Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the...Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.展开更多
Aiming to address the Unmanned Aerial Vehicle(UAV) formation collision avoidance problem in Three-Dimensional(3-D) low-altitude environments where dense various obstacles exist, a fluid-based path planning framework n...Aiming to address the Unmanned Aerial Vehicle(UAV) formation collision avoidance problem in Three-Dimensional(3-D) low-altitude environments where dense various obstacles exist, a fluid-based path planning framework named the Formation Interfered Fluid Dynamical System(FIFDS) with Moderate Evasive Maneuver Strategy(MEMS) is proposed in this study.First, the UAV formation collision avoidance problem including quantifiable performance indexes is formulated. Second, inspired by the phenomenon of fluids continuously flowing while bypassing objects, the FIFDS for multiple UAVs is presented, which contains a Parallel Streamline Tracking(PST) method for formation keeping and the traditional IFDS for collision avoidance. Third, to rationally balance flight safety and collision avoidance cost, MEMS is proposed to generate moderate evasive maneuvers that match up with collision risks. Comprehensively containing the time and distance safety information, the 3-D dynamic collision regions are modeled for collision prediction. Then, the moderate evasive maneuver principle is refined, which provides criterions of the maneuver amplitude and direction. On this basis, an analytical parameter mapping mechanism is designed to online optimize IFDS parameters. Finally, the performance of the proposed method is validated by comparative simulation results and real flight experiments using fixed-wing UAVs.展开更多
Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway s...Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway subgrade settlement interval prediction method using the secretary bird optimization(SBOA)algorithm to optimize the BP neural network under the premise of gray relational analysis is proposed.Findings–Using the SBOA algorithm to optimize the BP neural network,the optimal weights and thresholds are obtained,and the best parameter prediction model is combined.The data were collected from the sensors deployed through the subgrade settlement monitoring system,and the gray relational analysis is used to verify that all four influencing factors had a great correlation to the subgrade settlement,and the collected data are verified using the model.Originality/value–The experimental results show that the SBOA-BP model has higher prediction accuracy than the BP model,and the SBOA-BP model has a wider range of prediction intervals for a given confidence level,which can provide higher guiding value for practical engineering applications.展开更多
The purpose of this study is to analyze the galloping characteristics of the catenary positive feeder in fluctuating wind areas considering dynamic-wind angle of attack and aerodynamic damping.Firstly,the flow field m...The purpose of this study is to analyze the galloping characteristics of the catenary positive feeder in fluctuating wind areas considering dynamic-wind angle of attack and aerodynamic damping.Firstly,the flow field model of the catenary positive feeder was established,the fluctuating wind field was simulated by Davenport wind power spectrum and linear filtering method,and the wind speed at inlet in calculation domain was controlled by editing the profile file to simulate and calculate the aerodynamic characteristics of the positive feeder in the fluctuating wind area.Then,taking the positive feeder as the research object,the mathematical model of actual structure and the corresponding finite element model were established.By applying the wind load to the finite element model,the influence of aerodynamic damping caused by the self-movement of the positive feeder on the galloping response was analyzed,and the frequency domain characteristics of galloping displacement of the positive feeder considering aerodynamic damping were studied.Finally,the calculation method of aerodynamic damping by the Guidelines for Electrical Transmission Line Structural Loading(ASCE No.74)was used for the galloping response of the positive feeder and compared with the proposed method.The results show that when considering aerodynamic damping,the galloping amplitude of the positive feeder decreases significantly,and the first-order resonance effect on the vertical displacement and horizontal displacement decreases significantly.The galloping trajectories calculated by the two methods are consistent.Therefore,this study is of great significance to further clarify the ice-free galloping mechanism of the catenary positive feeder in violent wind areas.展开更多
To eliminate distortion caused by vertical drift and illusory slopes in atomic force microscopy(AFM)imaging,a lifting-wavelet-based iterative thresholding correction method is proposed in this paper.This method achiev...To eliminate distortion caused by vertical drift and illusory slopes in atomic force microscopy(AFM)imaging,a lifting-wavelet-based iterative thresholding correction method is proposed in this paper.This method achieves high-quality AFM imaging via line-by-line corrections for each distorted profile along the fast axis.The key to this line-by-line correction is to accurately simulate the profile distortion of each scanning row.Therefore,a data preprocessing approach is first developed to roughly filter out most of the height data that impairs the accuracy of distortion modeling.This process is implemented through an internal double-screening mechanism.A line-fitting method is adopted to preliminarily screen out the obvious specimens.Lifting wavelet analysis is then carried out to identify the base parts that are mistakenly filtered out as specimens so as to preserve most of the base profiles and provide a good basis for further distortion modeling.Next,an iterative thresholding algorithm is developed to precisely simulate the profile distortion.By utilizing the roughly screened base profile,the optimal threshold,which is used to screen out the pure bases suitable for distortion modeling,is determined through iteration with a specified error rule.On this basis,the profile distortion is accurately modeled through line fitting on the finely screened base data,and the correction is implemented by subtracting the modeling result from the distorted profile.Finally,the effectiveness of the proposed method is verified through experiments and applications.展开更多
With the development of civil aviation industry,the number of retired aircraft is increasing year by year.How to deal with retired aircraft,build aviation,avoid damage to the ecological environment,and develop their r...With the development of civil aviation industry,the number of retired aircraft is increasing year by year.How to deal with retired aircraft,build aviation,avoid damage to the ecological environment,and develop their residual value has attracted widespread attention internationally,and gradually formed dismantling industry for the commercial and reuse of retired aircraft.From the perspective of the industrial chain,the essence of aircraft dismantling is how to maximize the value of highvalue assets at the of their life cycle,that is,to balance the value of aircraft parts and the value of the whole aircraft,which is the last chain in the complete industrial of civil aircraft from design,manufacturing to usage and retirement.The paper studied the dismantling industrial modes of civil aircraft,analyzed the problems and challenges faced by aircraft dismantling,and put forward relevant measures and suggestions,which point out the direction for the development of domestic civil aircraft dismantling industry.展开更多
Active disturbance rejection control(ADRC)exhibits notable resilience against both internal and external disturbances.Its straightforward implementation further enhances its appeal for controlling a diverse class of s...Active disturbance rejection control(ADRC)exhibits notable resilience against both internal and external disturbances.Its straightforward implementation further enhances its appeal for controlling a diverse class of systems.However,the high-gain nature of the extended state observer,which is the core of ADRC,may degrade performance when faced with high-frequency sensing noise—a common challenge in real-world settings.This article addresses this issue through a specifically placed and particularly designed low-pass filterwhile preserving the ease of implementation characteristic of ADRC.This article proposes a simple tuning method for the filter-controller structure to improve the scheme’s design process.Theoretical results simplify the design process based on the Routh–Hurwitz criterion such that the additional low-pass filter does not affect the closedloop stability.The maximum power point tracking task on a wind turbine—a nonlinear system requiring the measurement of inherently noisy signals,such as electrical currents—is addressed to illustrate the design process of the proposed approach.Real-time experiments on a laboratory platform emulating a Permanent Magnet Synchronous Generator-based wind turbine endorse the enhanced scheme’s effectiveness in mitigating high-frequency sensing noise.展开更多
Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,...Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,given the challenges faced by health specialists to carry out continuous monitoring,the development of an intelligent anomaly detection system is proposed.Unlike other authors,where they use supervised techniques,this work proposes using unsupervised techniques due to the advantages they offer.These advantages include the lack of prior labeling of data,and the detection of anomalies previously not contemplated,among others.In the present work,an individualized methodology consisting of two phases is developed:characterizing the normal sitting pattern and determining abnormal samples.An analysis has been carried out between different unsupervised techniques to study which ones are more suitable for postural diagnosis.It can be concluded,among other aspects,that the utilization of dimensionality reduction techniques leads to improved results.Moreover,the normality characterization phase is deemed necessary for enhancing the system’s learning capabilities.Additionally,employing an individualized approach to the model aids in capturing the particularities of the various pathologies present among subjects.展开更多
Fine-grained aircraft target detection in remote sensing holds significant research valueand practical applications,particularly in military defense and precision strikes.Given the complex-ity of remote sensing images...Fine-grained aircraft target detection in remote sensing holds significant research valueand practical applications,particularly in military defense and precision strikes.Given the complex-ity of remote sensing images,where targets are often small and similar within categories,detectingthese fine-grained targets is challenging.To address this,we constructed a fine-grained dataset ofremotely sensed airplanes;for the problems of remote sensing fine-grained targets with obvious head-to-tail distributions and large variations in target sizes,we proposed the DWDet fine-grained tar-get detection and recognition algorithm.First,for the problem of unbalanced category distribution,we adopt an adaptive sampling strategy.In addition,we construct a deformable convolutional blockand improve the decoupling head structure to improve the detection effect of the model ondeformed targets.Then,we design a localization loss function,which is used to improve the model’slocalization ability for targets of different scales.The experimental results show that our algorithmimproves the overall accuracy of the model by 4.1%compared to the baseline model,and improvesthe detection accuracy of small targets by 12.2%.The ablation and comparison experiments alsoprove the effectiveness of our algorithm.展开更多
Background: Prematurely-born individuals tend to exhibit higher resting oxidative stress, although evidence suggests they may be more resistant to acute hypoxia-induced redox balance alterations. We aimed to investiga...Background: Prematurely-born individuals tend to exhibit higher resting oxidative stress, although evidence suggests they may be more resistant to acute hypoxia-induced redox balance alterations. We aimed to investigate the redox balance changes across a 3-day hypobaric hypoxic exposure at 3375 m in healthy adults born preterm(gestational age ≤ 32 weeks) and their term-born(gestational age ≥ 38 weeks)counterparts.Methods: Resting venous blood was obtained in normoxia(prior to altitude exposure), immediately upon arrival to altitude, and the following 3mornings. Antioxidant(superoxide dismutase(SOD), catalase, glutathione peroxidase(GPx), and ferric reducing antioxidant power(FRAP)),pro-oxidant(xanthine oxidase(XO) and myeloperoxidase(MPO)) enzyme activity, oxidative stress markers(advanced oxidation protein product(AOPP) and malondialdehyde(MDA)), nitric oxide(NO) metabolites(nitrites, nitrates, and total nitrite and nitrate(NOx)), and nitrotyrosine were measured in plasma.Results: SOD increased only in the preterm group(p < 0.05). Catalase increased at arrival in preterm group(p < 0.05). XO activity increased at Day 3 for the preterm group, while it increased acutely(arrival and Day 1) in control group. MPO increased in both groups throughout the3 days(p < 0.05). AOPP only increased at arrival in the preterm(p < 0.05) whereas it decreased at arrival up to Day 3(p < 0.05) for control.MDA decreased in control group from arrival onward. Nitrotyrosine decreased in both groups(p < 0.05). Nitrites increased on Day 3(p < 0.05)in control group and decreased on Day 1(p < 0.05) in preterm group.Conclusion: These data indicate that antioxidant enzymes seem to increase immediately upon hypoxic exposure in preterm adults. Conversely, the blunted pro-oxidant enzyme response to prolonged hypoxia exposure suggests that these enzymes may be less sensitive in preterm individuals.These findings lend further support to the potential hypoxic preconditioning effect of preterm birth.展开更多
Fiber-reinforced polymer(FRP)composites are renowned for their high mechanical strength,durability,and lightweight properties,making them integral to civil engineering,aerospace,and automotive manufacturing.Traditiona...Fiber-reinforced polymer(FRP)composites are renowned for their high mechanical strength,durability,and lightweight properties,making them integral to civil engineering,aerospace,and automotive manufacturing.Traditionally,the simulation and optimization of FRP materials have relied on finite element(FE)methods,which,while effective,often fall short in capturing the intricate behaviors of these composites under diverse conditions.Concrete examples in this regard involve modeling interfacial cracks,delaminations,or environmental effects that involve nonlinear phenomena.These degradation mechanisms exceed the capacity of classical FE models,as they are not detailed to the required level of detail.This aspect increases the time and computational resources required,leading to a need for optimization regarding fiber reinforcement configurations or multiple scenario load analysis.Thus,FE methods are inefficient compared to AI-based approaches that generalize material behavior based on extensive datasets.The advent of artificial intelligence(AI)has introduced advanced tools capable of enhancing the analysis and design of FRP materials.This review examines the current landscape of AI applications in FRP composite simulations,highlighting existing research gaps.Through a comprehensive bibliometric analysis,the study underscores the limited number of investigations focused on leveraging AI for FRP optimization.Furthermore,it synthesizes findings related to AI-driven simulation techniques,the mechanical properties of FRP composites,and strategies for predicting and improving their durability.This review comprehensively explores the potential of AI to overcome these limitations by synthesizing over 170 scientific works published between 2015 and 2025.Key findings highlight that supervised learning methods—especially neural networks,support vector machines,and gradient boosting models—achieve prediction accuracies above 90%for mechanical properties and defect classification.However,bibliometric analysis reveals that there are limited studies that address AI-driven optimization or standardized datasets for FRP applications.This review identifies eight core classification domains and eight regression domains where AI excels,including defect detection,bond strength prediction,and fiber orientation optimization.展开更多
Artificial intelligence(AI)-augmented contrast-enhanced ultrasonography(CEUS)is emerging as a powerful tool in liver imaging,particularly in enhancing the accuracy of Liver Imaging Reporting and Data System(known as L...Artificial intelligence(AI)-augmented contrast-enhanced ultrasonography(CEUS)is emerging as a powerful tool in liver imaging,particularly in enhancing the accuracy of Liver Imaging Reporting and Data System(known as LI-RADS)classi-fication.This review synthesized published data on the integration of machine learning and deep learning techniques into CEUS,revealing that AI algorithms can improve the detection and quantification of contrast enhancement patterns.Such improvements led to more consistent LI-RADS categorization,reduced interoperator variability,and enabled real-time analysis that streamlined work-flow.The enhanced sensitivity of AI tools facilitated better differentiation between benign and malignant lesions,ultimately optimizing patient management.These advances suggest that AI-augmented CEUS could transform liver imaging by providing rapid,reliable,and objective assessments.However,the review also highlighted the need for further large-scale,multicenter studies to fully validate these findings and ensure the safe integration of AI into routine clinical practice.INTRODUCTION International hepatology society guidelines have established contrast-enhanced computed tomography(CT)and contrast-enhanced magnetic resonance imaging(MRI)as the imaging modalities of choice for diagnosing hepatocellular carcinoma(HCC)lesions larger than 1 cm.MRI remains the gold standard for detecting small HCC nodules in cirrhotic livers due to its superior soft-tissue contrast and functional imaging capabilities.However,early or atypical presentations remain challenging for differential diagnosis,staging,and treatment planning.In these scenarios contrast-enhanced ultrasonography(CEUS)is a valuable second-line tool,offering real-time,radiation-free evaluation and repeatability for follow-up.A recent meta-analysis of head-to-head studies reported comparable diagnostic performance between CEUS and CT/MRI with pooled sensitivities and specificities of 0.67/0.88 for CEUS vs 0.60/0.98 for CT/MRI in non-HCC malignancies,and similar specificities for HCC diagnosis(0.70 for CEUS vs 0.59 for CT;0.81 for CEUS vs 0.79 for MRI)[1].Given the limitations of individual imaging modalities,hybrid techniques and multimodal approaches are gaining traction for improving lesion detection,especially in cases where standard methods fall short.Artificial intelligence(AI)has emerged as a powerful tool in medical imaging,enhancing diagnostic accuracy and reliability across platforms.In CEUS liver imaging dynamic enhancement patterns often challenge consistent interpretation across observers.AI holds particular promise for standardizing assessments.The growing complexity of liver tumor evaluation has also driven interest in approaches that integrate serum bio-markers with advanced imaging.However,no single strategy currently meets all the diagnostic and prognostic re-quirements.Recent studies highlighted the potential of AI to bridge this gap by enabling precise image interpretation and facilitating the integration of heterogeneous clinical and imaging data[2].Altogether the convergence of CEUS with AI and radiomics offers a dynamic,quantitative,and potentially reproducible paradigm for liver lesion assessment,comple-menting traditional imaging methods.This review aimed to provide an overview of current advances in AI-driven CEUS for liver lesion assessment with a particular focus on automated Liver Imaging Reporting and Data System(LI-RADS)classification,radiomics-based models,and future clinical integration.While another recent systematic review[3]provided a comprehensive analysis of AI applications in CEUS,our approach offers a targeted perspective,emphasizing LI-RADS-centered scoring,automated lesion characterization,and clinical utility,particularly in the context of HCC diagnosis and management.In the methodological process of this narrative mini-review,the literature selection was primarily based on targeted PubMed searches.ChatGPT-4o(OpenAI)[4]was employed to assist in refining query parameters and identifying relevant,up-to-date peer-reviewed sources on CEUS-based AI applications.展开更多
Hydroelectric power production from Garafiri dam and rainfall are essential elements with the observation of hydroelectric power production in West African power system,particularly in Guinea.This article focuses on t...Hydroelectric power production from Garafiri dam and rainfall are essential elements with the observation of hydroelectric power production in West African power system,particularly in Guinea.This article focuses on the study and the influence of climate variability on hydroelectric power production at Garafiri dam over 16-year period(2008-2023).The aim of this work is to show the correlation between rainfall anomalies and hydroelectric power production at Garafiri dam.The method used consists of calculating precipitation anomalies at Garafiri site and those for the production of hydroelectric power from Garafiri dam over the study period.This approach led us to calculate the anomalies,leading to the study on climatic variability,in order to establish correlation between rainfall and hydroelectric power dam’s production.The trend with the correlation found made it possible to carry out a significance test between these two variables.These results clearly show that rainfall in Garafiri site increases hydroelectric power production and vice versa,which explains the interdependence between these two parameters,i.e.climatic variability and hydroelectric power production.展开更多
In this editorial,we will discuss the article by Tang et al published in the recent issue of the World Journal of Gastrointestinal Oncology.They explored an innovative approach to enhancing gemcitabine(GEM)delivery an...In this editorial,we will discuss the article by Tang et al published in the recent issue of the World Journal of Gastrointestinal Oncology.They explored an innovative approach to enhancing gemcitabine(GEM)delivery and efficacy using human bone marrow mesenchymal stem cells(HU-BMSCs)-derived exosomes.The manufacture of GEM-loaded HU-BMSCs-derived exosomes(Exo-GEM)has been optimized.The Tang et al’s study demonstrated that Exo-GEM exhibits enhanced cytotoxicity and apoptosis-inducing effects compared to free GEM,highlighting the potential of exosome-based drug delivery systems as a more effective and targeted approach to chemotherapy in pancreatic cancer.Additional in vivo studies are required to confirm the safety and effectiveness of Exo-GEM before it can be considered for clinical use.展开更多
A space-based bistatic radar system composed of two space-based radars as the transmitter and the receiver respectively has a wider surveillance region and a better early warning capability for high-speed targets,and ...A space-based bistatic radar system composed of two space-based radars as the transmitter and the receiver respectively has a wider surveillance region and a better early warning capability for high-speed targets,and it can detect focused space targets more flexibly than the monostatic radar system or the ground-based radar system.However,the target echo signal is more difficult to process due to the high-speed motion of both space-based radars and space targets.To be specific,it will encounter the problems of Range Cell Migration(RCM)and Doppler Frequency Migration(DFM),which degrade the long-time coherent integration performance for target detection and localization inevitably.To solve this problem,a novel target detection method based on an improved Gram Schmidt(GS)-orthogonalization Orthogonal Matching Pursuit(OMP)algorithm is proposed in this paper.First,the echo model for bistatic space-based radar is constructed and the conditions for RCM and DFM are analyzed.Then,the proposed GS-orthogonalization OMP method is applied to estimate the equivalent motion parameters of space targets.Thereafter,the RCM and DFM are corrected by the compensation function correlated with the estimated motion parameters.Finally,coherent integration can be achieved by performing the Fast Fourier Transform(FFT)operation along the slow time direction on compensated echo signal.Numerical simulations and real raw data results validate that the proposed GS-orthogonalization OMP algorithm achieves better motion parameter estimation performance and higher detection probability for space targets detection.展开更多
To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on...To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on long shortterm memory(RPP-LSTM)network is proposed,which combines the memory characteristics of recurrent neural network(RNN)and the deep reinforcement learning algorithm.LSTM networks are used in this algorithm as Q-value networks for the deep Q network(DQN)algorithm,which makes the decision of the Q-value network has some memory.Thanks to LSTM network,the Q-value network can use the previous environmental information and action information which effectively avoids the problem of single-step decision considering only the current environment.Besides,the algorithm proposes a hierarchical reward and punishment function for the specific problem of UAV real-time path planning,so that the UAV can more reasonably perform path planning.Simulation verification shows that compared with the traditional feed-forward neural network(FNN)based UAV autonomous path planning algorithm,the RPP-LSTM proposed in this paper can adapt to more complex environments and has significantly improved robustness and accuracy when performing UAV real-time path planning.展开更多
The deterioration of unstable rock mass raised interest in evaluating rock mass quality.However,the traditional evaluation method for the geological strength index(GSI)primarily emphasizes the rock structure and chara...The deterioration of unstable rock mass raised interest in evaluating rock mass quality.However,the traditional evaluation method for the geological strength index(GSI)primarily emphasizes the rock structure and characteristics of discontinuities.It ignores the influence of mineral composition and shows a deficiency in assessing the integrity coefficient.In this context,hyperspectral imaging and digital panoramic borehole camera technologies are applied to analyze the mineral content and integrity of rock mass.Based on the carbonate mineral content and fissure area ratio,the strength reduction factor and integrity coefficient are calculated to improve the GSI evaluation method.According to the results of mineral classification and fissure identification,the strength reduction factor and integrity coefficient increase with the depth of rock mass.The rock mass GSI calculated by the improved method is mainly concentrated between 40 and 60,which is close to the calculation results of the traditional method.The GSI error rates obtained by the two methods are mostly less than 10%,indicating the rationality of the hyperspectral-digital borehole image coupled evaluation method.Moreover,the sensitivity of the fissure area ratio(Sr)to GSI is greater than that of the strength reduction factor(a),which means the proposed GSI is suitable for rocks with significant fissure development.The improved method reduces the influence of subjective factors and provides a reliable index for the deterioration evaluation of rock mass.展开更多
Wet flue gas desulphurization technology is widely used in the industrial process for its capability of efficient pollution removal.The desulphurization control system,however,is subjected to complex reaction mechanis...Wet flue gas desulphurization technology is widely used in the industrial process for its capability of efficient pollution removal.The desulphurization control system,however,is subjected to complex reaction mechanisms and severe disturbances,which make for it difficult to achieve certain practically relevant control goals including emission and economic performances as well as system robustness.To address these challenges,a new robust control scheme based on uncertainty and disturbance estimator(UDE)and model predictive control(MPC)is proposed in this paper.The UDE is used to estimate and dynamically compensate acting disturbances,whereas MPC is deployed for optimal feedback regulation of the resultant dynamics.By viewing the system nonlinearities and unknown dynamics as disturbances,the proposed control framework allows to locally treat the considered nonlinear plant as a linear one.The obtained simulation results confirm that the utilization of UDE makes the tracking error negligibly small,even in the presence of unmodeled dynamics.In the conducted comparison study,the introduced control scheme outperforms both the standard MPC and PID(proportional-integral-derivative)control strategies in terms of transient performance and robustness.Furthermore,the results reveal that a lowpass-filter time constant has a significant effect on the robustness and the convergence range of the tracking error.展开更多
The abrupt occurrence of the Zhongbao landslide is totally unexpected,resulting in the destruction of local infrastructure and river blockage.To review the deformation history of the Zhongbao landslide and prevent the...The abrupt occurrence of the Zhongbao landslide is totally unexpected,resulting in the destruction of local infrastructure and river blockage.To review the deformation history of the Zhongbao landslide and prevent the threat of secondary disasters,the small baseline subsets(SBAS)technology is applied to process 59 synthetic aperture radar(SAR)images captured from Sentinel-1A satellite.Firstly,the time series deformation of the Zhongbao landslide along the radar line of sight(LOS)direction is calculated by SBAS technology.Then,the projection transformation is conducted to determine the slope displacement.Furthermore,the Hurst exponent of the surface deformation along the two directions is calculated to quantify the hidden deformation development trend and identify the unstable deformation areas.Given the suddenness of the Zhongbao landslide failure,the multi-temporal interferometric synthetic aperture radar(InSAR)technology is the ideal tool to obtain the surface deformation history without any monitoring equipment.The obtained deformation process indicates that the Zhongbao landslide is generally stable with slow creep deformation before failure.Moreover,the Hurst exponent distribution on the landslide surface in different time stages reveals more deformation evolution information of the Zhongbao landslide,with partially unstable areas detected before the failure.Two potential unstable areas after the Zhongbao landslide disaster are revealed by the Hurst exponent distribution and verified by the GNSS monitoring results and deformation mechanism discussion.The method combining SBASInSAR and Hurst exponent proposed in this study could help prevent and control secondary landslide disasters.展开更多
Ship motions induced by waves have a significant impact on the efficiency and safety of offshore operations.Real-time prediction of ship motions in the next few seconds plays a crucial role in performing sensitive act...Ship motions induced by waves have a significant impact on the efficiency and safety of offshore operations.Real-time prediction of ship motions in the next few seconds plays a crucial role in performing sensitive activities.However,the obvious memory effect of ship motion time series brings certain difficulty to rapid and accurate prediction.Therefore,a real-time framework based on the Long-Short Term Memory(LSTM)neural network model is proposed to predict ship motions in regular and irregular head waves.A 15000 TEU container ship model is employed to illustrate the proposed framework.The numerical implementation and the real-time ship motion prediction in irregular head waves corresponding to the different time scales are carried out based on the container ship model.The related experimental data were employed to verify the numerical simulation results.The results show that the proposed method is more robust than the classical extreme short-term prediction method based on potential flow theory in the prediction of nonlinear ship motions.展开更多
基金supported by the Spanish Ministry of Science and Innovation under Projects PID2022-137680OB-C32 and PID2022-139187OB-I00.
文摘Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.
基金supported in part by the National Natural Science Foundations of China(Nos.61175084,61673042 and 62203046)the China Postdoctoral Science Foundation(No.2022M713006).
文摘Aiming to address the Unmanned Aerial Vehicle(UAV) formation collision avoidance problem in Three-Dimensional(3-D) low-altitude environments where dense various obstacles exist, a fluid-based path planning framework named the Formation Interfered Fluid Dynamical System(FIFDS) with Moderate Evasive Maneuver Strategy(MEMS) is proposed in this study.First, the UAV formation collision avoidance problem including quantifiable performance indexes is formulated. Second, inspired by the phenomenon of fluids continuously flowing while bypassing objects, the FIFDS for multiple UAVs is presented, which contains a Parallel Streamline Tracking(PST) method for formation keeping and the traditional IFDS for collision avoidance. Third, to rationally balance flight safety and collision avoidance cost, MEMS is proposed to generate moderate evasive maneuvers that match up with collision risks. Comprehensively containing the time and distance safety information, the 3-D dynamic collision regions are modeled for collision prediction. Then, the moderate evasive maneuver principle is refined, which provides criterions of the maneuver amplitude and direction. On this basis, an analytical parameter mapping mechanism is designed to online optimize IFDS parameters. Finally, the performance of the proposed method is validated by comparative simulation results and real flight experiments using fixed-wing UAVs.
文摘Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway subgrade settlement interval prediction method using the secretary bird optimization(SBOA)algorithm to optimize the BP neural network under the premise of gray relational analysis is proposed.Findings–Using the SBOA algorithm to optimize the BP neural network,the optimal weights and thresholds are obtained,and the best parameter prediction model is combined.The data were collected from the sensors deployed through the subgrade settlement monitoring system,and the gray relational analysis is used to verify that all four influencing factors had a great correlation to the subgrade settlement,and the collected data are verified using the model.Originality/value–The experimental results show that the SBOA-BP model has higher prediction accuracy than the BP model,and the SBOA-BP model has a wider range of prediction intervals for a given confidence level,which can provide higher guiding value for practical engineering applications.
基金supported by National Natural Science Foundation of China (No.51867013)Natural Science Foundation of Gansu Province (No.20JR5RA414)。
文摘The purpose of this study is to analyze the galloping characteristics of the catenary positive feeder in fluctuating wind areas considering dynamic-wind angle of attack and aerodynamic damping.Firstly,the flow field model of the catenary positive feeder was established,the fluctuating wind field was simulated by Davenport wind power spectrum and linear filtering method,and the wind speed at inlet in calculation domain was controlled by editing the profile file to simulate and calculate the aerodynamic characteristics of the positive feeder in the fluctuating wind area.Then,taking the positive feeder as the research object,the mathematical model of actual structure and the corresponding finite element model were established.By applying the wind load to the finite element model,the influence of aerodynamic damping caused by the self-movement of the positive feeder on the galloping response was analyzed,and the frequency domain characteristics of galloping displacement of the positive feeder considering aerodynamic damping were studied.Finally,the calculation method of aerodynamic damping by the Guidelines for Electrical Transmission Line Structural Loading(ASCE No.74)was used for the galloping response of the positive feeder and compared with the proposed method.The results show that when considering aerodynamic damping,the galloping amplitude of the positive feeder decreases significantly,and the first-order resonance effect on the vertical displacement and horizontal displacement decreases significantly.The galloping trajectories calculated by the two methods are consistent.Therefore,this study is of great significance to further clarify the ice-free galloping mechanism of the catenary positive feeder in violent wind areas.
基金supported by the National Natural Science Foundation of China under Grant No.21933006.
文摘To eliminate distortion caused by vertical drift and illusory slopes in atomic force microscopy(AFM)imaging,a lifting-wavelet-based iterative thresholding correction method is proposed in this paper.This method achieves high-quality AFM imaging via line-by-line corrections for each distorted profile along the fast axis.The key to this line-by-line correction is to accurately simulate the profile distortion of each scanning row.Therefore,a data preprocessing approach is first developed to roughly filter out most of the height data that impairs the accuracy of distortion modeling.This process is implemented through an internal double-screening mechanism.A line-fitting method is adopted to preliminarily screen out the obvious specimens.Lifting wavelet analysis is then carried out to identify the base parts that are mistakenly filtered out as specimens so as to preserve most of the base profiles and provide a good basis for further distortion modeling.Next,an iterative thresholding algorithm is developed to precisely simulate the profile distortion.By utilizing the roughly screened base profile,the optimal threshold,which is used to screen out the pure bases suitable for distortion modeling,is determined through iteration with a specified error rule.On this basis,the profile distortion is accurately modeled through line fitting on the finely screened base data,and the correction is implemented by subtracting the modeling result from the distorted profile.Finally,the effectiveness of the proposed method is verified through experiments and applications.
文摘With the development of civil aviation industry,the number of retired aircraft is increasing year by year.How to deal with retired aircraft,build aviation,avoid damage to the ecological environment,and develop their residual value has attracted widespread attention internationally,and gradually formed dismantling industry for the commercial and reuse of retired aircraft.From the perspective of the industrial chain,the essence of aircraft dismantling is how to maximize the value of highvalue assets at the of their life cycle,that is,to balance the value of aircraft parts and the value of the whole aircraft,which is the last chain in the complete industrial of civil aircraft from design,manufacturing to usage and retirement.The paper studied the dismantling industrial modes of civil aircraft,analyzed the problems and challenges faced by aircraft dismantling,and put forward relevant measures and suggestions,which point out the direction for the development of domestic civil aircraft dismantling industry.
文摘Active disturbance rejection control(ADRC)exhibits notable resilience against both internal and external disturbances.Its straightforward implementation further enhances its appeal for controlling a diverse class of systems.However,the high-gain nature of the extended state observer,which is the core of ADRC,may degrade performance when faced with high-frequency sensing noise—a common challenge in real-world settings.This article addresses this issue through a specifically placed and particularly designed low-pass filterwhile preserving the ease of implementation characteristic of ADRC.This article proposes a simple tuning method for the filter-controller structure to improve the scheme’s design process.Theoretical results simplify the design process based on the Routh–Hurwitz criterion such that the additional low-pass filter does not affect the closedloop stability.The maximum power point tracking task on a wind turbine—a nonlinear system requiring the measurement of inherently noisy signals,such as electrical currents—is addressed to illustrate the design process of the proposed approach.Real-time experiments on a laboratory platform emulating a Permanent Magnet Synchronous Generator-based wind turbine endorse the enhanced scheme’s effectiveness in mitigating high-frequency sensing noise.
基金FEDER/Ministry of Science and Innovation-State Research Agency/Project PID2020-112667RB-I00 funded by MCIN/AEI/10.13039/501100011033the Basque Government,IT1726-22+2 种基金by the predoctoral contracts PRE_2022_2_0022 and EP_2023_1_0015 of the Basque Governmentpartially supported by the Italian MIUR,PRIN 2020 Project“COMMON-WEARS”,N.2020HCWWLP,CUP:H23C22000230005co-funding from Next Generation EU,in the context of the National Recovery and Resilience Plan,through the Italian MUR,PRIN 2022 Project”COCOWEARS”(A framework for COntinuum COmputing WEARable Systems),N.2022T2XNJE,CUP:H53D23003640006.
文摘Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,given the challenges faced by health specialists to carry out continuous monitoring,the development of an intelligent anomaly detection system is proposed.Unlike other authors,where they use supervised techniques,this work proposes using unsupervised techniques due to the advantages they offer.These advantages include the lack of prior labeling of data,and the detection of anomalies previously not contemplated,among others.In the present work,an individualized methodology consisting of two phases is developed:characterizing the normal sitting pattern and determining abnormal samples.An analysis has been carried out between different unsupervised techniques to study which ones are more suitable for postural diagnosis.It can be concluded,among other aspects,that the utilization of dimensionality reduction techniques leads to improved results.Moreover,the normality characterization phase is deemed necessary for enhancing the system’s learning capabilities.Additionally,employing an individualized approach to the model aids in capturing the particularities of the various pathologies present among subjects.
基金supported by National Natural Science Foundation of China(No.62471034)Hebei Natural Science Foundation(No.F2023105001).
文摘Fine-grained aircraft target detection in remote sensing holds significant research valueand practical applications,particularly in military defense and precision strikes.Given the complex-ity of remote sensing images,where targets are often small and similar within categories,detectingthese fine-grained targets is challenging.To address this,we constructed a fine-grained dataset ofremotely sensed airplanes;for the problems of remote sensing fine-grained targets with obvious head-to-tail distributions and large variations in target sizes,we proposed the DWDet fine-grained tar-get detection and recognition algorithm.First,for the problem of unbalanced category distribution,we adopt an adaptive sampling strategy.In addition,we construct a deformable convolutional blockand improve the decoupling head structure to improve the detection effect of the model ondeformed targets.Then,we design a localization loss function,which is used to improve the model’slocalization ability for targets of different scales.The experimental results show that our algorithmimproves the overall accuracy of the model by 4.1%compared to the baseline model,and improvesthe detection accuracy of small targets by 12.2%.The ablation and comparison experiments alsoprove the effectiveness of our algorithm.
基金funded by the Swiss National Science Foundation(SNSF Grant No.320030L_192073 to GM)the Slovenian Research Agency(ARRS Grant No.N5-0152 to TD).
文摘Background: Prematurely-born individuals tend to exhibit higher resting oxidative stress, although evidence suggests they may be more resistant to acute hypoxia-induced redox balance alterations. We aimed to investigate the redox balance changes across a 3-day hypobaric hypoxic exposure at 3375 m in healthy adults born preterm(gestational age ≤ 32 weeks) and their term-born(gestational age ≥ 38 weeks)counterparts.Methods: Resting venous blood was obtained in normoxia(prior to altitude exposure), immediately upon arrival to altitude, and the following 3mornings. Antioxidant(superoxide dismutase(SOD), catalase, glutathione peroxidase(GPx), and ferric reducing antioxidant power(FRAP)),pro-oxidant(xanthine oxidase(XO) and myeloperoxidase(MPO)) enzyme activity, oxidative stress markers(advanced oxidation protein product(AOPP) and malondialdehyde(MDA)), nitric oxide(NO) metabolites(nitrites, nitrates, and total nitrite and nitrate(NOx)), and nitrotyrosine were measured in plasma.Results: SOD increased only in the preterm group(p < 0.05). Catalase increased at arrival in preterm group(p < 0.05). XO activity increased at Day 3 for the preterm group, while it increased acutely(arrival and Day 1) in control group. MPO increased in both groups throughout the3 days(p < 0.05). AOPP only increased at arrival in the preterm(p < 0.05) whereas it decreased at arrival up to Day 3(p < 0.05) for control.MDA decreased in control group from arrival onward. Nitrotyrosine decreased in both groups(p < 0.05). Nitrites increased on Day 3(p < 0.05)in control group and decreased on Day 1(p < 0.05) in preterm group.Conclusion: These data indicate that antioxidant enzymes seem to increase immediately upon hypoxic exposure in preterm adults. Conversely, the blunted pro-oxidant enzyme response to prolonged hypoxia exposure suggests that these enzymes may be less sensitive in preterm individuals.These findings lend further support to the potential hypoxic preconditioning effect of preterm birth.
文摘Fiber-reinforced polymer(FRP)composites are renowned for their high mechanical strength,durability,and lightweight properties,making them integral to civil engineering,aerospace,and automotive manufacturing.Traditionally,the simulation and optimization of FRP materials have relied on finite element(FE)methods,which,while effective,often fall short in capturing the intricate behaviors of these composites under diverse conditions.Concrete examples in this regard involve modeling interfacial cracks,delaminations,or environmental effects that involve nonlinear phenomena.These degradation mechanisms exceed the capacity of classical FE models,as they are not detailed to the required level of detail.This aspect increases the time and computational resources required,leading to a need for optimization regarding fiber reinforcement configurations or multiple scenario load analysis.Thus,FE methods are inefficient compared to AI-based approaches that generalize material behavior based on extensive datasets.The advent of artificial intelligence(AI)has introduced advanced tools capable of enhancing the analysis and design of FRP materials.This review examines the current landscape of AI applications in FRP composite simulations,highlighting existing research gaps.Through a comprehensive bibliometric analysis,the study underscores the limited number of investigations focused on leveraging AI for FRP optimization.Furthermore,it synthesizes findings related to AI-driven simulation techniques,the mechanical properties of FRP composites,and strategies for predicting and improving their durability.This review comprehensively explores the potential of AI to overcome these limitations by synthesizing over 170 scientific works published between 2015 and 2025.Key findings highlight that supervised learning methods—especially neural networks,support vector machines,and gradient boosting models—achieve prediction accuracies above 90%for mechanical properties and defect classification.However,bibliometric analysis reveals that there are limited studies that address AI-driven optimization or standardized datasets for FRP applications.This review identifies eight core classification domains and eight regression domains where AI excels,including defect detection,bond strength prediction,and fiber orientation optimization.
文摘Artificial intelligence(AI)-augmented contrast-enhanced ultrasonography(CEUS)is emerging as a powerful tool in liver imaging,particularly in enhancing the accuracy of Liver Imaging Reporting and Data System(known as LI-RADS)classi-fication.This review synthesized published data on the integration of machine learning and deep learning techniques into CEUS,revealing that AI algorithms can improve the detection and quantification of contrast enhancement patterns.Such improvements led to more consistent LI-RADS categorization,reduced interoperator variability,and enabled real-time analysis that streamlined work-flow.The enhanced sensitivity of AI tools facilitated better differentiation between benign and malignant lesions,ultimately optimizing patient management.These advances suggest that AI-augmented CEUS could transform liver imaging by providing rapid,reliable,and objective assessments.However,the review also highlighted the need for further large-scale,multicenter studies to fully validate these findings and ensure the safe integration of AI into routine clinical practice.INTRODUCTION International hepatology society guidelines have established contrast-enhanced computed tomography(CT)and contrast-enhanced magnetic resonance imaging(MRI)as the imaging modalities of choice for diagnosing hepatocellular carcinoma(HCC)lesions larger than 1 cm.MRI remains the gold standard for detecting small HCC nodules in cirrhotic livers due to its superior soft-tissue contrast and functional imaging capabilities.However,early or atypical presentations remain challenging for differential diagnosis,staging,and treatment planning.In these scenarios contrast-enhanced ultrasonography(CEUS)is a valuable second-line tool,offering real-time,radiation-free evaluation and repeatability for follow-up.A recent meta-analysis of head-to-head studies reported comparable diagnostic performance between CEUS and CT/MRI with pooled sensitivities and specificities of 0.67/0.88 for CEUS vs 0.60/0.98 for CT/MRI in non-HCC malignancies,and similar specificities for HCC diagnosis(0.70 for CEUS vs 0.59 for CT;0.81 for CEUS vs 0.79 for MRI)[1].Given the limitations of individual imaging modalities,hybrid techniques and multimodal approaches are gaining traction for improving lesion detection,especially in cases where standard methods fall short.Artificial intelligence(AI)has emerged as a powerful tool in medical imaging,enhancing diagnostic accuracy and reliability across platforms.In CEUS liver imaging dynamic enhancement patterns often challenge consistent interpretation across observers.AI holds particular promise for standardizing assessments.The growing complexity of liver tumor evaluation has also driven interest in approaches that integrate serum bio-markers with advanced imaging.However,no single strategy currently meets all the diagnostic and prognostic re-quirements.Recent studies highlighted the potential of AI to bridge this gap by enabling precise image interpretation and facilitating the integration of heterogeneous clinical and imaging data[2].Altogether the convergence of CEUS with AI and radiomics offers a dynamic,quantitative,and potentially reproducible paradigm for liver lesion assessment,comple-menting traditional imaging methods.This review aimed to provide an overview of current advances in AI-driven CEUS for liver lesion assessment with a particular focus on automated Liver Imaging Reporting and Data System(LI-RADS)classification,radiomics-based models,and future clinical integration.While another recent systematic review[3]provided a comprehensive analysis of AI applications in CEUS,our approach offers a targeted perspective,emphasizing LI-RADS-centered scoring,automated lesion characterization,and clinical utility,particularly in the context of HCC diagnosis and management.In the methodological process of this narrative mini-review,the literature selection was primarily based on targeted PubMed searches.ChatGPT-4o(OpenAI)[4]was employed to assist in refining query parameters and identifying relevant,up-to-date peer-reviewed sources on CEUS-based AI applications.
文摘Hydroelectric power production from Garafiri dam and rainfall are essential elements with the observation of hydroelectric power production in West African power system,particularly in Guinea.This article focuses on the study and the influence of climate variability on hydroelectric power production at Garafiri dam over 16-year period(2008-2023).The aim of this work is to show the correlation between rainfall anomalies and hydroelectric power production at Garafiri dam.The method used consists of calculating precipitation anomalies at Garafiri site and those for the production of hydroelectric power from Garafiri dam over the study period.This approach led us to calculate the anomalies,leading to the study on climatic variability,in order to establish correlation between rainfall and hydroelectric power dam’s production.The trend with the correlation found made it possible to carry out a significance test between these two variables.These results clearly show that rainfall in Garafiri site increases hydroelectric power production and vice versa,which explains the interdependence between these two parameters,i.e.climatic variability and hydroelectric power production.
基金Supported by the grants of China Medical University Hospital,No.DMR-112-173 and No.DMR-113-089the grant from Tungs’Taichung Metro Harbor Hospital,No.TTMHH-R1120013.
文摘In this editorial,we will discuss the article by Tang et al published in the recent issue of the World Journal of Gastrointestinal Oncology.They explored an innovative approach to enhancing gemcitabine(GEM)delivery and efficacy using human bone marrow mesenchymal stem cells(HU-BMSCs)-derived exosomes.The manufacture of GEM-loaded HU-BMSCs-derived exosomes(Exo-GEM)has been optimized.The Tang et al’s study demonstrated that Exo-GEM exhibits enhanced cytotoxicity and apoptosis-inducing effects compared to free GEM,highlighting the potential of exosome-based drug delivery systems as a more effective and targeted approach to chemotherapy in pancreatic cancer.Additional in vivo studies are required to confirm the safety and effectiveness of Exo-GEM before it can be considered for clinical use.
文摘A space-based bistatic radar system composed of two space-based radars as the transmitter and the receiver respectively has a wider surveillance region and a better early warning capability for high-speed targets,and it can detect focused space targets more flexibly than the monostatic radar system or the ground-based radar system.However,the target echo signal is more difficult to process due to the high-speed motion of both space-based radars and space targets.To be specific,it will encounter the problems of Range Cell Migration(RCM)and Doppler Frequency Migration(DFM),which degrade the long-time coherent integration performance for target detection and localization inevitably.To solve this problem,a novel target detection method based on an improved Gram Schmidt(GS)-orthogonalization Orthogonal Matching Pursuit(OMP)algorithm is proposed in this paper.First,the echo model for bistatic space-based radar is constructed and the conditions for RCM and DFM are analyzed.Then,the proposed GS-orthogonalization OMP method is applied to estimate the equivalent motion parameters of space targets.Thereafter,the RCM and DFM are corrected by the compensation function correlated with the estimated motion parameters.Finally,coherent integration can be achieved by performing the Fast Fourier Transform(FFT)operation along the slow time direction on compensated echo signal.Numerical simulations and real raw data results validate that the proposed GS-orthogonalization OMP algorithm achieves better motion parameter estimation performance and higher detection probability for space targets detection.
基金supported by the Natural Science Basic Research Prog ram of Shaanxi(2022JQ-593)。
文摘To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on long shortterm memory(RPP-LSTM)network is proposed,which combines the memory characteristics of recurrent neural network(RNN)and the deep reinforcement learning algorithm.LSTM networks are used in this algorithm as Q-value networks for the deep Q network(DQN)algorithm,which makes the decision of the Q-value network has some memory.Thanks to LSTM network,the Q-value network can use the previous environmental information and action information which effectively avoids the problem of single-step decision considering only the current environment.Besides,the algorithm proposes a hierarchical reward and punishment function for the specific problem of UAV real-time path planning,so that the UAV can more reasonably perform path planning.Simulation verification shows that compared with the traditional feed-forward neural network(FNN)based UAV autonomous path planning algorithm,the RPP-LSTM proposed in this paper can adapt to more complex environments and has significantly improved robustness and accuracy when performing UAV real-time path planning.
基金supported by the National Key R&D Program of China(Grant Nos.2021YFB3901403 and 2023YFC3007203).
文摘The deterioration of unstable rock mass raised interest in evaluating rock mass quality.However,the traditional evaluation method for the geological strength index(GSI)primarily emphasizes the rock structure and characteristics of discontinuities.It ignores the influence of mineral composition and shows a deficiency in assessing the integrity coefficient.In this context,hyperspectral imaging and digital panoramic borehole camera technologies are applied to analyze the mineral content and integrity of rock mass.Based on the carbonate mineral content and fissure area ratio,the strength reduction factor and integrity coefficient are calculated to improve the GSI evaluation method.According to the results of mineral classification and fissure identification,the strength reduction factor and integrity coefficient increase with the depth of rock mass.The rock mass GSI calculated by the improved method is mainly concentrated between 40 and 60,which is close to the calculation results of the traditional method.The GSI error rates obtained by the two methods are mostly less than 10%,indicating the rationality of the hyperspectral-digital borehole image coupled evaluation method.Moreover,the sensitivity of the fissure area ratio(Sr)to GSI is greater than that of the strength reduction factor(a),which means the proposed GSI is suitable for rocks with significant fissure development.The improved method reduces the influence of subjective factors and provides a reliable index for the deterioration evaluation of rock mass.
基金supported by the key project of the National Nature Science Foundation of China(51736002).
文摘Wet flue gas desulphurization technology is widely used in the industrial process for its capability of efficient pollution removal.The desulphurization control system,however,is subjected to complex reaction mechanisms and severe disturbances,which make for it difficult to achieve certain practically relevant control goals including emission and economic performances as well as system robustness.To address these challenges,a new robust control scheme based on uncertainty and disturbance estimator(UDE)and model predictive control(MPC)is proposed in this paper.The UDE is used to estimate and dynamically compensate acting disturbances,whereas MPC is deployed for optimal feedback regulation of the resultant dynamics.By viewing the system nonlinearities and unknown dynamics as disturbances,the proposed control framework allows to locally treat the considered nonlinear plant as a linear one.The obtained simulation results confirm that the utilization of UDE makes the tracking error negligibly small,even in the presence of unmodeled dynamics.In the conducted comparison study,the introduced control scheme outperforms both the standard MPC and PID(proportional-integral-derivative)control strategies in terms of transient performance and robustness.Furthermore,the results reveal that a lowpass-filter time constant has a significant effect on the robustness and the convergence range of the tracking error.
基金support from the National Key R&D Program of China(Grant No.2021YFB3901403)Project supported by graduate research and innovation foundation of Chongqing,China(Grant No.CYS23115)Special project for performance incentive and guidance of scientific research institutions in Chongqing(Grant No.cstc2021jxjl120011)are greatly appreciated。
文摘The abrupt occurrence of the Zhongbao landslide is totally unexpected,resulting in the destruction of local infrastructure and river blockage.To review the deformation history of the Zhongbao landslide and prevent the threat of secondary disasters,the small baseline subsets(SBAS)technology is applied to process 59 synthetic aperture radar(SAR)images captured from Sentinel-1A satellite.Firstly,the time series deformation of the Zhongbao landslide along the radar line of sight(LOS)direction is calculated by SBAS technology.Then,the projection transformation is conducted to determine the slope displacement.Furthermore,the Hurst exponent of the surface deformation along the two directions is calculated to quantify the hidden deformation development trend and identify the unstable deformation areas.Given the suddenness of the Zhongbao landslide failure,the multi-temporal interferometric synthetic aperture radar(InSAR)technology is the ideal tool to obtain the surface deformation history without any monitoring equipment.The obtained deformation process indicates that the Zhongbao landslide is generally stable with slow creep deformation before failure.Moreover,the Hurst exponent distribution on the landslide surface in different time stages reveals more deformation evolution information of the Zhongbao landslide,with partially unstable areas detected before the failure.Two potential unstable areas after the Zhongbao landslide disaster are revealed by the Hurst exponent distribution and verified by the GNSS monitoring results and deformation mechanism discussion.The method combining SBASInSAR and Hurst exponent proposed in this study could help prevent and control secondary landslide disasters.
文摘Ship motions induced by waves have a significant impact on the efficiency and safety of offshore operations.Real-time prediction of ship motions in the next few seconds plays a crucial role in performing sensitive activities.However,the obvious memory effect of ship motion time series brings certain difficulty to rapid and accurate prediction.Therefore,a real-time framework based on the Long-Short Term Memory(LSTM)neural network model is proposed to predict ship motions in regular and irregular head waves.A 15000 TEU container ship model is employed to illustrate the proposed framework.The numerical implementation and the real-time ship motion prediction in irregular head waves corresponding to the different time scales are carried out based on the container ship model.The related experimental data were employed to verify the numerical simulation results.The results show that the proposed method is more robust than the classical extreme short-term prediction method based on potential flow theory in the prediction of nonlinear ship motions.
