Marine thin plates are susceptible to welding deformation owing to their low structural stiffness.Therefore,the efficient and accurate prediction of welding deformation is essential for improving welding quality.The t...Marine thin plates are susceptible to welding deformation owing to their low structural stiffness.Therefore,the efficient and accurate prediction of welding deformation is essential for improving welding quality.The traditional thermal elastic-plastic finite element method(TEP-FEM)can accurately predict welding deformation.However,its efficiency is low because of the complex nonlinear transient computation,making it difficult to meet the needs of rapid engineering evaluation.To address this challenge,this study proposes an efficient prediction method for welding deformation in marine thin plate butt welds.This method is based on the coupled temperature gradient-thermal strain method(TG-TSM)that integrates inherent strain theory with a shell element finite element model.The proposed method first extracts the distribution pattern and characteristic value of welding-induced inherent strain through TEP-FEM analysis.This strain is then converted into the equivalent thermal load applied to the shell element model for rapid computation.The proposed method-particularly,the gradual temperature gradient-thermal strain method(GTG-TSM)-achieved improved computational efficiency and consistent precision.Furthermore,the proposed method required much less computation time than the traditional TEP-FEM.Thus,this study lays the foundation for future prediction of welding deformation in more complex marine thin plates.展开更多
The collection and annotation of lar ge-scale bird datasets are resource-intensive and time-consuming processes that significantly limit the scalability and accuracy of biodiversity monitoring systems.While self-super...The collection and annotation of lar ge-scale bird datasets are resource-intensive and time-consuming processes that significantly limit the scalability and accuracy of biodiversity monitoring systems.While self-supervised learning(SSL)has emerged as a promising approach for leveraging unannotated data,current SSL methods face two critical challenges in bird species recognition:(1)long-tailed data distributions that result in poor performance on underrepresented species;and(2)domain shift issues caused by data augmentation strategies designed to mitigate class imbalance.Here we present SDNet,a novel SSL-based bird recognition framework that integrates diffusion models with large language models(LLMs)to overcome these limitations.SDNet employs LLMs to generate semantically rich textual descriptions for tail-class species by prompting the models with species taxonomy,morphological attributes,and habitat information,producing detailed natural language priors that capture fine-grained visual characteristics(e.g.,plumage patterns,body proportions,and distinctive markings).These textual descriptions are subsequently used by a conditional diffusion model to synthesize new bird image samples through cross-attention mechanisms that fuse textual embeddings with intermediate visual feature representations during the denoising process,ensuring generated images preserve species-specific morphological details while maintaining photorealistic quality.Additionally,we incorporate a Swin Transformer as the feature extraction backbone whose hierarchical window-based attention mechanism and shifted windowing scheme enable multi-scale local feature extraction that proves particularly effective at capturing finegrained discriminative patterns(such as beak shape and feather texture)while mitigating domain shift between synthetic and original images through consistent feature representations across both data sources.SDNet is validated on both a self-constructed dataset(Bird_BXS)an d a publicly available benchmark(Birds_25),demonstrating substantial improvements over conventional SSL approaches.Our results indicate that the synergistic integration of LLMs,diffusion models,and the Swin Transformer architecture contributes significantly to recognition accuracy,particularly for rare and morphologically similar species.These findings highlight the potential of SDNet for addressing fundamental limitations of existing SSL methods in avian recognition tasks and establishing a new paradigm for efficient self-supervised learning in large-scale ornithological vision applications.展开更多
Introduced developing process of coal mine automation and communication technology,analyzed present features and characteristics of coal mine automation and communication technology,and put forward a few key technical...Introduced developing process of coal mine automation and communication technology,analyzed present features and characteristics of coal mine automation and communication technology,and put forward a few key technical problems needed to be solved.展开更多
In the face of the large number of people with motor function disabilities,rehabilitation robots have attracted more and more attention.In order to promote the active participation of the user's motion intention i...In the face of the large number of people with motor function disabilities,rehabilitation robots have attracted more and more attention.In order to promote the active participation of the user's motion intention in the assisted rehabilitation process of the robots,it is crucial to establish the human motion prediction model.In this paper,a hybrid prediction model built on long short-term memory(LSTM)neural network using surface electromyography(sEMG)is applied to predict the elbow motion of the users in advance.This model includes two sub-models:a back-propagation neural network and an LSTM network.The former extracts a preliminary prediction of the elbow motion,and the latter corrects this prediction to increase accuracy.The proposed model takes time series data as input,which includes the sEMG signals measured by electrodes and the continuous angles from inertial measurement units.The offline and online tests were carried out to verify the established hybrid model.Finally,average root mean square errors of 3.52°and 4.18°were reached respectively for offline and online tests,and the correlation coefficients for both were above 0.98.展开更多
According to the measurement principle of the traditional interferometer,a narrowband signal model is established and used,however,for wideband signals or multiple signals,this model is invalid.For the problems of dir...According to the measurement principle of the traditional interferometer,a narrowband signal model is established and used,however,for wideband signals or multiple signals,this model is invalid.For the problems of direction finding with interferometer for wideband signals and multiple signals scene,a frequency domain phase interferometer is proposed and the concrete implementation scheme is given.The proposed method computes the phase difference in frequency domain,and finds multi-target results with judging the spectrum amplitude changing,and uses the frequency phase difference to compute the arrival angle.Theoretical analysis and simulation results show that the proposed method effectively solves the problem of the angle estimation with phase interferometer for wideband signals,and has good performance in multiple signals scene with nonoverlapping spectrum or partially overlapping.In addition,the wider the signal bandwidth,the better direction finding performance of this algorithm.展开更多
The rudder mechanism of the X-rudder autonomous underwater cehicle(AUV)is relatively complex,and fault diagnosis capability is an important guarantee for its task execution in complex underwater environments.However,t...The rudder mechanism of the X-rudder autonomous underwater cehicle(AUV)is relatively complex,and fault diagnosis capability is an important guarantee for its task execution in complex underwater environments.However,traditional fault diagnosis methods currently rely on prior knowledge and expert experience,and lack accuracy.In order to improve the autonomy and accuracy of fault diagnosis methods,and overcome the shortcomings of traditional algorithms,this paper proposes an X-steering AUV fault diagnosis model based on the deep reinforcement learning deep Q network(DQN)algorithm,which can learn the relationship between state data and fault types,map raw residual data to corresponding fault patterns,and achieve end-to-end mapping.In addition,to solve the problem of few X-steering fault sample data,Dropout technology is introduced during the model training phase to improve the performance of the DQN algorithm.Experimental results show that the proposed model has improved the convergence speed and comprehensive performance indicators compared to the unimproved DQN algorithm,with precision,recall,F_(1-score),and accuracy reaching up to 100%,98.07%,99.02%,and 98.50% respectively,and the model’s accuracy is higher than other machine learning algorithms like back propagation,support vector machine.展开更多
As a novel lightweight metallic material with excellent heat and corrosion resistance,elastic disordered microporous metal rubber(EDMMR)functions as an effective damping and support element in high-temperature environ...As a novel lightweight metallic material with excellent heat and corrosion resistance,elastic disordered microporous metal rubber(EDMMR)functions as an effective damping and support element in high-temperature environments where traditional polymer rubber fails.In this paper,a multi-scale finite element model for EDMMR is constructed using virtual manufacturing technology(VMT).Thermo-mechanical coupling analysis reveals a distinct inward expansion and dissipation phenomenon in EDMMR under high-temperature conditions,distinguishing it from porous materials.This phenomenon has the potential to impact the overall dimensions of EDMMR through transmission and accumulation processes.The experimental results demonstrate a random distribution of internal micro springs in EDMMR,considering the contact composition of spring microelements and the pore structure.By incorporating material elasticity,a predictive method for the thermal expansion coefficient of EDMMR based on the Schapery model is proposed.Additionally,standardized processes are employed to manufacture multiple sets of cylindrical EDMMR samples with similar dimensions but varying porosities.Thermal expansion tests are conducted on these samples,and the accuracy of the predicted thermal expansion coefficient is quantitatively validated through residual analysis.This research indicates that EDMMR maintains good structural stability in high-temperature environments.The thermal expansion rate of the material exhibits an opposite trend to the variation of elastic modulus with temperature,as the porosity rate changes.展开更多
Orbital angular momentum(OAM)can achieve multifold increase of spectrum efficiency,but the hollow divergence characteristic and Line-of-Sight(LoS)path requirement impose the crucial challenges for vortex wave communic...Orbital angular momentum(OAM)can achieve multifold increase of spectrum efficiency,but the hollow divergence characteristic and Line-of-Sight(LoS)path requirement impose the crucial challenges for vortex wave communications.For air-to-ground vortex wave communications,where there exists the LoS path,this paper proposes a multi-user cooperative receive(MUCR)scheme to break through the communication distance limitation caused by the characteristic of vortex wave hollow divergence.In particular,we derive the optimal radial position corresponding to the maximum intensity,which is used to adjust the waist radius.Based on the waist radius and energy ring,the cooperative ground users having the minimum angular square difference are selected.Also,the signal compensation scheme is proposed to decompose OAM signals in air-to-ground vortex wave communications.Simulation results are presented to verify the superiority of our proposed MUCR scheme.展开更多
Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detecti...Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.展开更多
The Global Position System(GPS)is a reliable method for positioning in most scenarios,but it falls short in harsh environments like urban vehicular scenarios,where numerous trees or flyovers obstruct the signals.This ...The Global Position System(GPS)is a reliable method for positioning in most scenarios,but it falls short in harsh environments like urban vehicular scenarios,where numerous trees or flyovers obstruct the signals.This presents an unprecedented challenge for autonomous vehicles or applications requiring high accuracy.Fortunately,vehicular ad-hoc networks(VANET)offer an effective solution,where vehicle-to-vehicle(V2V)and vehicle-to-infrastructure(V2I)communications are used to enhance location awareness.In V2I communications,the roadside units(RSU)transmit beacon packets,and the vehicle receives numerous packets from different RSUs to establish communication.To further improve localization accuracy,a cross-covariance matrices-alternating least square(CCM-ALS)algorithm is proposed.The algorithm relies on ALS of the CCM for obtaining the position of vehicles in V2I communications.The algorithm is highly precise compared to traditional angle of arrival(AOA)positioning and not inferior to direct position determination(DPD)approaches while being low in complexity,which is crucial for moving vehicles.The numerical results verify the superiority of the proposed method.展开更多
Mobile robots represented by smart wheelchairs can assist elderly people with mobility difficulties.This paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots,wh...Mobile robots represented by smart wheelchairs can assist elderly people with mobility difficulties.This paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots,which can assist users to implement accurate navigation(e.g.,docking)in the environment without prior maps.In order to overcome the problem of repeated oscillations during the docking of traditional local path planning algorithms,this paper adopts a mode-switching method and uses feedback control to perform docking when approaching semantic goals.At last,comparative experiments were carried out in the real environment.Results show that our method is superior in terms of safety,comfort and docking accuracy.展开更多
A new version of differential evolution （DE） algorithm, in which immune concepts and methods are applied to determine the parameter setting, named immune self-adaptive differential evolution （ISDE）, is proposed to...A new version of differential evolution （DE） algorithm, in which immune concepts and methods are applied to determine the parameter setting, named immune self-adaptive differential evolution （ISDE）, is proposed to improve the performance of the DE algorithm. During the actual operation, ISDE seeks the optimal parameters arising from the evolutionary process, which enable ISDE to alter the algorithm for different optimization problems and improve the performance of ISDE by the control parameters＇ self-adaptation. The .performance of the proposed method is studied with the use of nine benchmark problems and compared with original DE algorithm ~nd-other well-known self-adaptive DE algorithms. The experiments conducted show that the ISDE clearly outperforms the other DE algorithms in all benchmark functions. Furthermore, ISDE is applied to develop the kinetic model for homogeneous mercury. （Hg） oxidation in flue gas, and satisfactory results are obtained.展开更多
The accurate model is the most important and basic condition for the application of advanced process control, but the conventional methods do not provide satisfactory results in the case of unstable processes. To effe...The accurate model is the most important and basic condition for the application of advanced process control, but the conventional methods do not provide satisfactory results in the case of unstable processes. To effec-tively control these processes, a novel identification method (Model Parameters and Initial States Identification si-multaneously in closed loop —MPISI) is proposed. The model parameters and initial states of state equation can be simultaneously identified using this method. The results of simulation and application show that this method has the advantageous of disturbance-rejection and robustness. This method proposes a novel way for the optimization and the advanced control of the process systems.展开更多
A three stage equilibrium model is developed for coal gasification in the Texaco type coal gasifiersbased on Aspen Plus to calculate the composition of product gas, carbon conversion, and gasification teml^erature. Th...A three stage equilibrium model is developed for coal gasification in the Texaco type coal gasifiersbased on Aspen Plus to calculate the composition of product gas, carbon conversion, and gasification teml^erature. The model is divided into three stages including pyrolysis and combustion stage, char gas reaction stage, and gas p.hase reaction stage. Part of the water produced in thepyrolysis and combust!on stag.e is assumed to be involved inthe second stage to react with the unburned carbon. Carbon conversion is then estimated in the second stage by steam participation ratio expressed as a function of temperature. And the gas product compositions are calculated from gas phase reactions in the third stage. The simulation results are consistent with published experimental data.展开更多
Electromyography(EMG)has already been broadly used in human-machine interaction(HMI)applications.Determining how to decode the information inside EMG signals robustly and accurately is a key problem for which we urgen...Electromyography(EMG)has already been broadly used in human-machine interaction(HMI)applications.Determining how to decode the information inside EMG signals robustly and accurately is a key problem for which we urgently need a solution.Recently,many EMG pattern recognition tasks have been addressed using deep learning methods.In this paper,we analyze recent papers and present a literature review describing the role that deep learning plays in EMG-based HMI.An overview of typical network structures and processing schemes will be provided.Recent progress in typical tasks such as movement classification,joint angle prediction,and force/torque estimation will be introduced.New issues,including multimodal sensing,inter-subject/inter-session,and robustness toward disturbances will be discussed.We attempt to provide a comprehensive analysis of current research by discussing the advantages,challenges,and opportunities brought by deep learning.We hope that deep learning can aid in eliminating factors that hinder the development of EMG-based HMI systems.Furthermore,possible future directions will be presented to pave the way for future research.展开更多
The Ni60A and Ni60A/SiC coatings were obtained by laser cladding on 0.45% C steel. The microstructure and hardness of the coatings were studied by SEM and XRD. The erosion resistances of Ni60A and Ni60A/SiC coatings w...The Ni60A and Ni60A/SiC coatings were obtained by laser cladding on 0.45% C steel. The microstructure and hardness of the coatings were studied by SEM and XRD. The erosion resistances of Ni60A and Ni60A/SiC coatings were also investigated. The results show that the structure of different coatings is up to the temperature gradient and solidifying velocity in metal-melting region during laser cladding process. The coatings consist of a cladding layer, in which dendritic crystal and bulky cell-like crystal exist mainly, and a thermo-affected layer. Ni60A/SiC coating has higher microhardness than that of Ni60A coating, which is mainly caused by SiC and complicated phases formed by Ni, Cr, Fe, C and Si. It is obvious from the erosion test that the Ni60A/SiC coating has high erosion resistance.展开更多
Particle swarm optimization (PSO) is an efficient, robust and simple optimization algorithm. Most studies are mainly concentrated on better understanding of the standard PSO control parameters, such as acceleration co...Particle swarm optimization (PSO) is an efficient, robust and simple optimization algorithm. Most studies are mainly concentrated on better understanding of the standard PSO control parameters, such as acceleration coefficients, etc. In this paper, a more simple strategy of PSO algorithm called θ-PSO is proposed. In θ-PSO, an increment of phase angle vector replaces the increment of velocity vector and the positions are decided by the mapping of phase angles. Benchmark testing of nonlinear func- tions is described and the results show that the performance of θ-PSO is much more effective than that of the standard PSO.展开更多
It is necessary to rely on the rail gauge to determine whether the object beside the track will affect train operation safety or not.A convenient and fast method based on line segment detector(LSD)and the least square...It is necessary to rely on the rail gauge to determine whether the object beside the track will affect train operation safety or not.A convenient and fast method based on line segment detector(LSD)and the least square curve fitting to identify the rail in the image is proposed in this paper.The image in front of the train can be obtained through the camera on-board.After preprocessing,it will be divided equally along the longitudinal axis.Utilizing the characteristics of the LSD algorithm,the edges are approximated into multiple line segments.After screening the terminals of the line segments,it can generate the mathematical model of the rail in the image based on the least square.Experiments show that the algorithm in this paper can fit the rail curve accurately and has good applicability and robustness.展开更多
OBJECTIVE: To explore the concept and norm of fracture healing with osteopathy in traditional Mongolian medicine (TMM). METHODS: Based on the correspondence between man and the universe (including psychosomatic integr...OBJECTIVE: To explore the concept and norm of fracture healing with osteopathy in traditional Mongolian medicine (TMM). METHODS: Based on the correspondence between man and the universe (including psychosomatic integration) in fracture healing with osteopathy in TMM, we used modern physio-psychological and biomechanical principles and methods to probe the integrated, dynamic and functional characteristics of fracture healing. RESULTS: Based on the integration of limbs and the body, unification of the body and function and harmony of man and nature (including psychoso-matic integration), fracture healing with osteopathy in TMM comprises the concept of natural functional healing of fractures, and follows the norm of considering physiological healing and psychological function as well as limb healing and motor function. CONCLUSION: Fracture healing with osteopathy in TMM is characterized by a lack of trauma without future complications. This therapy makes the concept of fracture healing develop in the direction of humanity, behaviorism and integration.展开更多
Robotics has aroused huge attention since the 1950s.Irrespective of the uniqueness that industrial applications exhibit,conventional rigid robots have displayed noticeable limitations,particularly in safe cooperation ...Robotics has aroused huge attention since the 1950s.Irrespective of the uniqueness that industrial applications exhibit,conventional rigid robots have displayed noticeable limitations,particularly in safe cooperation as well as with environmental adaption.Accordingly,scientists have shifted their focus on soft robotics to apply this type of robots more effectively in unstructured environments.For decades,they have been committed to exploring sub-fields of soft robotics(e.g.,cutting-edge techniques in design and fabrication,accurate modeling,as well as advanced control algorithms).Although scientists have made many different efforts,they share the common goal of enhancing applicability.The presented paper aims to brief the progress of soft robotic research for readers interested in this field,and clarify how an appropriate control algorithm can be produced for soft robots with specific morphologies.This paper,instead of enumerating existing modeling or control methods of a certain soft robot prototype,interprets for the relationship between morphology and morphology-dependent motion strategy,attempts to delve into the common issues in a particular class of soft robots,and elucidates a generic solution to enhance their performance.展开更多
基金Supported by the National Natural Science Foundation of China under Grant No.51975138the High-Tech Ship Scientific Research Project from the Ministry of Industry and Information Technology under Grant No.CJ05N20the National Defense Basic Research Project under Grant No.JCKY2023604C006.
文摘Marine thin plates are susceptible to welding deformation owing to their low structural stiffness.Therefore,the efficient and accurate prediction of welding deformation is essential for improving welding quality.The traditional thermal elastic-plastic finite element method(TEP-FEM)can accurately predict welding deformation.However,its efficiency is low because of the complex nonlinear transient computation,making it difficult to meet the needs of rapid engineering evaluation.To address this challenge,this study proposes an efficient prediction method for welding deformation in marine thin plate butt welds.This method is based on the coupled temperature gradient-thermal strain method(TG-TSM)that integrates inherent strain theory with a shell element finite element model.The proposed method first extracts the distribution pattern and characteristic value of welding-induced inherent strain through TEP-FEM analysis.This strain is then converted into the equivalent thermal load applied to the shell element model for rapid computation.The proposed method-particularly,the gradual temperature gradient-thermal strain method(GTG-TSM)-achieved improved computational efficiency and consistent precision.Furthermore,the proposed method required much less computation time than the traditional TEP-FEM.Thus,this study lays the foundation for future prediction of welding deformation in more complex marine thin plates.
基金supported by the National Natural Science Foundation of China(32471964)。
文摘The collection and annotation of lar ge-scale bird datasets are resource-intensive and time-consuming processes that significantly limit the scalability and accuracy of biodiversity monitoring systems.While self-supervised learning(SSL)has emerged as a promising approach for leveraging unannotated data,current SSL methods face two critical challenges in bird species recognition:(1)long-tailed data distributions that result in poor performance on underrepresented species;and(2)domain shift issues caused by data augmentation strategies designed to mitigate class imbalance.Here we present SDNet,a novel SSL-based bird recognition framework that integrates diffusion models with large language models(LLMs)to overcome these limitations.SDNet employs LLMs to generate semantically rich textual descriptions for tail-class species by prompting the models with species taxonomy,morphological attributes,and habitat information,producing detailed natural language priors that capture fine-grained visual characteristics(e.g.,plumage patterns,body proportions,and distinctive markings).These textual descriptions are subsequently used by a conditional diffusion model to synthesize new bird image samples through cross-attention mechanisms that fuse textual embeddings with intermediate visual feature representations during the denoising process,ensuring generated images preserve species-specific morphological details while maintaining photorealistic quality.Additionally,we incorporate a Swin Transformer as the feature extraction backbone whose hierarchical window-based attention mechanism and shifted windowing scheme enable multi-scale local feature extraction that proves particularly effective at capturing finegrained discriminative patterns(such as beak shape and feather texture)while mitigating domain shift between synthetic and original images through consistent feature representations across both data sources.SDNet is validated on both a self-constructed dataset(Bird_BXS)an d a publicly available benchmark(Birds_25),demonstrating substantial improvements over conventional SSL approaches.Our results indicate that the synergistic integration of LLMs,diffusion models,and the Swin Transformer architecture contributes significantly to recognition accuracy,particularly for rare and morphologically similar species.These findings highlight the potential of SDNet for addressing fundamental limitations of existing SSL methods in avian recognition tasks and establishing a new paradigm for efficient self-supervised learning in large-scale ornithological vision applications.
基金the National Science and Technology Progressthe Ministerial Science and Technology Progress twicethe Ministerial Science and Technology Progress
文摘Introduced developing process of coal mine automation and communication technology,analyzed present features and characteristics of coal mine automation and communication technology,and put forward a few key technical problems needed to be solved.
基金the National Key Research and Development Program of China(No.2020YFC2007500)the Science and Technology Commission of Shanghai Municipality(No.20DZ2220400)。
文摘In the face of the large number of people with motor function disabilities,rehabilitation robots have attracted more and more attention.In order to promote the active participation of the user's motion intention in the assisted rehabilitation process of the robots,it is crucial to establish the human motion prediction model.In this paper,a hybrid prediction model built on long short-term memory(LSTM)neural network using surface electromyography(sEMG)is applied to predict the elbow motion of the users in advance.This model includes two sub-models:a back-propagation neural network and an LSTM network.The former extracts a preliminary prediction of the elbow motion,and the latter corrects this prediction to increase accuracy.The proposed model takes time series data as input,which includes the sEMG signals measured by electrodes and the continuous angles from inertial measurement units.The offline and online tests were carried out to verify the established hybrid model.Finally,average root mean square errors of 3.52°and 4.18°were reached respectively for offline and online tests,and the correlation coefficients for both were above 0.98.
文摘According to the measurement principle of the traditional interferometer,a narrowband signal model is established and used,however,for wideband signals or multiple signals,this model is invalid.For the problems of direction finding with interferometer for wideband signals and multiple signals scene,a frequency domain phase interferometer is proposed and the concrete implementation scheme is given.The proposed method computes the phase difference in frequency domain,and finds multi-target results with judging the spectrum amplitude changing,and uses the frequency phase difference to compute the arrival angle.Theoretical analysis and simulation results show that the proposed method effectively solves the problem of the angle estimation with phase interferometer for wideband signals,and has good performance in multiple signals scene with nonoverlapping spectrum or partially overlapping.In addition,the wider the signal bandwidth,the better direction finding performance of this algorithm.
基金Supported by the National Natural Science Foundation of China under Grant Nos.52071099,52071104National Key Project of Research and Development Program under Grant No.2021YFC2801300Research Fund from National Key Laboratory of Autonomous Marine Vehicle Technology under Grant No.2023-SXJQR-SYSJJ01.
文摘The rudder mechanism of the X-rudder autonomous underwater cehicle(AUV)is relatively complex,and fault diagnosis capability is an important guarantee for its task execution in complex underwater environments.However,traditional fault diagnosis methods currently rely on prior knowledge and expert experience,and lack accuracy.In order to improve the autonomy and accuracy of fault diagnosis methods,and overcome the shortcomings of traditional algorithms,this paper proposes an X-steering AUV fault diagnosis model based on the deep reinforcement learning deep Q network(DQN)algorithm,which can learn the relationship between state data and fault types,map raw residual data to corresponding fault patterns,and achieve end-to-end mapping.In addition,to solve the problem of few X-steering fault sample data,Dropout technology is introduced during the model training phase to improve the performance of the DQN algorithm.Experimental results show that the proposed model has improved the convergence speed and comprehensive performance indicators compared to the unimproved DQN algorithm,with precision,recall,F_(1-score),and accuracy reaching up to 100%,98.07%,99.02%,and 98.50% respectively,and the model’s accuracy is higher than other machine learning algorithms like back propagation,support vector machine.
基金Supported by National Natural Science Foundation of China(Grant Nos.U2330202,52175162,51805086,51975123)Fujian Provincial Technological Innovation Key Research and Industrialization Projects(Grant Nos.2023XQ005,2024XQ010)Project of Guangdong Provincial Science and Technology Bureau of Jiangmen City(Grant No.2023780200030009506)。
文摘As a novel lightweight metallic material with excellent heat and corrosion resistance,elastic disordered microporous metal rubber(EDMMR)functions as an effective damping and support element in high-temperature environments where traditional polymer rubber fails.In this paper,a multi-scale finite element model for EDMMR is constructed using virtual manufacturing technology(VMT).Thermo-mechanical coupling analysis reveals a distinct inward expansion and dissipation phenomenon in EDMMR under high-temperature conditions,distinguishing it from porous materials.This phenomenon has the potential to impact the overall dimensions of EDMMR through transmission and accumulation processes.The experimental results demonstrate a random distribution of internal micro springs in EDMMR,considering the contact composition of spring microelements and the pore structure.By incorporating material elasticity,a predictive method for the thermal expansion coefficient of EDMMR based on the Schapery model is proposed.Additionally,standardized processes are employed to manufacture multiple sets of cylindrical EDMMR samples with similar dimensions but varying porosities.Thermal expansion tests are conducted on these samples,and the accuracy of the predicted thermal expansion coefficient is quantitatively validated through residual analysis.This research indicates that EDMMR maintains good structural stability in high-temperature environments.The thermal expansion rate of the material exhibits an opposite trend to the variation of elastic modulus with temperature,as the porosity rate changes.
基金supported in part by National Natural Science Foundation of China under Grant 62441115 and 62201427in part by the Ministry of Industry and Information Technology of the People’s Republic of China under Grant CBG01N23-01-04.
文摘Orbital angular momentum(OAM)can achieve multifold increase of spectrum efficiency,but the hollow divergence characteristic and Line-of-Sight(LoS)path requirement impose the crucial challenges for vortex wave communications.For air-to-ground vortex wave communications,where there exists the LoS path,this paper proposes a multi-user cooperative receive(MUCR)scheme to break through the communication distance limitation caused by the characteristic of vortex wave hollow divergence.In particular,we derive the optimal radial position corresponding to the maximum intensity,which is used to adjust the waist radius.Based on the waist radius and energy ring,the cooperative ground users having the minimum angular square difference are selected.Also,the signal compensation scheme is proposed to decompose OAM signals in air-to-ground vortex wave communications.Simulation results are presented to verify the superiority of our proposed MUCR scheme.
文摘Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.
基金supported by the National Natural Science Foundation of China(62371225).
文摘The Global Position System(GPS)is a reliable method for positioning in most scenarios,but it falls short in harsh environments like urban vehicular scenarios,where numerous trees or flyovers obstruct the signals.This presents an unprecedented challenge for autonomous vehicles or applications requiring high accuracy.Fortunately,vehicular ad-hoc networks(VANET)offer an effective solution,where vehicle-to-vehicle(V2V)and vehicle-to-infrastructure(V2I)communications are used to enhance location awareness.In V2I communications,the roadside units(RSU)transmit beacon packets,and the vehicle receives numerous packets from different RSUs to establish communication.To further improve localization accuracy,a cross-covariance matrices-alternating least square(CCM-ALS)algorithm is proposed.The algorithm relies on ALS of the CCM for obtaining the position of vehicles in V2I communications.The algorithm is highly precise compared to traditional angle of arrival(AOA)positioning and not inferior to direct position determination(DPD)approaches while being low in complexity,which is crucial for moving vehicles.The numerical results verify the superiority of the proposed method.
基金the Technology Project Managed by the State Grid Corporation of China(No.5108-202218280A-2-249-XG)。
文摘Mobile robots represented by smart wheelchairs can assist elderly people with mobility difficulties.This paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots,which can assist users to implement accurate navigation(e.g.,docking)in the environment without prior maps.In order to overcome the problem of repeated oscillations during the docking of traditional local path planning algorithms,this paper adopts a mode-switching method and uses feedback control to perform docking when approaching semantic goals.At last,comparative experiments were carried out in the real environment.Results show that our method is superior in terms of safety,comfort and docking accuracy.
基金Supported by the National Natural Science Foundation of China （20506003, 20776042） and the National High-Tech Research and Development Program of China （2007AA04Z 164）.
文摘A new version of differential evolution （DE） algorithm, in which immune concepts and methods are applied to determine the parameter setting, named immune self-adaptive differential evolution （ISDE）, is proposed to improve the performance of the DE algorithm. During the actual operation, ISDE seeks the optimal parameters arising from the evolutionary process, which enable ISDE to alter the algorithm for different optimization problems and improve the performance of ISDE by the control parameters＇ self-adaptation. The .performance of the proposed method is studied with the use of nine benchmark problems and compared with original DE algorithm ~nd-other well-known self-adaptive DE algorithms. The experiments conducted show that the ISDE clearly outperforms the other DE algorithms in all benchmark functions. Furthermore, ISDE is applied to develop the kinetic model for homogeneous mercury. （Hg） oxidation in flue gas, and satisfactory results are obtained.
基金Supported by the Common Project Plan of Beijing Municipal Education Commission (No.100100435).
文摘The accurate model is the most important and basic condition for the application of advanced process control, but the conventional methods do not provide satisfactory results in the case of unstable processes. To effec-tively control these processes, a novel identification method (Model Parameters and Initial States Identification si-multaneously in closed loop —MPISI) is proposed. The model parameters and initial states of state equation can be simultaneously identified using this method. The results of simulation and application show that this method has the advantageous of disturbance-rejection and robustness. This method proposes a novel way for the optimization and the advanced control of the process systems.
基金Supported by the Major State Basic Research Development Program of China(2012CB720500)the National Natural Science Foundation of China(U1162202,61174118)+1 种基金the National Science Fund for Outstanding Young Scholars(61222303)Shanghai Leading Academic Discipline Project(B504)
文摘A three stage equilibrium model is developed for coal gasification in the Texaco type coal gasifiersbased on Aspen Plus to calculate the composition of product gas, carbon conversion, and gasification teml^erature. The model is divided into three stages including pyrolysis and combustion stage, char gas reaction stage, and gas p.hase reaction stage. Part of the water produced in thepyrolysis and combust!on stag.e is assumed to be involved inthe second stage to react with the unburned carbon. Carbon conversion is then estimated in the second stage by steam participation ratio expressed as a function of temperature. And the gas product compositions are calculated from gas phase reactions in the third stage. The simulation results are consistent with published experimental data.
基金supported in part by the National Natural Science Foundation of China(U181321461773369+2 种基金61903360)the Selfplanned Project of the State Key Laboratory of Robotics(2020-Z12)China Postdoctoral Science Foundation funded project(2019M661155)。
文摘Electromyography(EMG)has already been broadly used in human-machine interaction(HMI)applications.Determining how to decode the information inside EMG signals robustly and accurately is a key problem for which we urgently need a solution.Recently,many EMG pattern recognition tasks have been addressed using deep learning methods.In this paper,we analyze recent papers and present a literature review describing the role that deep learning plays in EMG-based HMI.An overview of typical network structures and processing schemes will be provided.Recent progress in typical tasks such as movement classification,joint angle prediction,and force/torque estimation will be introduced.New issues,including multimodal sensing,inter-subject/inter-session,and robustness toward disturbances will be discussed.We attempt to provide a comprehensive analysis of current research by discussing the advantages,challenges,and opportunities brought by deep learning.We hope that deep learning can aid in eliminating factors that hinder the development of EMG-based HMI systems.Furthermore,possible future directions will be presented to pave the way for future research.
文摘The Ni60A and Ni60A/SiC coatings were obtained by laser cladding on 0.45% C steel. The microstructure and hardness of the coatings were studied by SEM and XRD. The erosion resistances of Ni60A and Ni60A/SiC coatings were also investigated. The results show that the structure of different coatings is up to the temperature gradient and solidifying velocity in metal-melting region during laser cladding process. The coatings consist of a cladding layer, in which dendritic crystal and bulky cell-like crystal exist mainly, and a thermo-affected layer. Ni60A/SiC coating has higher microhardness than that of Ni60A coating, which is mainly caused by SiC and complicated phases formed by Ni, Cr, Fe, C and Si. It is obvious from the erosion test that the Ni60A/SiC coating has high erosion resistance.
基金the National Natural Science Foundation of China (Nos. 60625302 and 60704028)the Program for ChangjiangScholars and Innovative Research Team in University (No. IRT0721)+2 种基金the 111 Project (No. B08021)the Major State Basic Research De-velopment Program of Shanghai (No. 07JC14016)ShanghaiLeading Academic Discipline Project (No. B504) of China
文摘Particle swarm optimization (PSO) is an efficient, robust and simple optimization algorithm. Most studies are mainly concentrated on better understanding of the standard PSO control parameters, such as acceleration coefficients, etc. In this paper, a more simple strategy of PSO algorithm called θ-PSO is proposed. In θ-PSO, an increment of phase angle vector replaces the increment of velocity vector and the positions are decided by the mapping of phase angles. Benchmark testing of nonlinear func- tions is described and the results show that the performance of θ-PSO is much more effective than that of the standard PSO.
基金National Natural Science Foundation of China(No.61763023).
文摘It is necessary to rely on the rail gauge to determine whether the object beside the track will affect train operation safety or not.A convenient and fast method based on line segment detector(LSD)and the least square curve fitting to identify the rail in the image is proposed in this paper.The image in front of the train can be obtained through the camera on-board.After preprocessing,it will be divided equally along the longitudinal axis.Utilizing the characteristics of the LSD algorithm,the edges are approximated into multiple line segments.After screening the terminals of the line segments,it can generate the mathematical model of the rail in the image based on the least square.Experiments show that the algorithm in this paper can fit the rail curve accurately and has good applicability and robustness.
基金Supported by the Project of the 2009 State Natural Science Fund (No. 30960518)a Project of the 2012 State Natural Science Fund (No. 81260513)a Major Project of the Scitech Plan of the Inner Mongolian Autonomous Region(2010-2012)
文摘OBJECTIVE: To explore the concept and norm of fracture healing with osteopathy in traditional Mongolian medicine (TMM). METHODS: Based on the correspondence between man and the universe (including psychosomatic integration) in fracture healing with osteopathy in TMM, we used modern physio-psychological and biomechanical principles and methods to probe the integrated, dynamic and functional characteristics of fracture healing. RESULTS: Based on the integration of limbs and the body, unification of the body and function and harmony of man and nature (including psychoso-matic integration), fracture healing with osteopathy in TMM comprises the concept of natural functional healing of fractures, and follows the norm of considering physiological healing and psychological function as well as limb healing and motor function. CONCLUSION: Fracture healing with osteopathy in TMM is characterized by a lack of trauma without future complications. This therapy makes the concept of fracture healing develop in the direction of humanity, behaviorism and integration.
文摘Robotics has aroused huge attention since the 1950s.Irrespective of the uniqueness that industrial applications exhibit,conventional rigid robots have displayed noticeable limitations,particularly in safe cooperation as well as with environmental adaption.Accordingly,scientists have shifted their focus on soft robotics to apply this type of robots more effectively in unstructured environments.For decades,they have been committed to exploring sub-fields of soft robotics(e.g.,cutting-edge techniques in design and fabrication,accurate modeling,as well as advanced control algorithms).Although scientists have made many different efforts,they share the common goal of enhancing applicability.The presented paper aims to brief the progress of soft robotic research for readers interested in this field,and clarify how an appropriate control algorithm can be produced for soft robots with specific morphologies.This paper,instead of enumerating existing modeling or control methods of a certain soft robot prototype,interprets for the relationship between morphology and morphology-dependent motion strategy,attempts to delve into the common issues in a particular class of soft robots,and elucidates a generic solution to enhance their performance.
