Semantic secure communication is an emerging field that combines the principles of source-channel coding with the need for secure data transmission.It is of great significance in modern communications to protect the c...Semantic secure communication is an emerging field that combines the principles of source-channel coding with the need for secure data transmission.It is of great significance in modern communications to protect the confidentiality and privacy of sensitive information and prevent information leaks and malicious attacks.This paper presents a novel approach to semantic secure communication through the utilization of joint source-channel coding,which is based on the design of an automated joint source-channel coding algorithm and an encryption and decryption algorithm based on semantic security.The traditional and state-of-the-art joint source-channel coding algorithms are selected as two baselines for different comparison purposes.Experimental results demonstrate that our proposed algorithm outperforms the first baseline algorithm,the traditional source-channel coding,by 61.21%in efficiency under identical channel conditions(SNR=15 dB).In security,our proposed method can resist 2 more types of attacks compared to the two baselines,exhibiting nearly no increases in time consumption and error rate compared to the state-of-the-art joint source-channel coding algorithm while the secure semantic communication is supported.展开更多
【目的】应用线粒体DNA条形码技术对尤犀金龟属(Eupatorus Burmeister,1847)昆虫物种界定进行探索,以解决该属物种形态鉴定困难的问题。【方法】基于尤犀金龟属物种线粒体cox1和cox2基因序列数据集,使用Automatic Barcode Gap Discovery...【目的】应用线粒体DNA条形码技术对尤犀金龟属(Eupatorus Burmeister,1847)昆虫物种界定进行探索,以解决该属物种形态鉴定困难的问题。【方法】基于尤犀金龟属物种线粒体cox1和cox2基因序列数据集,使用Automatic Barcode Gap Discovery(ABGD)和Bayesian Poisson Tree Processes(bPTP)对3个形态种进行分子物种界定,并与形态学鉴定结果进行比较。【结果】使用ABGD方法时,cox1数据集的界定结果与形态学鉴定结果一致,cox2数据集的界定结果与形态学鉴定结果存在差异;使用bPTP方法时,2种数据集的界定结果均远高于形态学鉴定结果,且均存在不同程度的过度划分。【结论】cox1是更适合用于鉴定尤犀金龟属昆虫的DNA条形码,使用ABGD方法时,其数据集界定结果与形态学鉴定结果一致。利用分子界定与形态特征鉴定相结合,可极大地提高鉴定效率和准确性。展开更多
Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive cont...Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control(MPC),which utilizes an extensive mathe-matical model of the voltage regulation system to optimize the control actions over a defined prediction horizon.This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints,thereby improving stability and performance under dynamic conditions.Thefindings were compared with those derived from an optimal proportional integral derivative(PID)con-troller designed using the artificial bee colony(ABC)algorithm.Although the ABC-PID method adjusts the PID parameters based on historical data,it may be difficult to adapt to real-time changes in system dynamics under constraints.Comprehensive simulations assessed both frameworks,emphasizing performance metrics such as disturbance rejection,response to load changes,and resilience to uncertainties.The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation;however,MPC excelled in controlling overshoot and settling time—recording 0.0%and 0.25 s,respectively.This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior,which exhibited settling times and overshoots exceeding 0.41 s and 5.0%,respectively.The controllers were implemented using MATLAB/Simulink software,indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.展开更多
BACKGROUND A total of 100 patients diagnosed with mixed hemorrhoids from October 2022 to September 2023 in our hospital were randomly divided into groups by dice rolling and compared with the efficacy of different tre...BACKGROUND A total of 100 patients diagnosed with mixed hemorrhoids from October 2022 to September 2023 in our hospital were randomly divided into groups by dice rolling and compared with the efficacy of different treatment options.AIM To analyze the clinical effect and prognosis of mixed hemorrhoids treated with polidocanol injection combined with automatic elastic thread ligation operation(RPH).METHODS A total of 100 patients with mixed hemorrhoids who visited our hospital from October 2022 to September 2023 were selected and randomly divided into the control group(n=50)and the treatment group(n=50)by rolling the dice.The procedure for prolapse and hemorrhoids(PPH)was adopted in the control group,while polidocanol foam injection+RPH was adopted in the treatment group.The therapeutic effects,operation time,wound healing time,hospital stay,pain situation(24 hours post-operative pain score,first defecation pain score),quality of life(QOL),incidence of complications(post-operative hemorrhage,edema,infection),incidence of anal stenosis 3 months post-operatively and recurrence rate 1 year post-operatively of the two groups were compared.RESULTS Compared with the control group,the total effective rate of treatment group was higher,and the difference was significant(P<0.05).The operation time/wound healing time/hospital stay in the treatment group were shorter than those in the control group(P<0.05).The pain scores at 24 hours after operation/first defecation pain score of the treatment group was significantly lower than those in the control group(P<0.05).After surgery,the QOL scores of the two groups decreased,with the treatment group having higher scores than that of the control group(P<0.05).Compared with the control group,the incidence of postoperative complications in the treatment group was lower,and the difference was significant(P<0.05);However,there was no significant difference in the incidence of postoperative bleeding between the two groups(P>0.05);There was no significant difference in the incidence of anal stenosis 3 months after operation and the recurrence rate 1 year after operation between the two groups(P>0.05).CONCLUSION For patients with mixed hemorrhoids,the therapeutic effect achieved by using polidocanol injection combined with RPH was better.The wounds of the patients healed faster,the postoperative pain was milder,QOL improved,and the incidence of complications was lower,and the short-term and long-term prognosis was good.展开更多
In order to increase the stability of the Mongolia power system, a single-phase automatic reclosing device (SPAR) was introduced on double-circuit power lines built with a size of 330 kV, operating on a voltage of 220...In order to increase the stability of the Mongolia power system, a single-phase automatic reclosing device (SPAR) was introduced on double-circuit power lines built with a size of 330 kV, operating on a voltage of 220 kV and a length of 250 km. These overhead power lines (L-213, L-214) connect the 220/110/35 kV “Songino” substation with the “Mandal” substation and form system networks. This paper presents the challenges encountered when implementing single-phase automatic reclosing (SPAR) devices and compares the changes in power system parameters before and after SPAR deployment for a long 220 kV line. Simulations and analyses were carried out using DIgSILENT PowerFactory software, focusing on rotor angle stability, and the overall impact on the power system during short-circuit faults. The evaluation also utilized measurement data from the Wide Area Monitoring System (WAMS) to compare system behavior pre- and post-implementation of SPAR. The findings reveal that SPAR significantly enhances system reliability and stability, effectively mitigating the risk of oscillations and stability loss triggered by short circuits. This improvement contributes to a more resilient power system, reducing the potential for disturbances caused by faults.展开更多
In this paper,the author follows the trail of C.Malabou,Q.Meillassoux,and G.Deleuze and tries to test three philosophical concepts that seem to be particularly threatened in the era of automatic digital reproduction.T...In this paper,the author follows the trail of C.Malabou,Q.Meillassoux,and G.Deleuze and tries to test three philosophical concepts that seem to be particularly threatened in the era of automatic digital reproduction.These three concepts are plasticity(defended for many years by C.Malabou),contingency(reconstructed by Q.Meillassoux),and virtuality(developed by G.Deleuze).The main task of the text will be to reflect on which of these three concepts better protects our thinking against automation and stays faithful to the ideal of creativity.In what sense are plasticity,contingency,and the possibility of virtualization the a priori condition of any transformation,physical or intellectual,affective or conceptual metamorphosis?In what sense are these three concepts the only conditions for the survival of every living being?Would a being without contingency,plasticity,and disposition to virtualization simply be a dead being?展开更多
BACKGROUND Depression is a leading global health concern with high suicide rates and recurrence.Cognitive models suggest that mental pain and automatic thoughts are central to depression's impact.The hypothesis is...BACKGROUND Depression is a leading global health concern with high suicide rates and recurrence.Cognitive models suggest that mental pain and automatic thoughts are central to depression's impact.The hypothesis is that self-compassion will be negatively associated with mental pain,mediated by automatic thoughts.AIM To determine the mediating role of automatic thoughts in the relationship between self-compassion and mental pain in individuals with depression.METHODS This cross-sectional study included 389 inpatients with depression from Tianjin Anding Hospital.Participants completed the Self-Compassion Scale-Chinese Version(SCS-C),Automatic Thought Questionnaire(ATQ),and Orbach&Mikulincer Mental Pain Scale-Chinese Version(OMMP).Data were analyzed using Pearson correlations,multiple linear regressions,and mediation analysis.RESULTS The SCS-C total score was 68.95±14.89,ATQ was 87.02±28.91,and OMMP was 129.01±36.74.Correlation analysis showed mental pain was positively associated with automatic thoughts(r=0.802,P<0.001)and negatively with selfcompassion(r=-0.636,P<0.001).Regression analysis indicated automatic thoughts(β=0.623,P<0.001)and self-compassion(β=-0.301,P<0.001)significantly predicted mental pain.Mediation analysis confirmed automatic thoughts partially mediated the relationship between self-compassion and mental pain(ab=-0.269,95%CI:-0.363 to-0.212).CONCLUSION Self-compassion is inversely related to mental pain in depression,with automatic thoughts playing a mediating role.These findings suggest potential therapeutic targets for alleviating mental pain in depressed patients.展开更多
The 19th Workshop on Antarctic Meteorology and Climate(WAMC)and the 8th Year of Polar Prediction in the Southern Hemisphere(YOPP-SH)meeting were held in June 2024 at the Byrd Polar and Climate Research Center,The Ohio...The 19th Workshop on Antarctic Meteorology and Climate(WAMC)and the 8th Year of Polar Prediction in the Southern Hemisphere(YOPP-SH)meeting were held in June 2024 at the Byrd Polar and Climate Research Center,The Ohio State University,Columbus,Ohio.These hybrid events convened 79 participants from 15 nations to foster international collaboration on Antarctic meteorology,climate research,and forecasting.The WAMC featured presentations on automatic weather stations,numerical weather prediction,Antarctic sea ice dynamics,and extreme weather events.The YOPP-SH meeting emphasized the positive impacts of enhanced observations during the 2022 Winter Special Observing Period(SOP)on forecast accuracy and addressed the transition toward the Polar Coupled Analysis and Prediction for Services(PCAPS)initiative.The outcomes reflect significant advancements in polar meteorological research and underscore the importance of sustained collaborative efforts,including improved observational networks and advanced modeling systems,to address the unique challenges of Antarctic meteorology.Future workshops will continue to support and expand upon these critical themes.展开更多
A rotary sealing device that automatically compensates for wear is designed to address the issues of easy wear and the short service life of the rotary sealing device with automatic wear compensation in mining machine...A rotary sealing device that automatically compensates for wear is designed to address the issues of easy wear and the short service life of the rotary sealing device with automatic wear compensation in mining machinery.After the end face of the guide sleeve wears out,it still tightly adheres to the sealing valve seat under the pressure difference,achieving automatic wear compensation.Based on fluid-solid coupling technology,the structural strength of the rotary sealing device was checked.The influence of factors on the sealing performance of rotary sealing devices was studied using the control variable method.The results show that as the pressure of water increases,the leakage rate of the sealing device decreases,and after 30 MPa,the leakage rate is almost 0 mL/h.The temperature of the rotating sealing device increases with the increase of rotation speed or pressure,and the temperature is more affected by the rotation speed factor.The frictional torque increases with increasing pressure and is independent of rotational speed.Comprehensive analysis shows that the wear resistance and reliability level of the sealing guide sleeve material is PVDF>PEEK>PE>PA.This study designs a high-pressure automatic compensation wear rotary sealing device and selects the optimal sealing material,providing technical support for the application of high-pressure water jet in mining machinery.展开更多
Coasts are subject to multiple natural hazards,which are increasing nowadays.Coastal flooding and erosion are some of the most common hazards affecting coastlines.Being aware of the vulnerability of coasts is importan...Coasts are subject to multiple natural hazards,which are increasing nowadays.Coastal flooding and erosion are some of the most common hazards affecting coastlines.Being aware of the vulnerability of coasts is important to achieve integrated coastal management.The coastal vulnerability index(CVI)is a common index used to assess coastal vulnerability because it is easily calculated.However,given that its calculation includes numerous manual steps,it requires considerable time,which is often unavailable,to produce accurate and utilizable results.In this work,we developed a ModelBuilder model by using the tools provided by ArcGIS Pro(ESRI).Through this model,we automatized most of the steps involved in CVI calculation.We applied the ModelBuilder model in the northern Peloponnese,for which the CVI has already been calculated in three other works.We were thus able to assess the effectiveness of our ModelBuilder model.Our results demonstrated that through the ModelBuilder,most of the processes could effectively be automatized without problems,and our results are consistent with the findings of previous works in our study area.展开更多
Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalizat...Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalization performance in for crack detection.Complex network model can generate redundant feature maps and computational complexity.Therefore,this paper proposes a novel model compression framework based on deep learning to detect road cracks,which can improve the detection efficiency and accuracy.A distillation loss function is proposed to compress the teacher model,followed by channel pruning.Meanwhile,a multi-dilation model is proposed to improve the accuracy of the model pruned.The proposed method is tested on the public database CrackForest dataset(CFD).The experimental results show that the proposed method is more efficient and accurate than other state-of-art methods.展开更多
Brain tumors are neoplastic diseases caused by the proliferation of abnormal cells in brain tissues,and their appearance may lead to a series of complex symptoms.However,current methods struggle to capture deeper brai...Brain tumors are neoplastic diseases caused by the proliferation of abnormal cells in brain tissues,and their appearance may lead to a series of complex symptoms.However,current methods struggle to capture deeper brain tumor image feature information due to the variations in brain tumor morphology,size,and complex background,resulting in low detection accuracy,high rate of misdiagnosis and underdiagnosis,and challenges in meeting clinical needs.Therefore,this paper proposes the CMS-YOLO network model for multi-category brain tumor detection,which is based on the You Only Look Once version 10(YOLOv10s)algorithm.This model innovatively integrates the Convolutional Medical UNet extended block(CMUNeXt Block)to design a brand-new CSP Bottleneck with 2 convolutions(C2f)structure,which significantly enhances the ability to extract features of the lesion area.Meanwhile,to address the challenge of complex backgrounds in brain tumor detection,a Multi-Scale Attention Aggregation(MSAA)module is introduced.The module integrates features of lesions at different scales,enabling the model to effectively capture multi-scale contextual information and enhance detection accuracy in complex scenarios.Finally,during the model training process,the Shape-IoU loss function is employed to replace the Complete-IoU(CIoU)loss function for optimizing bounding box regression.This ensures that the predicted bounding boxes generated by the model closely match the actual tumor contours,thereby further enhancing the detection precision.The experimental results show that the improved method achieves 94.80%precision,93.60%recall,96.20%score,and 79.60%on the MRI for Brain Tumor with Bounding Boxes dataset.Compared to the YOLOv10s model,this represents improvements of 1.0%,1.1%,1.0%,and 1.1%,respectively.The method can achieve automatic detection and localization of three distinct categories of brain tumors—glioma,meningioma,and pituitary tumor,which can accurately detect and identify brain tumors,assist doctors in early diagnosis,and promote the development of early treatment.展开更多
Esophageal cancer(EC),a common malignant tumor of the digestive tract,requires early diagnosis and timely treatment to improve patient prognosis.Automated detection of EC using medical imaging has the potential to inc...Esophageal cancer(EC),a common malignant tumor of the digestive tract,requires early diagnosis and timely treatment to improve patient prognosis.Automated detection of EC using medical imaging has the potential to increase screening efficiency and diagnostic accuracy,thereby significantly improving long-term survival rates and the quality of life of patients.Recent advances in deep learning(DL),particularly convolutional neural networks,have demons-trated remarkable performance in medical imaging analysis.These techniques have shown significant progress in the automated identification of malignant tumors,quantitative analysis of lesions,and improvement in diagnostic accuracy and efficiency.This article comprehensively examines the research progress of DL in medical imaging for EC,covering various imaging modalities such as digital pathology,endoscopy,computed tomography,etc.It explores the clinical value and application prospects of DL in EC screening and diagnosis.Additionally,the article addresses several critical challenges that must be overcome for the clinical translation of DL techniques,including constructing high-quality datasets,promoting multimodal feature fusion,and optimizing artificial intelligence-clinical workflow integration.By providing a detailed overview of the current state of DL in EC imaging and highlighting the key challenges and future directions,this article aims to guide future research and facilitate the clinical implementation of DL technologies in EC management,ultimately contributing to better patient outcomes.展开更多
The inherent limitations of 2D object detection,such as inadequate spatial reasoning and susceptibility to environmental occlusions,pose significant risks to the safety and reliability of autonomous driving systems.To...The inherent limitations of 2D object detection,such as inadequate spatial reasoning and susceptibility to environmental occlusions,pose significant risks to the safety and reliability of autonomous driving systems.To address these challenges,this paper proposes an enhanced 3D object detection framework(FastSECOND)based on an optimized SECOND architecture,designed to achieve rapid and accurate perception in autonomous driving scenarios.Key innovations include:(1)Replacing the Rectified Linear Unit(ReLU)activation functions with the Gaussian Error Linear Unit(GELU)during voxel feature encoding and region proposal network stages,leveraging partial convolution to balance computational efficiency and detection accuracy;(2)Integrating a Swin-Transformer V2 module into the voxel backbone network to enhance feature extraction capabilities in sparse data;and(3)Introducing an optimized position regression loss combined with a geometry-aware Focal-EIoU loss function,which incorporates bounding box geometric correlations to accelerate network convergence.While this study currently focuses exclusively on the detection of the Car category,with experiments conducted on the Car class of the KITTI dataset,future work will extend to other categories such as Pedestrian and Cyclist to more comprehensively evaluate the generalization capability of the proposed framework.Extensive experimental results demonstrate that our framework achieves a more effective trade-off between detection accuracy and speed.Compared to the baseline SECOND model,it achieves a 21.9%relative improvement in 3D bounding box detection accuracy on the hard subset,while reducing inference time by 14 ms.These advancements underscore the framework’s potential for enabling real-time,high-precision perception in autonomous driving applications.展开更多
With the development of machine translation technology,automatic pre-editing has attracted increasing research attention for its important role in improving translation quality and efficiency.This study utilizes UAM C...With the development of machine translation technology,automatic pre-editing has attracted increasing research attention for its important role in improving translation quality and efficiency.This study utilizes UAM Corpus Tool 3.0 to annotate and categorize 99 key publications between 1992 and 2024,tracing the research paths and technological evolution of automatic pre-translation editing.The study finds that current approaches can be classified into four categories:controlled language-based approaches,text simplification approaches,interlingua-based approaches,and large language model-driven approaches.By critically examining their technical features and applicability in various contexts,this review aims to provide valuable insights to guide the future optimization and expansion of pre-translation editing systems.展开更多
Traditional calibration method for the digital inclinometer relies on manual inspection,and results in its disadvantages of complicated process,low-efficiency and human errors easy to be introduced.To improve both the...Traditional calibration method for the digital inclinometer relies on manual inspection,and results in its disadvantages of complicated process,low-efficiency and human errors easy to be introduced.To improve both the calibration accuracy and efficiency of digital inclinometer,an automatic digital inclinometer calibration system was developed in this study,and a new display tube recognition algorithm was proposed.First,a high-precision automatic turntable was taken as the reference to calculate the indication error of the inclinometer.Then,the automatic inclinometer calibration control process and the digital inclinometer zero-setting function were formulated.For display tube recognition,a new display tube recognition algorithm combining threading method and feature extraction method was proposed.Finally,the calibration system was calibrated by photoelectric autocollimator and regular polygon mirror,and the calibration system error and repeatability were calculated via a series of experiments.The experimental results showed that the indication error of the proposed calibration system was less than 4",and the repeatability was 3.9".A digital inclinometer with the resolution of 0.1°was taken as a testing example,within the calibration points'range of[-90°,90°],the repeatability of the testing was 0.085°,and the whole testing process was less than 90 s.The digital inclinometer indication error is mainly introduced by the digital inclinometer resolution according to the uncertainty evaluation.展开更多
Vibration cutting has emerged as a promising method for creating surface functional microstructures.However,achieving precise tool setting is a time-consuming process that significantly impacts process efficiency.This...Vibration cutting has emerged as a promising method for creating surface functional microstructures.However,achieving precise tool setting is a time-consuming process that significantly impacts process efficiency.This study proposes an intelligent approach for tool setting in vibration cutting using machine vision and hearing,divided into two steps.In the first step,machine vision is employed to achieve rough precision in tool setting within tens of micrometers.Subsequently,in the second step,machine hearing utilizes sound pickup to capture vibration audio signals,enabling fine tool adjustment within 1μm precision.The relationship between the spectral intensity of vibration audio and cutting depth is analyzed to establish criteria for tool–workpiece contact.Finally,the efficacy of this approach is validated on an ultra-precision platform,demonstrating that the automated tool-setting process takes no more than 74 s.The total cost of the vision and hearing sensors is less than$1500.展开更多
Instance segmentation is crucial in various domains,such as autonomous driving and robotics.However,there is scope for improvement in the detection speed of instance-segmentation algorithms for edge devices.Therefore,...Instance segmentation is crucial in various domains,such as autonomous driving and robotics.However,there is scope for improvement in the detection speed of instance-segmentation algorithms for edge devices.Therefore,it is essential to enhance detection speed while maintaining high accuracy.In this study,we propose you only look once-layer fusion(YOLO-LF),a lightweight instance segmentation method specifically designed to optimize the speed of instance segmentation for autonomous driving applications.Based on the You Only Look Once version 8 nano(YOLOv8n)framework,we introduce a lightweight convolutional module and design a lightweight layer aggrega-tion module called Reparameterization convolution and Partial convolution Efficient Layer Aggregation Networks(RPELAN).This module effectively reduces the impact of redundant information generated by traditional convolutional stacking on the network size and detection speed while enhancing the capability to process feature information.We experimentally verified that our generalized one-stage detection network lightweight method based on Grouped Spatial Convolution(GSconv)enhances the detection speed while maintaining accuracy across various state-of-the-art(SOTA)networks.Our experiments conducted on the publicly available Cityscapes dataset demonstrated that YOLO-LF maintained the same accuracy as yolov8n(mAP@0.537.9%),the model volume decreased by 14.3%from 3.259 to=2.804 M,and the Frames Per Second(FPS)increased by 14.48%from 57.47 to 65.79 compared with YOLOv8n,thereby demonstrating its potential for real-time instance segmentation on edge devices.展开更多
The autonomous landing guidance of fixed-wing aircraft in unknown structured scenes presents a substantial technological challenge,particularly regarding the effectiveness of solutions for monocular visual relative po...The autonomous landing guidance of fixed-wing aircraft in unknown structured scenes presents a substantial technological challenge,particularly regarding the effectiveness of solutions for monocular visual relative pose estimation.This study proposes a novel airborne monocular visual estimation method based on structured scene features to address this challenge.First,a multitask neural network model is established for segmentation,depth estimation,and slope estimation on monocular images.And a monocular image comprehensive three-dimensional information metric is designed,encompassing length,span,flatness,and slope information.Subsequently,structured edge features are leveraged to filter candidate landing regions adaptively.By leveraging the three-dimensional information metric,the optimal landing region is accurately and efficiently identified.Finally,sparse two-dimensional key point is used to parameterize the optimal landing region for the first time and a high-precision relative pose estimation is achieved.Additional measurement information is introduced to provide the autonomous landing guidance information between the aircraft and the optimal landing region.Experimental results obtained from both synthetic and real data demonstrate the effectiveness of the proposed method in monocular pose estimation for autonomous aircraft landing guidance in unknown structured scenes.展开更多
During normal de-ballasting operations for floating docks,each ballast pump independently manages a specific group of ballast tanks.However,when a pump malfunctions,a connection valve between the two groups of ballast...During normal de-ballasting operations for floating docks,each ballast pump independently manages a specific group of ballast tanks.However,when a pump malfunctions,a connection valve between the two groups of ballast water systems is opened.This allows the adjacent pump to serve as a helper pump,simultaneously controlling two groups of ballast water systems.This study explores a full-scale floating dock’s dynamic behaviours during the de-ballasting operations under this situation through a numerical model.In the developed numerical model,the dock is described as a six-degree-of-freedom rigid body which is subjected to hydrostatic,hydrodynamic,and mooring loads.A hydraulic model of the piping network of the malfunctioning pump and the helper pump is proposed.A modified P-controller regulates opening angles of all tank valves for minimal pitch and roll.Two configurations of the floating dock,i.e.,a single floating dock and a floating dock with an onboard vessel,are considered.The numerical results show that the optimal helper pumps can be identified regarding the pumps’total de-ballasting capacity and the dock’s stability.The most severe scenarios can be determined in term of the dock’s maximum draught differences caused by its roll and pitch.The observed maximum draught differences remain small relative to the dock’s width,indicating the effectiveness of employing helper pumps and the proposed automatic ballast control strategy for one-pump malfunction scenarios.展开更多
基金supported in part by the National Key R&D Program of China under Grant 2022YFB3103500in part by the National Natural Science Foundation of China under Grant 62302195.
文摘Semantic secure communication is an emerging field that combines the principles of source-channel coding with the need for secure data transmission.It is of great significance in modern communications to protect the confidentiality and privacy of sensitive information and prevent information leaks and malicious attacks.This paper presents a novel approach to semantic secure communication through the utilization of joint source-channel coding,which is based on the design of an automated joint source-channel coding algorithm and an encryption and decryption algorithm based on semantic security.The traditional and state-of-the-art joint source-channel coding algorithms are selected as two baselines for different comparison purposes.Experimental results demonstrate that our proposed algorithm outperforms the first baseline algorithm,the traditional source-channel coding,by 61.21%in efficiency under identical channel conditions(SNR=15 dB).In security,our proposed method can resist 2 more types of attacks compared to the two baselines,exhibiting nearly no increases in time consumption and error rate compared to the state-of-the-art joint source-channel coding algorithm while the secure semantic communication is supported.
文摘【目的】应用线粒体DNA条形码技术对尤犀金龟属(Eupatorus Burmeister,1847)昆虫物种界定进行探索,以解决该属物种形态鉴定困难的问题。【方法】基于尤犀金龟属物种线粒体cox1和cox2基因序列数据集,使用Automatic Barcode Gap Discovery(ABGD)和Bayesian Poisson Tree Processes(bPTP)对3个形态种进行分子物种界定,并与形态学鉴定结果进行比较。【结果】使用ABGD方法时,cox1数据集的界定结果与形态学鉴定结果一致,cox2数据集的界定结果与形态学鉴定结果存在差异;使用bPTP方法时,2种数据集的界定结果均远高于形态学鉴定结果,且均存在不同程度的过度划分。【结论】cox1是更适合用于鉴定尤犀金龟属昆虫的DNA条形码,使用ABGD方法时,其数据集界定结果与形态学鉴定结果一致。利用分子界定与形态特征鉴定相结合,可极大地提高鉴定效率和准确性。
文摘Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control(MPC),which utilizes an extensive mathe-matical model of the voltage regulation system to optimize the control actions over a defined prediction horizon.This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints,thereby improving stability and performance under dynamic conditions.Thefindings were compared with those derived from an optimal proportional integral derivative(PID)con-troller designed using the artificial bee colony(ABC)algorithm.Although the ABC-PID method adjusts the PID parameters based on historical data,it may be difficult to adapt to real-time changes in system dynamics under constraints.Comprehensive simulations assessed both frameworks,emphasizing performance metrics such as disturbance rejection,response to load changes,and resilience to uncertainties.The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation;however,MPC excelled in controlling overshoot and settling time—recording 0.0%and 0.25 s,respectively.This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior,which exhibited settling times and overshoots exceeding 0.41 s and 5.0%,respectively.The controllers were implemented using MATLAB/Simulink software,indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.
文摘BACKGROUND A total of 100 patients diagnosed with mixed hemorrhoids from October 2022 to September 2023 in our hospital were randomly divided into groups by dice rolling and compared with the efficacy of different treatment options.AIM To analyze the clinical effect and prognosis of mixed hemorrhoids treated with polidocanol injection combined with automatic elastic thread ligation operation(RPH).METHODS A total of 100 patients with mixed hemorrhoids who visited our hospital from October 2022 to September 2023 were selected and randomly divided into the control group(n=50)and the treatment group(n=50)by rolling the dice.The procedure for prolapse and hemorrhoids(PPH)was adopted in the control group,while polidocanol foam injection+RPH was adopted in the treatment group.The therapeutic effects,operation time,wound healing time,hospital stay,pain situation(24 hours post-operative pain score,first defecation pain score),quality of life(QOL),incidence of complications(post-operative hemorrhage,edema,infection),incidence of anal stenosis 3 months post-operatively and recurrence rate 1 year post-operatively of the two groups were compared.RESULTS Compared with the control group,the total effective rate of treatment group was higher,and the difference was significant(P<0.05).The operation time/wound healing time/hospital stay in the treatment group were shorter than those in the control group(P<0.05).The pain scores at 24 hours after operation/first defecation pain score of the treatment group was significantly lower than those in the control group(P<0.05).After surgery,the QOL scores of the two groups decreased,with the treatment group having higher scores than that of the control group(P<0.05).Compared with the control group,the incidence of postoperative complications in the treatment group was lower,and the difference was significant(P<0.05);However,there was no significant difference in the incidence of postoperative bleeding between the two groups(P>0.05);There was no significant difference in the incidence of anal stenosis 3 months after operation and the recurrence rate 1 year after operation between the two groups(P>0.05).CONCLUSION For patients with mixed hemorrhoids,the therapeutic effect achieved by using polidocanol injection combined with RPH was better.The wounds of the patients healed faster,the postoperative pain was milder,QOL improved,and the incidence of complications was lower,and the short-term and long-term prognosis was good.
文摘In order to increase the stability of the Mongolia power system, a single-phase automatic reclosing device (SPAR) was introduced on double-circuit power lines built with a size of 330 kV, operating on a voltage of 220 kV and a length of 250 km. These overhead power lines (L-213, L-214) connect the 220/110/35 kV “Songino” substation with the “Mandal” substation and form system networks. This paper presents the challenges encountered when implementing single-phase automatic reclosing (SPAR) devices and compares the changes in power system parameters before and after SPAR deployment for a long 220 kV line. Simulations and analyses were carried out using DIgSILENT PowerFactory software, focusing on rotor angle stability, and the overall impact on the power system during short-circuit faults. The evaluation also utilized measurement data from the Wide Area Monitoring System (WAMS) to compare system behavior pre- and post-implementation of SPAR. The findings reveal that SPAR significantly enhances system reliability and stability, effectively mitigating the risk of oscillations and stability loss triggered by short circuits. This improvement contributes to a more resilient power system, reducing the potential for disturbances caused by faults.
文摘In this paper,the author follows the trail of C.Malabou,Q.Meillassoux,and G.Deleuze and tries to test three philosophical concepts that seem to be particularly threatened in the era of automatic digital reproduction.These three concepts are plasticity(defended for many years by C.Malabou),contingency(reconstructed by Q.Meillassoux),and virtuality(developed by G.Deleuze).The main task of the text will be to reflect on which of these three concepts better protects our thinking against automation and stays faithful to the ideal of creativity.In what sense are plasticity,contingency,and the possibility of virtualization the a priori condition of any transformation,physical or intellectual,affective or conceptual metamorphosis?In what sense are these three concepts the only conditions for the survival of every living being?Would a being without contingency,plasticity,and disposition to virtualization simply be a dead being?
文摘BACKGROUND Depression is a leading global health concern with high suicide rates and recurrence.Cognitive models suggest that mental pain and automatic thoughts are central to depression's impact.The hypothesis is that self-compassion will be negatively associated with mental pain,mediated by automatic thoughts.AIM To determine the mediating role of automatic thoughts in the relationship between self-compassion and mental pain in individuals with depression.METHODS This cross-sectional study included 389 inpatients with depression from Tianjin Anding Hospital.Participants completed the Self-Compassion Scale-Chinese Version(SCS-C),Automatic Thought Questionnaire(ATQ),and Orbach&Mikulincer Mental Pain Scale-Chinese Version(OMMP).Data were analyzed using Pearson correlations,multiple linear regressions,and mediation analysis.RESULTS The SCS-C total score was 68.95±14.89,ATQ was 87.02±28.91,and OMMP was 129.01±36.74.Correlation analysis showed mental pain was positively associated with automatic thoughts(r=0.802,P<0.001)and negatively with selfcompassion(r=-0.636,P<0.001).Regression analysis indicated automatic thoughts(β=0.623,P<0.001)and self-compassion(β=-0.301,P<0.001)significantly predicted mental pain.Mediation analysis confirmed automatic thoughts partially mediated the relationship between self-compassion and mental pain(ab=-0.269,95%CI:-0.363 to-0.212).CONCLUSION Self-compassion is inversely related to mental pain in depression,with automatic thoughts playing a mediating role.These findings suggest potential therapeutic targets for alleviating mental pain in depressed patients.
基金support from the Office of Polar Programs of the National Science Foundation(Grant Nos.2205398,2233182,1951720,1951603,2301362).
文摘The 19th Workshop on Antarctic Meteorology and Climate(WAMC)and the 8th Year of Polar Prediction in the Southern Hemisphere(YOPP-SH)meeting were held in June 2024 at the Byrd Polar and Climate Research Center,The Ohio State University,Columbus,Ohio.These hybrid events convened 79 participants from 15 nations to foster international collaboration on Antarctic meteorology,climate research,and forecasting.The WAMC featured presentations on automatic weather stations,numerical weather prediction,Antarctic sea ice dynamics,and extreme weather events.The YOPP-SH meeting emphasized the positive impacts of enhanced observations during the 2022 Winter Special Observing Period(SOP)on forecast accuracy and addressed the transition toward the Polar Coupled Analysis and Prediction for Services(PCAPS)initiative.The outcomes reflect significant advancements in polar meteorological research and underscore the importance of sustained collaborative efforts,including improved observational networks and advanced modeling systems,to address the unique challenges of Antarctic meteorology.Future workshops will continue to support and expand upon these critical themes.
基金Supported by Jiangsu Provincial Natural Science Foundation(Grant No.BK20231497)Jiangsu Provincial Post graduate Research&Practice Innovation Program(Grant No.KYCX25_2982)+3 种基金China University of Mining and Technology Graduate Innovation Program(Grant No.2025WLKXJ094)National Natural Science Foundation of China(Grant No.51975573)National Key R&D Program of China(Grant No.2022YFC2905600)Priority Academic Program Development of Jiangsu Higher Education Institute of China.
文摘A rotary sealing device that automatically compensates for wear is designed to address the issues of easy wear and the short service life of the rotary sealing device with automatic wear compensation in mining machinery.After the end face of the guide sleeve wears out,it still tightly adheres to the sealing valve seat under the pressure difference,achieving automatic wear compensation.Based on fluid-solid coupling technology,the structural strength of the rotary sealing device was checked.The influence of factors on the sealing performance of rotary sealing devices was studied using the control variable method.The results show that as the pressure of water increases,the leakage rate of the sealing device decreases,and after 30 MPa,the leakage rate is almost 0 mL/h.The temperature of the rotating sealing device increases with the increase of rotation speed or pressure,and the temperature is more affected by the rotation speed factor.The frictional torque increases with increasing pressure and is independent of rotational speed.Comprehensive analysis shows that the wear resistance and reliability level of the sealing guide sleeve material is PVDF>PEEK>PE>PA.This study designs a high-pressure automatic compensation wear rotary sealing device and selects the optimal sealing material,providing technical support for the application of high-pressure water jet in mining machinery.
文摘Coasts are subject to multiple natural hazards,which are increasing nowadays.Coastal flooding and erosion are some of the most common hazards affecting coastlines.Being aware of the vulnerability of coasts is important to achieve integrated coastal management.The coastal vulnerability index(CVI)is a common index used to assess coastal vulnerability because it is easily calculated.However,given that its calculation includes numerous manual steps,it requires considerable time,which is often unavailable,to produce accurate and utilizable results.In this work,we developed a ModelBuilder model by using the tools provided by ArcGIS Pro(ESRI).Through this model,we automatized most of the steps involved in CVI calculation.We applied the ModelBuilder model in the northern Peloponnese,for which the CVI has already been calculated in three other works.We were thus able to assess the effectiveness of our ModelBuilder model.Our results demonstrated that through the ModelBuilder,most of the processes could effectively be automatized without problems,and our results are consistent with the findings of previous works in our study area.
基金supported in part by the Jiangsu Province Construction System Science and Technology Project(No.2024ZD056)the Research Development Fund of Xi’an Jiaotong-Liverpool University(No.RDF-24-01-097).
文摘Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalization performance in for crack detection.Complex network model can generate redundant feature maps and computational complexity.Therefore,this paper proposes a novel model compression framework based on deep learning to detect road cracks,which can improve the detection efficiency and accuracy.A distillation loss function is proposed to compress the teacher model,followed by channel pruning.Meanwhile,a multi-dilation model is proposed to improve the accuracy of the model pruned.The proposed method is tested on the public database CrackForest dataset(CFD).The experimental results show that the proposed method is more efficient and accurate than other state-of-art methods.
基金supported in part by the National Natural Science Foundation of China under Grants 61861007in part by the Guizhou Province Science and Technology Planning Project ZK[2021]303in part by the Guizhou Province Science Technology Support Plan under Grants[2022]264,[2023]096,[2023]412 and[2023]409.
文摘Brain tumors are neoplastic diseases caused by the proliferation of abnormal cells in brain tissues,and their appearance may lead to a series of complex symptoms.However,current methods struggle to capture deeper brain tumor image feature information due to the variations in brain tumor morphology,size,and complex background,resulting in low detection accuracy,high rate of misdiagnosis and underdiagnosis,and challenges in meeting clinical needs.Therefore,this paper proposes the CMS-YOLO network model for multi-category brain tumor detection,which is based on the You Only Look Once version 10(YOLOv10s)algorithm.This model innovatively integrates the Convolutional Medical UNet extended block(CMUNeXt Block)to design a brand-new CSP Bottleneck with 2 convolutions(C2f)structure,which significantly enhances the ability to extract features of the lesion area.Meanwhile,to address the challenge of complex backgrounds in brain tumor detection,a Multi-Scale Attention Aggregation(MSAA)module is introduced.The module integrates features of lesions at different scales,enabling the model to effectively capture multi-scale contextual information and enhance detection accuracy in complex scenarios.Finally,during the model training process,the Shape-IoU loss function is employed to replace the Complete-IoU(CIoU)loss function for optimizing bounding box regression.This ensures that the predicted bounding boxes generated by the model closely match the actual tumor contours,thereby further enhancing the detection precision.The experimental results show that the improved method achieves 94.80%precision,93.60%recall,96.20%score,and 79.60%on the MRI for Brain Tumor with Bounding Boxes dataset.Compared to the YOLOv10s model,this represents improvements of 1.0%,1.1%,1.0%,and 1.1%,respectively.The method can achieve automatic detection and localization of three distinct categories of brain tumors—glioma,meningioma,and pituitary tumor,which can accurately detect and identify brain tumors,assist doctors in early diagnosis,and promote the development of early treatment.
基金Supported by Funding for Clinical Trials from the Nanjing Drum Tower Hospital,Affiliated Hospital of Medical School,Nanjing University,No.2021-LCYJ-MS-11.
文摘Esophageal cancer(EC),a common malignant tumor of the digestive tract,requires early diagnosis and timely treatment to improve patient prognosis.Automated detection of EC using medical imaging has the potential to increase screening efficiency and diagnostic accuracy,thereby significantly improving long-term survival rates and the quality of life of patients.Recent advances in deep learning(DL),particularly convolutional neural networks,have demons-trated remarkable performance in medical imaging analysis.These techniques have shown significant progress in the automated identification of malignant tumors,quantitative analysis of lesions,and improvement in diagnostic accuracy and efficiency.This article comprehensively examines the research progress of DL in medical imaging for EC,covering various imaging modalities such as digital pathology,endoscopy,computed tomography,etc.It explores the clinical value and application prospects of DL in EC screening and diagnosis.Additionally,the article addresses several critical challenges that must be overcome for the clinical translation of DL techniques,including constructing high-quality datasets,promoting multimodal feature fusion,and optimizing artificial intelligence-clinical workflow integration.By providing a detailed overview of the current state of DL in EC imaging and highlighting the key challenges and future directions,this article aims to guide future research and facilitate the clinical implementation of DL technologies in EC management,ultimately contributing to better patient outcomes.
基金funded by the National Key R&D Program of China(Grant No.2022YFB4703400)the China Three Gorges Corporation(Grant No.2324020012)+2 种基金the National Natural Science Foundation of China(Grant No.62476080)the Jiangsu Province Natural Science Foundation(Grant No.BK20231186)Key Laboratory about Maritime Intelligent Network Information Technology of the Ministry of Education(EKLMIC202405).
文摘The inherent limitations of 2D object detection,such as inadequate spatial reasoning and susceptibility to environmental occlusions,pose significant risks to the safety and reliability of autonomous driving systems.To address these challenges,this paper proposes an enhanced 3D object detection framework(FastSECOND)based on an optimized SECOND architecture,designed to achieve rapid and accurate perception in autonomous driving scenarios.Key innovations include:(1)Replacing the Rectified Linear Unit(ReLU)activation functions with the Gaussian Error Linear Unit(GELU)during voxel feature encoding and region proposal network stages,leveraging partial convolution to balance computational efficiency and detection accuracy;(2)Integrating a Swin-Transformer V2 module into the voxel backbone network to enhance feature extraction capabilities in sparse data;and(3)Introducing an optimized position regression loss combined with a geometry-aware Focal-EIoU loss function,which incorporates bounding box geometric correlations to accelerate network convergence.While this study currently focuses exclusively on the detection of the Car category,with experiments conducted on the Car class of the KITTI dataset,future work will extend to other categories such as Pedestrian and Cyclist to more comprehensively evaluate the generalization capability of the proposed framework.Extensive experimental results demonstrate that our framework achieves a more effective trade-off between detection accuracy and speed.Compared to the baseline SECOND model,it achieves a 21.9%relative improvement in 3D bounding box detection accuracy on the hard subset,while reducing inference time by 14 ms.These advancements underscore the framework’s potential for enabling real-time,high-precision perception in autonomous driving applications.
基金supported by Chunhui Collaborative Research Project funded by the Ministry of Education of China[Grant No.202200490]Humanities and Social Sciences Research Project funded by the Ministry of Education of China[Grant No.23YJAZH139].
文摘With the development of machine translation technology,automatic pre-editing has attracted increasing research attention for its important role in improving translation quality and efficiency.This study utilizes UAM Corpus Tool 3.0 to annotate and categorize 99 key publications between 1992 and 2024,tracing the research paths and technological evolution of automatic pre-translation editing.The study finds that current approaches can be classified into four categories:controlled language-based approaches,text simplification approaches,interlingua-based approaches,and large language model-driven approaches.By critically examining their technical features and applicability in various contexts,this review aims to provide valuable insights to guide the future optimization and expansion of pre-translation editing systems.
基金the National Natural Science Foundation of China(No.61927822)。
文摘Traditional calibration method for the digital inclinometer relies on manual inspection,and results in its disadvantages of complicated process,low-efficiency and human errors easy to be introduced.To improve both the calibration accuracy and efficiency of digital inclinometer,an automatic digital inclinometer calibration system was developed in this study,and a new display tube recognition algorithm was proposed.First,a high-precision automatic turntable was taken as the reference to calculate the indication error of the inclinometer.Then,the automatic inclinometer calibration control process and the digital inclinometer zero-setting function were formulated.For display tube recognition,a new display tube recognition algorithm combining threading method and feature extraction method was proposed.Finally,the calibration system was calibrated by photoelectric autocollimator and regular polygon mirror,and the calibration system error and repeatability were calculated via a series of experiments.The experimental results showed that the indication error of the proposed calibration system was less than 4",and the repeatability was 3.9".A digital inclinometer with the resolution of 0.1°was taken as a testing example,within the calibration points'range of[-90°,90°],the repeatability of the testing was 0.085°,and the whole testing process was less than 90 s.The digital inclinometer indication error is mainly introduced by the digital inclinometer resolution according to the uncertainty evaluation.
基金the financial support for this research provided by the National Natural Science Foundation of China(Grant Nos.52275470,124115301,and 52105458)the Natural Science Foundation of Beijing(Grant No.3222009).
文摘Vibration cutting has emerged as a promising method for creating surface functional microstructures.However,achieving precise tool setting is a time-consuming process that significantly impacts process efficiency.This study proposes an intelligent approach for tool setting in vibration cutting using machine vision and hearing,divided into two steps.In the first step,machine vision is employed to achieve rough precision in tool setting within tens of micrometers.Subsequently,in the second step,machine hearing utilizes sound pickup to capture vibration audio signals,enabling fine tool adjustment within 1μm precision.The relationship between the spectral intensity of vibration audio and cutting depth is analyzed to establish criteria for tool–workpiece contact.Finally,the efficacy of this approach is validated on an ultra-precision platform,demonstrating that the automated tool-setting process takes no more than 74 s.The total cost of the vision and hearing sensors is less than$1500.
基金supported by Science and Technology Research Youth Project of Chongqing Municipal Education Commission(No.KJQN202301104)Cooperative Project between universities in Chongqing and Affiliated Institutes of Chinese Academy of Sciences(No.HZ2021011)+1 种基金Chongqing Municipal Science and Technology Commission Technology Innovation and Application Development Special Project(No.2022TIAD-KPX0040)Action Plan for Quality Development of Chongqing University of Technology Graduate Education(Grant No.gzlcx20242014).
文摘Instance segmentation is crucial in various domains,such as autonomous driving and robotics.However,there is scope for improvement in the detection speed of instance-segmentation algorithms for edge devices.Therefore,it is essential to enhance detection speed while maintaining high accuracy.In this study,we propose you only look once-layer fusion(YOLO-LF),a lightweight instance segmentation method specifically designed to optimize the speed of instance segmentation for autonomous driving applications.Based on the You Only Look Once version 8 nano(YOLOv8n)framework,we introduce a lightweight convolutional module and design a lightweight layer aggrega-tion module called Reparameterization convolution and Partial convolution Efficient Layer Aggregation Networks(RPELAN).This module effectively reduces the impact of redundant information generated by traditional convolutional stacking on the network size and detection speed while enhancing the capability to process feature information.We experimentally verified that our generalized one-stage detection network lightweight method based on Grouped Spatial Convolution(GSconv)enhances the detection speed while maintaining accuracy across various state-of-the-art(SOTA)networks.Our experiments conducted on the publicly available Cityscapes dataset demonstrated that YOLO-LF maintained the same accuracy as yolov8n(mAP@0.537.9%),the model volume decreased by 14.3%from 3.259 to=2.804 M,and the Frames Per Second(FPS)increased by 14.48%from 57.47 to 65.79 compared with YOLOv8n,thereby demonstrating its potential for real-time instance segmentation on edge devices.
基金co-supported by the Science and Technology Innovation Program of Hunan Province,China(No.2023RC3023)the National Natural Science Foundation of China(No.12272404)。
文摘The autonomous landing guidance of fixed-wing aircraft in unknown structured scenes presents a substantial technological challenge,particularly regarding the effectiveness of solutions for monocular visual relative pose estimation.This study proposes a novel airborne monocular visual estimation method based on structured scene features to address this challenge.First,a multitask neural network model is established for segmentation,depth estimation,and slope estimation on monocular images.And a monocular image comprehensive three-dimensional information metric is designed,encompassing length,span,flatness,and slope information.Subsequently,structured edge features are leveraged to filter candidate landing regions adaptively.By leveraging the three-dimensional information metric,the optimal landing region is accurately and efficiently identified.Finally,sparse two-dimensional key point is used to parameterize the optimal landing region for the first time and a high-precision relative pose estimation is achieved.Additional measurement information is introduced to provide the autonomous landing guidance information between the aircraft and the optimal landing region.Experimental results obtained from both synthetic and real data demonstrate the effectiveness of the proposed method in monocular pose estimation for autonomous aircraft landing guidance in unknown structured scenes.
基金The article is a result of joined research performed during the project:“A Floating Dock Digital Twin towards Efficient,Safer and Autonomous Docking Operations”-NOR/POLNOR/DigiFloDock/0009/2019-00 which is cofinanced by the programme“Applied research”under the Norwegian Financial Mechanisms 2014-2021 POLNOR 2019-Digital and Industry.
文摘During normal de-ballasting operations for floating docks,each ballast pump independently manages a specific group of ballast tanks.However,when a pump malfunctions,a connection valve between the two groups of ballast water systems is opened.This allows the adjacent pump to serve as a helper pump,simultaneously controlling two groups of ballast water systems.This study explores a full-scale floating dock’s dynamic behaviours during the de-ballasting operations under this situation through a numerical model.In the developed numerical model,the dock is described as a six-degree-of-freedom rigid body which is subjected to hydrostatic,hydrodynamic,and mooring loads.A hydraulic model of the piping network of the malfunctioning pump and the helper pump is proposed.A modified P-controller regulates opening angles of all tank valves for minimal pitch and roll.Two configurations of the floating dock,i.e.,a single floating dock and a floating dock with an onboard vessel,are considered.The numerical results show that the optimal helper pumps can be identified regarding the pumps’total de-ballasting capacity and the dock’s stability.The most severe scenarios can be determined in term of the dock’s maximum draught differences caused by its roll and pitch.The observed maximum draught differences remain small relative to the dock’s width,indicating the effectiveness of employing helper pumps and the proposed automatic ballast control strategy for one-pump malfunction scenarios.
