Traditional psychological treatment methods often require a long time and have limited effects.Researchers have begun to explore the combination of brain-computer interface(BCI)technology and mental health,providing n...Traditional psychological treatment methods often require a long time and have limited effects.Researchers have begun to explore the combination of brain-computer interface(BCI)technology and mental health,providing new possibilities for the treatment and rehabilitation of mental illnesses.This paper reviews the advantages,existing risks,and challenges of BCI technology in mental health treatment,and prospects the future development of research on BCI and mental health.展开更多
Microwave ablation(MWA)is a minimally invasive technique for treating hepatic tumors,necessitating precise monitoring to ensure treatment efficacy and minimize damage to surrounding tissues.This study explores the pot...Microwave ablation(MWA)is a minimally invasive technique for treating hepatic tumors,necessitating precise monitoring to ensure treatment efficacy and minimize damage to surrounding tissues.This study explores the potential of photoacoustic imaging(PAI)in monitoring MWA by examining ex vivo porcine liver tissues.In this study,a comprehensive analysis of photoacoustic signals was performed to compare the main lobe width(MLW)between ablated and normal regions in porcine liver tissue.Histological staining with succinate dehydrogenase(SDH)and shear wave elastography(SWE)were employed to validate the changes in tissue elasticity after ablation.The analysis demonstrated a notable reduction in the MLW of the average A-lines in ablated tissues compared to nonablated regions(p<0.01).This reduction,attributed to increased tissue density and enhanced elasticity,indicates accelerated sound propagation in thermally ablated areas,which then serves as a critical parameter for mapping tissue characteristics.The reconstruction of the MLW distribution successfully delineated the ablated regions,and was consistent with the results of SDH staining and SWE.In addition,MLW-based imaging exhibited higher spatial resolution compared to SWE.Incorporating MLW analysis into PAI may be a promising strategy to improve the accuracy and effectiveness of MWA monitoring in clinical settings.展开更多
t In this paper an overall scheme of the task management system of ternary optical computer （TOC） is proposed, and the software architecture chart is given. The function and accomplishment of each module in the syst...t In this paper an overall scheme of the task management system of ternary optical computer （TOC） is proposed, and the software architecture chart is given. The function and accomplishment of each module in the system are described in general. In addition, according to the aforementioned scheme a prototype of TOC task management system is implemented, and the feasibility, rationality and completeness of the scheme are verified via running and testing the prototype.展开更多
Reconfiguration is the key to produce an applicable ternary optical computer （TOC）. The method to implement the reconfiguration function determines whether a TOC can step into applied fields or not. In this work, a ...Reconfiguration is the key to produce an applicable ternary optical computer （TOC）. The method to implement the reconfiguration function determines whether a TOC can step into applied fields or not. In this work, a design of the reconfiguration circuit based on field programmable gates array （FPGA） is proposed, and the structure of the entire hardware system is discussed.展开更多
The division operation is not frequent relatively in traditional applications, but it is increasingly indispensable and important in many modern applications. In this paper, the implementation of modified signed-digit...The division operation is not frequent relatively in traditional applications, but it is increasingly indispensable and important in many modern applications. In this paper, the implementation of modified signed-digit （MSD） floating-point division using Newton-Raphson method on the system of ternary optical computer （TOC） is studied. Since the addition of MSD floating-point is carry-free and the digit width of the system of TOC is large, it is easy to deal with the enough wide data and transform the division operation into multiplication and addition operations. And using data scan and truncation the problem of digits expansion is effectively solved in the range of error limit. The division gets the good results and the efficiency is high. The instance of MSD floating-point division shows that the method is feasible.展开更多
Aiming at the problems such as low throughput and unbalanced load of data center network caused by traditional multipath routing strategy,a dynamic load balancing strategy for flow classification oriented to Fat-Tree ...Aiming at the problems such as low throughput and unbalanced load of data center network caused by traditional multipath routing strategy,a dynamic load balancing strategy for flow classification oriented to Fat-Tree topology based on the software defined network(SDN)architecture is proposed,named DLB-FC.Multi-index evaluation methods such as link state information and network traffic characteristics are considered.DLB-FC mechanism can dynamically adjust the flow classification threshold to differentiate between large and small flows.The scheme selects different forwarding paths to meet the transmission performance requirements of different flow characteristics.On this basis,an SDN simulation platform is built for performance testing.The simulation results show that DLB-FC algorithm can dynamically distinguish large flows from small flows and achieve load balancing effectively.Compared with equal-cost multi-path(ECMP),global first fit(GFF)and minmum total delay load routing(MTDLR)algorithms,DLB-FC scheme improves the network throughput and link utilization of the data center network effectively.The transmission delay is also reduced with better load balance.展开更多
In the new era,the impact of emerging productive forces has permeated every sector of industry.As the core production factor of these forces,data plays a pivotal role in industrial transformation and social developmen...In the new era,the impact of emerging productive forces has permeated every sector of industry.As the core production factor of these forces,data plays a pivotal role in industrial transformation and social development.Consequently,many domestic universities have introduced majors or courses related to big data.Among these,the Big Data Management and Applications major stands out for its interdisciplinary approach and emphasis on practical skills.However,as an emerging field,it has not yet accumulated a robust foundation in teaching theory and practice.Current instructional practices face issues such as unclear training objectives,inconsistent teaching methods and course content,insufficient integration of practical components,and a shortage of qualified faculty-factors that hinder both the development of the major and the overall quality of education.Taking the statistics course within the Big Data Management and Applications major as an example,this paper examines the challenges faced by statistics education in the context of emerging productive forces and proposes corresponding improvement measures.By introducing innovative teaching concepts and strategies,the teaching system for professional courses is optimized,and authentic classroom scenarios are recreated through illustrative examples.Questionnaire surveys and statistical analyses of data collected before and after the teaching reforms indicate that the curriculum changes effectively enhance instructional outcomes,promote the development of the major,and improve the quality of talent cultivation.展开更多
In recent years,due to the scarcity of domestic radioisotopes,the Chinese government has strongly supported the development of dedicated radioisotope production facilities.This paper presents conceptual design simulat...In recent years,due to the scarcity of domestic radioisotopes,the Chinese government has strongly supported the development of dedicated radioisotope production facilities.This paper presents conceptual design simulations of an 11 MeV,50μA,H^(-) compact superconducting cyclotron for radioisotope production.This paper focuses primarily on four aspects:magnet system design,central region configuration,beam dynamics analysis,and extraction system design.This paper outlines the cyclotron's primary parameters and key steps in the development process.展开更多
Passive source imaging can reconstruct body wave reflections similar to those of active sources through seismic interferometry(SI).It has become a low-cost,environmentally friendly alternative to active source seismic...Passive source imaging can reconstruct body wave reflections similar to those of active sources through seismic interferometry(SI).It has become a low-cost,environmentally friendly alternative to active source seismic,showing great potential.However,this method faces many challenges in practical applications,including uneven distribution of underground sources and complex survey environments.These situations seriously affect the reconstruction quality of virtual shot records,resulting in unguaranteed imaging results and greatly limiting passive source seismic exploration applications.In addition,the quality of the reconstructed records is directly related to the time length of the noise records,but in practice it is often difficult to obtain long-term,high-quality noise segments containing body wave events.To solve the above problems,we propose a deep learning method for reconstructing passive source virtual shot records and apply it to passive source time-lapse monitoring.This method combines the UNet network and the BiLSTM(Bidirectional Long Short-Term Memory)network for extracting spatial features and temporal features respectively.It introduces the spatial attention mechanism to establish a hybrid SUNet-BiLSTM-Attention(SBA)network for supervised training.Through pre-training and fine-tuning training,the network can accurately reconstruct passive source virtual shot records directly from short-time noisy segments containing body wave events.The experimental results of theoretical data show that the virtual shot records reconstructed by the network have high resolution and signal to noise ratio(SNR),providing high-quality data for subsequent monitoring and imaging.Finally,to further validate the effectiveness of proposed method,we applied it to field data collected from gas storage in northwest China.The reconstruction results of field data effectively improve the quality of virtual records and obtain more reliable time-lapse imaging monitoring results,which have significant practical value.展开更多
In fire rescue scenarios,traditional manual operations are highly dangerous,as dense smoke,low visibility,extreme heat,and toxic gases not only hinder rescue efficiency but also endanger firefighters’safety.Although ...In fire rescue scenarios,traditional manual operations are highly dangerous,as dense smoke,low visibility,extreme heat,and toxic gases not only hinder rescue efficiency but also endanger firefighters’safety.Although intelligent rescue robots can enter hazardous environments in place of humans,smoke poses major challenges for human detection algorithms.These challenges include the attenuation of visible and infrared signals,complex thermal fields,and interference frombackground objects,all ofwhichmake it difficult to accurately identify trapped individuals.To address this problem,we propose VIF-YOLO,a visible–infrared fusion model for real-time human detection in dense smoke environments.The framework introduces a lightweight multimodal fusion(LMF)module based on learnable low-rank representation blocks to end-to-end integrate visible and infrared images,preserving fine details while enhancing salient features.In addition,an efficient multiscale attention(EMA)mechanism is incorporated into the YOLOv10n backbone to improve feature representation under low-light conditions.Extensive experiments on our newly constructedmultimodal smoke human detection(MSHD)dataset demonstrate thatVIF-YOLOachievesmAP50 of 99.5%,precision of 99.2%,and recall of 99.3%,outperforming YOLOv10n by a clear margin.Furthermore,when deployed on the NVIDIA Jetson Xavier NX,VIF-YOLO attains 40.6 FPS with an average inference latency of 24.6 ms,validating its real-time capability on edge-computing platforms.These results confirm that VIF-YOLO provides accurate,robust,and fast detection across complex backgrounds and diverse smoke conditions,ensuring reliable and rapid localization of individuals in need of rescue.展开更多
Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional ...Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance.展开更多
With an increase in internet-connected devices and a dependency on online services,the threat of Distributed Denial of Service(DDoS)attacks has become a significant concern in cybersecurity.The proposed system follows...With an increase in internet-connected devices and a dependency on online services,the threat of Distributed Denial of Service(DDoS)attacks has become a significant concern in cybersecurity.The proposed system follows a multi-step process,beginning with the collection of datasets from different edge devices and network nodes.To verify its effectiveness,experiments were conducted using the CICDoS2017,NSL-KDD,and CICIDS benchmark datasets alongside other existing models.Recursive feature elimination(RFE)with random forest is used to select features from the CICDDoS2019 dataset,on which a BiLSTM model is trained on local nodes.Local models are trained until convergence or stability criteria are met while simultaneously sharing the updates globally for collaborative learning.A centralised server evaluates real-time traffic using the global BiLSTM model,which triggers alerts for potential DDoS attacks.Furthermore,blockchain technology is employed to secure model updates and to provide an immutable audit trail,thereby ensuring trust and accountability among network nodes.This research introduces a novel decentralized method called Federated Random Forest Bidirectional Long Short-Term Memory(FRF-BiLSTM)for detecting DDoS attacks,utilizing the advanced Bidirectional Long Short-Term Memory Networks(BiLSTMs)to analyze sequences in both forward and backward directions.The outcome shows the proposed model achieves a mean accuracy of 97.1%with an average training delay of 88.7 s and testing delay of 21.4 s.The model demonstrates scalability and the best detection performance in large-scale attack scenarios.展开更多
In order to assure quality and control process in the development of the aircraft collaborative design software, a maturity assessment model is proposed. The requirements designing—house of quality is designed to eva...In order to assure quality and control process in the development of the aircraft collaborative design software, a maturity assessment model is proposed. The requirements designing—house of quality is designed to evaluate the maturity degree of the solution, and the evaluation results can help to manage and control the development process. Furthermore, a fuzzy evaluation method based on the minimum deviation is proposed to deal with the fuzzy information. The quantitative evaluation result of the maturity degree can be calculated by optimizing the semantic discount factor aim for the minimum deviation. Finally, this model is illustrated and analyzed by an example study of the aircraft collaborative design software.展开更多
This paper investigates the distributed fault-tolerant consensus tracking problem of nonlinear multi-agent systems with general incipient and abrupt time-varying actuator faults under cyber-attacks.First,a decentraliz...This paper investigates the distributed fault-tolerant consensus tracking problem of nonlinear multi-agent systems with general incipient and abrupt time-varying actuator faults under cyber-attacks.First,a decentralized unknown input observer is established to estimate relative states and actuator faults.Second,the estimated and output neighboring information is combined with distributed fault-tolerant consensus tracking controllers.Criteria of reaching leader-following exponential consensus tracking of multi-agent systems under both connectivity-maintained and connectivity-mixed attacks are derived with average dwelling time,attack frequency,and attack activation rate technique,respectively.Simulation example verifies the effectiveness of the fault-tolerant consensus tracking algorithm.展开更多
Based on the features of marine environmental data and processing requirements, a cloud computing archi- tecture of marine environment information is proposed, which provides a new cloud technology framework for the i...Based on the features of marine environmental data and processing requirements, a cloud computing archi- tecture of marine environment information is proposed, which provides a new cloud technology framework for the integration and sharing of marine environmental information resources. A physical layer, software platform layer and an application layer are illustrated systematically, at the same time, a corresponding solu- tions for many difficult technical problems such as parallel query processing of multi-dimensional, spatio- temporal information, data slice storage, software service flow customization, analysis, reorganization and so on. A prototype system is developed and many different data-size experiments and a comparative analy- sis are done based on it. The experiment results show that the cloud platform based on this framework can achieve high performance and scalability when dealing with large-scale marine data.展开更多
Energy consumption and acoustic noise can be significantly reduced through perching in the sustained flights of small Unmanned Aerial Vehicles(UAVs).However,the existing flying perching robots lack good adaptability o...Energy consumption and acoustic noise can be significantly reduced through perching in the sustained flights of small Unmanned Aerial Vehicles(UAVs).However,the existing flying perching robots lack good adaptability or loading capacity in unstructured environments.Aiming at solving these problems,a deformable UAV perching mechanism with strong adaptability and high loading capacity,which is inspired by the structure and movements of birds'feet,is presented in this paper.Three elastic toes,an inverted crank slider mechanism used to realize the opening and closing movements,and a gear mechanism used to deform between two configurations are included in this mechanism.With experiments on its performance towards different objects,Results show that it can perch on various objects reliably,and its payload is more than 15 times its weight.By integrating it with a quadcopter,it can perch on different types of targets in outdoor environments,such as tree branches,cables,eaves,and spherical lamps.In addition,the energy consumption of the UAV perching system when perching on objects can be reduced to 0.015 times that of hovering.展开更多
As a coprocessor, field-programmable gate array （FPGA） is the hardware computing processor accelerating the computing capacity of coraputers. To efficiently manage the hardware free resources for the placing of task...As a coprocessor, field-programmable gate array （FPGA） is the hardware computing processor accelerating the computing capacity of coraputers. To efficiently manage the hardware free resources for the placing of tasks on FPGA and take full advantage of the partially reconfigurable units, good utilization of chip resources is an important and necessary work. In this paper, a new method is proposed to find the complete set of maximal free resource rectangles based on the cross point of edge lines of running tasks on FPGA area, and the prove process is provided to make sure the correctness of this method.展开更多
Fault diagnosis is a key issue of the CCBII(computer controlled brake II) braking system, because the CCBII braking system is very complicated and nonlinear, which may exhibit isolated and multi-component coupled faul...Fault diagnosis is a key issue of the CCBII(computer controlled brake II) braking system, because the CCBII braking system is very complicated and nonlinear, which may exhibit isolated and multi-component coupled faults. A parity space-based method was proposed for fault diagnosis of CCBII braking systems. Firstly, the mathematical models were established according to three function modules of CCBII braking systems where the air fluid theory was utilized. Then, parity vector and threshold function were designed for each output of the system so as to identify more system faults. Fault character matrix was built based on the causal relationship between the output and the fault according to the system function and internal structure. Finally, fault detection and isolation can be realized by the comparison of the observed system output and the fault character matrix. Simulation results show that the proposed method is entirely feasible and effective.展开更多
Laser scanning confocal endomicroscope(LSCEM)has emerged as an imaging modality which provides noninvasive,in vivo imaging of biological tissue on a microscopic scale.Scientific visualizations for LSCEM datasets captu...Laser scanning confocal endomicroscope(LSCEM)has emerged as an imaging modality which provides noninvasive,in vivo imaging of biological tissue on a microscopic scale.Scientific visualizations for LSCEM datasets captured by current imaging systems require these datasets to be fully acquired and brought to a separate rendering machine.To extend the features and capabilities of this modality,we propose a system which is capable of performing realtime visualization of LSCEM datasets.Using field-programmable gate arrays,our system performs three tasks in parallel:(1)automated control of dataset acquisition;(2)imaging-rendering system synchronization;and(3)realtime volume rendering of dynamic datasets.Through fusion of LSCEM imaging and volume rendering processes,acquired datasets can be visualized in realtime to provide an immediate perception of the image quality and biological conditions of the subject,further assisting in realtime cancer diagnosis.Subsequently,the imaging procedure can be improved for more accurate diagnosis and reduce the need for repeating the process due to unsatisfactory datasets.展开更多
Unmanned combat system is one of the important means to capture information superiority,carry out precision strike and accomplish special combat tasks in information war.Unmanned attack strategy plays a crucial role i...Unmanned combat system is one of the important means to capture information superiority,carry out precision strike and accomplish special combat tasks in information war.Unmanned attack strategy plays a crucial role in unmanned combat system,which has to ensure the attack by unmanned surface vehicles(USVs)from failure.To meet the challenge,we propose a task allocation algorithm called distributed auction mechanism task allocation with grey wolf optimization(DAGWO).The traditional grey wolf optimization(GWO)algorithm is improved with a distributed auction mechanism(DAM)to constrain the initialization of wolves,which improves the optimization process according to the actual situation.In addition,one unmanned aerial vehicle(UAV)is employed as the central control system to establish task allocation model and construct fitness function for the multiple constraints of USV attack problem.The proposed DAGWO algorithm can not only ensure the diversity of wolves,but also avoid the local optimum problem.Simulation results show that the proposed DAGWO algorithm can effectively solve the problem of attack task allocation among multiple USVs.展开更多
文摘Traditional psychological treatment methods often require a long time and have limited effects.Researchers have begun to explore the combination of brain-computer interface(BCI)technology and mental health,providing new possibilities for the treatment and rehabilitation of mental illnesses.This paper reviews the advantages,existing risks,and challenges of BCI technology in mental health treatment,and prospects the future development of research on BCI and mental health.
基金supported by the National Natural Science Foundation of China(82427808,61875085)the Jiangsu Provincial University Natural Science Foundation(25KJB413004)+1 种基金the Nanjing Health Science and Technology Development Foundation(ZKX24043)the Fundamental Research Funds for the Central Universities(NJ2024029).
文摘Microwave ablation(MWA)is a minimally invasive technique for treating hepatic tumors,necessitating precise monitoring to ensure treatment efficacy and minimize damage to surrounding tissues.This study explores the potential of photoacoustic imaging(PAI)in monitoring MWA by examining ex vivo porcine liver tissues.In this study,a comprehensive analysis of photoacoustic signals was performed to compare the main lobe width(MLW)between ablated and normal regions in porcine liver tissue.Histological staining with succinate dehydrogenase(SDH)and shear wave elastography(SWE)were employed to validate the changes in tissue elasticity after ablation.The analysis demonstrated a notable reduction in the MLW of the average A-lines in ablated tissues compared to nonablated regions(p<0.01).This reduction,attributed to increased tissue density and enhanced elasticity,indicates accelerated sound propagation in thermally ablated areas,which then serves as a critical parameter for mapping tissue characteristics.The reconstruction of the MLW distribution successfully delineated the ablated regions,and was consistent with the results of SDH staining and SWE.In addition,MLW-based imaging exhibited higher spatial resolution compared to SWE.Incorporating MLW analysis into PAI may be a promising strategy to improve the accuracy and effectiveness of MWA monitoring in clinical settings.
基金Project supported by the National Natural Science Foundation of China(Grant No.61073049)the Ph D Programs Foundation of the Ministry of Education of China(Grant No.20093108110016)the Shanghai Leading Academic Discipline Project(Grant No.J50103)
文摘t In this paper an overall scheme of the task management system of ternary optical computer （TOC） is proposed, and the software architecture chart is given. The function and accomplishment of each module in the system are described in general. In addition, according to the aforementioned scheme a prototype of TOC task management system is implemented, and the feasibility, rationality and completeness of the scheme are verified via running and testing the prototype.
基金Project supported by the National Natural Science Foundation of China(Grant No.61073049)the Shanghai Leading Academic Discipline Project(Grant No.J50103)the Doctorate Foundation of Education Ministry of China(Grant No.20093108110016)
文摘Reconfiguration is the key to produce an applicable ternary optical computer （TOC）. The method to implement the reconfiguration function determines whether a TOC can step into applied fields or not. In this work, a design of the reconfiguration circuit based on field programmable gates array （FPGA） is proposed, and the structure of the entire hardware system is discussed.
基金Project supported by the Shanghai Leading Academic Discipline Project(Grant No.J50103)the National Natural Science Foundation of China(Grant No.61073049)
文摘The division operation is not frequent relatively in traditional applications, but it is increasingly indispensable and important in many modern applications. In this paper, the implementation of modified signed-digit （MSD） floating-point division using Newton-Raphson method on the system of ternary optical computer （TOC） is studied. Since the addition of MSD floating-point is carry-free and the digit width of the system of TOC is large, it is easy to deal with the enough wide data and transform the division operation into multiplication and addition operations. And using data scan and truncation the problem of digits expansion is effectively solved in the range of error limit. The division gets the good results and the efficiency is high. The instance of MSD floating-point division shows that the method is feasible.
基金Supported by the National Natural Science Foundation of China(No.61672270)Jiangsu Provionce Teaching Reform Project for Cloud Computing Technology and Application Talent Training(No.201802130049).
文摘Aiming at the problems such as low throughput and unbalanced load of data center network caused by traditional multipath routing strategy,a dynamic load balancing strategy for flow classification oriented to Fat-Tree topology based on the software defined network(SDN)architecture is proposed,named DLB-FC.Multi-index evaluation methods such as link state information and network traffic characteristics are considered.DLB-FC mechanism can dynamically adjust the flow classification threshold to differentiate between large and small flows.The scheme selects different forwarding paths to meet the transmission performance requirements of different flow characteristics.On this basis,an SDN simulation platform is built for performance testing.The simulation results show that DLB-FC algorithm can dynamically distinguish large flows from small flows and achieve load balancing effectively.Compared with equal-cost multi-path(ECMP),global first fit(GFF)and minmum total delay load routing(MTDLR)algorithms,DLB-FC scheme improves the network throughput and link utilization of the data center network effectively.The transmission delay is also reduced with better load balance.
文摘In the new era,the impact of emerging productive forces has permeated every sector of industry.As the core production factor of these forces,data plays a pivotal role in industrial transformation and social development.Consequently,many domestic universities have introduced majors or courses related to big data.Among these,the Big Data Management and Applications major stands out for its interdisciplinary approach and emphasis on practical skills.However,as an emerging field,it has not yet accumulated a robust foundation in teaching theory and practice.Current instructional practices face issues such as unclear training objectives,inconsistent teaching methods and course content,insufficient integration of practical components,and a shortage of qualified faculty-factors that hinder both the development of the major and the overall quality of education.Taking the statistics course within the Big Data Management and Applications major as an example,this paper examines the challenges faced by statistics education in the context of emerging productive forces and proposes corresponding improvement measures.By introducing innovative teaching concepts and strategies,the teaching system for professional courses is optimized,and authentic classroom scenarios are recreated through illustrative examples.Questionnaire surveys and statistical analyses of data collected before and after the teaching reforms indicate that the curriculum changes effectively enhance instructional outcomes,promote the development of the major,and improve the quality of talent cultivation.
文摘In recent years,due to the scarcity of domestic radioisotopes,the Chinese government has strongly supported the development of dedicated radioisotope production facilities.This paper presents conceptual design simulations of an 11 MeV,50μA,H^(-) compact superconducting cyclotron for radioisotope production.This paper focuses primarily on four aspects:magnet system design,central region configuration,beam dynamics analysis,and extraction system design.This paper outlines the cyclotron's primary parameters and key steps in the development process.
基金supported by the CNPC-SWPU Innovation Alliance Technology Cooperation Project(2020CX020000)the Natural Science Foundation of Sichuan Province(24NSFSC0808)the China Scholarship Council(202306440144).
文摘Passive source imaging can reconstruct body wave reflections similar to those of active sources through seismic interferometry(SI).It has become a low-cost,environmentally friendly alternative to active source seismic,showing great potential.However,this method faces many challenges in practical applications,including uneven distribution of underground sources and complex survey environments.These situations seriously affect the reconstruction quality of virtual shot records,resulting in unguaranteed imaging results and greatly limiting passive source seismic exploration applications.In addition,the quality of the reconstructed records is directly related to the time length of the noise records,but in practice it is often difficult to obtain long-term,high-quality noise segments containing body wave events.To solve the above problems,we propose a deep learning method for reconstructing passive source virtual shot records and apply it to passive source time-lapse monitoring.This method combines the UNet network and the BiLSTM(Bidirectional Long Short-Term Memory)network for extracting spatial features and temporal features respectively.It introduces the spatial attention mechanism to establish a hybrid SUNet-BiLSTM-Attention(SBA)network for supervised training.Through pre-training and fine-tuning training,the network can accurately reconstruct passive source virtual shot records directly from short-time noisy segments containing body wave events.The experimental results of theoretical data show that the virtual shot records reconstructed by the network have high resolution and signal to noise ratio(SNR),providing high-quality data for subsequent monitoring and imaging.Finally,to further validate the effectiveness of proposed method,we applied it to field data collected from gas storage in northwest China.The reconstruction results of field data effectively improve the quality of virtual records and obtain more reliable time-lapse imaging monitoring results,which have significant practical value.
基金funded by the National Natural Science Foundation of China under Grant 62306128the Leading Innovation Project of Changzhou Science and Technology Bureau underGrant CQ20230072+2 种基金the Basic Science Research Project of Jiangsu Provincial Department of Education under Grant 23KJD520003the Science and Technology Development Plan Project of Jilin Provinceunder Grant 20240101382JCthe National KeyR esearch and Development Program of China under Grant 2023YFF1105102.
文摘In fire rescue scenarios,traditional manual operations are highly dangerous,as dense smoke,low visibility,extreme heat,and toxic gases not only hinder rescue efficiency but also endanger firefighters’safety.Although intelligent rescue robots can enter hazardous environments in place of humans,smoke poses major challenges for human detection algorithms.These challenges include the attenuation of visible and infrared signals,complex thermal fields,and interference frombackground objects,all ofwhichmake it difficult to accurately identify trapped individuals.To address this problem,we propose VIF-YOLO,a visible–infrared fusion model for real-time human detection in dense smoke environments.The framework introduces a lightweight multimodal fusion(LMF)module based on learnable low-rank representation blocks to end-to-end integrate visible and infrared images,preserving fine details while enhancing salient features.In addition,an efficient multiscale attention(EMA)mechanism is incorporated into the YOLOv10n backbone to improve feature representation under low-light conditions.Extensive experiments on our newly constructedmultimodal smoke human detection(MSHD)dataset demonstrate thatVIF-YOLOachievesmAP50 of 99.5%,precision of 99.2%,and recall of 99.3%,outperforming YOLOv10n by a clear margin.Furthermore,when deployed on the NVIDIA Jetson Xavier NX,VIF-YOLO attains 40.6 FPS with an average inference latency of 24.6 ms,validating its real-time capability on edge-computing platforms.These results confirm that VIF-YOLO provides accurate,robust,and fast detection across complex backgrounds and diverse smoke conditions,ensuring reliable and rapid localization of individuals in need of rescue.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2024-00406320)the Institute of Information&Communica-tions Technology Planning&Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization Program Grant funded by the Korea government(MSIT)(IITP-2026-RS-2023-00259678).
文摘Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance.
基金supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2025S1A5A2A01005171)by the BK21 programat Chungbuk National University(2025).
文摘With an increase in internet-connected devices and a dependency on online services,the threat of Distributed Denial of Service(DDoS)attacks has become a significant concern in cybersecurity.The proposed system follows a multi-step process,beginning with the collection of datasets from different edge devices and network nodes.To verify its effectiveness,experiments were conducted using the CICDoS2017,NSL-KDD,and CICIDS benchmark datasets alongside other existing models.Recursive feature elimination(RFE)with random forest is used to select features from the CICDDoS2019 dataset,on which a BiLSTM model is trained on local nodes.Local models are trained until convergence or stability criteria are met while simultaneously sharing the updates globally for collaborative learning.A centralised server evaluates real-time traffic using the global BiLSTM model,which triggers alerts for potential DDoS attacks.Furthermore,blockchain technology is employed to secure model updates and to provide an immutable audit trail,thereby ensuring trust and accountability among network nodes.This research introduces a novel decentralized method called Federated Random Forest Bidirectional Long Short-Term Memory(FRF-BiLSTM)for detecting DDoS attacks,utilizing the advanced Bidirectional Long Short-Term Memory Networks(BiLSTMs)to analyze sequences in both forward and backward directions.The outcome shows the proposed model achieves a mean accuracy of 97.1%with an average training delay of 88.7 s and testing delay of 21.4 s.The model demonstrates scalability and the best detection performance in large-scale attack scenarios.
基金supported by the National Natural Science Foundation for Youth of China(61802174)the Natural Science Foundation for Youth of Jiangsu Province(BK20181016)+1 种基金the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(18KJB520019)the Scientific Research Foundation of Nanjing Institute of Technology of China(YKJ201614)
文摘In order to assure quality and control process in the development of the aircraft collaborative design software, a maturity assessment model is proposed. The requirements designing—house of quality is designed to evaluate the maturity degree of the solution, and the evaluation results can help to manage and control the development process. Furthermore, a fuzzy evaluation method based on the minimum deviation is proposed to deal with the fuzzy information. The quantitative evaluation result of the maturity degree can be calculated by optimizing the semantic discount factor aim for the minimum deviation. Finally, this model is illustrated and analyzed by an example study of the aircraft collaborative design software.
基金supported by the National Key R&D Program of China(2018AAA0102804)National Natural Science Foundation of China(62020106003,62103250,61773201)+1 种基金Fundamental Research Funds for the Central Universities(NC2020002,NP2020103)Shanghai Sailing Program(21YF1414000)。
文摘This paper investigates the distributed fault-tolerant consensus tracking problem of nonlinear multi-agent systems with general incipient and abrupt time-varying actuator faults under cyber-attacks.First,a decentralized unknown input observer is established to estimate relative states and actuator faults.Second,the estimated and output neighboring information is combined with distributed fault-tolerant consensus tracking controllers.Criteria of reaching leader-following exponential consensus tracking of multi-agent systems under both connectivity-maintained and connectivity-mixed attacks are derived with average dwelling time,attack frequency,and attack activation rate technique,respectively.Simulation example verifies the effectiveness of the fault-tolerant consensus tracking algorithm.
基金the Ocean Public Welfare Scientific Research Project of State Oceanic Administration of China under contract No.201105033
文摘Based on the features of marine environmental data and processing requirements, a cloud computing archi- tecture of marine environment information is proposed, which provides a new cloud technology framework for the integration and sharing of marine environmental information resources. A physical layer, software platform layer and an application layer are illustrated systematically, at the same time, a corresponding solu- tions for many difficult technical problems such as parallel query processing of multi-dimensional, spatio- temporal information, data slice storage, software service flow customization, analysis, reorganization and so on. A prototype system is developed and many different data-size experiments and a comparative analy- sis are done based on it. The experiment results show that the cloud platform based on this framework can achieve high performance and scalability when dealing with large-scale marine data.
基金supported by the National Key R&D Program of China[Grant No.2020YFB1313000]National Natural Science Foundation of China[Grant No.51975070,62003060,62073211].
文摘Energy consumption and acoustic noise can be significantly reduced through perching in the sustained flights of small Unmanned Aerial Vehicles(UAVs).However,the existing flying perching robots lack good adaptability or loading capacity in unstructured environments.Aiming at solving these problems,a deformable UAV perching mechanism with strong adaptability and high loading capacity,which is inspired by the structure and movements of birds'feet,is presented in this paper.Three elastic toes,an inverted crank slider mechanism used to realize the opening and closing movements,and a gear mechanism used to deform between two configurations are included in this mechanism.With experiments on its performance towards different objects,Results show that it can perch on various objects reliably,and its payload is more than 15 times its weight.By integrating it with a quadcopter,it can perch on different types of targets in outdoor environments,such as tree branches,cables,eaves,and spherical lamps.In addition,the energy consumption of the UAV perching system when perching on objects can be reduced to 0.015 times that of hovering.
基金Project supported by the Shanghai Leading Academic Discipline Project(Grant No.J50103)the Natural Science Foundation of Jiangxi Province(Grant No.2010GZS0031)the Science Technology Project of Jiangxi Province(Grant No.2010BGB00604)
文摘As a coprocessor, field-programmable gate array （FPGA） is the hardware computing processor accelerating the computing capacity of coraputers. To efficiently manage the hardware free resources for the placing of tasks on FPGA and take full advantage of the partially reconfigurable units, good utilization of chip resources is an important and necessary work. In this paper, a new method is proposed to find the complete set of maximal free resource rectangles based on the cross point of edge lines of running tasks on FPGA area, and the prove process is provided to make sure the correctness of this method.
基金Projects(61071096,61073103,61003233) supported by the National Natural Science Foundation of ChinaProjects(20100162110012,20110162110042) supported by Doctoral Program of Higher Education,China
文摘Fault diagnosis is a key issue of the CCBII(computer controlled brake II) braking system, because the CCBII braking system is very complicated and nonlinear, which may exhibit isolated and multi-component coupled faults. A parity space-based method was proposed for fault diagnosis of CCBII braking systems. Firstly, the mathematical models were established according to three function modules of CCBII braking systems where the air fluid theory was utilized. Then, parity vector and threshold function were designed for each output of the system so as to identify more system faults. Fault character matrix was built based on the causal relationship between the output and the fault according to the system function and internal structure. Finally, fault detection and isolation can be realized by the comparison of the observed system output and the fault character matrix. Simulation results show that the proposed method is entirely feasible and effective.
文摘Laser scanning confocal endomicroscope(LSCEM)has emerged as an imaging modality which provides noninvasive,in vivo imaging of biological tissue on a microscopic scale.Scientific visualizations for LSCEM datasets captured by current imaging systems require these datasets to be fully acquired and brought to a separate rendering machine.To extend the features and capabilities of this modality,we propose a system which is capable of performing realtime visualization of LSCEM datasets.Using field-programmable gate arrays,our system performs three tasks in parallel:(1)automated control of dataset acquisition;(2)imaging-rendering system synchronization;and(3)realtime volume rendering of dynamic datasets.Through fusion of LSCEM imaging and volume rendering processes,acquired datasets can be visualized in realtime to provide an immediate perception of the image quality and biological conditions of the subject,further assisting in realtime cancer diagnosis.Subsequently,the imaging procedure can be improved for more accurate diagnosis and reduce the need for repeating the process due to unsatisfactory datasets.
基金the National Natural Science Foundation of China(No.61625304)。
文摘Unmanned combat system is one of the important means to capture information superiority,carry out precision strike and accomplish special combat tasks in information war.Unmanned attack strategy plays a crucial role in unmanned combat system,which has to ensure the attack by unmanned surface vehicles(USVs)from failure.To meet the challenge,we propose a task allocation algorithm called distributed auction mechanism task allocation with grey wolf optimization(DAGWO).The traditional grey wolf optimization(GWO)algorithm is improved with a distributed auction mechanism(DAM)to constrain the initialization of wolves,which improves the optimization process according to the actual situation.In addition,one unmanned aerial vehicle(UAV)is employed as the central control system to establish task allocation model and construct fitness function for the multiple constraints of USV attack problem.The proposed DAGWO algorithm can not only ensure the diversity of wolves,but also avoid the local optimum problem.Simulation results show that the proposed DAGWO algorithm can effectively solve the problem of attack task allocation among multiple USVs.
