With the rapid development of the Internet of Things(IoT),artificial intelligence,and big data,wastesorting systems must balance high accuracy,low latency,and resource efficiency.This paper presents an edge-friendly i...With the rapid development of the Internet of Things(IoT),artificial intelligence,and big data,wastesorting systems must balance high accuracy,low latency,and resource efficiency.This paper presents an edge-friendly intelligent waste-sorting system that integrates a lightweight visual neural network,a pentagonal-trajectory robotic arm,and IoT connectivity to meet the requirements of real-time response and high accuracy.A lightweight object detection model,YOLO-WasNet(You Only Look Once for Waste Sorting Network),is proposed to optimize performance on edge devices.YOLO-WasNet adopts a lightweight backbone,applies Spatial Pyramid Pooling-Fast(SPPF)and Convolutional Block Attention Module(CBAM),and replaces traditional C3 modules(Cross Stage Partial Bottleneck with 3 convolutions)with efficient C2f blocks(Cross Stage Partial Bottleneck with 2 Convolutions fast)in the neck.Additionally,a Depthwise Parallel Triple-attention Convolution(DPT-Conv)operator is introduced to enhance feature extraction.Experiments on a custom dataset of nine waste categories conforming to Shanghai’s sorting standard(7,917 images)show that YOLO-WasNet achieves a mean average precision(mAP50)of 96.8%and a precision of 96.9%,while reducing computational cost by 30%compared to YOLOv5s.On a Raspberry Pi 4B,inference time is reduced from 480 to 350 ms,ensuring real-time performance.This system offers a practical and viable solution for low-cost,efficient automated waste management in smart cities.展开更多
The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.Thi...The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.This paper presents a novel sparrow search algorithm(SSA)-tuned proportional-integral(PI)controller for grid-connected photovoltaic(PV)systems,designed to optimize dynamic perfor-mance,energy extraction,and power quality.Key contributions include the development of a systematic SSA-based optimization frame-work for real-time PI parameter tuning,ensuring precise voltage and current regulation,improved maximum power point tracking(MPPT)efficiency,and minimized total harmonic distortion(THD).The proposed approach is evaluated against conventional PSO-based and P&O controllers through comprehensive simulations,demonstrating its superior performance across key metrics:a 39.47%faster response time compared to PSO,a 12.06%increase in peak active power relative to P&O,and a 52.38%reduction in THD,ensuring compliance with IEEE grid standards.Moreover,the SSA-tuned PI controller exhibits enhanced adaptability to dynamic irradiancefluc-tuations,rapid response time,and robust grid integration under varying conditions,making it highly suitable for real-time smart grid applications.This work establishes the SSA-tuned PI controller as a reliable and efficient solution for improving PV system performance in grid-connected scenarios,while also setting the foundation for future research into multi-objective optimization,experimental valida-tion,and hybrid renewable energy systems.展开更多
Significance:Over 80%of cervical cancer cases occur in lower-to-middle income countries(LMIC’s).This is partly because current screening techniques lack affordability,accessibility,and/or reliability for use in LMIC...Significance:Over 80%of cervical cancer cases occur in lower-to-middle income countries(LMIC’s).This is partly because current screening techniques lack affordability,accessibility,and/or reliability for use in LMIC’s.Aim:To develop an optical technique for cervical cancer screening that is affordable,accessible,and reliable for use in LMIC’s.Approach:We developed a portable diffuse reflectance spectroscopy(DRS)system,which costs$<2500 USD to manufacture,and employs a Raspberry Pi to extract the absorption(μα)and reduced scattering(μ′s)coefficients of biological tissue.The system was subject to travel and intentional rough handling.It was further used to capture 320 DRS spectra taken from 64 tissue-mimicking phantoms.Two users collected phantom data,one“expert”,and one“novice”in biomedical optics.The system was also used to collect 335 spectra from colon,small intestine,and rectal tissue of a fresh ex vivo porcine specimen.A previously described artificial intelligence model was used to extract optical properties,and a GradientBoostingClassifier identified the organ of origin for ex vivo spectra.Results:System alignment was robust to intentional rough handling and travel.Phantomμαandμ′s were predicted with average root-mean square error of<10%,regardless of user.Regarding ex vivo data,the system predicted the organ of origin with 80–90%accuracy.Statistical differences between predicted wereμαobserved in all three organs(P<0.001–0.03),and betweenμ′s in two organs(P<0.001–0.07).Conclusions:The DRS system has the potential to be affordable,reliable,and accessible for cervical screening in LMIC’s.展开更多
Background Interconnection of different power systems has a major effect on system stability.This study aims to design an optimal load frequency control(LFC)system based on a proportional-integral(PI)controller for a ...Background Interconnection of different power systems has a major effect on system stability.This study aims to design an optimal load frequency control(LFC)system based on a proportional-integral(PI)controller for a two-area power system.Methods Two areas were connected through an AC tie line in parallel with a DC link to stabilize the frequency of oscillations in both areas.The PI parameters were tuned using the cuckoo search algorithm(CSA)to minimize the integral absolute error(IAE).A state matrix was provided,and the stability of the system was verified by calculating the eigenvalues.The frequency response was investigated for load variation,changes in the generator rate constraint,the turbine time constant,and the governor time constant.Results The CSA was compared with particle swarm optimization algorithm(PSO)under identical conditions.The system was modeled based on a state-space mathematical representation and simulated using MATLAB.The results demonstrated the effectiveness of the proposed controller based on both algorithms and,it is clear that CSA is superior to PSO.Conclusion The CSA algorithm smoothens the system response,reduces ripples,decreases overshooting and settling time,and improves the overall system performance under different disturbances.展开更多
To enhance the frequency stability and lower the regulation mileage payment of a multiarea integrated energy system(IES)that supports the power Internet of Things(IoT),this paper proposes a data-driven cooperative met...To enhance the frequency stability and lower the regulation mileage payment of a multiarea integrated energy system(IES)that supports the power Internet of Things(IoT),this paper proposes a data-driven cooperative method for automatic generation control(AGC).The method consists of adaptive fractional-order proportional-integral(FOPI)controllers and a novel efficient integration exploration multiagent twin delayed deep deterministic policy gradient(EIE-MATD3)algorithm.The FOPI controllers are designed for each area based on the performancebased frequency regulation market mechanism.The EIE-MATD3 algorithm is used to tune the coefficients of the FOPI controllers in real time using centralized training and decentralized execution.The algorithm incorporates imitation learning and efficient integration exploration to obtain a more robust coordinated control strategy.An experiment on the four-area China Southern Grid(CSG)real-time digital system shows that the proposed method can improve the control performance and reduce the regulation mileage payment of each area in the IES.展开更多
车地无线技术方案会直接影响地铁P I S的最终运行效果。随着国内城市地铁线路的不断建设以及技术的不断发展进步,对于PIS中车地无线技术方案的选择成为地铁通信系统从业人员关注的重点。本文着重介绍了当前国内地铁PIS中的车地无线技术...车地无线技术方案会直接影响地铁P I S的最终运行效果。随着国内城市地铁线路的不断建设以及技术的不断发展进步,对于PIS中车地无线技术方案的选择成为地铁通信系统从业人员关注的重点。本文着重介绍了当前国内地铁PIS中的车地无线技术方案,并针对PIS当前及未来业务需求、应用现状及技术原理进行分析,提出了适合于PIS业务需求的技术方案。展开更多
文摘With the rapid development of the Internet of Things(IoT),artificial intelligence,and big data,wastesorting systems must balance high accuracy,low latency,and resource efficiency.This paper presents an edge-friendly intelligent waste-sorting system that integrates a lightweight visual neural network,a pentagonal-trajectory robotic arm,and IoT connectivity to meet the requirements of real-time response and high accuracy.A lightweight object detection model,YOLO-WasNet(You Only Look Once for Waste Sorting Network),is proposed to optimize performance on edge devices.YOLO-WasNet adopts a lightweight backbone,applies Spatial Pyramid Pooling-Fast(SPPF)and Convolutional Block Attention Module(CBAM),and replaces traditional C3 modules(Cross Stage Partial Bottleneck with 3 convolutions)with efficient C2f blocks(Cross Stage Partial Bottleneck with 2 Convolutions fast)in the neck.Additionally,a Depthwise Parallel Triple-attention Convolution(DPT-Conv)operator is introduced to enhance feature extraction.Experiments on a custom dataset of nine waste categories conforming to Shanghai’s sorting standard(7,917 images)show that YOLO-WasNet achieves a mean average precision(mAP50)of 96.8%and a precision of 96.9%,while reducing computational cost by 30%compared to YOLOv5s.On a Raspberry Pi 4B,inference time is reduced from 480 to 350 ms,ensuring real-time performance.This system offers a practical and viable solution for low-cost,efficient automated waste management in smart cities.
文摘The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.This paper presents a novel sparrow search algorithm(SSA)-tuned proportional-integral(PI)controller for grid-connected photovoltaic(PV)systems,designed to optimize dynamic perfor-mance,energy extraction,and power quality.Key contributions include the development of a systematic SSA-based optimization frame-work for real-time PI parameter tuning,ensuring precise voltage and current regulation,improved maximum power point tracking(MPPT)efficiency,and minimized total harmonic distortion(THD).The proposed approach is evaluated against conventional PSO-based and P&O controllers through comprehensive simulations,demonstrating its superior performance across key metrics:a 39.47%faster response time compared to PSO,a 12.06%increase in peak active power relative to P&O,and a 52.38%reduction in THD,ensuring compliance with IEEE grid standards.Moreover,the SSA-tuned PI controller exhibits enhanced adaptability to dynamic irradiancefluc-tuations,rapid response time,and robust grid integration under varying conditions,making it highly suitable for real-time smart grid applications.This work establishes the SSA-tuned PI controller as a reliable and efficient solution for improving PV system performance in grid-connected scenarios,while also setting the foundation for future research into multi-objective optimization,experimental valida-tion,and hybrid renewable energy systems.
文摘Significance:Over 80%of cervical cancer cases occur in lower-to-middle income countries(LMIC’s).This is partly because current screening techniques lack affordability,accessibility,and/or reliability for use in LMIC’s.Aim:To develop an optical technique for cervical cancer screening that is affordable,accessible,and reliable for use in LMIC’s.Approach:We developed a portable diffuse reflectance spectroscopy(DRS)system,which costs$<2500 USD to manufacture,and employs a Raspberry Pi to extract the absorption(μα)and reduced scattering(μ′s)coefficients of biological tissue.The system was subject to travel and intentional rough handling.It was further used to capture 320 DRS spectra taken from 64 tissue-mimicking phantoms.Two users collected phantom data,one“expert”,and one“novice”in biomedical optics.The system was also used to collect 335 spectra from colon,small intestine,and rectal tissue of a fresh ex vivo porcine specimen.A previously described artificial intelligence model was used to extract optical properties,and a GradientBoostingClassifier identified the organ of origin for ex vivo spectra.Results:System alignment was robust to intentional rough handling and travel.Phantomμαandμ′s were predicted with average root-mean square error of<10%,regardless of user.Regarding ex vivo data,the system predicted the organ of origin with 80–90%accuracy.Statistical differences between predicted wereμαobserved in all three organs(P<0.001–0.03),and betweenμ′s in two organs(P<0.001–0.07).Conclusions:The DRS system has the potential to be affordable,reliable,and accessible for cervical screening in LMIC’s.
基金Supported by the Russian Science Foundation(Agreement 23-41-10001,https://rscf.ru/project/23-41-10001/).
文摘Background Interconnection of different power systems has a major effect on system stability.This study aims to design an optimal load frequency control(LFC)system based on a proportional-integral(PI)controller for a two-area power system.Methods Two areas were connected through an AC tie line in parallel with a DC link to stabilize the frequency of oscillations in both areas.The PI parameters were tuned using the cuckoo search algorithm(CSA)to minimize the integral absolute error(IAE).A state matrix was provided,and the stability of the system was verified by calculating the eigenvalues.The frequency response was investigated for load variation,changes in the generator rate constraint,the turbine time constant,and the governor time constant.Results The CSA was compared with particle swarm optimization algorithm(PSO)under identical conditions.The system was modeled based on a state-space mathematical representation and simulated using MATLAB.The results demonstrated the effectiveness of the proposed controller based on both algorithms and,it is clear that CSA is superior to PSO.Conclusion The CSA algorithm smoothens the system response,reduces ripples,decreases overshooting and settling time,and improves the overall system performance under different disturbances.
基金upported by National Natural Science Foundation of China(52307118).
文摘To enhance the frequency stability and lower the regulation mileage payment of a multiarea integrated energy system(IES)that supports the power Internet of Things(IoT),this paper proposes a data-driven cooperative method for automatic generation control(AGC).The method consists of adaptive fractional-order proportional-integral(FOPI)controllers and a novel efficient integration exploration multiagent twin delayed deep deterministic policy gradient(EIE-MATD3)algorithm.The FOPI controllers are designed for each area based on the performancebased frequency regulation market mechanism.The EIE-MATD3 algorithm is used to tune the coefficients of the FOPI controllers in real time using centralized training and decentralized execution.The algorithm incorporates imitation learning and efficient integration exploration to obtain a more robust coordinated control strategy.An experiment on the four-area China Southern Grid(CSG)real-time digital system shows that the proposed method can improve the control performance and reduce the regulation mileage payment of each area in the IES.
文摘车地无线技术方案会直接影响地铁P I S的最终运行效果。随着国内城市地铁线路的不断建设以及技术的不断发展进步,对于PIS中车地无线技术方案的选择成为地铁通信系统从业人员关注的重点。本文着重介绍了当前国内地铁PIS中的车地无线技术方案,并针对PIS当前及未来业务需求、应用现状及技术原理进行分析,提出了适合于PIS业务需求的技术方案。
