为了保持领先的竞争优势，中国蓝星（集团）股份有限公司不断致力于优化生产流程并提高能源效率。为了更好地了解所有12个工厂（遍布全国9个省）的情况，中国蓝星公司转为使用OSIsoft PI System。通过改进的性能监控和计算能力，中国蓝...为了保持领先的竞争优势，中国蓝星（集团）股份有限公司不断致力于优化生产流程并提高能源效率。为了更好地了解所有12个工厂（遍布全国9个省）的情况，中国蓝星公司转为使用OSIsoft PI System。通过改进的性能监控和计算能力，中国蓝星公司促进了生产，提高了效率并降低了成本。展开更多
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
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.
