Smart metering has gained considerable attention as a research focus due to its reliability and energy-efficient nature compared to traditional electromechanical metering systems. Existing methods primarily focus on d...Smart metering has gained considerable attention as a research focus due to its reliability and energy-efficient nature compared to traditional electromechanical metering systems. Existing methods primarily focus on data management,rather than emphasizing efficiency. Accurate prediction of electricity consumption is crucial for enabling intelligent grid operations,including resource planning and demandsupply balancing. Smart metering solutions offer users the benefits of effectively interpreting their energy utilization and optimizing costs. Motivated by this,this paper presents an Intelligent Energy Utilization Analysis using Smart Metering Data(IUA-SMD)model to determine energy consumption patterns. The proposed IUA-SMD model comprises three major processes:data Pre-processing,feature extraction,and classification,with parameter optimization. We employ the extreme learning machine(ELM)based classification approach within the IUA-SMD model to derive optimal energy utilization labels. Additionally,we apply the shell game optimization(SGO)algorithm to enhance the classification efficiency of the ELM by optimizing its parameters. The effectiveness of the IUA-SMD model is evaluated using an extensive dataset of smart metering data,and the results are analyzed in terms of accuracy and mean square error(MSE). The proposed model demonstrates superior performance,achieving a maximum accuracy of65.917% and a minimum MSE of0.096. These results highlight the potential of the IUA-SMD model for enabling efficient energy utilization through intelligent analysis of smart metering data.展开更多
In recent years, semiconductor survey meters have been developed and are in increasing demand worldwide. This study determined if it is possible to use the X-ray system installed in each medical facility to calculate ...In recent years, semiconductor survey meters have been developed and are in increasing demand worldwide. This study determined if it is possible to use the X-ray system installed in each medical facility to calculate the time constant of a semiconductor survey meter and confirm the meter’s function. An additional filter was attached to the medical X-ray system to satisfy the standards of N-60 to N-120, more copper plates were added as needed, and the first and second half-value layers were calculated to enable comparisons of the facility’s X-ray system quality with the N-60 to N-120 quality values. Next, we used a medical X-ray system to measure the leakage dose and calculate the time constant of the survey meter. The functionality of the meter was then checked and compared with the energy characteristics of the meter. The experimental results showed that it was possible to use a medical X-ray system to reproduce the N-60 to N-120 radiation quality values and to calculate the time constant from the measured results, assuming actual leakage dosimetry for that radiation quality. We also found that the calibration factor was equivalent to that of the energy characteristics of the survey meter.展开更多
In order to more accurately detect the accuracy of word-wheel water meter digits, 2000 water meter pictures were produced, and an improved Faster-RCNN algorithm for detecting water meter digits was proposed. The impro...In order to more accurately detect the accuracy of word-wheel water meter digits, 2000 water meter pictures were produced, and an improved Faster-RCNN algorithm for detecting water meter digits was proposed. The improved Faster-RCNN algorithm uses ResNet50 combined with FPN (Feature Pyramid Network) structure instead of the original ResNet50 as the feature extraction network, which can enhance the accuracy of the model for small-sized digit recognition;the use of ROI Align instead of ROI Pooling can eliminate the error caused by the quantization process of the ROI Pooling twice, so that the candidate region is more accurately mapped to the feature map, and the accuracy of the model is further enhanced. The experiment proves that the improved Faster-RCNN algorithm can reach 91.8% recognition accuracy on the test set of homemade dataset, which meets the accuracy requirements of automatic meter reading technology for water meter digital recognition, which is of great significance for solving the problem of automatic meter reading of mechanical water meters and promoting the intelligent development of water meters.展开更多
Ensuring food safety is paramount worldwide.Developing effective detection methods to ensure food safety can be challenging owing to trace hazards,long detection time,and resource-poor sites,in addition to the matrix ...Ensuring food safety is paramount worldwide.Developing effective detection methods to ensure food safety can be challenging owing to trace hazards,long detection time,and resource-poor sites,in addition to the matrix effects of food.Personal glucose meter(PGM),a classic point-of-care testing device,possesses unique application advantages,demonstrating promise in food safety.Currently,many studies have used PGM-based biosensors and signal amplification technologies to achieve sensitive and specific detection of food hazards.Signal amplification technologies have the potential to greatly improve the analytical performance and integration of PGMs with biosensors,which is crucial for solving the challenges associated with the use of PGMs for food safety analysis.This review introduces the basic detection principle of a PGM-based sensing strategy,which consists of three key factors:target recognition,signal transduction,and signal output.Representative studies of existing PGM-based sensing strategies combined with various signal amplification technologies(nanomaterial-loaded multienzyme labeling,nucleic acid reaction,DNAzyme catalysis,responsive nanomaterial encapsulation,and others)in the field of food safety detection are reviewed.Future perspectives and potential opportunities and challenges associated with PGMs in the field of food safety are discussed.Despite the need for complex sample preparation and the lack of standardization in the field,using PGMs in combination with signal amplification technology shows promise as a rapid and cost-effective method for food safety hazard analysis.展开更多
Purpose-For billing purposes,heavy-haul locomotives in Sweden are equipped with on-board energy meters,which can record several parameters,e.g.,used energy,regenerated energy,speed and position.Since there is a strong...Purpose-For billing purposes,heavy-haul locomotives in Sweden are equipped with on-board energy meters,which can record several parameters,e.g.,used energy,regenerated energy,speed and position.Since there is a strong demand for improving energy efficiency in Sweden,data from the energy meters can be used to obtain a better understanding of the detailed energy usage of heavy-haul trains and identify potential for future improvements.Design/methodology/approach-To monitor energy efficiency,the present study,therefore,develops key performance indicators(KPIs),which can be calculated with energy meter data to reflect the energy efficiency of heavy-haul trains in operation.Energy meter data of IORE class locomotives,hauling highly uniform 30-tonne axle load trains with 68 wagons,together with additional data sources,are analysed to identify significant parameters for describing driver influence on energy usage.Findings-Results show that driver behaviour varies significantly and has the single largest influence on energy usage.Furthermore,parametric studies are performed with help of simulation to identify the influence of different operational and rolling stock conditions,e.g.,axle loads and number of wagons,on energy usage.Originality/value-Based on the parametric studies,some operational parameters which have significant impact on energy efficiency are found and then the KPIs are derived.In the end,some possible measures for improving energy performance in heavy-haul operations are given.展开更多
This study aims to develop a low-cost refractometer for measuring the sucrose content of fruit juice,which is an important factor affecting human health.While laboratory-grade refractometers are expensive and unsuitab...This study aims to develop a low-cost refractometer for measuring the sucrose content of fruit juice,which is an important factor affecting human health.While laboratory-grade refractometers are expensive and unsuitable for personal use,existing low-cost commercial options lack stability and accuracy.To address this gap,we propose a refractometer that replaces the expensive CCD sensor and light source with a conventional LED and a reasonably priced CMOS sensor.By analyzing the output waveform pattern of the CMOS sensor,we achieve high precision with a personal-use-appropriate accuracy of 0.1%.We tested the proposed refractometer by conducting 100 repeated measurements on various fruit juice samples,and the results demonstrate its reliability and consistency.Running on a 48 MHz ARM processor,the algorithm can acquire data within 0.2 seconds.Our low-cost refractometer is suitable for personal health management and small-scale production,providing an affordable and reliable method for measuring sucrose concentration in fruit juice.It improves upon the existing low-cost options by offering better stability and accuracy.This accessible tool has potential applications in optimizing the sucrose content of fruit juice for better health and quality control.展开更多
准确检测变电站中的设备缺陷并及时进行处理是保证电力系统安全运行的重要措施.针对表计缺陷图像背景复杂、目标尺寸不一、外形差别大等问题,提出基于改进YOLOv5(you only look once的第5个版本)的变电站表计缺陷检测算法.为了提高泛化...准确检测变电站中的设备缺陷并及时进行处理是保证电力系统安全运行的重要措施.针对表计缺陷图像背景复杂、目标尺寸不一、外形差别大等问题,提出基于改进YOLOv5(you only look once的第5个版本)的变电站表计缺陷检测算法.为了提高泛化能力、解决训练过程中样本不平衡问题,利用旋转和改变图像亮度的方法进行数据增广.通过引入坐标注意力机制,在聚焦缺陷特征的同时,能突出缺陷特征的差异.为了使边界框回归更快速准确,将EDIOU loss(effective distance intersection over union loss)代替CIOU loos(complete intersection over union loss).实验结果表明:6种算法中,该文算法的准确度、召回率和mAP(mean average preciscion)均最高,分别达85.1%,86.6%,87.3%.因此,该文算法具有优越性.展开更多
文摘Smart metering has gained considerable attention as a research focus due to its reliability and energy-efficient nature compared to traditional electromechanical metering systems. Existing methods primarily focus on data management,rather than emphasizing efficiency. Accurate prediction of electricity consumption is crucial for enabling intelligent grid operations,including resource planning and demandsupply balancing. Smart metering solutions offer users the benefits of effectively interpreting their energy utilization and optimizing costs. Motivated by this,this paper presents an Intelligent Energy Utilization Analysis using Smart Metering Data(IUA-SMD)model to determine energy consumption patterns. The proposed IUA-SMD model comprises three major processes:data Pre-processing,feature extraction,and classification,with parameter optimization. We employ the extreme learning machine(ELM)based classification approach within the IUA-SMD model to derive optimal energy utilization labels. Additionally,we apply the shell game optimization(SGO)algorithm to enhance the classification efficiency of the ELM by optimizing its parameters. The effectiveness of the IUA-SMD model is evaluated using an extensive dataset of smart metering data,and the results are analyzed in terms of accuracy and mean square error(MSE). The proposed model demonstrates superior performance,achieving a maximum accuracy of65.917% and a minimum MSE of0.096. These results highlight the potential of the IUA-SMD model for enabling efficient energy utilization through intelligent analysis of smart metering data.
文摘In recent years, semiconductor survey meters have been developed and are in increasing demand worldwide. This study determined if it is possible to use the X-ray system installed in each medical facility to calculate the time constant of a semiconductor survey meter and confirm the meter’s function. An additional filter was attached to the medical X-ray system to satisfy the standards of N-60 to N-120, more copper plates were added as needed, and the first and second half-value layers were calculated to enable comparisons of the facility’s X-ray system quality with the N-60 to N-120 quality values. Next, we used a medical X-ray system to measure the leakage dose and calculate the time constant of the survey meter. The functionality of the meter was then checked and compared with the energy characteristics of the meter. The experimental results showed that it was possible to use a medical X-ray system to reproduce the N-60 to N-120 radiation quality values and to calculate the time constant from the measured results, assuming actual leakage dosimetry for that radiation quality. We also found that the calibration factor was equivalent to that of the energy characteristics of the survey meter.
文摘In order to more accurately detect the accuracy of word-wheel water meter digits, 2000 water meter pictures were produced, and an improved Faster-RCNN algorithm for detecting water meter digits was proposed. The improved Faster-RCNN algorithm uses ResNet50 combined with FPN (Feature Pyramid Network) structure instead of the original ResNet50 as the feature extraction network, which can enhance the accuracy of the model for small-sized digit recognition;the use of ROI Align instead of ROI Pooling can eliminate the error caused by the quantization process of the ROI Pooling twice, so that the candidate region is more accurately mapped to the feature map, and the accuracy of the model is further enhanced. The experiment proves that the improved Faster-RCNN algorithm can reach 91.8% recognition accuracy on the test set of homemade dataset, which meets the accuracy requirements of automatic meter reading technology for water meter digital recognition, which is of great significance for solving the problem of automatic meter reading of mechanical water meters and promoting the intelligent development of water meters.
基金supported by the Natural Science Foundation of Shandong Province(Grant No.:ZR2020QC250)China Agriculture Research System(Grant No.:CARS-38)+1 种基金Modern Agricultural Technology Industry System of Shandong Province(Grant No.:SDAIT10-10)Key Technology Research and Development Program of Shandong(Grant Nos.:2021CXGC010809 and 2021TZXD012).
文摘Ensuring food safety is paramount worldwide.Developing effective detection methods to ensure food safety can be challenging owing to trace hazards,long detection time,and resource-poor sites,in addition to the matrix effects of food.Personal glucose meter(PGM),a classic point-of-care testing device,possesses unique application advantages,demonstrating promise in food safety.Currently,many studies have used PGM-based biosensors and signal amplification technologies to achieve sensitive and specific detection of food hazards.Signal amplification technologies have the potential to greatly improve the analytical performance and integration of PGMs with biosensors,which is crucial for solving the challenges associated with the use of PGMs for food safety analysis.This review introduces the basic detection principle of a PGM-based sensing strategy,which consists of three key factors:target recognition,signal transduction,and signal output.Representative studies of existing PGM-based sensing strategies combined with various signal amplification technologies(nanomaterial-loaded multienzyme labeling,nucleic acid reaction,DNAzyme catalysis,responsive nanomaterial encapsulation,and others)in the field of food safety detection are reviewed.Future perspectives and potential opportunities and challenges associated with PGMs in the field of food safety are discussed.Despite the need for complex sample preparation and the lack of standardization in the field,using PGMs in combination with signal amplification technology shows promise as a rapid and cost-effective method for food safety hazard analysis.
文摘Purpose-For billing purposes,heavy-haul locomotives in Sweden are equipped with on-board energy meters,which can record several parameters,e.g.,used energy,regenerated energy,speed and position.Since there is a strong demand for improving energy efficiency in Sweden,data from the energy meters can be used to obtain a better understanding of the detailed energy usage of heavy-haul trains and identify potential for future improvements.Design/methodology/approach-To monitor energy efficiency,the present study,therefore,develops key performance indicators(KPIs),which can be calculated with energy meter data to reflect the energy efficiency of heavy-haul trains in operation.Energy meter data of IORE class locomotives,hauling highly uniform 30-tonne axle load trains with 68 wagons,together with additional data sources,are analysed to identify significant parameters for describing driver influence on energy usage.Findings-Results show that driver behaviour varies significantly and has the single largest influence on energy usage.Furthermore,parametric studies are performed with help of simulation to identify the influence of different operational and rolling stock conditions,e.g.,axle loads and number of wagons,on energy usage.Originality/value-Based on the parametric studies,some operational parameters which have significant impact on energy efficiency are found and then the KPIs are derived.In the end,some possible measures for improving energy performance in heavy-haul operations are given.
文摘This study aims to develop a low-cost refractometer for measuring the sucrose content of fruit juice,which is an important factor affecting human health.While laboratory-grade refractometers are expensive and unsuitable for personal use,existing low-cost commercial options lack stability and accuracy.To address this gap,we propose a refractometer that replaces the expensive CCD sensor and light source with a conventional LED and a reasonably priced CMOS sensor.By analyzing the output waveform pattern of the CMOS sensor,we achieve high precision with a personal-use-appropriate accuracy of 0.1%.We tested the proposed refractometer by conducting 100 repeated measurements on various fruit juice samples,and the results demonstrate its reliability and consistency.Running on a 48 MHz ARM processor,the algorithm can acquire data within 0.2 seconds.Our low-cost refractometer is suitable for personal health management and small-scale production,providing an affordable and reliable method for measuring sucrose concentration in fruit juice.It improves upon the existing low-cost options by offering better stability and accuracy.This accessible tool has potential applications in optimizing the sucrose content of fruit juice for better health and quality control.
文摘准确检测变电站中的设备缺陷并及时进行处理是保证电力系统安全运行的重要措施.针对表计缺陷图像背景复杂、目标尺寸不一、外形差别大等问题,提出基于改进YOLOv5(you only look once的第5个版本)的变电站表计缺陷检测算法.为了提高泛化能力、解决训练过程中样本不平衡问题,利用旋转和改变图像亮度的方法进行数据增广.通过引入坐标注意力机制,在聚焦缺陷特征的同时,能突出缺陷特征的差异.为了使边界框回归更快速准确,将EDIOU loss(effective distance intersection over union loss)代替CIOU loos(complete intersection over union loss).实验结果表明:6种算法中,该文算法的准确度、召回率和mAP(mean average preciscion)均最高,分别达85.1%,86.6%,87.3%.因此,该文算法具有优越性.
