As modern power systems grow in complexity,accurate and efficient fault detection has become increasingly important.While many existing reviews focus on a single modality,this paper presents a comprehensive survey fro...As modern power systems grow in complexity,accurate and efficient fault detection has become increasingly important.While many existing reviews focus on a single modality,this paper presents a comprehensive survey from a dual-modality perspective-infrared imaging and voiceprint analysis-two complementary,non-contact techniques that capture different fault characteristics.Infrared imaging excels at detecting thermal anomalies,while voiceprint signals provide insight into mechanical vibrations and internal discharge phenomena.We review both traditional signal processing and deep learning-based approaches for each modality,categorized by key processing stages such as feature extraction and classification.The paper highlights how these modalities address distinct fault types and how they may be fused to improve robustness and accuracy.Representative datasets are summarized,and practical challenges such as noise interference,limited fault samples,and deployment constraints are discussed.By offering a cross-modal,comparative analysis,this work aims to bridge fragmented research and guide future development in intelligent fault detection systems.The review concludes with research trends including multimodal fusion,lightweight models,and self-supervised learning.展开更多
The SiO_(2) inverse opal photonic crystals(PC)with a three-dimensional macroporous structure were fabricated by the sacrificial template method,followed by infiltration of a pyrene derivative,1-(pyren-8-yl)but-3-en-1-...The SiO_(2) inverse opal photonic crystals(PC)with a three-dimensional macroporous structure were fabricated by the sacrificial template method,followed by infiltration of a pyrene derivative,1-(pyren-8-yl)but-3-en-1-amine(PEA),to achieve a formaldehyde(FA)-sensitive and fluorescence-enhanced sensing film.Utilizing the specific Aza-Cope rearrangement reaction of allylamine of PEA and FA to generate a strong fluorescent product emitted at approximately 480 nm,we chose a PC whose blue band edge of stopband overlapped with the fluorescence emission wavelength.In virtue of the fluorescence enhancement property derived from slow photon effect of PC,FA was detected highly selectively and sensitively.The limit of detection(LoD)was calculated to be 1.38 nmol/L.Furthermore,the fast detection of FA(within 1 min)is realized due to the interconnected three-dimensional macroporous structure of the inverse opal PC and its high specific surface area.The prepared sensing film can be used for the detection of FA in air,aquatic products and living cells.The very close FA content in indoor air to the result from FA detector,the recovery rate of 101.5%for detecting FA in aquatic products and fast fluorescence imaging in 2 min for living cells demonstrate the reliability and accuracy of our method in practical applications.展开更多
As a vital and integral component of transportation infrastructure,pavement has a direct and tangible impact on socio-economic sustainability.In recent years,an influx of groundbreaking and state-of-the-art materials,...As a vital and integral component of transportation infrastructure,pavement has a direct and tangible impact on socio-economic sustainability.In recent years,an influx of groundbreaking and state-of-the-art materials,structures,equipment,and detection technologies related to road engineering have continually and progressively emerged,reshaping the landscape of pavement systems.There is a pressing and growing need for a timely summarization of the current research status and a clear identification of future research directions in these advanced and evolving technologies.Therefore,Journal of Road Engineering has undertaken the significant initiative of introducing a comprehensive review paper with the overarching theme of“advanced road materials,structures,equipment,and detection technologies”.This extensive and insightful review meticulously gathers and synthesizes research findings from 39 distinguished scholars,all of whom are affiliated with 19 renowned universities or research institutions specializing in the diverse and multidimensional field of highway engineering.It covers the current state and anticipates future development directions in the four major and interconnected domains of road engineering:advanced road materials,advanced road structures and performance evaluation,advanced road construction equipment and technology,and advanced road detection and assessment technologies.展开更多
The YOLOx-s network does not sufficiently meet the accuracy demand of equipment detection in the autonomous inspection of distribution lines by Unmanned Aerial Vehicle(UAV)due to the complex background of distribution...The YOLOx-s network does not sufficiently meet the accuracy demand of equipment detection in the autonomous inspection of distribution lines by Unmanned Aerial Vehicle(UAV)due to the complex background of distribution lines,variable morphology of equipment,and large differences in equipment sizes.Therefore,aiming at the difficult detection of power equipment in UAV inspection images,we propose a multi-equipment detection method for inspection of distribution lines based on the YOLOx-s.Based on the YOLOx-s network,we make the following improvements:1)The Receptive Field Block(RFB)module is added after the shallow feature layer of the backbone network to expand the receptive field of the network.2)The Coordinate Attention(CA)module is added to obtain the spatial direction information of the targets and improve the accuracy of target localization.3)After the first fusion of features in the Path Aggregation Network(PANet),the Adaptively Spatial Feature Fusion(ASFF)module is added to achieve efficient re-fusion of multi-scale deep and shallow feature maps by assigning adaptive weight parameters to features at different scales.4)The loss function Binary Cross Entropy(BCE)Loss in YOLOx-s is replaced by Focal Loss to alleviate the difficulty of network convergence caused by the imbalance between positive and negative samples of small-sized targets.The experiments take a private dataset consisting of four types of power equipment:Transformers,Isolators,Drop Fuses,and Lightning Arrestors.On average,the mean Average Precision(mAP)of the proposed method can reach 93.64%,an increase of 3.27%.The experimental results show that the proposed method can better identify multiple types of power equipment of different scales at the same time,which helps to improve the intelligence of UAV autonomous inspection in distribution lines.展开更多
Personal protective equipment(PPE)donning detection for medical staff is a key link of medical operation safety guarantee and is of great significance to combat COVID-19.However,the lack of dedicated datasets makes th...Personal protective equipment(PPE)donning detection for medical staff is a key link of medical operation safety guarantee and is of great significance to combat COVID-19.However,the lack of dedicated datasets makes the scarce research on intelligence monitoring of workers’PPE use in the field of healthcare.In this paper,we construct a dress codes dataset for medical staff under the epidemic.And based on this,we propose a PPE donning automatic detection approach using deep learning.With the participation of health care personnel,we organize 6 volunteers dressed in different combinations of PPE to simulate more dress situations in the preset structured environment,and an effective and robust dataset is constructed with a total of 5233 preprocessed images.Starting from the task’s dual requirements for speed and accuracy,we use the YOLOv4 convolutional neural network as our learning model to judge whether the donning of different PPE classes corresponds to the body parts of the medical staff meets the dress codes to ensure their self-protection safety.Experimental results show that compared with three typical deeplearning-based detection models,our method achieves a relatively optimal balance while ensuring high detection accuracy(84.14%),with faster processing time(42.02 ms)after the average analysis of 17 classes of PPE donning situation.Overall,this research focuses on the automatic detection of worker safety protection for the first time in healthcare,which will help to improve its technical level of risk management and the ability to respond to potentially hazardous events.展开更多
In order to solve the problem of metal impurities mixed in the production line of wood pulp nonwoven raw materials,intelligent metal detection and disposal automation equipment is designed.Based on the principle of el...In order to solve the problem of metal impurities mixed in the production line of wood pulp nonwoven raw materials,intelligent metal detection and disposal automation equipment is designed.Based on the principle of electromagnetic induction,the precise positioning of metal coordinates is realized by initial inspection and multi-directional re-inspection.Based on a geometry optimization driving algorithm,the cutting area is determined by locating the center of the circle that covers the maximum area.This approach aims to minimize the cutting area and maximize the use of materials.Additionally,the method strives to preserve as many fabrics at the edges as possible by employing the farthest edge covering circle algorithm.Based on a speed compensation algorithm,the flexible switching of upper and lower rolls is realized to ensure the maximum production efficiency.Compared with the metal detection device in the existing production line,the designed automation equipment has the advantages of higher detection sensitivity,more accurate metal coordinate positioning,smaller cutting material areas and higher production efficiency,which can make the production process more continuous,automated and intelligent.展开更多
With the development of the technology of the Internet of Things,more and more operational data can be collected from air conditioning systems.Unfortunately,the most of existing air conditioning controllers mainly pro...With the development of the technology of the Internet of Things,more and more operational data can be collected from air conditioning systems.Unfortunately,the most of existing air conditioning controllers mainly provide controlling functions more than storing,processing or computing the measured data.This study develops an online fault detection configuration on the equipment side of air conditioning systems to realize these functions.Modbus communication is served to collect real-time operational data.The calculating programs are embedded to identify whether the measured signals exceed their limits or not,and to detect if sensor reading is frozen and other faults in relation to the operational performance are generated or not.The online fault detection configuration is tested on an actual variable-air-volume(VAV)air handling unit(AHU).The results show that the time ratio of fault detection exceeds 95.00%,which means that the configuration exhibits an acceptable fault detection effect.展开更多
This study presents an automated system for monitoring Personal Protective Equipment(PPE)compliance using advanced computer vision techniques in industrial settings.Despite strict safety regulations,manual monitoring ...This study presents an automated system for monitoring Personal Protective Equipment(PPE)compliance using advanced computer vision techniques in industrial settings.Despite strict safety regulations,manual monitoring of PPE compliance remains inefficient and prone to human error,particularly in harsh environmental conditions like in Saudi Arabia’s Eastern Province.The proposed solution leverages the state-of-the-art YOLOv11 deep learning model to detect multiple safety equipment classes,including safety vests,hard hats,safety shoes,gloves,and their absence(no_hardhat,no_safety_vest,no_safety_shoes,no_gloves)along with person detection.The system is designed to perform real-time detection of safety gear while maintaining accuracy despite challenging conditions such as extreme heat,dust,and variable lighting.In this regard,a state-of-the-art augmented and rich dataset obtained from real-life CCTV,warehouse,and smartphone footage has been investigated using YOLOv11,the latest in its family.Preliminary testing indicates the highest detection rate of 98.6% across various environmental conditions,significantly improving workplace safety compliance and reducing the resources required for manual checks.Additionally,a userfriendly administrative interface provides immediate notification upon detection of breaches so that corrective action can be taken immediately.This initiative contributes to Industry 4.0 practice development and reinforces Saudi Vision 2030’s emphasis on workplace safety and technology.展开更多
在ARIES采集站的测试、检修过程中,比较常见的故障是Equipment not detected(检测不到设备)的故障。本文根据Equipment not detected出现时的故障现象,有针对性地分析了放电接口板被烧毁、采集站电源异常、不能正常传输时对应的电源电路...在ARIES采集站的测试、检修过程中,比较常见的故障是Equipment not detected(检测不到设备)的故障。本文根据Equipment not detected出现时的故障现象,有针对性地分析了放电接口板被烧毁、采集站电源异常、不能正常传输时对应的电源电路,准确地定位了故障的原因,并介绍了相应的故障排除方法。展开更多
Multi-source information fusion （MSIF） is imported into structural damage diagnosis methods to improve the validity of damage detection. After the introduction of the basic theory, the function model, classification...Multi-source information fusion （MSIF） is imported into structural damage diagnosis methods to improve the validity of damage detection. After the introduction of the basic theory, the function model, classifications and mathematical methods of MSIF, a structural damage detection method based on MSIF is presented, which is to fuse two or more damage character vectors from different structural damage diagnosis methods on the character-level. In an experiment of concrete plates, modal information is measured and analyzed. The structural damage detection method based on MSIF is taken to localize cracks of concrete plates and it is proved to be effective. Results of damage detection by the method based on MSIF are compared with those from the modal strain energy method and the flexibility method. Damage, which can hardly be detected by using the single damage identification method, can be diagnosed by the damage detection method based on the character-level MSIF technique. Meanwhile multi-location damage can be identified by the method based on MSIF. This method is sensitive to structural damage and different mathematical methods for MSIF have different preconditions and applicabilities for diversified structures. How to choose mathematical methods for MSIF should be discussed in detail in health monitoring systems of actual structures.展开更多
The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety(OHS)is of prime importance.Like in other developing countries,this indust...The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety(OHS)is of prime importance.Like in other developing countries,this industry pays very little,rather negligible attention to OHS practices in Pakistan,resulting in the occurrence of a wide variety of accidents,mishaps,and near-misses every year.One of the major causes of such mishaps is the non-wearing of safety helmets(hard hats)at construction sites where falling objects from a height are unavoid-able.In most cases,this leads to serious brain injuries in people present at the site in general and the workers in particular.It is one of the leading causes of human fatalities at construction sites.In the United States,the Occupational Safety and Health Administration(OSHA)requires construction companies through safety laws to ensure the use of well-defined personal protective equipment(PPE).It has long been a problem to ensure the use of PPE because round-the-clock human monitoring is not possible.However,such monitoring through technological aids or automated tools is very much possible.The present study describes a systema-tic strategy based on deep learning(DL)models built on the You-Only-Look-Once(YOLOV5)architecture that could be used for monitoring workers’hard hats in real-time.It can indicate whether a worker is wearing a hat or not.The proposed system usesfive different models of the YOLOV5,namely YOLOV5n,YOLOv5s,YOLOv5 m,YOLOv5l,and YOLOv5x for object detection with the support of PyTorch,involving 7063 images.The results of the study show that among the DL models,the YOLOV5x has a high performance of 95.8%in terms of the mAP,while the YOLOV5n has the fastest detection speed of 70.4 frames per second(FPS).The proposed model can be successfully used in practice to recognize the hard hat worn by a worker.展开更多
An internal defect meter is an instrument to detect the internal inclusion defects of cold-rolled strip steel.The detection accuracy of the equipment can be evaluated based on the similarity of the multiple detection ...An internal defect meter is an instrument to detect the internal inclusion defects of cold-rolled strip steel.The detection accuracy of the equipment can be evaluated based on the similarity of the multiple detection data obtained for the same steel coil.Based on the cosine similarity model and eigenvalue matrix model,a comprehensive evaluation method to calculate the weighted average of similarity is proposed.Results show that the new method is consistent with and can even replace artificial evaluation to realize the automatic evaluation of strip defect detection results.展开更多
In order to make the combat effectiveness of weapon yield well, it is necessary to maintain and monitor the weapons. Based on the requirement of weapon equipment, an integrated maintaining system is developed, which c...In order to make the combat effectiveness of weapon yield well, it is necessary to maintain and monitor the weapons. Based on the requirement of weapon equipment, an integrated maintaining system is developed, which can be used to carry out the maintenance by using electricity periodically and function detection at module level. So, the breakdown rate of product component is deceased a lot, product reliability is improved, and the life cycle is prolonged. Thus, combat effectiveness of weapon equipment is improved.展开更多
Based on the use of Global Navigation Satellite System (GNSS) for meteorological detection in the world, we used the GNSS/MET detection equipment in the meteorological departments of Liaoning Province of China and its...Based on the use of Global Navigation Satellite System (GNSS) for meteorological detection in the world, we used the GNSS/MET detection equipment in the meteorological departments of Liaoning Province of China and its data to study and summarize the maintenance methods of GNSS/MET (Global Navigation Satellite System Meteorology) detection equipment and the application of water vapor products in operational systems. The results show that: 1) For GNSS/MET failures, specific inspections and classifications can be performed according to different phenomena;2) The GNSS water vapor measurement station samples every 30 seconds, forming one set of GNSS data every hour, and can detonate the atmospheric precipitation by solving the original data;3) Using the “Navigation Satellite Remote Sensing Water Vapor Application Management System”, the GNSS/MET water vapor products can be directly displayed. We can get the conclusion that GNSS/MET has far-reaching significance for studying the law of atmospheric water vapor changes and enhancing the ability to monitor severe weather such as heavy rain and strong convection.展开更多
The requirements for the construction of a new power system inevitably pose significant challenges and changes to the operation and maintenance of the power grid.To ensure the safe and stable operation of ultra-high v...The requirements for the construction of a new power system inevitably pose significant challenges and changes to the operation and maintenance of the power grid.To ensure the safe and stable operation of ultra-high voltage(UHV)transmission equipment,this work reports on the principles and preliminary results of using electroluminescence(EL)-based photon counting(PC)methods for early detection of micro-defects in GIS/GIL insulation spacer.In this study,the impact of voltage,gas pressure,and gas composition on the photon response of insulation is examined.Furthermore,the corresponding relationship between defect status and photon response characteristics is explored,along with the discussion of the EL mechanism and its evolution induced by defects.The research results demonstrate that PC measurement exhibits high sensitivity to variations in millimeter-scale defect size,position,and morphology at lower electric fields before partial discharge(PD)initiation.With this regard,this paper reveals promising prospects for the early detection of micro-defects in UHV transmission equipment using PC measurement-based methods.展开更多
In recent years, the world of science has started to produce advanced materials and technology in the nanoscale, which known as nanotechnology. The use of nanotechnology has become wide spread in all branches of scien...In recent years, the world of science has started to produce advanced materials and technology in the nanoscale, which known as nanotechnology. The use of nanotechnology has become wide spread in all branches of science, one of the important branches is the field of transportation. The application of nanotechnology in pavements showed great promise and the potential to change commonly used materials, which makes transportation more efficient, smart looking, stronger and durable that all lead to the extension of their life cycle of the roads. So, there is an essential need to prepare advanced nanotechnology tools and detection systems contain very recent instruments needed for nanotechnology studies, since the physical, chemical and biological properties of the material at nanoscale differ in fundamental and valuable ways from that at normal scale. In this work the different techniques in measuring and detection techniques in nanotechnology will be discussed the method of operation and accuracy of each technique will be evaluated, the main applications of each technique in industrial and construction field will be evaluated.展开更多
The problems in equipment fault detection include data dimension explosion,computational complexity,low detection accuracy,etc.To solve these problems,a device anomaly detection algorithm based on enhanced long short-...The problems in equipment fault detection include data dimension explosion,computational complexity,low detection accuracy,etc.To solve these problems,a device anomaly detection algorithm based on enhanced long short-term memory(LSTM)is proposed.The algorithm first reduces the dimensionality of the device sensor data by principal component analysis(PCA),extracts the strongly correlated variable data among the multidimensional sensor data with the lowest possible information loss,and then uses the enhanced stacked LSTM to predict the extracted temporal data,thus improving the accuracy of anomaly detection.To improve the efficiency of the anomaly detection,a genetic algorithm(GA)is used to adjust the magnitude of the enhancements made by the LSTM model.The validation of the actual data from the pumps shows that the algorithm has significantly improved the recall rate and the detection speed of device anomaly detection,with the recall rate of 97.07%,which indicates that the algorithm is effective and efficient for device anomaly detection in the actual production environment.展开更多
When operating an equipment or a power system at the high voltage, problems associated with partial discharge （PD） can be tracked down to electromagnetic emission, acoustic emission or chemical reactions such as the...When operating an equipment or a power system at the high voltage, problems associated with partial discharge （PD） can be tracked down to electromagnetic emission, acoustic emission or chemical reactions such as the formation of ozone and nitrous oxide gases. The high voltage equipment and high voltage installation owners have come to terms with the need for conditions monitoring the^process of PD in the equipments such as power transformers, gas insulated substations （GIS）, and cable installations. This paper reviews the available PD detection methods （involving high voltage equipment） such as electrical detection, chemical detection, acoustic detection, and optical detection. Advantages and disadvantages of each method have been explored and compared. The review suggests that optical detection techniques provide many advantages in the consideration of accuracy and suitability for the applications when compared to other techniques.展开更多
This study explores the potential of Artificial Intelligence(AI)in early screening and prognosis of Dry Eye Disease(DED),aiming to enhance the accuracy of therapeutic approaches for eye-care practitioners.Despite the ...This study explores the potential of Artificial Intelligence(AI)in early screening and prognosis of Dry Eye Disease(DED),aiming to enhance the accuracy of therapeutic approaches for eye-care practitioners.Despite the promising opportunities,challenges such as diverse diagnostic evidence,complex etiology,and interdisciplinary knowledge integration impede the interpretability,reliability,and applicability of AI-based DED detection methods.The research conducts a comprehensive review of datasets,diagnostic evidence,and standards,as well as advanced algorithms in AI-based DED detection over the past five years.The DED diagnostic methods are categorized into three groups based on their relationship with AI techniques:(1)those with ground truth and/or comparable standards,(2)potential AI-based methods with significant advantages,and(3)supplementary methods for AI-based DED detection.The study proposes suggested DED detection standards,the combination of multiple diagnostic evidence,and future research directions to guide further investigations.Ultimately,the research contributes to the advancement of ophthalmic disease detection by providing insights into knowledge foundations,advanced methods,challenges,and potential future perspectives,emphasizing the significant role of AI in both academic and practical aspects of ophthalmology.展开更多
基金supported by Science and Technology Project of State Grid Corporation of China(52094024003D).
文摘As modern power systems grow in complexity,accurate and efficient fault detection has become increasingly important.While many existing reviews focus on a single modality,this paper presents a comprehensive survey from a dual-modality perspective-infrared imaging and voiceprint analysis-two complementary,non-contact techniques that capture different fault characteristics.Infrared imaging excels at detecting thermal anomalies,while voiceprint signals provide insight into mechanical vibrations and internal discharge phenomena.We review both traditional signal processing and deep learning-based approaches for each modality,categorized by key processing stages such as feature extraction and classification.The paper highlights how these modalities address distinct fault types and how they may be fused to improve robustness and accuracy.Representative datasets are summarized,and practical challenges such as noise interference,limited fault samples,and deployment constraints are discussed.By offering a cross-modal,comparative analysis,this work aims to bridge fragmented research and guide future development in intelligent fault detection systems.The review concludes with research trends including multimodal fusion,lightweight models,and self-supervised learning.
基金supported by the National Natural Science Foundation of China(21663032 and 22061041)the Open Sharing Platform for Scientific and Technological Resources of Shaanxi Province(2021PT-004)the National Innovation and Entrepreneurship Training Program for College Students of China(S202110719044)。
文摘The SiO_(2) inverse opal photonic crystals(PC)with a three-dimensional macroporous structure were fabricated by the sacrificial template method,followed by infiltration of a pyrene derivative,1-(pyren-8-yl)but-3-en-1-amine(PEA),to achieve a formaldehyde(FA)-sensitive and fluorescence-enhanced sensing film.Utilizing the specific Aza-Cope rearrangement reaction of allylamine of PEA and FA to generate a strong fluorescent product emitted at approximately 480 nm,we chose a PC whose blue band edge of stopband overlapped with the fluorescence emission wavelength.In virtue of the fluorescence enhancement property derived from slow photon effect of PC,FA was detected highly selectively and sensitively.The limit of detection(LoD)was calculated to be 1.38 nmol/L.Furthermore,the fast detection of FA(within 1 min)is realized due to the interconnected three-dimensional macroporous structure of the inverse opal PC and its high specific surface area.The prepared sensing film can be used for the detection of FA in air,aquatic products and living cells.The very close FA content in indoor air to the result from FA detector,the recovery rate of 101.5%for detecting FA in aquatic products and fast fluorescence imaging in 2 min for living cells demonstrate the reliability and accuracy of our method in practical applications.
基金support from the European Union's Horizon 2020 Research and Innovation Program under the Marie Skłodowska-Curie grant agreement No.101024139,the RILEM technical committee TC 279 WMR(valorisation of waste and secondary materials for roads),RILEM technical committee TC-264 RAP(asphalt pavement recycling)the Swiss National Science Foundation(SNF)grant 205121_178991/1 for the project titled“Urban Mining for Low Noise Urban Roads and Optimized Design of Street Canyons”,National Natural Science Foundation of China(No.51808462,51978547,52005048,52108394,52178414,52208420,52278448,52308447,52378429)+9 种基金China Postdoctoral Science Foundation(No.2023M730356)National Key R&D Program of China(No.2021YFB2601302)Natural Science Basic Research Program of Shaanxi(Program No.2023-JC-QN-0472)Postdoctoral Science Foundation of Anhui Province(2022B627)Shaanxi Provincial Science and Technology Department(No.2022 PT30)Key Technological Special Project of Xinxiang City(No.22ZD013)Key Laboratory of Intelligent Manufacturing of Construction Machinery(No.IMCM2021KF02)the Applied Basic Research Project of Sichuan Science and Technology Department(Free Exploration Type)(Grant No.2020YJ0039)Key R&D Support Plan of Chengdu Science and Technology Project-Technology Innovation R&D Project(Grant No.2019-YF05-00002-SN)the China Postdoctoral Science Foundation(Grant No.2018M643520).
文摘As a vital and integral component of transportation infrastructure,pavement has a direct and tangible impact on socio-economic sustainability.In recent years,an influx of groundbreaking and state-of-the-art materials,structures,equipment,and detection technologies related to road engineering have continually and progressively emerged,reshaping the landscape of pavement systems.There is a pressing and growing need for a timely summarization of the current research status and a clear identification of future research directions in these advanced and evolving technologies.Therefore,Journal of Road Engineering has undertaken the significant initiative of introducing a comprehensive review paper with the overarching theme of“advanced road materials,structures,equipment,and detection technologies”.This extensive and insightful review meticulously gathers and synthesizes research findings from 39 distinguished scholars,all of whom are affiliated with 19 renowned universities or research institutions specializing in the diverse and multidimensional field of highway engineering.It covers the current state and anticipates future development directions in the four major and interconnected domains of road engineering:advanced road materials,advanced road structures and performance evaluation,advanced road construction equipment and technology,and advanced road detection and assessment technologies.
基金supported by the National Natural Science Foundation of China under Grants 62362040,61662033supported by the Science and Technology Project of the State Grid Jiangxi Electric Power Co.,Ltd.of China under Grant 521820210006.
文摘The YOLOx-s network does not sufficiently meet the accuracy demand of equipment detection in the autonomous inspection of distribution lines by Unmanned Aerial Vehicle(UAV)due to the complex background of distribution lines,variable morphology of equipment,and large differences in equipment sizes.Therefore,aiming at the difficult detection of power equipment in UAV inspection images,we propose a multi-equipment detection method for inspection of distribution lines based on the YOLOx-s.Based on the YOLOx-s network,we make the following improvements:1)The Receptive Field Block(RFB)module is added after the shallow feature layer of the backbone network to expand the receptive field of the network.2)The Coordinate Attention(CA)module is added to obtain the spatial direction information of the targets and improve the accuracy of target localization.3)After the first fusion of features in the Path Aggregation Network(PANet),the Adaptively Spatial Feature Fusion(ASFF)module is added to achieve efficient re-fusion of multi-scale deep and shallow feature maps by assigning adaptive weight parameters to features at different scales.4)The loss function Binary Cross Entropy(BCE)Loss in YOLOx-s is replaced by Focal Loss to alleviate the difficulty of network convergence caused by the imbalance between positive and negative samples of small-sized targets.The experiments take a private dataset consisting of four types of power equipment:Transformers,Isolators,Drop Fuses,and Lightning Arrestors.On average,the mean Average Precision(mAP)of the proposed method can reach 93.64%,an increase of 3.27%.The experimental results show that the proposed method can better identify multiple types of power equipment of different scales at the same time,which helps to improve the intelligence of UAV autonomous inspection in distribution lines.
基金supported by the grants from the Natural Science Foundation of China(No.72161034).
文摘Personal protective equipment(PPE)donning detection for medical staff is a key link of medical operation safety guarantee and is of great significance to combat COVID-19.However,the lack of dedicated datasets makes the scarce research on intelligence monitoring of workers’PPE use in the field of healthcare.In this paper,we construct a dress codes dataset for medical staff under the epidemic.And based on this,we propose a PPE donning automatic detection approach using deep learning.With the participation of health care personnel,we organize 6 volunteers dressed in different combinations of PPE to simulate more dress situations in the preset structured environment,and an effective and robust dataset is constructed with a total of 5233 preprocessed images.Starting from the task’s dual requirements for speed and accuracy,we use the YOLOv4 convolutional neural network as our learning model to judge whether the donning of different PPE classes corresponds to the body parts of the medical staff meets the dress codes to ensure their self-protection safety.Experimental results show that compared with three typical deeplearning-based detection models,our method achieves a relatively optimal balance while ensuring high detection accuracy(84.14%),with faster processing time(42.02 ms)after the average analysis of 17 classes of PPE donning situation.Overall,this research focuses on the automatic detection of worker safety protection for the first time in healthcare,which will help to improve its technical level of risk management and the ability to respond to potentially hazardous events.
基金National Key Research and Development Program of China(Nos.2022YFB4700600 and 2022YFB4700605)。
文摘In order to solve the problem of metal impurities mixed in the production line of wood pulp nonwoven raw materials,intelligent metal detection and disposal automation equipment is designed.Based on the principle of electromagnetic induction,the precise positioning of metal coordinates is realized by initial inspection and multi-directional re-inspection.Based on a geometry optimization driving algorithm,the cutting area is determined by locating the center of the circle that covers the maximum area.This approach aims to minimize the cutting area and maximize the use of materials.Additionally,the method strives to preserve as many fabrics at the edges as possible by employing the farthest edge covering circle algorithm.Based on a speed compensation algorithm,the flexible switching of upper and lower rolls is realized to ensure the maximum production efficiency.Compared with the metal detection device in the existing production line,the designed automation equipment has the advantages of higher detection sensitivity,more accurate metal coordinate positioning,smaller cutting material areas and higher production efficiency,which can make the production process more continuous,automated and intelligent.
基金Research Project of China Ship Development and Design Center,Wuhan,China。
文摘With the development of the technology of the Internet of Things,more and more operational data can be collected from air conditioning systems.Unfortunately,the most of existing air conditioning controllers mainly provide controlling functions more than storing,processing or computing the measured data.This study develops an online fault detection configuration on the equipment side of air conditioning systems to realize these functions.Modbus communication is served to collect real-time operational data.The calculating programs are embedded to identify whether the measured signals exceed their limits or not,and to detect if sensor reading is frozen and other faults in relation to the operational performance are generated or not.The online fault detection configuration is tested on an actual variable-air-volume(VAV)air handling unit(AHU).The results show that the time ratio of fault detection exceeds 95.00%,which means that the configuration exhibits an acceptable fault detection effect.
文摘This study presents an automated system for monitoring Personal Protective Equipment(PPE)compliance using advanced computer vision techniques in industrial settings.Despite strict safety regulations,manual monitoring of PPE compliance remains inefficient and prone to human error,particularly in harsh environmental conditions like in Saudi Arabia’s Eastern Province.The proposed solution leverages the state-of-the-art YOLOv11 deep learning model to detect multiple safety equipment classes,including safety vests,hard hats,safety shoes,gloves,and their absence(no_hardhat,no_safety_vest,no_safety_shoes,no_gloves)along with person detection.The system is designed to perform real-time detection of safety gear while maintaining accuracy despite challenging conditions such as extreme heat,dust,and variable lighting.In this regard,a state-of-the-art augmented and rich dataset obtained from real-life CCTV,warehouse,and smartphone footage has been investigated using YOLOv11,the latest in its family.Preliminary testing indicates the highest detection rate of 98.6% across various environmental conditions,significantly improving workplace safety compliance and reducing the resources required for manual checks.Additionally,a userfriendly administrative interface provides immediate notification upon detection of breaches so that corrective action can be taken immediately.This initiative contributes to Industry 4.0 practice development and reinforces Saudi Vision 2030’s emphasis on workplace safety and technology.
文摘在ARIES采集站的测试、检修过程中,比较常见的故障是Equipment not detected(检测不到设备)的故障。本文根据Equipment not detected出现时的故障现象,有针对性地分析了放电接口板被烧毁、采集站电源异常、不能正常传输时对应的电源电路,准确地定位了故障的原因,并介绍了相应的故障排除方法。
基金The National High Technology Research and Develop-ment Program of China(863Program)(No.2006AA04Z416)the Na-tional Science Fund for Distinguished Young Scholars(No.50725828)the Excellent Dissertation Program for Doctoral Degree of Southeast University(No.0705)
文摘Multi-source information fusion （MSIF） is imported into structural damage diagnosis methods to improve the validity of damage detection. After the introduction of the basic theory, the function model, classifications and mathematical methods of MSIF, a structural damage detection method based on MSIF is presented, which is to fuse two or more damage character vectors from different structural damage diagnosis methods on the character-level. In an experiment of concrete plates, modal information is measured and analyzed. The structural damage detection method based on MSIF is taken to localize cracks of concrete plates and it is proved to be effective. Results of damage detection by the method based on MSIF are compared with those from the modal strain energy method and the flexibility method. Damage, which can hardly be detected by using the single damage identification method, can be diagnosed by the damage detection method based on the character-level MSIF technique. Meanwhile multi-location damage can be identified by the method based on MSIF. This method is sensitive to structural damage and different mathematical methods for MSIF have different preconditions and applicabilities for diversified structures. How to choose mathematical methods for MSIF should be discussed in detail in health monitoring systems of actual structures.
文摘The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety(OHS)is of prime importance.Like in other developing countries,this industry pays very little,rather negligible attention to OHS practices in Pakistan,resulting in the occurrence of a wide variety of accidents,mishaps,and near-misses every year.One of the major causes of such mishaps is the non-wearing of safety helmets(hard hats)at construction sites where falling objects from a height are unavoid-able.In most cases,this leads to serious brain injuries in people present at the site in general and the workers in particular.It is one of the leading causes of human fatalities at construction sites.In the United States,the Occupational Safety and Health Administration(OSHA)requires construction companies through safety laws to ensure the use of well-defined personal protective equipment(PPE).It has long been a problem to ensure the use of PPE because round-the-clock human monitoring is not possible.However,such monitoring through technological aids or automated tools is very much possible.The present study describes a systema-tic strategy based on deep learning(DL)models built on the You-Only-Look-Once(YOLOV5)architecture that could be used for monitoring workers’hard hats in real-time.It can indicate whether a worker is wearing a hat or not.The proposed system usesfive different models of the YOLOV5,namely YOLOV5n,YOLOv5s,YOLOv5 m,YOLOv5l,and YOLOv5x for object detection with the support of PyTorch,involving 7063 images.The results of the study show that among the DL models,the YOLOV5x has a high performance of 95.8%in terms of the mAP,while the YOLOV5n has the fastest detection speed of 70.4 frames per second(FPS).The proposed model can be successfully used in practice to recognize the hard hat worn by a worker.
文摘An internal defect meter is an instrument to detect the internal inclusion defects of cold-rolled strip steel.The detection accuracy of the equipment can be evaluated based on the similarity of the multiple detection data obtained for the same steel coil.Based on the cosine similarity model and eigenvalue matrix model,a comprehensive evaluation method to calculate the weighted average of similarity is proposed.Results show that the new method is consistent with and can even replace artificial evaluation to realize the automatic evaluation of strip defect detection results.
文摘In order to make the combat effectiveness of weapon yield well, it is necessary to maintain and monitor the weapons. Based on the requirement of weapon equipment, an integrated maintaining system is developed, which can be used to carry out the maintenance by using electricity periodically and function detection at module level. So, the breakdown rate of product component is deceased a lot, product reliability is improved, and the life cycle is prolonged. Thus, combat effectiveness of weapon equipment is improved.
文摘Based on the use of Global Navigation Satellite System (GNSS) for meteorological detection in the world, we used the GNSS/MET detection equipment in the meteorological departments of Liaoning Province of China and its data to study and summarize the maintenance methods of GNSS/MET (Global Navigation Satellite System Meteorology) detection equipment and the application of water vapor products in operational systems. The results show that: 1) For GNSS/MET failures, specific inspections and classifications can be performed according to different phenomena;2) The GNSS water vapor measurement station samples every 30 seconds, forming one set of GNSS data every hour, and can detonate the atmospheric precipitation by solving the original data;3) Using the “Navigation Satellite Remote Sensing Water Vapor Application Management System”, the GNSS/MET water vapor products can be directly displayed. We can get the conclusion that GNSS/MET has far-reaching significance for studying the law of atmospheric water vapor changes and enhancing the ability to monitor severe weather such as heavy rain and strong convection.
基金supported by the National Natural Science Foundation of China under Grant No.52125703.
文摘The requirements for the construction of a new power system inevitably pose significant challenges and changes to the operation and maintenance of the power grid.To ensure the safe and stable operation of ultra-high voltage(UHV)transmission equipment,this work reports on the principles and preliminary results of using electroluminescence(EL)-based photon counting(PC)methods for early detection of micro-defects in GIS/GIL insulation spacer.In this study,the impact of voltage,gas pressure,and gas composition on the photon response of insulation is examined.Furthermore,the corresponding relationship between defect status and photon response characteristics is explored,along with the discussion of the EL mechanism and its evolution induced by defects.The research results demonstrate that PC measurement exhibits high sensitivity to variations in millimeter-scale defect size,position,and morphology at lower electric fields before partial discharge(PD)initiation.With this regard,this paper reveals promising prospects for the early detection of micro-defects in UHV transmission equipment using PC measurement-based methods.
文摘In recent years, the world of science has started to produce advanced materials and technology in the nanoscale, which known as nanotechnology. The use of nanotechnology has become wide spread in all branches of science, one of the important branches is the field of transportation. The application of nanotechnology in pavements showed great promise and the potential to change commonly used materials, which makes transportation more efficient, smart looking, stronger and durable that all lead to the extension of their life cycle of the roads. So, there is an essential need to prepare advanced nanotechnology tools and detection systems contain very recent instruments needed for nanotechnology studies, since the physical, chemical and biological properties of the material at nanoscale differ in fundamental and valuable ways from that at normal scale. In this work the different techniques in measuring and detection techniques in nanotechnology will be discussed the method of operation and accuracy of each technique will be evaluated, the main applications of each technique in industrial and construction field will be evaluated.
基金National Key R&D Program of China(No.2020YFB1707700)。
文摘The problems in equipment fault detection include data dimension explosion,computational complexity,low detection accuracy,etc.To solve these problems,a device anomaly detection algorithm based on enhanced long short-term memory(LSTM)is proposed.The algorithm first reduces the dimensionality of the device sensor data by principal component analysis(PCA),extracts the strongly correlated variable data among the multidimensional sensor data with the lowest possible information loss,and then uses the enhanced stacked LSTM to predict the extracted temporal data,thus improving the accuracy of anomaly detection.To improve the efficiency of the anomaly detection,a genetic algorithm(GA)is used to adjust the magnitude of the enhancements made by the LSTM model.The validation of the actual data from the pumps shows that the algorithm has significantly improved the recall rate and the detection speed of device anomaly detection,with the recall rate of 97.07%,which indicates that the algorithm is effective and efficient for device anomaly detection in the actual production environment.
文摘When operating an equipment or a power system at the high voltage, problems associated with partial discharge （PD） can be tracked down to electromagnetic emission, acoustic emission or chemical reactions such as the formation of ozone and nitrous oxide gases. The high voltage equipment and high voltage installation owners have come to terms with the need for conditions monitoring the^process of PD in the equipments such as power transformers, gas insulated substations （GIS）, and cable installations. This paper reviews the available PD detection methods （involving high voltage equipment） such as electrical detection, chemical detection, acoustic detection, and optical detection. Advantages and disadvantages of each method have been explored and compared. The review suggests that optical detection techniques provide many advantages in the consideration of accuracy and suitability for the applications when compared to other techniques.
基金funded by the National Natural Science Foundation of China Natural(Nos.U22A2041,82071915,and 62372047)the Shenzhen Key Laboratory of Intelligent Bioinformatics(No.ZDSYS20220422103800001)+5 种基金the Shenzhen Science and Technology Program(No.KQTD20200820113106007)the Guangdong Basic and Applied Basic Research Foundation(No.2022A1515220015)the Zhuhai Technology and Research Foundation(Nos.ZH22036201210034PWC,2220004000131,and 2220004002412)the Project of Humanities and Social Science of MOE(Ministry of Education in China)(No.22YJCZH213)the Science and Technology Research Program of Chongqing Municipal Education Commission(Nos.KJZD-K202203601,KJQN0202203605,and KJQN202203607)the Natural Science Foundation of Chongqing China(No.cstc2021jcyj-msxmX1108).
文摘This study explores the potential of Artificial Intelligence(AI)in early screening and prognosis of Dry Eye Disease(DED),aiming to enhance the accuracy of therapeutic approaches for eye-care practitioners.Despite the promising opportunities,challenges such as diverse diagnostic evidence,complex etiology,and interdisciplinary knowledge integration impede the interpretability,reliability,and applicability of AI-based DED detection methods.The research conducts a comprehensive review of datasets,diagnostic evidence,and standards,as well as advanced algorithms in AI-based DED detection over the past five years.The DED diagnostic methods are categorized into three groups based on their relationship with AI techniques:(1)those with ground truth and/or comparable standards,(2)potential AI-based methods with significant advantages,and(3)supplementary methods for AI-based DED detection.The study proposes suggested DED detection standards,the combination of multiple diagnostic evidence,and future research directions to guide further investigations.Ultimately,the research contributes to the advancement of ophthalmic disease detection by providing insights into knowledge foundations,advanced methods,challenges,and potential future perspectives,emphasizing the significant role of AI in both academic and practical aspects of ophthalmology.
