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Module-Level Fault Diagnosis of Photovoltaic Array based on Wireless sensor networks and Inverter Activated I-V Scanning 5

Module-Level Fault Diagnosis of Photovoltaic Array based on ...

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5th IEEE international Electrical and Energy conference, CIEEC 2022

作者： Zhang, Jingwei Ai, Changqing Zheng, Zijun Ding, Kun Chen, Xihui Liu, Yongiie Chen, Ling Hohai University College of Mechanical and Electrical Engineering Changzhou China Changzhou Key Laboratory of Photovoltaic System Integration and Production Equipment Technology Changzhou China Huaiyin Normal University School of Physics and Electronic Electrical Engineering Huai'an China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

Photovoltaic (PV) technology has become a promising renewable energy in the recent decade. the intelligent operation and maintenance of PV system is one of the hottest research issues in PV industry. In this paper, the module-level fault diagnosis is proposed based on the developed wireless sensor networks and inverter activated current-voltage (I-V) scanning. the voltage waveform of PV module is measured during the scanning of I-V characteristic curve activated by the inverter. then, the open-circuit voltage and voltage at maximum power point are obtained and used to diagnose the pattern of partial shading, short-circuit of bypass diodes of PV module, and open-circuit faults. Using the real-time monitoring in the developed virtual reality (VR) environment, the efficiency of fault localization and diagnosis of PV array is enhanced. the experimental results show that the proposed module-level fault diagnosis of PV array based on wireless sensor networks and inverter activated I-V scanning is effective and feasible, especially suitable for the PV string that few PV modules are slightly shaded or one of its bypass diodes is short-circuited. © 2022 IEEE.

关键词： Failure analysis

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Enhancing Multi-crop Disease Prediction Using Convolutional Neural networks: A Global Perspective 5th

Enhancing Multi-crop Disease Prediction Using Convolutional ...

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5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Garigipati, Nagababu Nivrithi, Bathini ShreshtaReddy, Dachireddy Eesha, Mummaneni Nikitha, Patlavath G. Narayanamma Institute of Technology and Science Telangana Hyderabad India

ISBN: (纸本)9789819780426

Given the recent rate of population expansion, it is anticipated that by 2050, global crop productivity will need to double. Getting this production result is significantly hampered by pests and diseases. It is essential to offer efficient methods for automatically diagnosing, classifying, and foretelling diseases and pests in agricultural crops. Machine learning techniques can be applied to the data being worked on to extract information and relationships in order to automate this process. Deep learning techniques are employed in the study, which focuses on the categorization, detection, and pest and disease prediction tasks in the agricultural industry. We seek to advance precision agriculture and smart farming by supporting the development of techniques that will allow farmers to use less pesticides and chemicals while protecting and improving the quality and yield of their crops. We’ll employ image classification method, in which the user uploads a photo of the diseased plant or crop, and the system uses deep learning techniques to identify the disease type. And this disease identification will offer advice on how to treat or avoid that crop. the goal is to create a uniform approach for all features and deliver results for all crops throughout the majority of regions in India that are as accurate as possible. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional Neural networks Deep learning Image processing Plant diseases Prediction

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Enhancing Structural Cracks Segmentation through Generative Image Synthesis 5

Enhancing Structural Cracks Segmentation Through Generative ...

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5th international conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Liao, Yunping Zhang, Tao Yang, Yong Li, Boyi Li, Hui Yan, Qi Southwest University College of Computer and Information Science & College of Software Chongqing China Ministry of Natural Resources Chongqing Institute of Geology and Mineral Resources Technology Innovation Center of Geohazards Automatic Monitoring Chongqing China Chongqing University School of Civil Engineering Chongqing China

ISBN: (纸本)9798350309034

Using deep neural networks to detect cracks in architectural structures presents significant challenges due to the irregular and diverse nature of cracks, along with variations in lighting conditions and backgrounds. Moreover, the absence of a sufficient number of crack sample images can significantly impact the accuracy of crack detection. In this study, we propose a novel method to enhance the semantic segmentation accuracy of structural crack images. this is achieved by generating images and their corresponding masks. Our approach cleverly combines synthetic crack data generated through unconditional GAN-based methods with real-world data, addressing the issue of limited data availability. Additionally, we employ transformer-based architecture, specifically the SegFormer segmentation model, to assess our methodology. Experimental results demonstrate that the SegFormer small model, trained iteratively on this integrated dataset, achieved an impressive mIoU of 87.13%, marking a significant improvement over the 85.59% obtained on the original dataset. Our work demonstrates the power of generative techniques in computer vision tasks by offering a viable solution in the face of challenging data constraints, especially in the realm of cracks detection. © 2023 IEEE.

关键词： computer vision crack detection generative adversarial network image processing segmentation

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Deep Learning Methods for Ship Classification: From Visible to Infrared Images 5

Deep Learning Methods for Ship Classification: From Visible ...

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5th international conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Liu, Tianci Qin, Hengjia Zhan, Zhuo Liu, Yunpeng Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Chinese Academy of Sciences Key Laboratory of Opto-Electronic Information Processing Shenyang110016 China Key Laboratory of Image Understanding and Computer Vision Liaoning Province Shenyang110016 China The Third Military Representative Office of the Air Force Equipment Department in Shenyang Region Liaoning Province Shenyang110027 China

ISBN: (纸本)9798350357950

Deep learning methods have achieved excellent performances on visual tasks of target recognition and classification. the rapid development of autonomous seafaring vessels comes up with the requirement to recognize other maritime ships day and night. However, the recognition of ships based on the deep neural networks may not always access the results as expectation when the ships are under the nighttime environment. To this issue, we consider the ship recognition task under the deep learning framework with paired visible images and infrared images. In this article, we propose an end-to-end convolutional network based on visible images and infrared images of the autonomous seafaring vessels. To demonstrate the effectiveness of our model, we choose the V AIS dataset to test the performance of classifying the maritime ships. Experimental results show that the proposed network outperforms the state-of-the-art methods based on the V AIS database. © 2023 IEEE.

关键词： deep learning infrared images neural network Ship recognition

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Identifying Abnormal Sessions via Adversarial Learning on Heterogeneous Information Network 5

Identifying Abnormal Sessions via Adversarial Learning on He...

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5th international conference on Intelligent Medicine and Image processing, IMIP 2023

作者： Gong, Tao Qiu, Dehong Huazhong University of Science and Technology Academy of Cyberspace Security Wuhan China Huazhong University of Science and Technology School of Software Engineering Wuhan China

ISBN: (纸本)9798350337396

Existing anomaly detection is mostly based on abnormal traffic and statistical analysis, but the structural information of the attacker's network is also of great significance to anomaly detection. We convert the problem of identifying abnormal sessions in the network into a problem of link classification on a heterogeneous graph. However, the number of sessions in the real network is extremely large, forming a heterogeneous network with complex structural information, and it is difficult to extract its effective features. therefore, we use the idea of Hin2Vec to extract features from heterogeneous graphs, and use the generative adversarial network (GAN) to improve. the negative samples is needed in the model training process of feature extraction. through the traditional negative sampling method, the samples are often too trivial to effectively fit the model, and it is difficult to sample appropriate negative samples. therefore, we use the generator of GAN to generate high-quality negative samples. On this basis, an anomaly detection model is constructed to identify abnormal sessions. We named our model HinDet. In this paper, heterogeneous graph embedding is applied to the field of anomaly detection. We have improved Hin2Vec and the negative sampling method. It solves the problems of difficult feature extraction and excessive noise of negative samples, and constructs an anomaly detection model to identify abnormal sessions. We experiment on two security domain datasets. Experimental results show that the use of GAN to generate negative samples can enhance the detection ability of the anomaly detection model. HinDet has good abnormal session recognition ability. © 2023 IEEE.

关键词： Generative adversarial networks

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FRCNN based Deep Learning for Identification and Classification of Alopecia Areata 5

FRCNN based Deep Learning for Identification and Classificat...

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5th IEEE international conference on Electrical, Computer and Communication Technologies, ICECCT 2023

作者： Saraswathi, C. Pushpa, B. Annamalai University Department of Computer and Information Science Chidambaram608002 India

ISBN: (纸本)9781665493604

Most patients suffer from scalp hair issues like dermatitis, baldness, and so on as an outcome of unhealthy lifestyles, hormonal imbalance, and so on. the most common type of alopecia is alopecia areata (AA), which is typically detected and diagnosed using medical image processing models. However, they are unreliable for striking or intersecting hairs and are directly influenced by design variables. Deep Learning (DL) in imaging data was thus used to detect and diagnose AA. Similarly, various DL models recognized and diagnosed various scalp hair conditions. Likewise, various scalp hair conditions were recognized and diagnosed by the various DL models. Hence, this paper proposes a Faster Residual Convolutional Neural Network (FRCNN) model to recognize AA and scalp conditions together for many individuals with different kinds of baldness. the main goal of the FRCNN is to use a Region-Of-Interest (ROI) projection layer to enhance the recognition accuracy of AA and scalp hair symptoms. In this FRCNN model, the given scalp and AA images are initially fed to the ROI projection layer and deep convolutional layer to extract feature maps, which are aggregated by the ROI pooling to get the final feature vector representation. then, the obtained feature vector is passed to the Fully Connected (FC) layer accompanied by the softmax classifier to recognize the various conditions of AA. Finally, the test results show that the FRCNN on hair and scalp image databases achieves an accuracy of 84.3% compared to the other models. © 2023 IEEE.

关键词： Convolutional neural networks

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An Empirical Study on Neural networks Pruning: Trimming for Reducing Memory or Workload 5

An Empirical Study on Neural Networks Pruning: Trimming for ...

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5th international conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Xiao, Kaiwen Cai, Xiaodong Xu, Ke School of Artificial Intelligence Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China

ISBN: (纸本)9798350393521

Most of existing studies on neural network pruning only consider memory-based pruning strategies. However pruning for computational workload is often more important in hardware deployments due to a greater focus on model computation reductions. In addition, most pruning schemes restore model accuracy during pruning at the expense of adding hyperparameters, extending training time and training complexity. this work proposes a statistical-based globally soft iterative pruning scheme. With little extra calculation, an extremely sparse model can be obtained without additional hyperparameters and extended training time. Moreover, this work proposes the concept of computational intensity to balance model memory and computational workload during pruning. Focusing on memory orientated pruning, we can achieve 303×, 100× and 25× parameter compression on LeNet-5 (MNIST), VGG (CIFAR-10) and AlexNet (ImageNet) models, respectively. In particular, combined with cluster quantization, the LeNet-5 model parameters can be compressed to 3232×. Focusing on workload orientated pruning, we can reduce the computation by 7}.6× on the AlexNet model, without accuracy loss, significantly higher than prior work. In addition, in order to verify the versatility of the pruning method, we also migrate the pruning task to the object detection and implement 10× parameter compression and 2.8× computation compression for YOLOv2 with reduced mAP within 1%. © 2023 IEEE.

关键词： Iterative methods

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Automatic Creation of Masks for Marking Histological Images of the Epithelium of the Paranasal Sinuses 5

Automatic Creation of Masks for Marking Histological Images ...

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5th international conference on Informatics and Data-Driven Medicine (IDDM)

作者： Nechyporenko, Alina Hubarenko, Yevhen Hubarenko, Maryna Kalnytska, Violeta Alekseeva, Victoriia Gargin, Vitaliy Tech Univ Appl Sci Div Mol Biotechnol & Funct Genom Hsch Ring 1 D-15745 Wildau Germany Kharkiv Natl Univ Radio Elect 14 Nauky Ave UA-61116 Kharkiv Ukraine Kharkiv Natl Med Univ 4 Nauky Ave UA-61000 Kharkiv Ukraine Kharkiv Int Med Univ 38 Molochna Str UA-61001 Kharkiv Ukraine

the article discusses the approach to solving the problem of reducing time spent on the preparation of medical images for teaching neural networks, by reducing the time of creating masks for images. the task is considered on the example of processing images of the mucous membrane of the paranasal sinus. the specifics of the task did not allow effectively using existing software solutions. During the study, a software solution was proposed, which made it possible to radically reduce the time of creating masks for images. the article also analyzes the shortcomings of the automated creation of masks, as well as the directions of their solution. the loss of time due to the adjustment of the color palette can be reduced even more to 1-2 minutes, the average deviation is 7.61%

关键词： Neural networks masks microscopic images epithelium inflammatory changes

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Hydrogen Cyanide Gas sensor using Doped/Undoped Graphene Nanoribbon 5

Hydrogen Cyanide Gas Sensor using Doped/Undoped Graphene Nan...

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5th international conference on Multimedia, Signal processing and Communication Technologies (IMPACT)

作者： Solanki, Kamal Majumder, Manoj Kumar Int Inst Informat Technol Naya Raipur Dept Elect & Commun Naya Raipur 493661 Chhattisgarh India

ISBN: (纸本)9781665476478

Hydrogen cyanide (HCN) is a gas with an acid, unsteady, almond-similar odor, and an expensive precursor to various chemical compounds ranging from pharmaceuticals to polymers. the sensitivity of HCN gas molecules in doped/undoped armchair graphene nanoribbon is investigated using the industry standard virtual nanolab. While the pure ArGNR is not overly sensitive to HCN gas molecules, its sensitivity significantly increases upon adding dopants. A linear combination of atomic orbital (LCAO) calculator has been used to analyze the adsorption energy, charge transfer and band structure of doped/undoped ArGNR for HCN gas molecule sensing. the results demonstrated that phosphorus-doped ArGNR exhibit a considerable adsorption energy of -0.74 eV which is 55% more than that of an undoped ArGNR while being the most responsive to the HCN molecule. Accordingly, the phosphorus-doped ArGNR is being considered for applications like HCN gas sensing because of its high binding energy and charge transfer values.

关键词： Hydrogen Cyanide Adsorption Doping Sensitivity

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An Automatic Navigation System for New Technical Advanced Drones for Different Alpplications 5

An Automatic Navigation System for New Technical Advanced Dr...

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5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Talwandi, Navjot Singh thakur, Payal Khare, Shanu Chandigarh University Dept of Computer Science Engineering Punjab India

ISBN: (纸本)9798350383522

Autonomous pilot technology integrated into drones has ushered in a new era of unmanned aircraft systems, creating a multitude of applications this abstract examines the importance of the importance and potential of autonomous pilot aircraft, underscoring its transformative impact on industries ranging from agriculture to surveillance and beyond Navigation requires intelligent a unmanned aerial vehicles (UAVs) will be equipped with sophisticated artificial and sensor systems, enabling them to operate autonomously without constant human intervention A rapidly growing field in robotics and aerospace technology Tires It is showing to do so, the paper provides a comprehensive overview, including key concepts, challenges, and recent developments in autonomous automation enter the box. It also discusses, and educates, the critical importance of real-time operations, the critical role of obstacle detection and avoidance systems, and the use of GPS and optical-based navigation delves into how machine learning and artificial intelligence enhance the autonomy of drones. In addition, the paper explores the many potential applications and benefits of autonomous drone communications, from search and rescue missions to precision agricultural delivery Growing interest and investment in this technology in 2013 highlights its potential to transform industries, promising a future marked by improved safety and performance. © 2024 IEEE.

关键词： and applications artificial intelligence Autonomous drones GPS machine learning navigation obstacle avoidance real-time data processing vision-based navigation

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