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EFFECTS OF CUTTING AND WELDING ON ELECTRIC STEELS' FERROMAGNETIC MATERIAL

EFFECTS OF CUTTING AND WELDING ON ELECTRIC STEELS' FERROMAGN...

2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Agarhari, Abishek Singh, Thakur Meenu Balaji, N.V. Arul Antran Vijay, S. Borade, Monali Ravindra Department of Computer Science & Engineering Invertis University Uttar Pradesh Bareilly243123 India School of Mechenical Engineering Dev Bhoomi Uttarakhand University Uttarakhand 248007 India Department of Computer Science & Engineering School of Engineering & Technology K. R. Mangalam University Haryana Gurugram122103 India Department of Computer Science Karpagam Academy of Higher Education Coimbatore641021 India Department of Computer Science Engineering Karpagam College of Engineering Coimbatore641032 India AI & Ds Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350355338

This study examines the crucial function that silicon steels, sometimes referred to as electric steels, serve in electromagnetic applications like transformers and motors. The need of preserving ferromagnetic characteristics for effective energy exchange is emphasized in particular. In order to fill in knowledge gaps about the precise effects of the two processes on these materials, this investigation presents a methodology that emphasizes the significance of careful material selection, thorough recording of the cutting and welding procedures, and an organized method of measurement and *** study recognizes the broad implications for industrial, power distribution, and automotive sectors, where ferromagnetic properties are necessary for electrical equipment to remain reliable. The study attempts to provide understanding of the unique effects of the two processes on electric steels, suggesting mitigating measures via a thorough analysis of the body of current literature. The transparent and reproducible technique presented in this study contributes to our knowledge of how electric steels behave in various contexts. The ultimate goal of the study is to improve production methods and guarantee that electromagnetic gear operates as well it can in our environment of growing electricity. © 2024 IEEE.

关键词： Automotive industry

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Semantic-Aware Pseudo-Labeling for Unsupervised Meta-Learning

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5475-5488页

作者： Ouyang, Tianran Dong, Xingping Ye, Mang Du, Bo Shao, Ling Shen, Jianbing Wuhan University School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan430072 China University of Chinese Academy of Sciences UCAS-Terminus AI Lab Beijing101408 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science 314100 China

In unsupervised meta-learning, the clustering-based pseudo-labeling approach is an attractive framework, since it is model-agnostic, allowing it to synergize with supervised algorithms to learn from unlabeled data. However, the pseudo-labels suffer from clustering noise and semantic chaos problems, further impacting the effectiveness of meta-learning. In this paper, we analyze and optimize the pseudo-labeling process, including encoding and clustering, aiming to generate semantic-like pseudo-labels to narrow the gap between unsupervised and supervised meta-learning. First, during the encoding, we observe that the embedding space of existing methods lacks clustering-friendly properties, which is the primary reason for clustering noise. To address this issue, we minimize the inter-to-intra-class similarity ratio to generate clustering-friendly embedding features and validate our approach through comprehensive experiments. Then, during the clustering, we find that the semantic quality of pseudo-labels is not adequately controlled, resulting in semantic chaos of pseudo-labels. We propose a semantic-stability index to measure the semantic quality of pseudo-labels quantitatively. Based on this index, we propose the Semantic-aware Pseudo-label Reassignment mechanism to generate semantic-like pseudo-labels for all samples. Our approach is model-agnostic and can easily be integrated into existing supervised methods. To demonstrate its generalization ability, we integrate it into two representative algorithms: MAML and EP. The results on three main few-shot benchmarks clearly show that the proposed method achieves significant improvement compared to state-of-the-art models. Notably, our approach also outperforms the corresponding supervised method in three tasks. © 1979-2012 IEEE.

关键词： Latent semantic analysis

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A Constructive Method Based on Dynamic Solution Space for Travelling Salesman Problem 11

A Constructive Method Based on Dynamic Solution Space for Tr...

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11th International Conference on Advanced Cloud and Big Data, CBD 2023

作者： Bai, Xiaoxin Luli, Luli Wu, Hanqian Southeast University School of Computer Science and Engineering China Southeast University School of Cyber Science and Engineering China Suzhou Centennail College China Southeast University Key Laboratory of Computer Network and Information Integration of Ministry of Education China

ISBN: (纸本)9798350345346

Neural combinatorial optimization (NCO) has emerged as a promising field to solve the combinatorial optimization problem (COP) as automatically learning the solvers less relying on domain-specific expert knowledge. Constructive Method (ConsM) is the most widely used approach in NCO for solving various COPs and their variations. However, few literature elucidates the underlying principles of ConsM, hindering further optimization. In this paper, we analyze the efficiency principle of ConsM for COPs from the perspective of discrete solution space and summarize its design principles. These principles can serve as guidelines for designing novel constructive policy. Based on these principles, we reviewed the existing ConsM models and identified that the design of existing decoder introduced certain noises, which negatively impacted the policy performance. Therefore, we designed a novel decoder in line with the aforementioned principles. Notably, our method can be applied to all ConsM models and yielded significant improvements compared to the original. We evaluated our method on Traveling Salesman Problem(TSP) and the experimental results verified the effectiveness and efficiency of our method, supporting our analysis of ConsM. In particular, we reduced the average optimality gap from 1.16% to 0.62% for TSP100 compared to Sym-NCOs, keeping the hyperparameters constant. © 2023 IEEE.

关键词： Combinatorial optimization

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A Real-Time Semantic Segmentation Network for Robotic Arm Grasp 1

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41st computer Graphics International Conference, CGI 2024

作者： Liu, Li Zhou, Xinlei He, Mingwei Yu, Feng Peng, Tao Hu, Xinrong Jiang, Minghua School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Engineering Research Center of Hubei Province for Clothing Information Wuhan China

ISBN: (数字)9783031820243

ISBN: (纸本)9783031820236

A robotic arm equipped with a vision system can significantly improve production efficiency. However, due to the complexity of the robotic arm’s working environment, the useful information about the objects recognized by the visual system is significantly reduced. To solve this problem, we propose a real-time semantic segmentation network to achieve high-precision and efficient robotic arm grasp. First, we propose an enhanced feature extraction module for the encoding stage, which can improve feature extraction capabilities without increasing or even reducing the amount of model calculations and parameters. Then, we propose an enhanced feature reconstruction module for the decoding stage, which can better preserve essential features in the feature map restoration stage. To validate the efficacy of our proposed approach, experiments are conducted on the rigid dataset Jacquard and the flexible dataset Electric Wires. Experimental results show that our method can quickly and accurately identify target objects, achieving the best trade-off between accuracy and inference speed. Our method achieves an mIOU of 94.66% and an inference speed of 99.39 FPS on the Jacquard dataset. On the Electric Wires dataset, our method achieves an mIOU of 96.70% with an inference speed of 98.44 FPS. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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An Automatic Error Recognition approach for Machine Translation Results based on Deep Learning 2

An Automatic Error Recognition approach for Machine Translat...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Rekha, M.N. Prasad, Mudarakola Lakshmi Mukherjee, Saptarshi Nikam, Sudam Vasant Sharma, Swati Reddy, Pundru Chandra Shaker Jss Science and Technology University Electrical and Electronics Engineering Sri Jayachamarajendra College of Engineering Karnataka Mysore India Institute of Aeronautical Engineering Computer Science and Engineering Telangana Dundigal India Maulana Abul Kalam Azad University of Technology M.Tech-Bioinformatics West Bengal India Bharati Vidyapeeth College of Engineering Instrumentation Engineering Navi Mumbai India Presidency University School of Computer Science Engineering and Information Science Karnataka Bangalore India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798350387933

At present, the speedy growth of natural-language-processing(NLP) has produced considerable evolution for the topic of machine-translation. There have been many deep neural network-based machine-translation(MT) methods that have become increasingly broad. Still, there currently lacks reliable automatic-error-detection(AED) systems for MT outcomes. One important metric for gauging the efficacy of an engineering English translation application is the accuracy of the corresponding deep learning model's translation. This research presents a deep learning-based solution for automatic mistake identification in machine translation results as a means of closing this gap. In this research, we propose a deep generative model to synthesis the training data that will be utilized to train a model to correct grammatical errors in English used in international trade. Next, the learner's corpus is corrected using the grammatical fault alteration approach, and 'error-correct' sentence pairs are created using the exacted aim sentences and by hand interpreted standard sentences and fed back to the error generation technique for retraining. The model's error detection and correction capabilities are enhanced through the establishment of a connection amid the grammatical error recognition and correction model. The suggested error recognition method has been shown to considerably increase the stealthiest of the trigger, guarantee the efficacy of the backdoor assault, and make the trigger resistant to specific data augmentation operations in experiments using datasets like GTRSB. © 2024 IEEE.

关键词： Error detection

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SCB-LEDN: Lightweight and Efficient Object Detection Network for Student Classroom Behavior 41st

SCB-LEDN: Lightweight and Efficient Object Detection Networ...

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41st computer Graphics International Conference, CGI 2024

作者： Jiang, Minghua Zheng, Xingwei Wang, Cheng He, Mingwei Liu, Li Yu, Feng School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Engineering Research Center of Hubei Province for Clothing Information Wuhan China

ISBN: (纸本)9783031818059

Object detection techniques in computer vision are increasingly employed in education to analyze student performance and evaluate teaching effectiveness. However, current classroom behavior recognition methods have problems such as decreased accuracy, high memory usage, and slow detection speed. In order to address these challenges, we design a lightweight object detection network (SCB-LEDN) based on the classroom monitoring perspective to enhance the detection accuracy of objects while reducing the model’s parameters and accelerating real-time inference. First, we create the student classroom behavior dataset (SCB-E), which includes five behaviors: raising hands, reading, sleeping, writing, and using a mobile phone. Secondly, We propose the partial detection head (PHead), effectively reducing parameter count and improving detection speed. We introduce the diverse branch block (DBB) in the feature extraction layer and create the C2DB module, enhancing student classroom behavior recognition accuracy. We introduce the EfficientRepBiPAN structure in the neck layer, enabling practical preservation and fusion of spatial information. The experimental results show that our method achieves an average precision (mAP) of 91.5% on the SCB-E dataset and reduces the parameters by 20% compared to the baseline method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Students

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Trans-EffNet: A Hybrid Model for Brain Tumor Detection Using EfficientNet and Transformer Encoder 10

Trans-EffNet: A Hybrid Model for Brain Tumor Detection Using...

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10th IEEE Smart World Congress, SWC 2024

作者： Nizamani, Abdul Haseeb Chen, Zhigang Nizamani, Ahsan Ahmed Bhatti, Mughair Aslam Ma, Hauwei Du, Wencai Central South University School of Computer Science and Engineering Changsha410083 China Hainan University School of Information and Communication Engineering Hainan China University Guangzhou Guangzhou China The University of Saint Joseph China

ISBN: (纸本)9798331520861

Accurate classification of brain tumors from MRI is critical for effective diagnosis and treatment. In this study, we introduce Trans-EffNet, a hybrid model combining pre-trained EfficientNet architectures with a transformer encoder to enhance brain tumor classification accuracy. By leveraging EfficientNet's deep CNN capabilities for localized feature extraction and the transformer encoder for capturing global contextual relationships, our model improves the identification of intricate tumor characteristics. Fine-tuned with ImageNet-derived weights and utilizing extensive data augmentation, Trans-EffNet was validated on both multi-class and binary datasets. Trans-EffNetB1 achieved 99.49 % accuracy on the multi-class dataset, while Trans-EffNetB2 recorded 99.83 % accuracy on the binary dataset, with perfect precision, recall, and F1-Score. These results underscore Trans-EffNet's robustness and potential as a significant advancement in brain tumor detection and classification. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Aggregate Signature with Single-Transaction Verification for Blockchain 9

Aggregate Signature with Single-Transaction Verification for...

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9th IEEE Smart World Congress, SWC 2023

作者： Wang, Chen Shen, Jian Ma, Qingru Dai, Hong-Ning Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou China Hong Kong Baptist University Department of Computer Science China

ISBN: (纸本)9798350319804

Blockchain has gradually become one of the important technologies concerned by many cross fields. Since the concept of blockchain has been proposed and implemented, a large number of new environments (such as smart cities, smart grids, electronic medicine, etc.) have been enabled by blockchain, while more security features, such as tamper resistance and traceability, have been realized. The verification of transaction records in the blockchain is particularly important for the implementation of the blockchain. Some researchers have achieved the recording and signing of transaction information on the blockchain through specific structures. However, the aggregation of transaction signatures and the verification of single transactions are still worthy of further study. An aggregate signature is proposed in this paper for blockchain, supporting single-transaction verification. This scheme can increase the flexibility of blockchain transaction information verification. When the committer deletes a single signature and only retains the aggregation result, it can effectively verify a single transaction. The analysis shows that the signature generated in this scheme is unforgeable and the verification of a single transaction is efficient. © 2023 IEEE.

关键词： Aggregate signature blockchain cybersecurity verification

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MFENet: Multi-scale and Local Frequency Enhancement Network for Skin Lesion Classification 1

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41st computer Graphics International Conference, CGI 2024

作者： Jin, Yuyu Xiao, Zhiyong Yuan, Jiaqi He, Mingwei Liu, Li Yu, Feng Jiang, Minghua School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Engineering Research Center of Hubei Province for Clothing Information Wuhan China

ISBN: (数字)9783031820243

ISBN: (纸本)9783031820236

Dermatoscopy is a common diagnostic tool used by dermatologists to examine skin lesions. However, existing classification methods often rely on spatial domain feature extraction. Dermatoscopic images are frequently affected by various sources of noise, such as hair and pigmented areas, resulting in non-robust feature extraction. Therefore, relying solely on spatial domain feature extraction is limited and cannot fully differentiate the subtle features of skin lesions. This paper proposes a multi-scale and local frequency enhancement network (MFENet) for skin lesion, which significantly improves the diagnostic accuracy and interpretability of dermatoscopic images. Specifically, we introduce the multi-scale and local of frequency (MLF) module to reduce noise interference and enhance edge, shape, and texture features. The module selects low-frequency information and suppresses high-frequency noise at different scales. It further enhances details and texture information in the image by processing local frequency blocks. This makes important morphological features, such as the shape and edge, more prominent in diagnosis. Additionally, a lesion channel attention (LCA) module is proposed to further optimize the extraction and amplification of key features by learning dependencies between channels. The results show that the algorithm achieves optimal performance on the ISIC 2017 and HAM10000 datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Distributed Secure Storage Scheme Based on Sharding Blockchain

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computers, Materials & Continua 2022年第3期70卷 4485-4502页

作者： Jin Wang Chenchen Han Xiaofeng Yu Yongjun Ren R.Simon Sherratt School of Computer Science and Mathematics Fujian University of TechnologyFuzhou350118China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China School of Business Nanjing UniversityNanjing210093China School of Computer School of SoftwareSchool of Cyberspace SecurityEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science&TechnologyNanjing210044China Department of Biomedical Engineering University of ReadingRG66AYUK

Distributed storage can store data in multiple devices or servers to improve data ***,in today’s explosive growth of network data,traditional distributed storage scheme is faced with some severe challenges such as insufficient performance,data tampering,and data lose.A distributed storage scheme based on blockchain has been proposed to improve security and efficiency of traditional distributed *** this scheme,the following improvements have been made in this *** paper first analyzes the problems faced by distributed *** proposed to build a new distributed storage blockchain scheme with sharding *** proposed scheme realizes the partitioning of the network and nodes by means of blockchain sharding technology,which can improve the efficiency of data verification between *** addition,this paper uses polynomial commitment to construct a new verifiable secret share scheme called *** new scheme is one of the foundations for building our improved distributed storage blockchain *** with the previous scheme,our new scheme does not require a trusted third party and has some new features such as homomorphic and batch *** security of VSS can be further *** comparisons show that the proposed scheme significantly reduces storage and communication costs.

关键词： Blockchain distributed storage verifiable secret share polynomial commitment

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