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Digital Meter Detection and Reading Recognition Based on Improved YOLOv9

Digital Meter Detection and Reading Recognition Based on Imp...

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cognitive computing and complex data (ICCD), international conference on the

作者： Wenhao Wang Chenjin Sun Taoyang Jin Zhenyang Yu Yanlun Wu Xin Chen Faculty of Computer Engineering Huaiyin Institue of Technology Huaian China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Aiming at the problems of low precision, high cost and poor timeliness of traditional digital instrument detection and reading recognition methods in petrochemical explosion environment, a digital instrument detection network and character detection and recognition network based on improved YOLOv9 are proposed. Firstly, the original backbone network is replaced by a lightweight Mobilenetv2 network, which is used to extract the instrument feature information and reduce the parameters and computational complexity of the model. Then, iRMB attention mechanism is embedded to enhance the feature representation and perception ability of the network and improve the model efficiency. Finally, the HWD_Adown downsampling module is designed to include as much information as possible in the downsampling process and improve the detection accuracy. The experimental results show that the number of parameters of the improved identification method is obviously reduced on the test set, and the average accuracy of instrument detection (mAP50) reaches 88.5%, which is 2.2% higher than the original model, and the effect is remarkable in digital instrument positioning. The average accuracy of character recognition (mAP50) reaches 98.8%.

关键词： Meters Accuracy Attention mechanisms Computational modeling Feature extraction Petrochemicals Explosions Character recognition data mining Computational complexity

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State of Health Assessment of Lithium Batteries Based on Fusion Model and Feature Extraction

State of Health Assessment of Lithium Batteries Based on Fus...

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cognitive computing and complex data (ICCD), international conference on the

作者： Qiwei Zhang Yulong Li Peipei Cai Rongkai Zhao Tian Peng Smart Energy Division Huadong Engineering Corporation Limited Hangzhou China Automation Engineering Room Hangzhou Huachen Power Control Engineering Corporation Limited Hangzhou China Beidou Innovation Studio Hangzhou Huachen Power Control Engineering Corporation Limited Hangzhou China Faculty of Automation Huaiyin Institute of Technology Huai’an China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Lithium battery State of Health (SOH) is crucial for stable operation and reducing failure risks. This paper identifies three key health features—IC curve peak value, constant voltage drop discharge time, and IC curve peak voltage—through Pearson correlation analysis. A multi-model fusion approach is utilized to construct and train three individual models: Temporal Convolutional Network (TCN), Extreme Learning Machine (ELM), and Long Short-Term Memory Network (LSTM). Additionally, three pairwise fusion models—TCN-LSTM, TCN-ELM, and ELM-LSTM—and a comprehensive TCN-LSTM-ELM model are developed. Validation using NASA dataset shows that TCN-LSTM-ELM fusion model achieves the highest prediction accuracy.

关键词： Training Accuracy Computational modeling Voltage Predictive models Lithium batteries data models Integrated circuit modeling Optimization Long short term memory

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Efficient Thyroid Disease Prediction using Features Selection and Meta-Classifiers 6

Efficient Thyroid Disease Prediction using Features Selectio...

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6th international conference on computing Methodologies and Communication, ICCMC 2022

作者： Priyadharsini, D. Sasikala, S. Hindusthan College of Arts and Science Department of Computer Applications Coimbatore India Hindusthan College of Arts and Science Department of Computer Science with Cognitive Systems Coimbatore India

ISBN: (纸本)9781665410281

Prediction of Thyroid is a complex axiom in medical research. Machine learning methods are more powerful and compact for the healthcare industry to handle the massive amount of healthcare records. The methods in machine learning provides the facility to use different kind of data values which are used for prediction. data cleaning techniques are used for enhancing the dataset to provide accurate results. The noisy and missing values are handled using data pre-processing methods. In this work, Adaboost and Bagging techniques are used for thyroid classification. The methods are executed, and the results are compared to show the effective method for thyroid prediction. © 2022 ieee.

关键词： Adaptive boosting

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A Two Dimensional Feature Engineering Method for Relation Extraction 5

A Two Dimensional Feature Engineering Method for Relation Ex...

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5th international conference on Control, Robotics, and Intelligent System, CCRIS 2024

作者： Wang, Hao Chen, Yanping Yang, Weizhe Lin, Chuan Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry Huaxi District Guizhou China State Key Laboratory of Public Big Data Huaxi District Guizhou China Guizhou University Huaxi District Guizhou China

ISBN: (数字)9781510685871

ISBN: (纸本)9781510685864

Transforming a sentence into a 2D representation, similar to table structures, reveals a semantic plane where elements can represent potential relations between paired entities. The 2D representation effectively addresses the common issue of overlapping relations. However, previous works directly transformed the representation from raw input, neglecting the utilization of prior knowledge, which is essential to support the entity relation extraction task. In this paper, we propose a 2D feature engineering method. The method innovatively integrates prior knowledge within 2D sentence representations, marking an advancement over previous strategies, and employs an attention mechanism to build the hitherto overlooked association between entities and prior knowledge. The fusion of prior knowledge and targeted attention enhances the model’s relational discernment capabilities. Evaluations on three benchmark datasets—ACE05 Chinese, ACE05 English, and CLTC—demonstrate the efficacy of the proposed method. It surpasses existing techniques, achieving state-of-the-art performance with F1 score improvements of 0.88%, 3.27%, and 2.83% respectively. The results indicate that 2D feature engineering can take advantage of a 2D sentence representation and make full use of prior knowledge. © 2024 SPIE.

关键词： Semantics

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DF-SegDiff: A Diffusion Segmentation Model using a New Distributed Parallel computing Algorithm

DF-SegDiff: A Diffusion Segmentation Model using a New Distr...

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cognitive computing and complex data (ICCD), international conference on the

作者： Hancang Mi Hong-Seng Gan Xiaoyi Wang Akinobu Shimizu Muhammad Hanif Ramlee Mehmet Zubeyir Unlu School of AI and Advanced Computing Xi’an Jiaotong – Liverpool University Suzhou China School of Mathematics and Physics Xi’an Jiaotong – Liverpool University Suzhou China Institute of Engineering Tokyo University of Agriculture and Technology Tokyo Japan Department of Biomedical Engineering and Health Sciences Universiti Teknologi Malaysia Johor Bahru Malaysia Department of Electric and Electronics Engineering Izmir Institute of Technology Turkey

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Brain tumours are among the most life-threatening diseases, and automatic segmentation of brain tumours from medical images is crucial for clinicians to identify and quantify tumour regions with high precision. While traditional segmentation models have laid the groundwork, diffusion models have since been developed to better manage complex medical data. However, diffusion models often face challenges related to insufficient parallel computing power and inefficient GPU utilization. To address these issues, we propose the DF-SegDiff model, which includes diffusion segmentation, parallel data processing, a distributed training model, a dynamic balancing parameter and model fusion. This approach significantly reduces training time while achieving an average Dice score of 0.87, with several samples reaching Dice values close to 0.94. By combining BRATS2020 with the Medical Segmentation Decathlon dataset, we also integrated a comprehensive dataset containing 800 training samples and 53 test samples. Evaluation of the model using Dice, IoU, and other relevant metrics demonstrates that our method outperforms current state-of-the-art techniques.

关键词： Training Measurement Image segmentation Computational modeling Graphics processing units Parallel processing Diffusion models data models Tumors Biomedical imaging

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NAND-Tree: A 3D NAND Flash Based Processing In Memory Accelerator for Tree-Based Models on Large-Scale Tabular data 24

NAND-Tree: A 3D NAND Flash Based Processing In Memory Accele...

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43rd international conference on Computer-Aided Design, ICCAD 2024

作者： Zhong, Hongtao Li, Taixin Chen, Yiming Tang, Wenjun Wu, Juejian Yang, Huazhong Li, Xueqing Electronic Engineering BNRist Tsinghua University China

ISBN: (纸本)9798400710773

Tabular data are a widely used format in data science, and tree-based Machine Learning (ML) models are powerful tools and outperform Deep Neural Network (DNN) with higher accuracy for tasks on tabular data. However, computing multiple trees on massive tabular data via conventional von Neumann architectures suffers from irregular memory accesses. Prior work utilizes Analog Content Addressable Memories (ACAMs) to gain great speedup, but the analog matching method is vulnerable to device and voltage variations, and the limited density of 2D memory makes frequent data movement still inevitable for large-scale tabular data. In this work, we propose NAND-Tree: a 3D NAND flash based digital storage-level processing in memory (PIM) accelerator for Tree-based ML models on tabular data. Unlike prior 3D NAND flash based accelerators with limited memory density due to complex peripherals and specific array structure, NAND-Tree makes slight modifications to commercial 3D NAND flashes. The additional peripherals only increase slight hardware overhead, which enables NAND-Tree to achieve 2.5Gb/mm2 memory density (0.89×) compared with commercial 3D NAND flashes. Besides, to tackle the low performance of original storage-level PIM architecture, we propose two strategies, namely Block Grouping (BG) and In-cell Bit-parallel Comparison (IBC) taking advantage of the multi-bit property of 3D NAND flash, for higher parallelism and lower comparison latency, respectively. Results show that the proposed NAND-Tree achieves on average 9.4×, 2.0×, and 15.4× speedup and 218.3×, 50.0×, and 9.7× higher energy efficiency over CPU, GPU, the state-of-the-art SLC 3D memory based PIM solution, respectively. © 2024 Copyright is held by the owner/author(s).

关键词： Deep neural networks

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Graph Neural Networks for Optimizing Reliability in Vehicle-to-Infrastructure Communication in Autonomous Systems 9

Graph Neural Networks for Optimizing Reliability in Vehicle-...

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9th international conference on Communication and Electronics Systems, ICCES 2024

作者： Neelamegam, G. Revathy, J. Kannukkiniyal, M. Devendran, M. Akshya, J. Choudhry, Mani Deepak Vsb College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Christ the King Engineering College Department of Artificial Intelligence and Data Science Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode India Hindustan institute of technology Department of Computer Science and Engineering Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350377972

High reliance on autonomous systems necessitates efficient and reliable data exchange through Vehicle-to-Infrastructure (V2I) communication to have appropriate and stable network performance in dynamic vehicular environments. With heuristic approaches, Dijkstra's algorithm, and Deep Q-Networks (DQN), several methods are proposed for optimizing latency reduction, packet delivery rate improvement, and power efficiency in the direction of V2I communications. However, these approaches suffer from important challenges: low adaptability of the network to real-time dynamics, suboptimal resource usage, and difficulties in scalability towards complex large-scale networks. In this scenario, we propose a framework that incorporates a GNN inside autonomous systems for V2I communication to optimize performance. The GNN will process real-time graph features, including signal strength, packet loss, and mobility speed, as it dynamically changes in adaptation to the evolution of network behaviour and other functions. This work shows the superiority of the proposed GNN model for the efficiency of experimentally conducted simulations compared to the current models;it achieved latency reduced by up to 28%, 5-10% PDR, and 20% energy efficiency across the various scenarios with urban, rural, highway, and suburban networks. The results validate robustness and scalability for practical implementations by the proposed approach in real-world autonomous systems. Further development is possible with this framework: for example, the use of multi-agent reinforcement learning techniques within cooperative decision-making and the enhancement through blockchain-based security mechanisms to ensure the integrity of the data. © 2024 ieee.

关键词： Graph neural networks

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Small UAVs-supported Autonomous Generation of Fine-grained 3D Indoor Radio Environmental Maps 42

Small UAVs-supported Autonomous Generation of Fine-grained 3...

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42nd ieee international conference on Distributed computing Systems (ICDCS)

作者： Mendes, Ken Lemic, Filip Famaey, Jeroen Univ Antwerp Internet & Data Lab IDLab Imec Antwerp Belgium

ISBN: (数字)9781665488792

ISBN: (纸本)9781665488792

Radio Environmental Maps (REMs) are a powerful tool for enhancing the cognitive awareness of various communication and networked agents by providing localized radio measurements of an environment of interest. Generating REMs is a laborious undertaking, especially in complex 3-Dimensional (3D) environments, such as indoors. To address this issue, we propose a system for autonomous generation of fine-grained REMs of indoor 3D spaces. In the system, multiple small indoor Unmanned Aerial Vehicles (UAVs) are used for 3D sampling of signal quality indicators. The collected readings are streamlined to a Machine Learning (ML) system for its training and, once trained, the system is able to predict the signal quality at unknown 3D locations. The system enables autonomous REM generation and can be straightforwardly deployed in new environments. The system also supports REM sampling without self-interference and is technology-agnostic, as long as the REM-sampling receivers features suitable sizes and weights to be carried by the UAVs. In the demonstration, we instantiate the system design using two UAVs and show its capability of visiting 72 waypoints within 10 min and gathering thousands Wi-Fi samples. Our results also include an instantiation of the ML system for predicting the Received Signal Strength (RSS) of known Wi-Fi Access Points (APs) at locations not visited by the UAVs.

关键词： Wireless communication Training Three-dimensional displays Interference cancellation Machine learning Receivers Autonomous aerial vehicles

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Enhanced-AlexNet for Smart Detection of Tomato Seedling Disease

Enhanced-AlexNet for Smart Detection of Tomato Seedling Dise...

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cognitive computing and complex data (ICCD), international conference on the

作者： Joel Richmond Adeshida Lie Liu Ahmed N. Abdalla Yongfei Ju Bo Chen Faculty of Electronic Information Engineering Huaiyin Institute of Technology Huai’an City China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

The accurate identification of seedling diseases in tomato plants is crucial for ensuring optimal crop yields and agricultural productivity. However, traditional image identification methods often rely on visual inspection and experience, can lead to misdiagnosis and inappropriate pesticide use, thereby affecting both yield and quality. This paper addresses this challenges by presenting an Enhanced-AlexNet model designed to improve the identification of tomato seedling diseases through advanced image analysis techniques. The Enhanced-AlexNet model improved the overfitting problem of the standard AlexNet network model by using data augmentation and random inactivation of partial neurons. The model was trained using ten types of tomato data and a total of 4600 images were used, with 70% allocated for training and 30% for testing. Through comprehensive testing, with the original AlexNet model, LeNet model and the ResNet model our proposed model showed better performance than other network models and the overfitting problem of the original AlexNet model was greatly improved after improvement. The accuracy of the Enhanced-AlexNet model is 95.8%, which is 2.4% higher than the original model making it more effective in providing early and accurate disease diagnosis.

关键词： Training Accuracy data augmentation data models Medical diagnosis Crop yield Diseases Overfitting Testing Convergence

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Real-time In-situ Defect Detection in 3D Printing of Continuous Fiber-Reinforced Composites 4

Real-time In-situ Defect Detection in 3D Printing of Continu...

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4th ieee international conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Yue, Zhijian Shen, Zhen Dong, Xiangyang Fang, Qihang Wang, Weixing Dong, Xisong Xiong, Gang Wang, Fei-Yue University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Beijing100190 China Qingdao Academy of Intelligent Industries Intelligent Manufacturing Center Qingdao266109 China Chinese Academy of Sciences Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan523808 China Chinese Academy of Sciences State Key Laboratory for Management and Control for Complex Systems Institute of Automation Beijing100190 China

ISBN: (纸本)9798350349252

With the development of continuous fiber-reinforced composites (CFRCs) 3D printing technology, timely, efficient and accurate detection of fiber path defects is essential for ensuring product quality and performance. However, challenges still need to be addressed in this field, such as inadequate training data coverage and the complexity of fiber path structures. These factors hinder the capability of traditional methods to accurately assess the status of printed products. Therefore, developing accurate and robust fiber path defect detection techniques is crucial for CFRCs 3D printing technology. In this paper, we propose a precise detection method for fiber path defects in CFRCs 3D printing using the YOLOv7 object detection model combined with a Squeeze-and-Excitation (SE) attention mechanism. By introducing the SE attention mechanism, we enhance the perception capability of the YOLOv7 model for crucial features, thereby improving the accuracy of fiber path defect detection. The experimental results show that the YOLOv7 model, when augmented with an attention mechanism, achieves an average accuracy of 93.9% in identifying fiber path defects during the printing process. Compared to models without this attention mechanism, the results show that there is 10.7% increase, with a notably enhanced learning capacity during the training phase, which substantiates the efficacy and practicality of the proposed approach. © 2024 ieee.

关键词： Fiber reinforced plastics

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