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Research on intelligent Measurement System of Workpiece Outer Dimension 3

Research on Intelligent Measurement System of Workpiece Oute...

引用

3rd international conference on Machine learning and Computer Application, ICMLCA 2022

作者： Liu, Bo Gao, Hongwei Jiang, Yueqiu Zhang, Xin Shenyang Ligong University Liaoning Province Shenyang China

ISBN: (纸本)9781510664814

In the field of machinery manufacturing, due to the backward detection methods of the outer dimensions of the workpiece, the high labor intensity of the detection and the high production cost, the problems of poor detection accuracy and low efficiency are caused. Therefore, this paper designs an industrial camera-based intelligent detection system for workpiece outer dimensions. The system uses industrial cameras to simulate human vision functions to accurately extract information from workpiece images and convert them into a form that can be understood and calculated by computers. Compared with the traditional manual workpiece external size detection method, the system has the advantages of good positioning flexibility, short sampling period, large amount of information, low cost, good stability, high precision and intelligence, etc., and the system realizes online non-contact detection, which has the characteristics of high accuracy, fast speed, and easy operation;at the same time, the system realizes the real-time measurement of the outer dimensions of the workpiece and the result output, and the detection accuracy is less than *** the actual production environment test, the system can meet the requirements of rapid and accurate detection of the outer dimensions of the workpiece, and effectively enhance the ability of automatic manufacturing and detection of products. © 2023 SPIE.

关键词： Cameras

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The optimization study of energy conversion efficiency of supercapacitor energy storage device based on SOA 3

The optimization study of energy conversion efficiency of su...

引用

2023 IEEE 3rd international conference on data Science and Computer Application, ICDSCA 2023

作者： Dong, Chunhai Jia, Yanqi Feng, Xiao Xu, Zhenbin Ge, Meng State Grid Tianjin Electric Power Company Materials Company Tianjin China Tianjin Power Transmission and Transformation Engineering Co. Ltd Tianjin China

ISBN: (纸本)9798350341546

As a power source with special properties between traditional capacitors and batteries, supercapacitors play a role in suppressing power fluctuations in the power grid, energy saving, and consumption reduction while storing energy. Supercapacitor energy storage system has strong nonlinear characteristics, and circuit electrical parameters have uncertainty, affecting the improvement of conversion efficiency and affecting the energy-saving effect of energy storage devices. By optimizing the control strategy of the supercapacitor energy storage device, the energy utilization rate of the energy storage device can be effectively improved. To this end, this paper investigates an intelligent optimization control strategy for supercapacitor energy storage devices, realizes adaptive adjustment of the control parameters of the energy storage device, and thus keeps the dynamic and steady-state performance of the supercapacitor energy storage device and the energy conversion efficiency in an optimal state at all times. © 2023 IEEE.

关键词： Bidirectional DC/DC Converter SOA Algorithm Supercapacitor

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Integrated data Link Interactive ELT Triggering Device Based on Fingerprint Capture 3

Integrated Data Link Interactive ELT Triggering Device Based...

引用

3rd international conference on intelligent Computing and Human-Computer Interaction, ICHCI 2022

作者： Song, Jinze Zhang, Xin'ai Wu, Haomin Shanghai Aircraft Design & Research Institute China

ISBN: (纸本)9781510661301

The ELT (Emergency Locator Transmitter) is a device used to locate an aircraft in an emergency situation, such as an accident, in order to facilitate search and rescue efforts. Conventional ELT devices have two triggering methods, namely automatic triggering when crash acceleration is reached and manual triggering when a button on the control panel is pressed. This paper proposes an ELT triggering device based on fingerprint collection technology. By integrating the fingerprint collection device into the control button and cross-linking it with the on-board data link, the fingerprint of the triggering person is collected, stored, and compared, so as to make a judgement or seek evidence, which effectively solves the problem of false triggering of traditional triggering devices and provides a basis for subsequent investigation of emergency situations. Experiments and use case study validate the usefulness of this new device. © 2023 SPIE.

关键词： Aircraft accidents

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Maize Plant Disease Prediction of UAV Images for Precision Agriculture Using Fusion of Multimodal 3

Maize Plant Disease Prediction of UAV Images for Precision A...

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3rd international conference on Computing and Information Technology, ICCIT 2023

作者： Mishra, Vinay Naik, Nenavath Srinivas Kumar, Santosh Alsamhi, Saeed Hamood Saif, Abdu Curry, Edward -Naya Raipur Department of Computer Science and Engineering Chhattisgarh India University of Galway Insight Centre for Data Analytics Galway Ireland Faculty of Engineering & Information Technology Taiz Yemen

ISBN: (纸本)9798350321487

The agriculture sector is vital to a country's economic and productive development. With technological advancements, we can identify plant diseases in their early stages, similar to how human diseases are diagnosed, and avoid significant financial loss. For example, a new technique has been developed to detect diseases in maize crops, a widely used raw product in the food industry and precision agriculture. Furthermore, farmers ensure the fields do not remain wet for extended periods to prevent fungus infection. Currently, machine learning and deep learning approaches are being utilized, making agriculture one of the most researched topics in AI. This study proposes a novel framework for predicting maize plant disease using a fusion of multi-deep learning models. The proposed framework uses a four-stage pipeline for detecting disease in maize plants by fusion of Xception, DenseNet-121, and ensembling of Xception and DenseNet-121 models to achieve excellent results on the OSF drone image dataset, which consists of 1821 high-resolution images of maize plants taken from unmanned aerial vehicles (UAVs). The accuracy of our proposed approach is 96.95%, which is highly effective, considering the challenges posed by drone images. © 2023 IEEE.

关键词： Drones

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Improving The Efficiency of Split learning Based on Multi-user and Parallel Training 3

Improving The Efficiency of Split Learning Based on Multi-us...

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3rd international conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2023

作者： Li, Yinhui Lu, Xiaofeng School of Cyberspace Security Beijing University of Post and Telecommunications Beijing100876 China

ISBN: (纸本)9781510671485

Federated learning has rapidly become a research hotspot in the field of distributed deep learning because raw data does not need to be shared. However, it is still characterized by problems in terms of privacy security and computing power. Split learning is a new distributed deep learning technology. By dividing the complete deep learning model, the client and server can participate in the training together to overcome some problems related to computing power and privacy and security. In this paper, the concept of split learning is introduced. This article introduces the use of multi-user and parallel training methods to improve the learning efficiency of split learning. © 2023 SPIE.

关键词： Computing power

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A Transfer learning Framework Based on Adaptive Spatial Spectral Relational Networks for Hyperspectral Image Classification 3

A Transfer Learning Framework Based on Adaptive Spatial Spec...

引用

3rd international conference on Machine learning and Computer Application, ICMLCA 2022

作者： Yuan, Sen Rao, Mengbin Ge, Jianjun Information Science Academy of China Electronics Technology Group Corporation Beijing100086 China

ISBN: (纸本)9781510664814

Hyperspectral image classification is one of the key technologies in hyperspectral image application. Since the number of bands in hyperspectral images is usually much larger than that in RGB images or multispectral images, it is necessary to reduce the dimensionality of the hyperspectral data of the target by using pre-trained models from other fields. However, this situation may lead to the loss of effective spectral information. Therefore, to overcome this problem, we propose a novel transfer learning framework based on Adaptive Spatial Spectral Relational Networks (ASSRN), which can be applied to knowledge transfer between different hyperspectral datasets. Finally, we conduct extensive experiments on several databases. The inspiring experimental results demonstrate the effectiveness of the proposed framework. © 2023 SPIE.

关键词： Image classification

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Face Identification and Verification in Thermal Images 3

Face Identification and Verification in Thermal Images

引用

3rd international conference on intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Jeba Sonia, J. Sheeba, Paul T. Jacob, Jeena SRMIST Department of Data Science and Business Systems Kattankulathur India Amal Jyothi College of Engineering Department of Computer Science and Engineering Kanjirappally India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Surveillance has become a crucial role in recent years, owing to the rise in crime rates. Existing daytime surveillance research has improved performance by employing deep learning algorithms to recognize and track objects. However, due to low illumination and/or poor weather conditions, achieving the same performance for night vision are difficult. Face detection is a crucial task in surveillance. A face detection module based on merging of thermal and visual pictures is proposed. The encoder-decoder network of the fusion module extracts effective features from the given thermal and visual images using depth-wise convolution. By recording radiation information at a greater wavelength, the thermal face solves most of the limitations that visual faces have. The range of facial imageries that can be recognized is expanded by developing a method that recognizes thermal faces. Face recognition in thermal images is having high research scope, and it has been discovered that when thermal images are trained, it performs better. © 2022 IEEE.

关键词： Face recognition

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Design and implementation of a physical security evaluation system for cryptographic chips based on machine learning 3

Design and implementation of a physical security evaluation ...

引用

3rd international conference on intelligent Computing and Human-Computer Interaction, ICHCI 2022

作者： Liu, Jiajing Wei, Congming Wen, Shengjun Wang, An School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China State Key Laboratory of Cryptology P.O. Box 5159 Beijing100878 China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China Information Center State Administration for Market Regulation Beijing100820 China

ISBN: (纸本)9781510661301

Side-channel attack is a commonly used attack method for recovering cryptographic chip keys, and plays an important role in the field of cryptographic chip physical security evaluation. Combining side-channel attacks with machine learning and replacing some steps of traditional side-channel attacks with machine learning methods can improve the efficiency of key-recovery from side-channel attacks to a certain extent. In practice, there is a problem that most existing cryptographic chip security evaluation systems cannot support the complete key recovery process, and fully improve the utilization of side information generated in the evaluation process. In this paper, we design a cryptographic chip physical security evaluation system based on machine learning. Through the integrated operation of power trace acquisition, preprocessing, analysis and evaluation, the correct key can be successfully recovered. © 2023 SPIE.

关键词： Machine learning

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An intelligent Machine learning Approach for Prediction of Road Accident Severity 3

An Intelligent Machine Learning Approach for Prediction of R...

引用

3rd IEEE international conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Ragam, Prashanth Rahul, Namana Sri Balaji, Pandi Jyothi, Sankati Danaveni, Pavithra School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India VIT-AP University Software Development Cell Andhra Pradesh Amaravati522237 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad500090 India

ISBN: (纸本)9798331533205

Road accidents are a primary global concern for public safety, with India having a very high death toll. This study presents an intelligent machine learning approach to predict the severity of road accidents, contributing to the development of more effective and preventive measures. We utilized a comprehensive dataset of road accidents in India, employing advanced machine learning algorithms, including Histogram-based Gradient Boosting (HGB), Random Forest (RF), Support Vector Machines (SVM), and K-Nearest Neighbors (KNN). Our methodology involved extensive data pre-processing, feature engineering, and model optimization. The Random Forest algorithm demonstrated superior performance, achieving an accuracy of 94.7% and an F1-score of 0.947. This research provides a robust predictive model and information on the key factors influencing the accident's severity. The novelty of our approach lies in the comprehensive feature engineering tailored to the Indian context and the application of ensemble learning techniques to capture complex, nonlinear relationships in accident data. The findings of this study have significant implications for policymakers, urban planners, and law enforcement agencies in their efforts to improve road safety. © 2025 IEEE.

关键词： Highway accidents

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Self-Supervised learning for Swine Ear-Tag Classification with Attention Module Model 3

Self-Supervised Learning for Swine Ear-Tag Classification wi...

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3rd international Signal Processing, Communications and engineering Management conference, ISPCEM 2023

作者： Pham-Duc, Thinh Ullah, Mohib Le-Tien, Thuong Cheikh, Faouzi Alaya Nordbo, Oyvind Beghdadi, Azeddine Phan-Xuan, Hanh Institut Galilée University Sorbonne Paris L2TI Nord France Norwegian University of Science and Technology Dept. of Computer Science Gjovik2815 Norway Uni. of Technology National University Vnu HoChiMinh City Viet Nam Norsvin Sa Storhamargata 44 Hamar2317 Norway

ISBN: (纸本)9798350307023

We proposed an attention-based deep framework for animal ear-tag classification in the livestock industry. In addition to spatial and channel attention, the model incorporated squeeze excitation block that performed channel-wise feature calibration to improve the model representation power. Moreover, this backbone was transferred to self-learning model on unlabeled data - Barlow Twins and combined with focal loss. We investigated the impact of ear-tag crop size from the original frame to achieve desirable efficiency and to reduce noise in the images, such as the pen or swine. The model was trained on manually annotated data collected at a swine farm in Norway to validate our approach. The results of model were presented in terms of two performance metrics: accuracy and F-score. A comprehensive comparison with state-of-the-art model was conducted, and more than 10% improvement was achieved. The ablation study was also performed and proved the effectiveness of our approach. © 2023 IEEE.

关键词： Agriculture

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