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Integrating Industry 4.0 Technologies for Enhanced Safety engineering: A Comprehensive Review and Analysis 25th

Integrating Industry 4.0 Technologies for Enhanced Safety En...

引用

25th international conference on Human-computer Interaction (HCI international)

作者： Hutchins, Savannah Jhaveri, Niral Duffy, Vincent G. Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9783031480461;9783031480478

Advancements in technology over the past few decades have led to rapid changes in manufacturing and process industries. This industry-wide shift towards automation and digitization is better known as Industry 4.0, or the fourth industrial revolution. As new Industry 4.0 technologies are adopted and "smart factories" become commonplace, the impact on the health and safety of manufacturing workers needs to be understood. The aim of this systematic review is to understand how Industry 4.0 technologies can be integrated without introducing new risks to worker health and safety through a literature review and database, content, and co-citation analyses. The literature analysis revealed publications analyzing connections between Industry 4.0 and worker health and safety, however, as an emerging field, relevant studies and publications are limited. More funding and research into the relationship between Industry 4.0 and worker OHS will be critical to the advancement, implementation, and acceptance of new technologies. Future research should likely concentrate on establishing concepts that incorporate human factors considerations into the design of integrated safety systems.

关键词： Industry 4.0 Smart Factory Health Safety engineering automation

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The Dependability Assessment Method for Electronic Modules Considering Updated Quality Management Principles

The Dependability Assessment Method for Electronic Modules C...

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2024 international Russian automation conference, RusAutoCon 2024

作者： Polesskiy, Sergey Korolev, Pavel Tsvetkov, Vyacheslav School of Computer Engineering Hse University Moscow Russia

The scientific article addresses the issue of assessing and ensuring the dependability of electronic modules in light of the significant disparity between the mathematically estimated dependability measures and those obtained from testing results. It has been revealed that the quality management system of the executing organization plays a crucial role in the development of electronic modules, influencing the final outcome of the product in terms of dependability and quality. Analysis of dependability assessment methods in different countries has led to the conclusion that they do not align with the updated requirements and principles of quality management in dependability assurance programs. Therefore, a modified method of assessment, namely the 'production quality coefficient,' based on the approach applied according to the RIAC-HDBK-217Plus handbook, is proposed. This method incorporates updated principles of quality management. The modified structure of the questionnaire for expert assessment allows for a systematic evaluation of the level (completeness) of necessary measures' implementation to minimize the proportion of failures belonging to specific categories. © 2024 IEEE.

关键词： Quality management

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Explore augmented reality and deep feature learning to visual field operation and maintenance for electric power equipment

Explore augmented reality and deep feature learning to visua...

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2024 international conference on automation Control, Algorithm, and Intelligent Bionics, ACAIB 2024

作者： Yuan, Qi Feng, Jianfeng Yan, Nannan Xue, Xiangyang Shen, Bin Yang, Yongcheng Liu, Ziqi Zhou, Yunjie Yuan, Jialiang State Grid Shanghai Electric Power Company Shanghai200122 China School of Big Data Institute of Brain-Like Intelligence Science and Technology Fudan University Shanghai200433 China Institute of Brain and Intelligence Science and Technology School of Computer Science Fudan University Shanghai200433 China

ISBN: (纸本)9781510682597

This work mainly studies the visual field operation and maintenance method based on human-computer interaction, and establishes the field operation and maintenance management module through the deep image feature analysis method to realize the operation and maintenance function of augmented reality (AR) intelligent devices. For the on-site electricity inspection, the visual image feature mining model based on deep convolutional neural network is established. The basic information, operation condition, box condition, etc. of the field collection and metering equipment for regular inspection needs to be recorded and uploaded to the deep network model for analysis. The experimental results show that the proposed method has high efficiency in equipment operation and maintenance. © 2024 SPIE.

关键词： Inspection equipment

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Enhancing Time Series Prediction in Industrial Processes: A Self-Attention BiLSTM Approach 7

Enhancing Time Series Prediction in Industrial Processes: A ...

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7th international conference on Robotics, Control and automation engineering, RCAE 2024

作者： Wen, Sicheng Xu, Jiajun University of Engineering Science Department of Electronic and Electrical Engineering Shanghai Shanghai China University of Shanghai for Science and Technology Department of Optical-Electrical and Computer Engineering Shanghai China

ISBN: (纸本)9798350355642

The development of data-driven soft sensors for modeling complex data, particularly in scenarios characterized by strong nonlinearity, high dimensionality, cross-correlation and autocorrelation, remains a significant challenge in process data analysis. Bidirectional Long Short-Term Memory (BiLSTM) networks are highly effective in capturing both forward and backward dependencies within sequences, thereby enhancing the extraction of temporal features from time series data. However, BiLSTM networks may struggle to capture global dependencies, especially in long sequences. To address this limitation, the self-attention mechanism, which excels in modeling global relationships across all positions in the input sequence, can be integrated with BiLSTM by applying weighted attention to the two different directions of BiLSTM, thereby enhancing its performance. This study proposes a novel method called SA-BiLSTM, which combines the strengths of the BiLSTM network and the self- attention mechanism. The self-attention layer captures global dependencies, while the BiLSTM layer extracts comprehensive temporal features in both directions, which are then passed to a fully connected layer for final prediction. Extensive comparisons with traditional methods such as BiLSTM, LSTM with added self-attention mechanism, LSTM, RNN, and SVM validate the superior performance of the proposed SA-BiLSTM method in handling complex time series data. © 2024 IEEE.

关键词： Spatio-temporal data

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Automated Lung Cancer Diagnosis Using Vision Transformers (ViT) on CT Scan Data 2

Automated Lung Cancer Diagnosis Using Vision Transformers (V...

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2nd IEEE international conference on IoT, Communication and automation Technology, ICICAT 2024

作者： Agarwal, Rohit Gayathri, B. Rao, Cherukupalli Mallikarjuna Alsalami, Zaid Monisha Jothi, R. Jaya Deepthi, K. GLA University Department of Computer Engineering & Applications Mathura India CMR College of Engineering & Technology Department of CSE India GRIET Bachupally Department of CSE Telangana Hyderabad India College of Technical Engineering the Islamic University Department of Computers Techniques Engineering Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of CSE Tamil Nadu Chennai600073 India Mohan Babu University Department of AIM Andhra Pradesh Tirupati India

ISBN: (纸本)9798350368109

In this study, it brings into focus, the use of Vision Transformers (ViTs) in the diagnostic of lung cancer with the aid of Computed Tomography (CT) scan data. Taking advantage of the self-attention properties of ViTs the presented model considers global dependencies in imaging data and as a result demonstrates higher diagnostic performance. The training and validation of the proposed model were done using the public dataset, namely the LIDC-IDRI dataset and the classification accuracy is 92%. 5%, a specificity of 88 and the overall accuracy was 91%. 2% for detecting tumours in tissues examined while having a sensitivity of 88. 8%. This shows that ViTs can be made as a substitute to the prior CNN-based models and can be used as a reliable and efficient diagnostic tool in the early stage of lung cancer. These findings may be helpful in making clinical decision and also for increasing survival rate among the patients of lung cancer. © 2024 IEEE.

关键词： Lung cancer

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Not What I was Trained for - Out-of-Distribution-Tests for Interactive AIs 15th

Not What I was Trained for - Out-of-Distribution-Tests for I...

引用

15th ACMSIGCHI conference on engineering Interactive Computing Systems (EICS)

作者： Severing, Benedikt Werger, Ole Hesenius, Marc Univ Duisburg Essen Inst Software Engn Schutzenbahn 70 D-45127 Essen Germany

ISBN: (纸本)9783031592348;9783031592355

Recent advances in AI demonstrate its capacities to not only automate more and more everyday tasks, but also make direct Human-AI-Interaction possible. When using AI models in production, we might face situations where the AI is confronted with input data that is very different from the data it was trained on. Such situations are called Out-of-Distribution situations and can result in misleading AI inferences. We argue that identification, handling, and prevention of Out-of-Distribution situations is key for creating production-ready interactive AI components. In this paper, we test the robustness of state-of-the-art AI/ML approaches in Out-of-Distribution situations and propose a research agenda to gather a deeper understanding of how to identify, handle, and prevent such situations in interactive applications.

关键词： Human-computer Interaction Software engineering Artificial Intelligence Machine Learning Testing Test automation Quality Assurance Out-of-Distribution Concept Shift

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A Cube Workpiece Positioning Method Based on Point Structured Light Scanning 5

A Cube Workpiece Positioning Method Based on Point Structure...

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5th international conference on Robotics and computer Vision, ICRCV 2023

作者： Cheng, Li Xu, Chaohai Chi, Shichun Mao, Xinyu Zhu, Sijun School of Mechanical Engineering Shenyang University Shenyang China Dep. Intelligent Detection and Equipment Shenyang Institute of Automation CAS Shenyang China AVIC XI'AN Aircraft Industry Group Company LTD Xi'an China

ISBN: (纸本)9798350326369

In the automation upgrading process of traditional labor-intensive industries, positioning method of workpiece affects the automated operation quality, efficiency, and system cost directly. Traditional workpiece positioning methods based on image vision and line structured light usually require higher professional knowledge or cost, which increase the difficulty for widely application. Therefore, this paper proposes a workpiece positioning method based on point structured light scanning for cube-shaped workpieces. Positioning accuracy is analyzed and the feasibility of this method is verified through numerical example. The result of this research provides a precise and low-cost workpiece positioning method for the automation upgrade of traditional labor-intensive industries. © 2023 IEEE.

关键词： automation

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Document Classification and Key Information Extraction Using Multimodal Transformers 9

Document Classification and Key Information Extraction Using...

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9th international conference on computer Science and engineering, UBMK 2024

作者： Baysan, Mehmet Selman Kizilay, Furkan Özmen, Ayşe Irem Ince, Gökhan Mdp Group Turkey Istanbul Technical University Computer Engineering Department Turkey

ISBN: (纸本)9798350365887

Companies manage and track their expenses either physically or through software applications. However, manual expense entry steps are prone to errors. Manual expense entry errors losses in terms of money, time and productivity. Therefore, this study presents a novel system on the automation of document information entry with a special focus on financial documents through machine I earning techniques. The methodology involves training LayoutLM models for sequence and token classification to categorize and extract detailed information from various financial documents such a s receipts and invoices. The proposed system integrates state-of-the-art models such as LayoutLMv2, LayoutLMv3, and fastText to achieve accurate document classification a nd information extraction. The designed system was implemented and tested on various types of receipts and invoices containing financial values, using evaluation metrics such as accuracy, precision, recall, and F1-score. The capability of the proposed system to achieve high accuracy, precision and F1 scores above 90 % across various document types and in automated document processing tasks reaffirms its suitability for document processing applications. © 2024 IEEE.

关键词： Financial data processing

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Enhancing Fruit and Vegetable Preservation with Support Vector Machine and IoT Connectivity

Enhancing Fruit and Vegetable Preservation with Support Vect...

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2024 IEEE international conference on automation and Computation, AUTOCOM 2024

作者： Satyaraj, D. Prabakaran, T. Blessy, Y.M. Jayanthi, L.N. Mouleeswaran, S.K. Velmurugan, S. Arjun College of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Joginpally B.R. Engineering College Department of Computer Science and Engineering Telangana Hyderabad India R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Commerce Saveetha College of Liberal Arts and Sciences Tamil Nadu Chennai India Dayananda Sagar University Department of Computer Science and Engineering Innovation Campus Karnataka Bangalore India T.J.S. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Channai India

ISBN: (纸本)9798350382723

This paper presents a new method for extending the shelf life of perishable food items by combining the use of Support Vector Machine (SVM) algorithms with Internet of Things (IoT) connection. Problems with early spoiling detection and inefficient storage condition control plague conventional approaches to fruit and vegetable preservation. Using SVM it analyze environmental variables including humidity, temperature, and gas concentrations that impact commodities with a near-term expiration date in real time via this research. Sensors, actuators, and a command centre can all communicate to one other without a hitch thanks to the IoT. With this integration, storage conditions may be dynamically adjusted according to SVM predictions, guaranteeing the best possible preservation. In light of the ever-changing nature of farming, the suggested system provides an adaptable and scalable answer to the growing need for effective and environmentally friendly food storage solutions. The experimental findings show that the suggested method may prolong the freshness and quality of produce while reducing the amount of resources wasted. The findings of this study will help bring data-driven technology into the food business more widely and improve smart agriculture practices. It highlights the efficacy of combining IoT connection with SVM algorithms for vegetables and fruits storage. Agricultural operations and customers may both profit from the results, which show a considerable decrease in waste, an increase in freshness, and better quality control. © 2024 IEEE.

关键词： Internet of things

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A Novel Reinforcement Q-Learning Control Approach for Lateral Vehicle Dynamics 10

A Novel Reinforcement Q-Learning Control Approach for Latera...

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10th international conference on Control, Instrumentation and automation, ICCIA 2024

作者： Samsami, Reza Arefi, Mohammad Mehdi Shiraz University School of Electrical and Computer Engineering Shiraz Iran

ISBN: (纸本)9798331509972

This paper introduces a novel reinforcement Q learning algorithm equipped with an actor-critic neural network (NN) structure to effectively control the lateral dynamics of an autonomous vehicle (AV), even when system dynamics are entirely unknown. To address the challenges posed by traditional persistence of excitation conditions, an experience replay technique is implemented, leveraging past data to train the adaptive critic law. A nonlinear lateral vehicle dynamic model is established, considering steering angle as the input and road curvature as the disturbance. Subsequently, a linearized model is derived using the Euler technique. The Q-function is formulated using the Hamilton-Jacobi-Bellman (HJB) equation, and its parameters are estimated through integral reinforcement learning (IRL) and an online actor-critic NN, ensuring closed-loop stability. Finally, the efficacy of the proposed approach is demonstrated through a simulation example conducted on an AV. © 2024 IEEE.

关键词： Reinforcement learning

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