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9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Bharathi, G.P. Kavitha, P. Abhang, Laxman Baburao Rathinasabapathi, G. Sharma, Vipin Sathish, T. Sri Sai Ram Institute of Technology Department of Computer And Communication Engineering West Tambaram Tamil Nadu Chennai600045 India R.M.K Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Kavaraipettai601206 India Pravara Rural Engineering College Department of Automation And Robotics Maharashtra Loni413736 India Panimalar Engineering College Department of Mechanical Engineering Tamil Nadu Chennai600123 India Medi-Caps University Department of Mechanical Engineering Madhya Pradesh Indore453331 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350365092

The extensive incorporation of machine vision into the fields of robotics and automation in a variety of different ways. The various uses of machine vision and the revolutionary impact it has on the capabilities of robotic systems. This investigation is fueled by improvements in imaging technology and artificial intelligence. In the abstract, major features such as object detection, navigation, and human-robot interaction are discussed in depth. The abstract also demonstrates how machine vision algorithms contribute to enhanced precision, efficiency, and adaptability in automation processes. The purpose of thisis to shed light on the role that machine vision plays in revolutionising industrial automation, collaborative robotics, and autonomous systems by conducting an examination of real-world implementations and case studies. This all-encompassing investigation not only highlights the current status of machine vision applications but also forecasts the future trajectory of this technology, thereby establishing it as a foundational component for the development of robotics and automation in a variety of fields. © 2024 IEEE.

关键词： Navigation systems

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Generative AI Co-Pilot for Rapid Prototyping of IEC 61499 Control Applications

Generative AI Co-Pilot for Rapid Prototyping of IEC 61499 Co...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Polina Ovsiannikova Tatiana Liakh Pranay Jhunjhunwala Valeriy Vyatkin Department of Electrical Engineering and Automation Aalto University Espoo Finland Department of Computer Science Electrical and Space Engineering Luleå Tekniska Universitet Sweden

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

This work develops a concept of a natural language co-pilot for the creation of IEC 61499 applications to accelerate initial testing and development. The co-pilot addresses the challenge of generating the correct function blocks that can be opened in popular IEC 61499 IDEs. In our case study, we focus on the generation of the centralised controller that operates particular equipment to perform a defined process. In addition, we have developed a scheme for how the co-pilot will be integrated into the FBME IDE as an AI assistant in the next steps. In the discussion, we provide valuable information for future co-pilot developers on the possible problems they might face in this area and how to overcome them.

关键词： IEC Standards Codes Process control XML Artificial intelligence Chatbots Testing Natural languages Encoding Vectors

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A Novel Attention-based Explainable Deep Learning Framework Towards Medical Image Classification 7

A Novel Attention-based Explainable Deep Learning Framework ...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Muoka, Gladys W. Yi, Ding Ukwuoma, Chiagoziem Chima Martin, Moindi Duke Aydin, Ahmet Arif Al-Antari, Mugahed A. University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Chengdu University of Technology Sichuan College of Nuclear Technology and Automation Engineering Chengdu China Unicaf University Lilongwe Department of Computer Science Lilongwe Malawi Inönü University Faculty of Engineering Department of Computer Engineering Turkey Sejong University College of Software & Convergence Technology Daeyang AI Center Department of Artificial Intelligence Seoul Korea Republic of

ISBN: (纸本)9798350383065

Deep learning applications for medical image classification have shown remarkable promise, particularly incorporating attention-based neural networks. This is particularly relevant in medical imaging, where the integration of Artificial Intelligence assists with various imaging tasks, including classification, segmentation, and detection. Deep learning is revolutionizing medical research and playing a significant role in advancing personalized clinical treatment. However, the lack of interpretability in these models presents a significant obstacle to their adoption in clinical practice. Therefore, there is a growing need for a comprehensive understanding of artificial intelligence systems and their internal mechanisms, capabilities, and limitations, which is the focus of the field of explainable AI. This study proposes a novel attention-based explainable deep learning framework for medical image classification tasks, including Covid-19, breast cancer (BreakHis), lung cancer (LC2500), and Retinal optical coherence tomography (OCT). The proposed framework recorded overall accuracies of 98% (Covid-19 Radiography), 95% (BreakHis), 99.8% (LC2500), and 95%(OCT). For visual analysis of the outcomes, we employ and use the LIME, SHAP, and ELI-5 to analyze the achieved results. The study's primary goal is to bridge the gap between the high performance achieved by attention-based models and the necessity for transparency and interpretability in medical image diagnostics. © 2023 IEEE.

关键词： Optical tomography

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Revolutionizing Logistics: "Drone Package Delivery Systems, a Multi-Technology Approach" 2

Revolutionizing Logistics: "Drone Package Delivery Systems, ...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Mahajan, Anusha Gupta, Arshi Gupta, Anishka Sharma, Arun Rani, Ritu Jaiswal, Garima Indira Gandhi Delhi Technical University for Women Mechanical and Automation Engineering Department New Delhi India Indira Gandhi Delhi Technical University for Women Centre of Excellemce-Artificial Intelligence New Delhi India Bennett University Computer Science Department Noida India

ISBN: (纸本)9798350360523

In the recent decade, the drone package delivery industry has faced many challenges, along with improving performance, protection, and adaptability to numerous environments. This research paper addresses those problems via the combination of various technologies. It integrates five critical technologies: battery-level management, object detection, face recognition, weather prediction, and optimal site selection for industrial setups. It introduces a real-time battery level monitoring system for stable and uninterrupted operations, enhances overall reliability, and implements face recognition technology to enforce protection and prevent unauthorized usage. It embarks on the ability of drones to revolutionize the logistics industry. Our approach makes drone deliveries faster, sustainable, and more efficient, with the ability to help regions via medical deliveries, disaster relief, and e-trade while fostering environment focus. By accommodating artificial intelligence algorithms and machine learning models, we aim to help create a future in which drones become the new technology for efficient and dependable package delivery. © 2024 IEEE.

关键词： Drones

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Guest Editorial Enabling Technologies and Systems for Industry 5.0: From Foundation Models to Foundation Intelligence

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2024年第11期54卷 6496-6499页

作者： Tang, Ying Tian, Yonglin Lin, Yilun Lv, Chen Fanti, Maria Pia Rowan University Department of Electrical and Computer Engineering GlassboroNJ08028 United States Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Shanghai AI Laboratory Urban Computing Laboratory Shanghai200232 China Nanyang Technological University School of Mechanical and Aerospace Engineering Jurong West639798 Singapore Polytechnic University of Bari Department of Electrical and Information Engineering Bari70126 Italy

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Field Trial of an ML-Assisted Phase-Sensitive OTDR for Traffic Monitoring in Smart Cities

Field Trial of an ML-Assisted Phase-Sensitive OTDR for Traff...

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International Conference on Transparent Optical Networks

作者： Robson A. Colares Evandro Conforti Letícia Rittner Darli A. A. Mello Department of Communications School of Electrical and Computer Engineering Universidade de Campinas (UNICAMP) Campinas SP Brasil Department of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering Universidade de Campinas (UNICAMP) Campinas SP Brasil

ISBN: (数字)9798350377323

ISBN: (纸本)9798350377330

We present a field trial of cost-effective distributed optical fiber sensing (DOFS) for traffic intensity estimation. The field trial uses a buried single-mode fiber installed along the university campus intended for communications and research experimentation. Acquired $\phi$-OTDR traces are concatenated and transformed into a space-time domain to form images. The resulting images are then processed by a MobileNetv2 model for car counting. Training is carried out by a set of collected images labeled by traffic information gathered automatically from camera videos processed with the novel YOLOv8 model. The results indicate that the implemented setup successfully counts heavy vehicles with high accuracy. The setup is also applied to data sets collected over entire days, showcasing its successful application for traffic monitoring in smart cities.

关键词： Training Optical fibers Smart cities Urban planning Optical fiber cables Estimation Traffic control

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An Interactive Platform for Farmers with Plant Disease Recognition Using Deep Learning Approach 1

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International Conference on Intelligent Systems and Human-Machine Collaboration, ICISHMC 2022

作者： Murthy, I. Aryan Mhatre, Niraj Ashish Oroskar, Rohit Vasudev Voriganti, Surya Kharatmol, Keerti Das, Poulami Department of Computer Engineering K.C. College of Engineering and Management Studies and Research Mumbai400603 India Automation and Robotics Specialist GlobalMed Inc TrentonK8V 5R5 Canada

ISBN: (数字)9789811984778

ISBN: (纸本)9789811984761

India’s primary sector is mainly contributed by the agriculture industry. The GDP from agriculture in India increased to 6630 billion INR in the fourth quarter of 2021 from 4076 billion INR in the third quarter of 2021. Due to the varied conditions of the subcontinent, the farmers may face different problems affecting the crop yield which ultimately affects the economy. Also, in case of the crops the decisions need to be immediate since if the crops are affected by a disease, then it can spread over the whole field affecting other crops. In order to help the farmers, come up with solutions to these problems, it has been aimed to create a platform on which the farmers can post their queries and other individuals can respond to those questions with answers. A deep learning approach has also been used to detect plant diseases. This will help the farmers to detect the disease of 14 different plant species with 38 sub-classes and also to post their queries in the proposed platform. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Crops

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Variable Weight Combination Model for Lane-changing Prediction of Human-Driven Vehicle

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-22页

作者： Wang, Xiang Zan, Yuyao Zheng, Jianying E, Wenjuan Tao, Yanyun Xiao, Yang Li, Tieshan School of Rail Transportation Soochow University Suzhou China Jiangsu Sutong Bridge Co. Ltd Nantong China School of Intelligent Manufacturing and Smart Transportation Suzhou City University Suzhou China Department of Computer Science The University of Alabama Tuscaloosa AL USA School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

Accurate lane-changing prediction is important to Advanced Driver Assistance Systems (ADAS). However, the prediction accuracy declines when the lateral motion is ambiguous during the early stage of lane change or when vehicles abruptly cease the process for safety concerns. This study proposes a variable weight combination (VWC) model to predict human-driven vehicles' (HDV) lane-changing. Speed gain and comfort gain are selected to describe lane change demand. An improved safety potential field model that considers different vehicle types is established to represent lane change conditions. Two Random Forest (RF) models are initially trained using vehicle and lane change demand, vehicle motion, and lane change condition as input features to extract the probabilities of different driving intentions. The dynamic weights of the two RF models are obtained based on the logistic function to achieve the final prediction. The results show that the accuracy of the proposed model reaches 89.14% on average within 3s duration before lane change point on NGSIM(I-80), the accuracy of the VWC model has increased by about 5% when compared to independent RF models and maintains an 83.69% accuracy even under challenging N(0, 0.2) noise condition. Notably, at a 3s prediction time, the VWC model outperforms Long Short-Term Memory and XGBoost by approximately 10%. Furthermore, the accuracy of the VWC model on NGSIM (US101) and HighD datasets sustains 86.81% and 88.02%, respectively, demonstrating its generalization ability. The research findings contribute to the advancement of ADAS by enabling the prediction of neighboring HDV driving intention, thereby enhancing driving safety and efficiency. IEEE

关键词： Hidden Markov models

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HISIM: Analytical Performance Modeling and Design Space Exploration of 2.5D/3D Integration for AI Computing

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Wang, Zhenyu Nalla, Pragnya Sudershan Sun, Jingbo Goksoy, A. Alper Mandal, Sumit K. Seo, Jae-Sun Chhabria, Vidya A. Zhang, Jeff Chakrabarti, Chaitali Ogras, Umit Y. Cao, Yu University of Minnesota Department of Electrical and Computer Engineering MinneapolisMN55455 United States Arizona State University School of Electrical Computer and Energy Engineering TempeAZ85287 United States Cornell Tech Department of Electrical and Computer Engineering New YorkNY10044 United States University of Wisconsin-Madison Department of Electrical and Computer Engineering MadisonWI53706 United States Indian Institute of Science Department of Computer Science and Automation Bengaluru560012 India

Monolithic designs face significant fabrication cost and data movement challenges, especially when executing complex and diverse AI models. Advanced 2.5D/3D packaging promises high bandwidth and connection density to overcome these challenges, yet it also introduces new electro-thermal constraints. This paper develops a suite of analytical performance models to enable efficient benchmarking of a 2.5D/3D heterogeneous system for energy-efficient AI computing. These models encompass various performance metrics related to computing units, network-on-chip, and network-on-package. The results are summarized into a new tool, HISIM, which is 104-106× faster than state-of-the-art AI benchmark tools. Furthermore, HISIM integrates rapid thermal simulation for the 2.5D/3D system, helping shed light on both the potential and limitations of 2.5D/3D heterogeneous integration on representative AI algorithms. The code of HISIM is available at https://***/mec-UMN/HISIM. © 1982-2012 IEEE.

关键词： Space research

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Reseed Skipping of Linear Congruential Generator for Multi-level Image Steganography Security of Financial Data

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SN computer Science 2023年第2期4卷 124页

作者： Danso, Juliana Mantebea Missah, Yaw Marfo Gyamfi, Enoch Opanin Dankwa, Stephen Kwabena, Sarpong Department of Computer Science Kwame Nkrumah University of Science and Technology (KNUST) Kumasi Ghana School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Cyber Security and Computer Engineering Technology School of Computing and Information Sciences C.K. Tedam University of Technology and Applied Sciences Upper East Region Navrongo Ghana School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

This paper proposed a multi-level image steganography model that integrates with image and plaintext encryption schemes. The model starts by partitioning the cover-image pixel positions into two regions of respective purposes, one is reserved to hold the data, and through the implementation of the dynamic difference expansion principle, the other sloughs some parts to the reserved region in responsive to the size of the data to embed. Continuously reseeding and initializing the parameter values of a Linear Congruential Generator (LCG), generate a set of random pixel positions as members of the reserved region. Before data embedding, the region reserved to hold the data as well as the data bits to embed, are then encrypted using AES with respective keys. Encryption keys are randomly generated using a Cryptographically Secure Pseudo-Random Number Generator (CSPRNG), the Fortuna algorithm. Multiplex security nature of using a randomization generator to reserve image region for encryption and encrypting plaintext data before performing steganography still ensures stability in computational complexities better than a logarithmic O(n log n). A visual inspection by evaluators who made 3000 choices to correctly differentiate stego images from its cover-images shows that only 47 choices were correct, 1884 choices were wrong, while 1069 choices were uncertain. An experiment shows an average Embedding Rates (ERs) of 0.00109 bpp and an average PSNR of 72. This concludes, as our integrated steganography and image encryption which implements the dynamic difference expansion principle, outputs encrypted stego mages that can deceive the naked human eyes into identifying them as rather cover-images, its embedding rate is faster (i.e. ER < 0.1), suitably good, and ensures better imperceptibility of cover-image alterations. When comparing these experimental results with existing methods, our proposed model shows significant competitiveness. We even show real-life scenarios on how this

关键词： AES encryption scheme Dynamic difference expansion principle Linear congruential generator Pseudo-random number generator Reserving room before encryption Steganography

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