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Context-Aware Recognition of Elevator Buttons Using a Sequential Training Methodology 24

Context-Aware Recognition of Elevator Buttons Using a Sequen...

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24th international conference on Control, automation and Systems, ICCAS 2024

作者： Ghosh, Arpan Joo, Kyeong-Jin Giraldo, Gilberto Galvis Kuc, Tae-Yong Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of Sungkyunkwan University Department of Electronic and Electrical Engineering Suwon16419 Korea Republic of

ISBN: (纸本)9788993215380

In this paper, we present a sequential training methodology aimed at improving the recognition of elevator buttons using the YOLOv5 object detection model. The methodology is structured into three distinct phases. In the first phase, we generate a synthetic dataset where elevator buttons, cropped from their original context, are placed on random image backgrounds. This phase is designed to help the model learn to identify buttons independently of their surroundings, ensuring a foundational understanding of button features without contextual distractions. In the second phase, we augment the cropped button dataset by applying various transformations such as random flips, rotations, and scaling. These augmentations increase the diversity and robustness of the training data, allowing the model to generalize better to variations in button appearances. The final phase involves training the model on images of full elevator panels. This step is crucial for helping the model understand the contextual placement and spatial relationships of the buttons within the panel, which is essential for accurate detection in real-world scenarios. Additionally, we enhance the real-time video input exposure to improve visibility under varying lighting conditions, addressing common challenges faced in practical applications. For post-processing, we integrate a Channel and Spatial Reliability Tracker (CSRT) to maintain button-tracking consistency in video sequences. This tracker helps ensure that once a button is detected, its position is reliably followed across frames, improving the overall accuracy and reliability of the system. This comprehensive approach, which combines the use of synthetic data, extensive data augmentation techniques, and contextual training on full panel images, aims to better simulate real-world scenarios. As a result, the proposed methodology significantly enhances the robustness and reliability of the YOLOv5 model in recognizing elevator buttons under diverse condi

关键词： Image enhancement

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The Identification of Health of Heart Through Predictive ML Based Technique in Integration with Existing System 2

The Identification of Health of Heart Through Predictive ML ...

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

作者： Prabakaran, S. Harish, S. Tamilselvan, B. Praveen Kumar, B. Gobinath, B. V.S.B. Engineering College Department of Computer Science and Engineering Karur India

ISBN: (纸本)9798350368109

New classes of diseases include;cardiovascular diseases: These are diseases associated with the heart and blood vessels and they are some of the classes of emerging infectious diseases that threaten human health around the world. for public health, is a big blow to complicated the issue that this is becoming a problem gradually, many researchers have been developing complicated systems that integrate patient's electronic health records, as well as many others, and artificial neural networks algorithms in a bid to produce high diagnostic results. most of the frequent artificial intelligence particularly in a context where full patient data are instantiated for the purpose of inferring the heart diseases. and thereby merely claiming the most primordial facets of our population's health. describes with exemplary accuracy the use of four separate classification methods: The used models in the current study are a) Multilayer Perceptron (MLP) b) RandomForest (RF) c) Support Vector Machine (SVM), and d) XGBoost. Before the development of the conceptual models, this study went through pre-processing and doing features extraction to these analyses These were done in line with what is regarded as the best approach when doing the last phase in a given research process. When evaluating the results, the following standards were used: accuracy, precision, recall, F1-score as it has been mentioned earlier, the XGBoost model shows a very high level of effectiveness The accuracy rate is rather high, 88%, which means the effectiveness of the instrument is high. Thus, this research study will fill this research gap of knowledge, as mentioned earlier, in a way that is appreciable. is a clear step in the right direction in addressing the high error rate that is associated with cardiovascular diagnosis while at the same time enhancing the validity with the MLP as well as the SVM. © 2024 IEEE.

关键词： Support vector machines

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Routing Protocols in Wireless Sensor Networks: A Comprehensive Review and Future Perspectives 7

Routing Protocols in Wireless Sensor Networks: A Comprehensi...

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7th international conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kaur, Puneet Kaur, Kiranbir Verma, Deepak Kumar Singh, Kuldeep Kaushik, Keshav Singh, Vineeta Guru Nanak Dev University Department of Computer Engineering & Technology Punjab Amritsar India Department of Computer Science and Engineering Uttar Pradesh Kalyanpur India Guru Nanak Dev University Department of Electronics Technology Punjab Amritsar India Amity University Amity School of Engineering and Technology Punjab Mohali India

ISBN: (纸本)9798350350067

Wireless Sensor Networks (WSNs) have significantly transformed diverse domains such as industrial automation, healthcare, disaster management, and environmental monitoring. The complex interaction of routing protocols, essential for enabling smooth data transport despite the specific difficulties in WSN environments, is the core of their success. The objective of this study is to get a accurate understanding of routing algorithms specifically projected for wireless sensor networks. Expanding upon the essential features and significant disputes faced in WSN systems, admitting limited energy resources, bandwidth constraints, and node processing capabilities, the discussion accentuates the significance of effective routing protocols. Furthermore, it thoroughly analyzes many forms of routing protocols, including basic hierarchical and flat schemes, as well as advanced location-based and data-centric protocols. The protocol category is thoroughly examined, uncovering its unique benefits, limitations, and suitability for various WSN applications. Furthermore, the review reveals the latest developments and upcoming trends in WSN routing protocols, brooding areas such as energy efficiency, dependability, scalability, and adaptive resilience to changing network considerations. In conclusion, the evaluation furnishes a forward-looking view by outlining potential research areas and possible pathways for meliorating the designs of WSN routing protocols. The main goal is to heighten the efficacy and practicality of WSNs. In this work, we examined a number of routing algorithms according to important criteria including mobility, scalability, energy efficiency, and Quality of Service (QoS). We looked at how each method handles node mobility, performs as the network expands, guarantees dependable data transmission, and optimizes energy use. We also looked at ways for choosing the cluster head (CH), energy-saving data aggregation strategies, redundancy-reduction mechanisms, and the

关键词： Routing algorithms

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Cybersecurity In The New Space Age

Cybersecurity In The New Space Age

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2023 international conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Singh, Ritesh Kumar Bhuvana, J. Iqbal, Mohammad Asif Noida Institute of Engineering & Technology Department of Computer Science and Engineering Uttar Pradesh Greater Noida India Computer Science Engineering Department Bangalore India Vivekananda Global University Department of Electrical Engineering Jaipur India

ISBN: (纸本)9798350357769

Technology breakthroughs, changing perspectives, and more investment are all contributing to a significant alteration of the space domain, making it more accessible to a wider variety of organizations. The space sector faces a growing danger scenario as a result of the impending deployment of new satellite constellations, which will dramatically increase the population of in-orbit satellites. The goal of this essay is to clarify the motives and features of adversary operations against satellites by doing a detailed analysis of past satellite security threats and events. Although historical patterns suggest that ground and radio frequency communications are the most common targets, the growing constellations of satellites that are predicted to be launched in the next several years can shift attention to vulnerabilities in the space sector. Proactive steps are required to identify and minimize any hazards in light of this impending trend. Along with providing an overview of the current issues facing the satellite industry-particularly those pertaining to security and operational requirements-the article also explores significant technical breakthroughs. It is crucial to comprehend and tackle these concerns in order to guarantee the ongoing security and dependability of satellite systems inside this dynamic space environment. © 2023 IEEE.

关键词： automation autonomy Commercial Off-The-Shelf (COTS) and security landscape cryptography CubeSats New Space satellite security Software-Defined Radios (SDRs) space industry space protocols

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Autonomous Drone Air Combat Policy Study in Amount Disadvantage Scenarios 4

Autonomous Drone Air Combat Policy Study in Amount Disadvant...

引用

4th international conference on Control Theory and Applications, ICoCTA 2024

作者： Li, Peng Zhu, Jing Zhai, Xiangping Nanjing University of Aeronautics and Astronautics College of Automation Engineering Nanjing China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China

ISBN: (纸本)9798331506155

This paper addresses the challenging issue of drone air combat maneuver decision-making by proposing an improved TR-PPO-RB algorithm. In simulated air combat scenarios, especially when drones are outnumbered, the performance of this algorithm is meticulously compared with the traditional PPO algorithm. Experimental data shows that the TR-PPO-RB algorithm has a significant advantage in increasing the win rate and the number of effective combat rounds, improving from 60.5% to 68.5% and from 80% to 98.9%, respectively. These results not only demonstrate the algorithm ' s excellent adaptability in complex battlefield environments but also confirm its stability. In the research, by introducing innovative rollback mechanisms and trust region optimization techniques, the TR-PPO-RB algorithm has achieved a breakthrough in the effectiveness of strategy learning and the robustness of the model. In the 1 vs 2 adversarial tests, the performance of the drones has been significantly enhanced. This study not only contributes new research methods and practical skills to the field of drone air combat decision-making but also lays a solid theoretical foundation and practical reference for future research endeavors. © 2024 IEEE.

关键词： Drones

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Implementation of Diamond Security System Using Machine Learning and Blockchain 8

Implementation of Diamond Security System Using Machine Lear...

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8th IEEE international conference on Computing, Communication, Control and automation, ICCUBEA 2024

作者： Dudhe, Swaraj Ravindra Ichole, Prajakta Aniruddha Bansode, Sudarshan Rameshwar Gaikwad, Kalyani Sandip Jadhav, Kavita Mahesh Nutan College of Engineering and Research Talegaon Dabhade Pune India

ISBN: (纸本)9798350391770

The pervasive challenges of counterfeit in the diamond industry, cybersecurity vulnerabilities and the looming threat of large-scale heists, akin to the infamous Antwerp money heist of 2003, underscore the critical need for innovative security solutions. The initiative addresses these pressing concerns through the fusion of machine learning algorithm and blockchain technology. The methodology entails a comprehensive dataset comprising thirty-two parameters for advanced detection and five dominant parameters for basic detection, catering to the both expert and novice users. Leveraging machine learning approaches which includes decision trees and ensemble learning, the system meticulously analyses these parameters to discern between natural and artificial diamonds with heightened accuracy. The accuracy of the models varies, with the highest achieving an impressive 98%, ensuring robust authentication and fraud prevention mechanisms. Furthermore, the verified data of natural diamonds is securely stored on a blockchain ledger, fortifying ownership records against tampering and ensuring transparency. This integrated approach not only safeguards the integrity of the diamond industry but also fosters trust and transparency among stakeholders. By providing a paradigm shift in security solutions, the system sets a new standard for authentication and fraud prevention in the diamond market, mitigating risks and empowering stakeholders with reliable, data-driven tools for decision-making. © 2024 IEEE.

关键词： Adversarial machine learning

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A Time-Sensitive Networking Traffic Scheduling Method Based on Q-Learning Routing Optimization 18

A Time-Sensitive Networking Traffic Scheduling Method Based ...

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18th international conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Li, Jin Wei, Min Huo, Chengjie Kim, Keecheon Chongqing University of Posts and Telecommunications China-Korea Belt and Road Joint Laboratory on Industrial Internet of Things Chongqing China Konkuk University Dept. of Computer Information & Communications Engineering Seoul Korea Republic of

ISBN: (纸本)9798350331011

With the rapid development of industrial automation, higher requirements are put forward for reliable and deterministic communication in industrial networks. And time-sensitive networking (TSN) is a promising technology that can satisfy such deterministic transmission requirements. Currently, TSN typically uses the shortest path routing (SPR) algorithm to determine the transmission path of traffic. However, the SPR algorithm may cause a high load on a single path, which makes it difficult to improve the schedulability and determinism of time-triggered (TT) traffic. In this paper, a TSN traffic scheduling method based on Q-learning routing optimization for TT traffic is proposed, and the transmission performance of the proposed method is tested. The results show that the delay and jitter of TT traffic are reduced after using this method. © 2024 IEEE.

关键词： Reinforcement learning

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Tech Meets Agriculture - Precise Plant Disease Detection Using Deep Learning Algorithm

Tech Meets Agriculture - Precise Plant Disease Detection Usi...

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2024 international conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Arunasakthi, K. Akilandeswari, V. Shalini, K.S. Archana, K. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

Plant disease prediction is crucial aspect of agricultural field. Plant diseases are one of the major concerns for agriculture in recent times;most of the crops are prone to damage caused by these diseases, which later hampers global food security. The traditional methods used for disease analysis rely strictly on visual inspection. These methods are time-consuming and prone to errors. Deep learning has therefore emerged as a significant and formidable instrument for the automation of plant disease detection. This project uses the deep learning models, YOLOv5 and YOLOv9. It ranks high on accuracy and ensures efficiency in the system to detect diseases related to plant leaves. It uses annotated datasets for several plant leaf diseases with a convolutional neural network to extractfeatures and enable classification. The system provides real-time automated solutions for the identification of plant diseases through the integration of deep learning models into agricultural practices, with a view to allowing growers to take timely action to avoid further damage to crops. The proposed technique not only heightens the rate of accuracy for disease detection but also contributes toward the growing dimension of precision agriculture, within which technology acts as an assistant in the optimization of crop management and enhancement of yield. © 2024 IEEE.

关键词： Convolutional neural networks

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Proactive Learning Renewable Energy Management Strategy for Isolated DC Microgrid 10

Proactive Learning Renewable Energy Management Strategy for ...

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10th IEEE international Power Electronics and Motion Control conference, IPEMC 2024 ECCE Asia

作者： Li, Fei Wu, Jiangpeng Zhu, Ren Rong, Jieqi Wang, Zini Li, Heng Zhang, Rui South University School of Automation Central Changsha China Changsha Preschool Education College School of Preschool Education Changsha China Central South University School of Computer Science and Engineering Changsha China Central South University School of Electronic Information Changsha China Changsha University School of Electronic Information and Electrical Engineering Changsha China

ISBN: (纸本)9798350351330

Complex weather conditions are an important factor restricting the development of solar power generation. In this paper, an proactive learning-based dynamic power management method for DC microgrid fuel cell/battery systems is proposed to cope with the uncertainty of solar power generation. A deep deterministic policy gradient algorithm is used to adaptively distribute power between fuel cell/battery systems. Secondly, the demand power curve is randomly generated using the historical data of microgrid PV to ensure the efficiency of the strategy under any photovoltaic power generation conditions. The findings indicate that this energy management strategy notably decreases both battery deterioration and fuel cell costs by 18.6% and 21.06% under PV-abundant scenarios, and by 5.07% and 9.13% under PV-scarce scenarios, respectively. These improvements bring the system's performance closer to that achieved by offline dynamic programming. © 2024 IEEE.

关键词： Solar power generation

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IoT-Based Plant Health Monitor Using NodeMCU and ESP8266 5

IoT-Based Plant Health Monitor Using NodeMCU and ESP8266

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5th international conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Tandel, Rajat Ebenezer, V. Santhiya, P. Edwin, Bijolin Stewart Kirubakaran, S. Thanka, Roshni Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Data Sciences and Cyber Security Coimbatore India

ISBN: (纸本)9798331518097

Plant Monitor Using NodeMCU(ESP8266)is a technology that is advanced;it uses IoT-Internet of Things to measure and monitor different plant phenomena. This system, which is based on sensors that collect data on different factors such as soil type, temperature, humidity, and other vital metrics vital for plant growth, is also integrated. Bringing IoT into the picture, the system can remotely sense the metrics and having control over other factors which include watering, shading, and even security provision for the plants to make it thrive. Besides that, it will also send notifications to users, hence allowing them to engage in the monitoring process through mobile applications with support from their smartphones. The Smart Plant Monitoring System though creates an impact on the efficiency of plant care also impacts on water conservation, automation of plant care process and better agricultural practices. © 2024 IEEE.

关键词： Efficiency

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