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检索条件"任意字段=International Conference on Advances in Intelligent Control and Computer Engineering"

共 47502 条记录，以下是181-190 订阅

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Real-Time Dynamic Image Stitching Using GNN-Based Feature Matching 11

Real-Time Dynamic Image Stitching Using GNN-Based Feature Ma...

引用

11th international conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Suh, Young Hoon Kim, Eun Ho Choi, Yeong Gwang Jeon, JaeWook Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon16419 Korea Republic of

ISBN: (纸本)9798331509293

Especially in static situations, image stitching has been routinely used to seamlessly merge many photographs into a panorama. Using it in dynamic and real-time environments, however, presents a number of difficulties, such as managing shifting objects, changing surroundings, and processing limitations. An image stitching look at that works well in dynamic real-time situations is presented in this paper. The method makes use of GNN-based feature matching, dynamic object removal, and real-time synthesis techniques. Comparative studies suggest that, in comparison to traditional methods like SIFT and ORB, the suggested approach meets real-time processing requirements while achieving greater registration precision, lower homography RMSE, and consistent panoramic outputs. The findings imply that the algorithm might be used in fields where precision and efficiency are required in dynamic circumstances, like parking management systems, sports broadcasting, and autonomous driving. © 2025 IEEE.

关键词： Photointerpretation

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Application of Wireless Sensor Technology in Urban Energy Management 24

Application of Wireless Sensor Technology in Urban Energy Ma...

引用

8th international conference on Electronic Information Technology and computer engineering, EITCE 2024

作者： Zhang, Yajuan Chen, Jie Gong, Zhijia College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

The rapid growth of urbanization has led to an increasing need for efficient energy management systems to optimize energy consumption and reduce environmental impacts. Wireless sensor technology emerges as a pivotal solution, providing advanced capabilities in real-time monitoring, control, and management of urban energy systems. This paper explores the practical applications of wireless sensor networks (WSN) in urban energy management, highlighting their role in smart grids, intelligent building systems, smart street lighting, public transportation, water management, and renewable energy integration. Additionally, it addresses the prominent challenges such as data security, sensor durability, network scalability, data management, energy consumption, and infrastructure integration. [1-2] The paper further discusses optimization pathways to enhance the effectiveness and sustainability of WSNs in urban energy management. By overcoming these challenges and leveraging the potential of wireless sensor technology, cities can achieve significant energy savings, operational efficiencies, and contribute to broader environmental sustainability goals. © 2024 Copyright held by the owner/author(s).

关键词： Green buildings

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Adaptive Electric Vehicle Charge controllers Using Deep Reinforcement Learning for Real-Time Optimization 1

Adaptive Electric Vehicle Charge Controllers Using Deep Rein...

引用

1st international conference on advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kavitha, N. Bdair, Mohammad Chandrasekar, P. Veerakumar, S. Kumar, M. Hema Kumar, Vinay Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of EEE Tamil Nadu Chennai India School of Architecture Computing & Engineering University of East London London United Kingdom Bannari Amman Institute of Technology Department of EEE Tamil Nadu Erode India Sona College of Technology Department of ECE Tamil Nadu Salem India B N M Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798331518578

The increasing adoption of electric vehicles necessitates intelligent charging systems to address challenges such as grid overload, renewable energy integration, and user prioritization. This paper presents a new framework in optimizing the efficiency of electric car charging: Hierarchical Multi-Agent Deep Reinforcement Learning. In this architecture, a global coordinator relies on Proximal Policy Optimization to coordinate across stations, and the local agents follow Deep Q-Networks to manage their resources at the station level. Test results: A comparison of the test results with regular methods shows an average waiting time reduced by 30%, a 15% increase in the usage of renewable energy, and a reduction of 22% in the system latency. For grid loads, consistent results from the operation of this architecture ensure that minimal unserved energy demand leads to the balanced serving of loads. This method, therefore, aligns well with sustainable energy goals through simultaneous improvement in effectiveness of charging infrastructure and on the flexible scale of solution. © 2025 IEEE.

关键词： Deep reinforcement learning

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Building AI-Powered Recommendation Engines Within Salesforce Ecosystems 1

Building AI-Powered Recommendation Engines Within Salesforce...

引用

1st international conference on advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Janakiraman, Arulselvan Shah, Bhumika Thummala, Venkata Reddy Kumar, Naresh Balasubramaniam, Vijayanand Goel, Om Anna University Tamil Nadu Chennai600025 India University of the Cumberlands WilliamsburgKY40769 United States Visvesvaraya Technological University Karnataka Belgaum India Greater Noida201306 India Bharathiar University Tamil Nadu Coimbatore641046 India ABES Engineering College U.P. Ghaziabad India

ISBN: (纸本)9798331518578

AI tool has become so useful and important for companies that it can either drive customers to a company or a company to a sale. This Recommendations Engine have become a crucial part of how to present the right recommendations to customers, to achieve better experience in Salesforce journey. So, many AI-powered recommendation engines extended within the Salesforce universe. These engines are usually very intelligent, utilizing advanced machine learning algorithms to apply customer data to make more personalized recommendations. Engines that integrate with Salesforce CRM can access a trove of customer data, enabling progressively more accurate and nuanced recommendations. Furthermore, AI-driven recommendation engines within the Salesforce ecosystem can be tailored to specific industry and business needs. For instance, in e-commerce, recommend engines suggest products interacts with customers browsing history and purchasing trends, as well as recommending tailored financial products and services in finance. While on the other hand AI-based recommendation engines present in the Salesforce ecosystem can further be said to implement improved internal processes for sales and marketing strategies as well as you can gain real-time access to customer preferences and behavior. This makes companies highly flexible and able to adapt to changing customer needs and tastes. © 2025 IEEE.

关键词： Sales

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Generative AI in Real-Time E-Commerce Fraud Detection: A Comparative and Ethical Analysis

Generative AI in Real-Time E-Commerce Fraud Detection: A Com...

引用

2025 international conference on intelligent control, Computing and Communications, IC3 2025

作者： Tyagi, Rashi Goyal, Gauri Tyagi, Shiva Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798331527495

This paper will discuss generative AI models, such as GANs, VAEs, and a hybrid GAN-VAE, towards real-time fraud detection in e-commerce. GANs perform outstandingly in synthesizing fraudulent data, while VAEs detect complex patterns of fraud. The proposed hybrid method combines the respective strengths and achieves higher accuracy and adaptability. Also, issues regarding ethical concerns related to privacy in data and biases are taken care of, illustrating the future possibility of generative AI in upgrading the frameworks for fraud detection in e-commerce. © 2025 IEEE.

关键词： Marketplaces

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Enhancement of Power Quality in a Standalone PV-Wind-Battery Hybrid Microgrid with ANN-LSTM controllers 1

Enhancement of Power Quality in a Standalone PV-Wind-Battery...

引用

1st international conference on advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Albtoush, Faten Al-Mattarneh, Hashem Rao, Burada Sankara Kumar, K. Siva Yadav, Ravindrakumar Devi, M. Malladhi, Nagarjuna Jadara University Department of Civil Engineering Irbid Jordan Yarmouk University Department of Civil Engineering Irbid Jordan CPGC Pvt. Ltd ERG Foundation Andhra Pradesh Visakhapatnam India Department of EEE Andhra Pradesh Puttur India Krishna School of Emerging Technology and Applied Research Gujarat Vadodara India New Prince Shri Bhavani College of Engineering and Technology Department of EEE Tamil Nadu Chennai India Vardhaman College of Engineering Department of ECE Telangana Hyderabad India

ISBN: (纸本)9798331518578

The global world is investigating renewable energy alternatives to fight climate change and lessen dependency on non-renewable resources. Although the usage of solar and wind energy has increased dramatically, variations in sun radiation and wind speed are still unpredictable. An energy storage system is necessary to convert various energy sources into electricity and maximize their use. Batteries are a popular choice for medium power applications, but because of their frequent maintenance needs and susceptibility to self-discharge, their storage capacity rapidly declines. Compared to batteries, hydrogen storage is a more cost-effective solution for high power applications. It can be used to generate electricity and for transportation. Using electrical energy, an electrolyzer can efficiently separate water into hydrogen and oxygen. However, the slow heat transfer dynamics make it difficult to produce hydrogen quickly, necessitating the use of a new management technique to increase production efficiency in the face of variations in wind speed, solar irradiance, buck, boost, and bidirectional DC circuits are employed to achieve a stable voltage at the dc-link, regardless of fluctuations in various working scenarios. A new control methodology is developed to maintain power quality and to regulate energy within the hybrid standalone Microgrid. The proposed control strategy utilizes LSTM-ANN controllers, which exhibit superior performance compared to conventional controllers. The results of this study illustrate the methodology for assessing performance across various scenarios through the use of two OPAL-RT units and Hardware-in-the-Loop (HIL) testing. © 2025 IEEE.

关键词： Hydrogen storage

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A Hybrid Deep Learning Models: Optimization for Forecasting Wireless 6G IoT Network 1

A Hybrid Deep Learning Models: Optimization for Forecasting ...

引用

1st international conference on advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Singh, B.P. Venkadeshwaran, Kuthalingam Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Ramnagar District India

ISBN: (纸本)9798331518578

New specifications for 6G mobile networks will surpass the performance targets of 5G networks. As more 5G networks are deployed, their limits become obvious, encouraging the exploration of networks using 6G as the future solution. This paper covers 6G technology's core privacy and security concerns. Monitoring real-time structures need secured wireless sensor networks (WSNs). Denial of service (DoS) outbreaks increase secure risks for WSNs and potentially undermine the whole system. This study presents a new approach to control and optimize the security of 6G wireless networks based on blockchain technology by means of a machine learning method. Utilizing a blockchain users datagram transit protocol in conjunction with reinforcement projection regression, this research manages security of organized 6G wireless sensor net. The network is then optimized via artificially democratized cuckoo glowworm remora optimizations. Numerous network characteristics pertaining to accuracy, end-to-end latency, packet delivery ratio, energy efficiency, and throughput have formed the basis of the simulation findings. Reduced network traffic may be achieved by figuring out which node and route are best for data transfer. 98% of Throughput, energy effectiveness of 96%, 97% accuracy, end to end latency of 51%, and packet delivery ratio of 95% were all achieved by the suggested method. © 2025 IEEE.

关键词： 5G mobile communication systems

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Development of Multi Objective Approach for Validation of PID controller for Buck Converter 1

Development of Multi Objective Approach for Validation of PI...

引用

1st international conference on advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Arun Kumar, K. Vegineni, Gopi Chand Suresh, C. Chowdari Marella, Bhagath Chandra Addanki, Sireesha Chimwal, Palak Big Data Architect India Sr.Software Engineer Enrollment and Eligibility Team India Electrical and Electronics Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Trichy Samayapuram621112 India Insights & Data Capgemini Department of Financial Services India System soft Technologies LLC India Bipin Tripathi Kumaon Institute of Technology Department of Electrical Engineering Almora Dwarahat India

ISBN: (纸本)9798331518578

Buck converters are a common converter of DCDC used for high energy conversion and power sources. These regulators are utilized in electronic gear to alter the result voltage because of changes in the heap current and information voltage. Therefore, in order to react to enquiries in real time, an efficient control system is required. In this work, an upgraded Sine Cosine Algorithm (ISCA) with proportional-integral-derivative (PID) controller is proposed for Mult objective optimization of a DC-DC buck converter. Through two distinct modifications, the limitations of the traditional SCA were therefore addressed, leading to the innovative use of nonlinear equations in conjunction with instrumental mechanisms to update the average location. By contrasting its response for frequency, response for increment, uncertainties of parametric, statistics of time-space indispensable error execution, objective capability, and resistance from aggravation with the previous methods-based PID for buck converters of DC-DC, the suggested ISCA-PID technique's robustness was evaluated. The results obtained later revealed that the suggested approach outperformed its algorithmic predecessors in terms of the DC-DC buck converter's remarkably improved transient responsiveness. © 2025 IEEE.

关键词： Buck converter

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Hyperspectral Image Analysis Using Maximum Abundance Classification 13

Hyperspectral Image Analysis Using Maximum Abundance Classif...

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13th international conference on intelligent control and Information Processing, ICICIP 2025

作者： Zhang, Nian Rouamba, Sougre-Nonma Mahmoud, Wagdy H. Thompson, Lara A. Department of Electrical and Computer Engineering University of the District of Columbia WashingtonDC United States

ISBN: (纸本)9798331516147

This paper presents a satellite hyperspectral image processing method that utilizes a maximum abundance classifier to categorize different regions of hyperspectral images into ground truth classes. First, the class names for each endmember and their corresponding columns in the signature matrix are identified, followed by the visualization of their spectral profiles. Abundance maps for the endmembers are then generated using the fully constrained least squares (FCLS) method. Afterward, the maximum abundance classifier is applied, and the resulting classified image is displayed with color-coded pixels. The abundance maps illustrate the spatial distribution of endmembers across the hyperspectral image, where the abundance values of each pixel represent the proportion of each endmember present. By determining the highest abundance value for each pixel and assigning it to the corresponding endmember class, the pixels within the hyperspectral images are classified. Experimental results demonstrate that the proposed MAC method effectively handles mixed pixels. In addition, it can effectively deal with the mixed pixel problem in hyperspectral images because it identifies components by calculating the abundance values for each pixel rather than relying solely on single spectral features. © 2025 IEEE.

关键词： Image segmentation

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Crop disease identification by integrating Internet of Things and Stacking algorithm 2

Crop disease identification by integrating Internet of Thing...

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2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Zhang, Hongmeng School of Computer and Information Engineering Guangxi Vocational Normal University Guangxi Nanning530007 China

ISBN: (纸本)9781510689053

Artificial intelligence plays an important role in agricultural pest control. In order to address the issue of agricultural crop diseases, this study proposes an intelligent agricultural disease recognition technology based on the Internet of Things. In this study, the Internet of Things technology is used to extract disease image data, and the images are preprocessed and segmented. Secondly, the Stacking algorithm training data is introduced to realize the classification and recognition of crop diseases. The recognition accuracy of the research model in the big spot disease is 0.975, and the recognition accuracy of corn rust in the 5th sample is 0.975. The recognition effect is better than that of similar models. In the comparison of recognition errors, the root mean square error of the research model is 0.097, and the error value is the lowest. Finally, in the comprehensive recognition comparison, the precision rate and recall rate of the research model are the best. The application effect of this research model in practical scenarios is very good. The research content will significantly improve the application of machine vision algorithm technology in the field of agriculture and provide suggestions for the development of modern agriculture. © 2025 SPIE.

关键词： Smart agriculture

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