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Energy Aware Heterogeneous Server Consolidation in a Cloud Datacenter 3

Energy Aware Heterogeneous Server Consolidation in a Cloud D...

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3rd international conference on Artificial intelligence, Computational Electronics and Communication System, AICECS 2024

作者： Mault, Samrajesh Viswanathan, K.K. McGill University Technology and Innovation Domain Montreal H3A 2M7 QC Canada Samarkand State University Department of Mathematical Modeling Samarkand 140104 Uzbekistan

ISBN: (纸本)9798350391244

Cloud computing has transformed the way data is stored, applications are developed and used. There is an exponential rise in cloud datacenters. Server consolidation in heterogeneous datacenters is an important area of research. Efforts to conserve energy in datacenters often conflict with the commercial goals of cloud providers. Most datacenter's servers' energy saving approaches are hardware centered. An idle server utilizes more than fifty percentage of its highest energy requirements. Today, as more applications and data are moved to cloud. Energy consumption of cloud datacenters is growing and reducing energy at the cloud datacenters is becoming more challenging. In this paper we presented a heterogeneous server consolidation for cloud datacenter. Our experiment confirms that Heterogeneous Server Consolidation (HSC) is efficient in lowering energy consumption. HSC proposed when compared with DVFS, MTDP closes around 26%, 21% blade enclosures respectively. © 2024 IEEE.

关键词： cloud computing datacenter energy load balancing

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A Systematic Review: How Computer Vision is Transforming Agriculture in Economic Growth 1

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3rd international conference on Artificial intelligence and Knowledge Processing (AIKP)

作者： Karanam, Santoshachandra Rao Kumar, A. B. Pradeep Yandrapati, Prakash Babu Tangudu, Naresh Peddada, Nagamani Bollipelly, PruthviRaj Goud GITAM Deemed Univ CSE Dept Hyderabad India Aditya Inst Technol & Management CSD & MCA Dept Tekkali Srikakulam India Anurag Univ IT Dept Hyderabad India

ISBN: (数字)9783031686177

ISBN: (纸本)9783031686160;9783031686177

Recent years have seen the emergence of computer vision, a subfield of artificial intelligence (AI), as a technology that has the potential to revolutionize agricultural practices. This might have an impact on many agricultural practices and crop management techniques. This is due to the fact that computer vision can examine pictures and identify patterns of data. This article provides a summary of the uses of computer vision in agriculture as well as the consequences such applications have had. The issues of precision agriculture, disease diagnosis, crop monitoring, and yield computation may be overcome with the use of computer vision technologies such as image recognition, object detection, and pattern analysis. Specifically, it investigates the ways in which these strategies are useful. In addition to this, it investigates the benefits, drawbacks, and possible future applications of computer vision in agriculture, with a particular emphasis on the potential for the sector to improve its levels of productivity, sustainability, and profitability. According to this in-depth analysis, computer vision has revolutionized the agricultural industry and contributed significantly to economic growth. In order to evaluate the financial impacts that computer vision has had on the agricultural industry, this study looks at a wide range of academic papers, publications, and reports. The findings highlight the advancements, benefits, challenges, and future opportunities presented by computer vision technology in the areas of crop monitoring, precision farming, animal management, and harvesting. According to the evaluation, computer vision has the potential to improve farming in terms of productivity, resource allocation, cost reduction, and sustainable practices.

关键词： Computer Vision Artificial intelligence Economic Growth Agriculture Management Agricultural Automation Machine Learning

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Machine Learning based human recognition via robust Features from audio signals 3

Machine Learning based human recognition via robust Features...

引用

3rd IEEE international conference on Artificial intelligence, ICAI 2023

作者： Sadique, Umar Khan, Muhammad Suleman Anwar, Shahzad Ahmad, Mehran University of Engineering and Technology AI in Healthcare Lab National Centre of AI Peshawar Pakistan University of Engineering and Technology Department of Electrical Engineering Bannu Campus Peshawar Pakistan University of Engineering and Technology Department of Mechatronics Engineering Peshawar Pakistan

ISBN: (纸本)9798350322125

Biometric verification techniques are commissioned throughout the world in different applications. Human voice recognition is one of the biometric techniques. This technique consists of identifying a human from their voice characteristic. This popular and beneficial biometric technique could be employed for identity human, security purposes, and many different applications. Human audio signal recognition consists of two phases i.e., Features Extraction and Classification. The proposed work consists of extracting features through the Mel Frequency Cepstral-Coefficient (MFCC) from the human audio signal, selecting robust features through Principal Component Analysis PCA, and classifying the selected features by comparing seven Machine Learning and proposed deep learning algorithms. Finally, compare the performance of different algorithms with different percentages of selected features to evaluate the acceptance rate of the correlated features. Support Vector Machine SVM shows the best performance with an Accuracy of 99.27% with F1-score and ROC values of 1.00. In the comparison with other methods, the Random Forest and CNN-ANN are 2nd top robust models with an accuracy of 98.7%. Some of the algorithm's accuracy decreased with fewer features, including Naïve Bayes accuracy suddenly decreases to 60% on 20% of total features. The experiment concludes the acceptance rate of correlated features in different ML and DL algorithms are different in speech processing data. © 2023 IEEE.

关键词： Learning algorithms

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Grid Integrated Renewable Energy Resources with Battery Storage Support for Optimized EV Charging 3

Grid Integrated Renewable Energy Resources with Battery Stor...

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3rd international conference on Power Electronics and IoT applications in Renewable Energy and its Control, PARC 2024

作者： Kumhar, Rajesh Sarkar, Ananda Mahato, Tarkesh Kr Lakra, Poonam National Institute of Technology Silchar Dept. of Electrical Engg. Assam Silchar India Birsa Institute of Technology Dept. of Electrical Engg. Sindri Sindri Dhanbad India

ISBN: (纸本)9798350309409

The use of grid-connected renewable energy sources, such as photovoltaic (PV) systems and battery storage, with electric vehicle (EV) charging infrastructure offers a viable solution to issues encountered in conventional charging stations. This paper investigates intelligent control approaches for EV charging, with a focus on the optimal use of PV energy and batteries for energy optimization. With the increasing popularity of EVs, there is a greater emphasis on sustainable transportation, making renewable energy integration critical. The article opens by discussing the shortcomings of typical EV charging, such as grid stress, capacity constraints, and reliance on nonrenewable sources. It offers a thorough overview of intelligent control strategies that incorporate modern algorithms and computational intelligence to maximize PV and battery use in charging infrastructure. The primary focus is on developing sophisticated optimal controllers for EV charging, demonstrating how intelligent management of PV energy based on aspects such as solar irradiance, battery condition, and grid demand enhances the charging process dramatically. Battery storage effectively captures excess PV energy, minimizing dependency on the grid during peak demand. The report highlights several advantages of intelligent control, including financial savings, reduced environmental impact, and increased energy efficiency. It investigates the predictive analytics potential for EV charging demand forecasting and dynamic allocation of PV and battery resources, hence boosting system efficiency. The paper also discusses essential factors such as hardware requirements, communication protocols, and interoperability in order to apply intelligent control in EV charging in a practical manner. It focuses on cybersecurity measures for safe functioning. The potential of PV and battery integration for energy optimization in EV charging is highlighted in this paper. It provides practical advice for academics, engin

关键词： MATLAB

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Application of Virtual Reality technology in Motion Simulation and Control of Industrial robot 3

Application of Virtual Reality Technology in Motion Simulati...

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3rd international conference on Artificial intelligence and Computer Engineering, ICAICE 2022

作者： Zhao, Wei Sichuan Aerospace Vocational College Sichuan Chengdu610100 China

ISBN: (纸本)9781510663473

Aiming at the problem of tracking and controlling the motion path of industrial robots in the process of research, design and development, this paper will take the common six-axis industrial robots as the research object, take advantage of the application advantages of VR technology, 3D modeling technology and Web3D interactive technology, take 3ds Max as the modeling tool and Unity3D virtual reality engine as the development platform, and build a virtual reality simulation experiment system of industrial robots from the perspective of visual interaction between virtual robots and real robots, so as to provide a comprehensive and feasible solution for the research of virtual motion simulation and control of industrial robots. The whole system adopts B/S architecture, and completes the design and deployment of the whole function according to MVC mode in *** environment, so as to support users with different roles to test the functions of each component module of industrial robot in virtual reality environment, and also simulate the trajectory planning and motion effect control of industrial robot in different scenes. The system will greatly improve the research and development efficiency of industrial robots, increase the efficiency and flexibility of industrial robots, break through the limitations of traditional testing methods on time and space, and provide experience and reference for the intelligent development of industrial robots. © 2023 SPIE.

关键词： Industrial robots

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A No-Reference Image Quality Assessment Method Based on Random Forest for Power Scenario 3

A No-Reference Image Quality Assessment Method Based on Rand...

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3rd international conference on Artificial intelligence and Computer Information technology, AICIT 2024

作者： Li, Junwei Zeng, Jijun Kong, Tianwen Su, Huaquan Wu, Yang Huo, Yejing Wu, Shiting Shi, Hanyu Information Center Guangdong Power Grid Co. Ltd. Guangzhou China Guangdong University of Technology Dept. School of Computer Science Guangzhou China Huizhou Boluo Power Supply Bureau Guangdong Power Grid Co. Ltd. Huizhou China Guangdong University of Finance and Economics Dept. School of Statistics and Mathematics Guangzhou China

ISBN: (纸本)9798350365443

No-reference image quality assessment (NR-IQA) is a crucial task in computer vision, aiming to evaluate the visual quality of images solely through distorted images without the need for original (reference) images. This evaluation method is particularly useful in practical applications where obtaining the original image is not feasible, such as in the task scenario discussed in this article: assessing whether a camera is blurred, overexposed, etc., in power grid scenarios. Traditional no-reference image quality assessment methods usually face issues such as over-reliance on manually designed features, which may not comprehensively capture all aspects of image quality, and good performance on specific datasets but significant performance degradation across different datasets or types of image distortion, resulting in poor consistency with actual subjective scores. Although with the continuous development of deep learning, many models are able to extract the features of an image with relatively high efficiency for effective evaluation, many of the models require high computational resources, which is not conducive to the needs of real-world scenarios where low-computing power is deployed to achieve timely image quality assessment. In order to solve these problems, we propose a random forest-based approach for reference-free image quality assessment in power scenarios. First, we collect original power scene images and perform image expansion, including abnormal and normal images, to increase the diversity of the dataset. The expanded images were merged with the original images to form a balanced dataset. Next, we take the merged dataset as input and perform data processing, including random cropping and simulated image damage, to enhance the model's adaptability to different observation viewpoints and damage scenarios. The processed images are used for feature extraction, and these features are subsequently used to train the random forest model. The experimental result

关键词： Image quality

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11th international conference on robot intelligence technology and applications, RiTA 2023

11th International Conference on Robot Intelligence Technolo...

引用

11th international conference on robot intelligence technology and applications, RiTA 2023

ISBN: (纸本)9783031706868

The proceedings contain 30 papers. The special focus in this conference is on robot intelligence technology and applications. The topics include: IoT-Based Underwater robotics for Water Quality Monitoring in Aquaculture: A Survey;Review of Recent Research on the Potential of Cellulose NanoCrystals (CNCs) in Improving Heat Transfer Processes;harnessing the Fourth Industrial Revolution: Trends and Challenges in Applying Industry 4.0 to Enhance Predictive Maintenance in Manufacturing, Specifically Mining;enhancing Thermal Conductivity and Heat Transfer Performance with Nanocellulose-Based Nanofluids: A Comprehensive Study;3D Guidewire Shape Reconstruction from Monoplane Fluoroscopic Images;shape-Sensitive Loss for Catheter and Guidewire Segmentation;a ZnO Nanowire Based Memristive Device and Its Application in Artificial Synapse;wide-Area Signalling Enabled by the High-Throughput-Satellite Communication: A Machine Learning Approach;a Neural Network Approach to Tool Wear Detection via Infrared Sensor Monitoring;A RFID Online Sensing Tag for Electric Power Materials;Analysis of a Hybrid Pump Controlled Hydraulic System Based on AMEsim;collaborative Music Performances with Humanoid robots and Humans;Comparison of PSO and SQP Methods for Energy Optimization of a Backstepping-Controlled Differential Mobile robot;TOSS: Real-Time Tracking and Moving Object Segmentation for Static Scene Mapping;path Planning of Multi-robots in Confined Spaces;Fusing UAV and Ultra-Wideband technology for Target Visual Intelligent Perception Without GNSS;perception of Social Behaviours in robots;evaluating the Energy Consumption of Path Planning Algorithms: A Comparative Study of A*, Dijkstra and Probabilistic Roadmap;inertial-Joint Learning-Aided Robust Pose Estimation for Legged robots;Guided Exploration Reinforcement Learning for UAV Pursuit-Evasion Games;Autonomous Crop Pollination Using a Double-Flap UAV Integrated with Real-Time SLAM technology: Design, Algorithms, and Experimental Validat

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Research on robot Control Coordinate System Conversion Method Adapted to Offline Measurement 5

Research on Robot Control Coordinate System Conversion Metho...

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2023 5th international conference on Artificial intelligence and Computer applications, ICAICA 2023

作者： Yan, Wang Yuan, Zhang Liqing, Zuo Xiaodong, Wen Jinsheng, Fu Qi, Li Tianjin Institute of Aerospace Mechanical and Electrical Equipment Tianjin Key Laboratory of Aerospace Intelligent Equipment Technology Tianjin China

ISBN: (纸本)9798350323313

To address the problem of transforming the control coordinate system of a robot, a method is proposed to adapt to the combination of offline measurement and position solving. Before the operation of the robot, the positions of the feature points are measured and fitted, and the translational and rotational travels betweenthem and the input target positions are solved for controlling the robot motion, i e., thecontrol coordinate system of the robot is transformed from the robot itself to the specified coordinate system. The effectiveness of the method is verified by comparing the 3D model with the simulation results of the numerical model. © 2023 IEEE.

关键词： Numerical methods

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Avenues of Graph Theoretic Approach of Analysing the LIDAR Data for Point-To-Point Floor Exploration by Indoor AGV 3rd

Avenues of Graph Theoretic Approach of Analysing the LIDAR D...

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3rd international conference on Machine Learning, Image Processing, Network Security and Data Sciences, MIND 2021

作者： Chaudhuri, Rapti Acharjee, Jashaswimalya Deb, Suman National Institute of Technology Agartala Agartala India

ISBN: (纸本)9789811958670

Realizing point-to-point augmented path is one of the most challenging tasks for any mobile robot navigation. The challenge is much higher when it is GPS-denied indoor environment. This paper presents analysis of graph theoretic approaches including applicable heuristic algorithms for achieving optimal point-to-point navigation using LIDAR sensor data as source input, ensuring total workspace coverage and minimization of action performed by the robot in carrying out realistic applications. Sampling-based approaches which uses arbitrary information gain formulation and learning-based techniques, both are studied extensively. Uniform sampling-based techniques are found feasible in exploring the state space without any complexity in geometrically modelling the configuration area ensuring embedded intelligence into the mobile robots in finding optimal execution. The navigation over both static as well as dynamic obstacles are analysed, and the observations are presented in a comparative manner. The paper concludes with the graphical and numerical analysis of the accuracy achieved using mentioned algorithms. It would definitely help in obtaining individual navigational strategy by the mobile robot to reach a specific goal. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mobile robots

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Utilizing Social Media Platforms for Artificial intelligence-Based Detection and Developing Effective Communications in Disaster-Affected Areas 3

Utilizing Social Media Platforms for Artificial Intelligence...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Waghmare, Ganesh Kumar, Saurabh Udhayakumar, R. Raja Mannar, B. Singh, Rajesh Rawat, Navneet Lexicon Management Institute of Leadership and Excellence Maharashtra Pune India Invertis University Invertis Institute of Computer Applications Bareilly India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of English Tamil Nadu Chennai India St.Theresa Intl College Lecturer - Business Administration Thailand Uttaranchal University Uttaranchal Institute of Technology India Graphic Era Deemed to Be University Department of Management Studies Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350399264

Disaster response organizations must have timely access to current and pertinent disaster information in order to formulate wise management decisions and decrease the impact of disasters. Social media has developed recently as a proactive avenue for disaster communications. It has made it easier for groups that deal with disasters to get a tonne of information in real time about them from the people that are affected. Researchers and professionals are now able to fully realise the significance of such a user-generated content for aiding sensible decision-making in catastrophe management thanks to advancements in artificial intelligence. This paper provides an overview of how artificial intelligence techniques are now being used to evaluate social media content connected to disasters in order to support disaster management at various stages. It gives a brief overview of a few increasingly digitally systems that use information from social media to manage disasters. This study also highlights a number of research opportunities and obstacles for handling disaster-related information on social media. © 2023 IEEE.

关键词： Disasters

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