检索结果-内蒙古大学图书馆

Intelligent Computational Model for Energy Efficiency and AI Automation of Network Devices in 5G Communication Environment

引用

Tsinghua Science and technology 2024年第6期29卷 1728-1751页

作者： Ashish Bagwari Jaganathan Logeshwaran M.Raja P.Devisivasankari Jyotshana Bagwari Vikas Rathi Asma Mohammed Elbashir Saad Department of Electronics and Communication Engineering Uttarakhand Technical UniversityDehradun 248007India Department of Electronics and Communication Engineering Sri Eshwar College of EngineeringCoimbatore 641202India Department of Computer Science and Engineering CMR Institute of TechnologyKarnataka 560037India Department of Information Science and Engineering CMR Institute of TechnologyKarnataka 560037India Advanced and Innovative Research Laboratory Dehradun 248001India Department of Electronics and Communication Engineering Graphic Era(Deemed to be University)Dehradun 248007India Department of Physics College of Science and HumanitiesPrince Sattam Bin Abdulaziz UniversityAl-Kharj 16273Saudi Arabia

Currently, the 4G network service has caused massive digital growth in high use. Video calling has become the go-to Internet application for many people, downloading even huge files in minutes. Everyone is looking for and buying only 4G Subscriber Identity Module (SIM)-capable mobiles. In this case, the expectation of 5G has increased in line with 2G, 3G, and 4G, where the G stands for generation, and it does not indicate Internet or Internet speed. 5G includes next-generation features that are more advanced than those available in 4G network services. The main objective of 5G is uninterrupted telecommunication service in hybrid energy storage system. This paper proposes an intelligent networking model to obtain the maximum energy efficiency and Artificial Intelligence (AI) automation of 5G networks. There is currently an issue where the signal cuts out when crossing an area with one tower and traveling to an area with another tower. The problem of “call drop”, where the call is disconnected while talking, is not present in 5G. The proposed Intelligent Computational Model (ICM) model obtained 96.31% network speed management, 90.63% battery capacity management, 92.27% network device management, 93.57% energy efficiency, and 88.41% AI automation.

关键词： hybrid energy storage system 4G digital growth 5G downloading Subscriber Identity Module(SIM) generation network energy efficiency Artificial Intelligence(Al)automation

来源：评论

学校读者我要写书评

暂无评论

Review of Novel Hybrid Electric Bicycle with Minimal Cost for Real Time Application 4

Review of Novel Hybrid Electric Bicycle with Minimal Cost fo...

引用

4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Felshiya Rajakumari, R. Siva Ramkumar, M. Chandrasekaran, Gokul Kumar, Neelam Sanjeev Kavitha, D. Bangalore College of Engineering and Technology Department of Electrical and Electronics Engineering Bangalore India SNS College of Technology Department of Electrical and Electronics Engineering Coimbatore India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India SRM Institute of Science & Technology Department of Computer Science and Engineering Vadapalani Chennai India Coimbatore India

ISBN: (纸本)9798331515720

This research work presents the design and development of a hybrid electric bicycle with a primary focus on energy conservation. A hybrid electric vehicle combines two or more energy conversion technologies with one or more storage units, aiming to optimize performance and efficiency. The ultimate goal of this research work is to create a personal transportation vehicle that promotes active mobility while addressing the limitations of traditional bicycles, such as limited range and power, without compromising the inherent benefits. This research proposes a hybrid-electric bicycle that enhances practicality by reducing the physical effort required while allowing for higher speeds. The proposed system incorporates a small permanent magnet DC motor mounted on the rear wheel, powered by lead-acid batteries with a voltage exceeding 24 volts. The hybrid electric bicycle can be recharged through three methods: manual pedaling, connection to a 220-volt AC socket, or solar energy. By combining solar energy with the energy generated through pedaling, the bicycle significantly extends the range achievable with electric power, storing the converted energy in the battery for sustained use. © 2024 IEEE.

关键词： DC motors

来源：评论

学校读者我要写书评

暂无评论

Development and Investigation of Vision System for a Small-Sized Mobile Humanoid Robot in a Smart Environment

引用

International Journal of Crowd Science 2025年第1期9卷 29-43页

作者： Abu-Jassar, Amer Tahseen Attar, Hani Amer, Ayman Lyashenko, Vyacheslav Yevsieiev, Vladyslav Solyman, Ahmed Department of Computer Science College of Information Technology Amman Arab University Amman11937 Jordan Faculty of Engineering Zarqa University Zarqa2000 Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Department of Media Systems and Technology Kharkiv National University of Radio Electronics Kharkiv61166 Ukraine Department of Electrical and Electronics Engineering Faculty of Engineering and Architecture Nişantaşı University Istanbul34398 Turkey

The conducted research aims to develop a computer vision system for a small-sized mobile humanoid robot. The decentralization of the servomotor control and the computer vision systems is investigated based on the hardware solution point of view, moreover, the required software level to achieve an efficient matched design is obtained. A computer vision system using the upgraded tiny-You Only Look Once (YOLO) network model is developed to allow recognizing and identifying objects and making decisions on interacting with them, which is recommended for crowd environment. During the research, a concept of a computer vision system was developed, which describes the interaction between the main elements, on the basis of which hardware modules were selected to implement the task. A structure of information interaction between hardware modules is proposed, and a connection scheme is developed, on the basis of which a model of a computer vision system is assembled for research, with the required algorithmic and software for solving the problem. To ensure the high speed of the computer vision system based on the ESP32-CAM module, the neural network was improved by replacing the Visual Geometry Group 16 (VGG-16) network as the base network for extracting the functions of the Single Shot Detector (SSD) network model with the tiny-YOLO lightweight network model, which made it possible to preserve the multidimensional structure of the network model feature graph, resulting in increasing the detection accuracy, while significantly reducing the amount of calculations generated by the network operation, thereby significantly increasing the detection speed, due to a limited set of objects. Finally, a number of experiments were carried out, both in static and dynamic environments, which showed a high accuracy of identifications. © The author(s) 2025.

关键词： Anthropomorphic robots

来源：评论

学校读者我要写书评

暂无评论

Optimization of Traffic System using Adaptive Response First Come First Serve

Optimization of Traffic System using Adaptive Response First...

引用

2024 International Conference on Signal Processing, Computation, electronics, Power and Telecommunication, IConSCEPT 2024

作者： Amutha, S. Singh, Dhruv Singh, Shashank Shekhar Jhandial, Anant Gupta Krishna, Kunal School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Electronics and Communication Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331540685

In this period of urbanization and adding vehicular traffic, the optimization of business operation systems is consummate to insure both the effectiveness of transportation networks and the safety of commuters. This exploration paper explores the operation of deep literacy ways to revise the field of business operation. By employing the power of deep learning, sensors and timers, our study aims to develop innovative results that ameliorate the delicacy, rigidity, and real- time responsiveness of business control systems. The primary focus of this exploration is to design a comprehensive deep literacy-grounded business operation system that incorporates colorful data sources. By assaying vast quantities of miscellaneous data, the system will acclimatize to dynamic business conditions and offer real- time perceptivity for effective business inflow operation, accident discovery, and traffic relief. The exploration will also explore the integration of machine literacy algorithms for intelligent business signal control, prioritizing green light allocation grounded on real- time demand and business viscosity. The proposed deep literacy result aims to significantly reduce business traffic, minimize trip times, and enhance overall road safety by furnishing optimized business operation strategies. The exploration will concentrate on the practical perpetration of the system in real- world business scripts, with the eventuality to gauge and acclimatize to colorful civic surroundings. This exploration bid seeks to lay the foundation for a future in which business systems seamlessly acclimatize to evolving business patterns, creating safer, more effective transportation networks, and contributing to a sustainable and intelligent civic ecosystem. The proposed dynamic signal control logic is 83.33 % more efficient in utilizing green time compared to the traditional fixed-time system. Estimated reduction in wasted green time is 25%. © 2024 IEEE.

关键词： Traffic signals

来源：评论

学校读者我要写书评

暂无评论

Implementation of YOLOv9 in Agricultural AI for Enhanced Weed Detection

Implementation of YOLOv9 in Agricultural AI for Enhanced Wee...

引用

2024 International Conference on Intelligent Computing and Sustainable Innovations in technology, IC-SIT 2024

作者： Duraisamy, Premkumar Deepika, A. Niranjani, V. Jeevitha, R. Sibishree, M. Oviya, A. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350369175

Weeds significantly hinder agricultural productivity by reducing crop yields and increasing production costs. Leveraging artificial intelligence (AI) is critical for equipping farmers with early detection capabilities to implement effective weed management strategies. Among AI technologies, deep learning (DL) techniques are especially effective for analyzing agricultural field images to identify weed species. This paper reviews state-of-the-art DL methodologies, with a focus on the YOLOv9 model for weed detection. Our findings highlight YOLOv9's superior effectiveness, achieving an accuracy of $\mathbf{9 0 \%}$, due to its robust architecture and advanced feature extraction. The model's proficiency in accurately identifying weeds is evident from our thematic analysis. We also explore various image acquisition devices, including robots, drones, and mobile phones. Comparative evaluations show that YOLOv9 consistently outperforms other DL techniques, achieving remarkable accuracy and speed. This study includes a comparative analysis of several algorithms like SSD, Mask R-CNN, and Fast R-CNN, underscoring YOLOv9's superior performance. This paper serves as a valuable resource for researchers and practitioners, guiding future efforts in weed management and precision agriculture by emphasizing YOLOv9's transformative potential. © 2024 IEEE.

关键词： Agricultural productivity Deep learning Image acquisition object detection Weed detection YOLOv9 algorithm

来源：评论

学校读者我要写书评

暂无评论

Brain Tumor Detection: A Comprehensive Review 5

Brain Tumor Detection: A Comprehensive Review

引用

5th International Conference on Electrical, Communication and computer engineering, ICECCE 2024

作者： Bhagat, Poonam Pandian, R. Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331529437

Brain tumor classification and segmentation are crucial medical imaging procedures that aid in diagnosis and treatment planning. This study provides a detailed analysis of the methodologies and techniques used in this subject, which includes conventional machine learning (ML), convolutional neural networks (CNNs), capsule networks (CapsNets), and vision transformers (ViTs). This survey identifies key challenges and opportunities in brain tumor analysis and delineates future research directions. Extant literature predominantly focuses on individual tasks such as tumor detection, segmentation, or grade estimation, indicating the necessity for integrated frameworks capable of simultaneous multi-tasking, particularly with multi-grade classification. While CNNs have been extensively utilized, alternative architectures such as CapsNets and ViTs offer promising avenues for enhancing diagnostic accuracy and efficiency. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Gene Array Based Cervical cancer categorization with Machine Learning Techniques

Gene Array Based Cervical cancer categorization with Machine...

引用

2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Nisha, K. Vijayalakshmi, A. Surya, N. Chennai Institute of Technology Department of Computer Science and Engineering Chennai India Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331541170

Cervical squamous cell carcinoma is anticipated to become the fourth most prevalent cancer globally and is expected to cause the death of one in three affected women. The World Health Organization (WHO) reports that 86% of all documented cases of cervical cancer occur in developing countries.. Gene expression profiling is still used extensively in the investigation of cervical cancer. Gene expression patterns have been investigated in biological networks, in particular a gene co-expression network, which emerges as an unconventional comprehensive method for microarray research. A component of the physiognomy in gene co-expression networks continues to be disproportionate even at the point in time when a particular biological issue has been ***, the Enhanced Bacterial colony Optimization Algorithm with Hilbert-Schmidt Independence Criterion (EBCO-HSIC) and the Support Vector Machine (SVM) method are bestowed in this investigation by selecting symbolic genes that encompass phases including normalisation, module detection, gene selection, and classification. The gene dataset is first percolated using the fuzzy C Means (FCM) approach, which is then used for both the purpose of normalisation and the removal of extraneous features. The EBO method is used in order to choose the suitable genes using the degree of accuracy in categorization as the fitness function. The approach that was devised focuses on gene importance as well as calibrating the dependence between differentially expressed genes (DEGs) in order to arbitrate a selection of the most informative genes by using an ultrafast beyond ascendable bat algorithm. In order to remedy this situation, HSIC, which is supported in part by EBO, is put into action. The SVM classifier is an effective tool for categorising high-dimensional data, such as that obtained from microarray gene expression. © 2024 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Leveraging machine learning for intelligent agriculture

引用

Discover Internet of Things 2025年第1期5卷 1-21页

作者： Sowmya, B.J. Meeradevi, A.K. Supreeth, S. Pradeep Kumar, D. Ravi Kumar, B.N. Rohith, S. Mishra, Divyansh Koushik, Abhishek Patil, Ankit U. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Computer Science Ramaiah Institute of Technology Bengaluru560054 India Department of Information Science and Engineering BMS Institute of Technology & Management Bengaluru560119 India Department of Electronics and Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India

Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilingual platform leveraging Generative AI to address farmers' diverse needs. The platform encompasses various features to enhance agricultural practices. An LLM-powered Government Scheme Advisor functions as a multilingual chatbot offering intelligent guidance on government agricultural schemes and subsidies. The Disease Detection module utilizes AI technology for real-time identification and treatment recommendations, minimizing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential information based on geographical data to assist farmers in optimizing soil quality. A Crop Recommendation feature employs Machine Learning algorithms to offer personalized crop recommendations, considering various factors and aiding informed decision-making. The Crop Planning Tool, with its intuitive user interface, simplifies planning planting schedules and managing resources. Additionally, the platform includes an MSP Center Locator to find nearby Minimum Support Price (MSP) centers based on location. By integrating these innovative solutions, this platform bridges the gap between conventional agricultural techniques and contemporary technology, equipping farmers with the resources and expertise essential for advancing productivity and sustainability. Multilingual support ensures accessibility for a wider audience, breaking down language barriers and promoting inclusivity in the agricultural sector. This work proposes an innovative, multilingual platform powered by Generative AI to address these issues. Key features include an LLM-driven chatbot for government scheme guidance, AI-based real-time disease detection, and location-based tools for soil testing and MSP center identification. Additionally, the platf

关键词： Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

Virtual Synchronous Generator-Based Positive Negative Sequence Control Scheme for Voltage Unbalance Mitigation in Low Inertia Islanded Microgrid 1

Virtual Synchronous Generator-Based Positive Negative Sequen...

引用

1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Sinha, Suruchi Ali, Nada Kumar, Deepak Anwar, Shamama Birla Institue of Technology Mesra Electrical and Electronics Engineering Ranchi India Birla Institute of Technology Mesra Computer Science and Engineering Ranchi India

ISBN: (纸本)9798350384246

The depletion of traditional energy sources, particularly fossil fuels, and increasing energy demands have prompted a combination of renewable energy sources (RES), Distributed Generation (DG) units, and the extensive uptake of micro grids are shaping the energy landscape. A micro grid operates either independently or in conjunction with the main power grid. In islanded mode, micro sources manage to supply power to loads while maintaining voltage and frequency within acceptable limits. However, islanded microgrids often face challenges related to poor power quality, with voltage unbalance being an important concern. Also, renewable energy systems lack inertia as they do not have any rotating parts. Therefore, this paper addresses the issue by proposing a positive-sequence (PS) in parallel with the negative sequence (NS) controller for a Voltage-Source Converter (VSC)-based DG unit for voltage unbalance reduction in microgrids. Additionally, to intensify the stability of the system, this paper also introduces a Virtual Synchronous Generator (VSG) control framework, utilizing inverter-interfaced distributed energy resources to impart virtual inertia to the system. The proposed controller's design concept is validated via simulation in Simulink/MATLAB, with the results confirming the efficacy of the controller as demonstrated. © 2024 IEEE.

关键词： Synchronous generators

来源：评论

学校读者我要写书评

暂无评论

Detection of Plant Diseases and Recommendation of Plant Cycle using Machine Learning 4

Detection of Plant Diseases and Recommendation of Plant Cycl...

引用

4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Divya, B. Alivn Ancy, A. Vivekanandan, G. Swapna, A. Kaniha, P. Indhu Priyadharshini, S. Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India Chennai Institute of Technology Dept of Electronics and Communication Engineering India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Plant diseases create significant challenges to productivity in agriculture, food security, and ecological stability. Pathogens and environmental variables interact to cause plant diseases. Pathogens include various microorganisms like bacteria, fungi, and viruses, each with unique modes of spread. Environmental conditions such as temperature, humidity, and soil quality impact disease development and severity. The impacts of plant diseases are far-reaching. It affects crop yields and quality. Outbreaks of plant diseases can lead to economic losses, and food shortages which ultimately lead to hunger and poverty. Effective management of plant diseases requires an integrated approach that combines detection, preventive measures, cultural practices, and chemical interventions. The solution has two modules. The first module focuses on detection of plant diseases and its management. The second module focuses on recommendations of the plant cycle. Our proposed system utilizes CNN algorithms trained on a large amount of datasets. Using image analysis the features are extracted and patterns are recognized. Therefore, the system can quickly identify the disorders.. The system also includes suggestions for preventive measures. The system integrates modules for recommending optimal plant cycles to the farmers and gardeners. It generates personalized recommendations for planting schedules and crop varieties. This system increases crop yields and decreases the risk of disease outbreaks. Our solution offers a holistic approach to plant health monitoring and management. © 2024 IEEE.

关键词： Productivity Plant diseases Pathogens Schedules Soil Feature extraction Stability analysis Pattern recognition Crop yield Thermal stability

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：