检索结果-内蒙古大学图书馆

1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Suresh Kumar, S. Chidambaram, G. Vijayalakshmi, S. Computer Science and Engineering PSG College of Technology Coimbatore India

ISBN: (纸本)9781665460842

Advances in digital communication play an important role in our daily lives in today's world. In general, information security systems fall into two categories: encryption and information hiding. The storage and transmission of sensitive personal data have become a partof everyday life for professional and personal well-being. Therefore, the issue of secure storage and transmission of confidential information is interest to many researchers. Steganography is a collective method to hide sensitive data in unobtrusive digital media such as video, audio, and images. One of the biggest challenges in developing steganography systems is finding the right balance between metrices like reliability, security, and data privacy. This project provides information about how the Bit Plane Complexity Steganography (BPCS) technology is applied toboth grayscale and color imaging techniques and assesses performance parameters such as data concealment, security and stability based on the peak signal-to-noise ratio (PSNR) and Mean Square Error (MSE) with various test cases to explain how it works for different files and images. © 2022 IEEE

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

Self-adaptive rolling motion for snake robots in unstructured environments based on torque control

引用

Biomimetic Intelligence & Robotics 2023年第3期3卷 59-66页

作者： Shihao Ma Fatao Qin Shufan Chen Longchuan Li Jianming Wang Zengzeng Wang Shuai Li Xuan Xiao School of Electronic and Information Engineering Tiangong UniversityTianjin 300382China College of Mechanical and Electrical Engineering Beijing University of Chemical TechnologyBeijing 100029China College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems Tianjin 300382China School of Computer Science and Technology Tiangong UniversityTianjin 300382China

Snake robots have great potential for exploring and operating in challenging unstructured environments,such as rubble,caves,and narrow ***,due to the complexity and unpredictability of unstructured environments,designing a controller that can achieve adaptive motion is *** paper proposes a self-adaptive torque-based rolling controller for snake robots,enabling compliant motion in unstructured ***,a controller is designed to modify the torque of each motor by focusing on the different motion states of the rolling ***,an experimental platform is established for snake robots to verify the effectiveness of the ***,a series of rolling experiments are conducted using the torque-based rolling *** conclusion,the self-adaptive torque-based rolling controller enhances snake robot adaptability and mobility.

关键词： Snake robot Torque-based control Self-adaptive rolling gait Unstructured environments

来源：评论

学校读者我要写书评

暂无评论

Investigation on Deep Learning and Machine Learning Approaches for Antenna Design Based on Radar Signal Processing 4

Investigation on Deep Learning and Machine Learning Approach...

引用

4th International Conference on Innovative Practices in technology and Management, ICIPTM 2024

作者： Govindasamy, Santhakumar Bhushan, Kavi Nijhawan, Ginni Kumar, B Santhosh Chandrashekar, Rakesh Issa, Ali Ashoor Sri Krishna College of Technology and Tamilnadu Electronics and Communication Engineering India Sir Chhotu Ram Institute of Engg. & Technology Department of Computer Science India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350307757

This paper gives a complete review of the use of Deep Learning (DL) and Machine Learning (ML) approaches in the area of antenna design for radar data processing. It analyzes the potential of DL and ML to overcome the constraints of conventional antenna design methodologies, especially in the face of complicated environmental variables and the necessity for interference reduction. By employing sophisticated computational techniques, the research reveals how these AI-based approaches may considerably boost the design and performance optimization of radar antennas. The integration of neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning into the design process offers promise for constructing adaptable and efficient radar systems. Empirical findings from the research illustrate the durability of ML models, notably Support Vector Machines (SVMs), in forecasting antenna performance, stressing its resilience even in high-noise environments. This investigation's results are crucial for the progress of intelligent radar systems and lay the way for future improvements in the industry. © 2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Autoencoder-based conditional optimal transport generative adversarial network for medical image generation

引用

Visual Informatics 2024年第1期8卷 15-25页

作者： Jun Wang Bohan Lei Liya Ding Xiaoyin Xu Xianfeng Gu Min Zhang School of Software Technology Zhejiang UniversityNingboZhejiangChina College of Computer Science&Technology Zhejiang UniversityHangzhouZhejiangChina Department of Pathology Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhouZhejiangChina College of Biomedical Engineering&Instrument Science Zhejiang UniversityHangzhouZhejiangChina Department of Computer Science State University of New York at Stony BrookStony BrookNYUSA

Medical image generation has recently garnered significant interest among ***,the primary generative models,such as Generative Adversarial Networks(GANs),often encounter challenges during training,including mode *** address these issues,we proposed the AECOT-GAN model(Autoencoder-based Conditional Optimal Transport Generative Adversarial Network)for the generation of medical images belonging to specific *** training process of our model comprises three fundamental *** training process of our model encompasses three fundamental ***,we employ an autoencoder model to obtain a low-dimensional manifold representation of real ***,we apply extended semi-discrete optimal transport to map Gaussian noise distribution to the latent space distribution and obtain corresponding labels *** procedure leads to the generation of new latent codes with known ***,we integrate a GAN to train the decoder further to generate medical *** evaluate the performance of the AE-COT-GAN model,we conducted experiments on two medical image datasets,namely DermaMNIST and *** model’s performance was compared with state-of-the-art generative *** show that the AE-COT-GAN model had excellent performance in generating medical ***,it effectively addressed the common issues associated with traditional GANs.

关键词： Medical image generation Mode collapse Mode mixing Optimal transport Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Data Driven Vibration Control:A Review

引用

IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1898-1917页

作者： Weiyi Yang Shuai Li Xin Luo Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714 Chongqing School University of Chinese Academy of SciencesChongqing 400714China Faculty of Information Technology and Electrical Engineering University of OuluOulu 90570 VTT-Technology Research Center of Finland Oulu 90570Finlan College of Computer and Information Science Southwest UniversityChongqing 400715China

With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing,aviation,and healthcare,the data driven vibration control(DDVC)has attracted broad interests from both the industrial and academic *** shaping(IS),as a simple and effective feedforward method,is greatly demanded in DDVC *** convolves the desired input command with impulse sequence without requiring parametric dynamics and the closed-loop system structure,thereby suppressing the residual vibration *** on a thorough investigation into the state-of-the-art DDVC methods,this survey has made the following efforts:1)Introducing the IS theory and typical input shapers;2)Categorizing recent progress of DDVC methods;3)Summarizing commonly adopted metrics for DDVC;and 4)Discussing the engineering applications and future trends of *** doing so,this study provides a systematic and comprehensive overview of existing DDVC methods from designing to optimizing perspectives,aiming at promoting future research regarding this emerging and vital issue.

关键词： Data driven vibration control(DDVC) data science designing method feedforward control industrial robot input shaping optimizing method residual vibration

来源：评论

学校读者我要写书评

暂无评论

An Effective Energy-Based Data Collection Method to Extend WSN Lifetime 3

An Effective Energy-Based Data Collection Method to Extend W...

引用

3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Mohanraj, A. Kumar, M.Ramesh Radha, V. Sreeraj, S. Computer Science & Engg. Sri Eshwar College of Engineering Coimbatore India Information Technology VSB College of Engineering Technical Campus Coimbatore India

ISBN: (纸本)9781665494007

Wireless sensor networks are crucial for gathering information and managing variables. It is composed of a number of sensor nodes that are randomly positioned across the neighborhood. The longevity of the community can be increased by collecting statistics. The power-efficient information gathering Scheme (Edge Computing-Enabled Wireless Sensor Networks is introduced inside the advised study effort in order to stabilize statistics accumulating and strengthen supervision among sensor nodes. The sensor nodes are first separated and hooked on two assemblies, vital and non-important nodes in particular. It is based on the speed at which guesstimate facts are generated and the amount of remaining energy in each sensor node. Relay nodes are positioned close to the anchor node in order to properly gather the statistics. By using non-essential nodes in the cluster region, data collection techniques offer a way to increase the statistics series pace. In order to distribute power across sensor nodes in a dynamic environment, the electricity version makes an estimate of the strength use at the transmitter and receiver. The simulation results are performed using a network simulator tool, and they are expressed in footings of packet shipping ratio, quit-to-quit delay, and statistics group cost, energy efficiency, and network dependability. © 2023 IEEE.

关键词： Sensor nodes

来源：评论

学校读者我要写书评

暂无评论

5G-Enabled V2X Communication with Cloud-Powered XGBoost Algorithm for Electric Transportation 10

5G-Enabled V2X Communication with Cloud-Powered XGBoost Algo...

引用

10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Gnana Jeslin, J. Sujatha, M. Aghalya, S. Jehan, C. Ishwarya, M.V. Vedasundara Vinayagam, P. Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Easwari Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India St. Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Vel Tech Multitech Dr.Rangarajan Dr.Sagunthala Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Thazhambur India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

The opportunity for electric transportation system optimization has never been greater with the combination of cloud-based machine learning algorithms and 5G-enabled Vehicle-to-Everything (V2X) connectivity. To improve the effectiveness, reliability, and longevity of electric vehicle (EV) networks, it provides a new architecture combining V2X communication enabled by 5G and an XGBoost algorithm driven by the cloud. Vehicles may exchange data in real time and make decisions with the help of infrastructure and the cloud via the seamless communication made possible by 5G networks' high-speed, low-latency connection. The XGBoost algorithm, hosted on cloud servers, can use this data to forecast several metrics, including traffic flow, energy usage, and the best way to charge. Cloud computing allows more sophisticated analysis and prediction models by moving the processing load from individual cars to large, powerful servers. It improves transportation efficiency, decreases congestion, minimizes energy usage, and optimizes charging infrastructure via simulation and real-world experiments. © 2024 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Light bottle transformer based large scale point cloud classification

引用

Optoelectronics Letters 2023年第6期19卷 377-384页

作者： XIE En ZHANG Zhiyong ZHANG Guodao CHEN Pingkuo GE Yisu College of Computer Information and Engineering Nanchang Institute of TechnologyNanchang 330044China School of Information Engineering East China University of TechnologyNanchang 330000China Department of Digital Media Technology Hangzhou Dianzi UniversityHangzhou 310023China Great Bay University Dongguan 523000China College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325100China

With the rapid development of computer vision,point clouds technique was widely used in practical applications,such as obstacle detection,roadside detection,smart city construction,***,how to efficiently identify the large scale point clouds is still an open *** relieving the large computation consumption and low accuracy problem in point cloud classification,a large scale point cloud classification framework based on light bottle transformer(light-Bot Net)is ***,the two-dimensional(2D)and three-dimensional(3D)feature values of large scale point cloud were extracted for constructing point cloud feature images,which employed the prior knowledge to normalize the point cloud ***,the feature images are input to the classification network,and the light-Bot Net network is applied for point cloud *** is an interesting attempt to combine the traditional image features with the transformer *** proving the performance of the proposed method,the large scale point cloud benchmark Oakland 3D is *** the experiments,the proposed method achieved 98.1%accuracy on the Oakland 3D *** with the other methods,it can both reduce the memory consumption and improve the classification accuracy in large scale point cloud classification.

关键词： computer cloud point

来源：评论

学校读者我要写书评

暂无评论

Predicting Flight Delays and Error Calculation Using Machine Learning Classifiers 5

Predicting Flight Delays and Error Calculation Using Machine...

引用

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Rajeshkumar, G. Vijaya, K. Sadesh, S. Arunkumar, P.M. Manoj Praveen, V. Kumar, M. Senthil Bannari Amman Institute of Technology Dept of Cse Sathyamangalam India Kongu Engineering College Department of Csd Perunduarai Erode India Velalar College of Engineering and Technology Department of Artificial Intelligence and Data Science Erode India Karpagam College of Engineering Department of Computer Science and Engineering Coimbatore641032 India Erode Sengunthar Engineering Collge Department of Artificial Intelligence and Data Science Perundurai India

ISBN: (纸本)9798350379945

Flight delays are a significant concern in the aviation industry, causing inconvenience for passengers and financial losses for airlines. Predicting flight delays accurately can help mitigate these issues by allowing both airlines and passengers to proactively manage schedules and resources. The delay might depend on weather, crew management, and resource planning. Existing prediction methods often lack accuracy or rely on historical data and statistical models, which may not capture the complex interactions and patterns present in flight operations to predict delays. This study proposes a novel approach for flight delay prediction using advanced machine learning techniques and tools. Specifically, machine-learning classifiers, such as Support Vector Machines (SVM) and Decision Tress, are employed to analyze a comprehensive live streaming dataset comprising various incorporated factors like flight operations, weather conditions, air traffic congestion, and aircraft performance. Proposed model is trained on a large dataset encompassing diverse routes and airlines and has achieved an accuracy of 95% in predicting delays by outperforming existing methods. © 2024 IEEE.

关键词： Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

MentalHealthAI: Utilizing Deep Learning for Accurate Stress Level Prediction 3

MentalHealthAI: Utilizing Deep Learning for Accurate Stress ...

引用

3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Bharathi, S.T. Raj, J. Relin Francis Krishnan, R. Santhana Rajalakshmi, V. Kumar, V. Vinoth Anchitaalagammai, J.V. Department of Information Technology PSNA College of Engineering and Technology Dindigul India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Management Studies SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798331532420

MentalHealthAI is an advanced deep learning system designed to accurately predict stress levels by integrating various data sources, including user surveys, heart rate metrics, sentiment scores from social media interactions, and demographic information. This innovative approach employs a Fully Connected Neural Network (FCNN) architecture, enabling the model to uncover complex relationships among multiple stress-related factors, such as PHQ-9 and GAD-7 questionnaire scores, heart rate variability, sleep patterns, and calculated sentiment scores. By utilizing Natural Language Processing (NLP) techniques, specifically the VADER model, the system analyzes social media text data to derive sentiment scores, providing insights into users' emotional states. This holistic assessment empowers individuals to understand the interplay between their mental health and daily activities, fostering proactive stress management. The advantages of MentalHealthAI include its capacity to deliver timely and personalized stress assessments, allowing users to make informed decisions about their mental well-being. By combining objective data from wearables with subjective sentiment analysis, the system offers a comprehensive perspective on stress dynamics. Ultimately, MentalHealthAI aims to enhance mental health support, providing valuable insights that encourage healthier lifestyles and improved emotional resilience. © 2024 IEEE.

关键词： Electronic health record

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：