检索结果-内蒙古大学图书馆

7th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2023

作者： Gong, Jiabao Fu, Weiming Kang, Yu Qin, Jiahu Xiao, Feng Department of Automation University of Science and Technology of China Hefei230027 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819733279

This paper proposes a multi-agent deep reinforcement learning (MADRL) based algorithm for charging control of multiple electric vehicles (EVs) in an electric vehicle charging station (EVCS) with dynamic operations. By taking the uncertainties of EV users’ behaviors and charging prices into consideration, a Markov game with multiple objectives is first established, where the individual objective of reducing EV charging costs and the collective objective of mitigating charging load fluctuations are both studied. Then, a multi-agent actor-double-critic (MAADC) algorithm with a unified advantage actor-critic framework is proposed, where two advantage functions are designed as the local and global critics, and the integrated actor is employed to balance the learning towards the individual and collective objectives. The simulation results obtained in a delicately built EVCS environment show the effectiveness of MAADC. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Advancing Hardware Implementation of Hyperdimensional Computing for Edge Intelligence

Advancing Hardware Implementation of Hyperdimensional Comput...

引用

IEEE International Conference on Artificial Intelligence Circuits and systems (AICAS)

作者： Eman Hassan Meriem Bettayeb Baker Mohammad Electrical Engineering and Computer Science Department System on Chip Lab Khalifa University of Science and Technology Abu Dhabi United Arab Emirates

ISBN: (数字)9798350383638

ISBN: (纸本)9798350383645

Hyperdimensional Computing (HDC) is a promising paradigm known for its energy efficiency and simplicity. Human brain functions inspire HDC, which operates in high-dimensional spaces with low-precision data, making it suitable for efficient machine-learning applications. This paper analyzes the State-of-the-Art (SOTA) significance and challenges of implementing HDC on diverse hardware platforms, including Application Specific Integrated Circuits (ASIC), Central Processing Units (CPU), and Field-Programmable Gate Arrays (FPGA), and explores its potential in In-Memory Computing (IMC). Also, the significance of hardware-accelerated Associative Memory (AM) in HDC systems is discussed, emphasizing its role in optimizing overall performance and efficiency.

关键词： Application specific integrated circuits Associative memory Circuits and systems Machine learning In-memory computing Hardware Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

Quantum K-Means Model based on Optimization Framework

Quantum K-Means Model based on Optimization Framework

引用

2023 Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2023

作者： Shao, Zexiang Zhang, Shibin Kumar, Sachin School of Cyberspace Security Chengdu University of Information Technology Chengdu610225 China Sichuan Key Laboratory of Advanced Cryptography and System Security Chengdu610225 China Ajay Kumar Garg Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9781665457033

The Quantum K-means clustering algorithm offers the advantage of quantum parallel computing, but suffers from issues related to cluster center initialization and sensitivity to noisy data due to its similarity with the K-means clustering algorithm. To address these challenges, we propose a quantum K-means model based on an optimization framework. Specifi-cally, we use a quantum genetic algorithm with pin optimization to adjust the fitness function for the Quantum K-means clustering algorithm. The optimally adapted quantum genetic algorithm reduces the sensitivity of cluster center initialization and improves the noise robustness of the Quantum K-means clus-tering algorithm using a noise reduction self-encoder. As a result, the proposed model achieves better clustering performance even in the presence of noise. Experimental results demonstrate that our proposed quantum K-means model has fewer clustering iterations, lower probability of falling into local optima, and improved noise robustness after optimizing the clustering cen-ters. The model's effectiveness in clustering is also demon-strated experimentally. © 2023 IEEE.

关键词： Noise abatement

来源：评论

学校读者我要写书评

暂无评论

Explaining Binary Obfuscation

Explaining Binary Obfuscation

引用

Cyber Security and Resilience (CSR), IEEE International Conference on

作者： Claudia Greco Michele Ianni Antonella Guzzo Giancarlo Fortino Department of Computer Science Modeling Electronics and System Engineering (DIMES) University of Calabria Italy

Binary obfuscation is a very broad set of techniques widely employed in the context of code protection from piracy. However, it is also used for malicious goals, e.g. virus writers often employ obfuscation in order to evade signature-based antivirus detection. Thus, the ability to detect if an executable has been obfuscated is of paramount importance, as it allows to thwart the execution of potentially malicious code. The task of detection, however, is not easy, since many different obfuscating transformations exist and the alteration of an original code is not always easily detectable. In this paper, we want to shed light on the blurry task of obfuscation detection. We will look at this task through the brand new lenses of explainable artificial intelligence (XAI), in order to finally sharpen the obscure landscape of obfuscated software. Thanks to XAI we will be able to identify the relevant features altered by the transformating obfuscation as well as the invariant ones, that can be used for obfuscation-resistant malware signatures. We show our findings thanks to an evaluation with a dataset of obfuscated and non-obfuscated binaries, explaining the important features that lead to the detection of obfuscating transformations.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Anaesthesia Monitoring: From Conventional to AI-Enabled Data Acquisition Methods 20

Anaesthesia Monitoring: From Conventional to AI-Enabled Data...

引用

20th International Conference on Smart Technologies, EUROCON 2023

作者： Liu, Xiaoxiao McGrath, Sean Flanagan, Colin Lei, Yiming Zeng, Liaoyuan University of Limerick Electronic and Computer Engineering Limerick Ireland School of Electronics Peking University Ministry of Education Engineering Research Center on Mobile Digital Hospital System Beijing China University of Electronic Science and Technology of China Department of Information and Communication Engineering Chengdu China

ISBN: (纸本)9781665463973

It is widely recognized that the timely acquisition and presentation of accurate physiological data is a vital aid to the anaesthesiologist in monitoring and maintaining the well-being of a patient undergoing surgery. Collecting, storing, synchronizing, integrating, evaluating, and retrieving pertinent information from patient data remains very difficult due to limited computer power, the need for specialized software, inconsistency between monitoring devices, and inadequate data storage. This paper introduces the general steps of data acquisition in anaesthesia and summarizes some data acquisition systems. It also introduces some applications of the burgeoning field of artificial intelligence (AI) to data acquisition, illustrating how AI techniques may be used to advantage to assist the anaesthesiologist in monitoring and interpreting such data. © 2023 IEEE.

关键词： Data acquisition

来源：评论

学校读者我要写书评

暂无评论

Optimal Kernel Extreme Learning Machine for COVID-19 Classification on Epidemiology Dataset

引用

computers, Materials & Continua 2022年第11期73卷 3305-3318页

作者： Saud S.Alotaibi Amal Al-Rasheed Sami Althahabi Manar Ahmed Hamza Abdullah Mohamed Abu Sarwar Zamani Abdelwahed Motwakel Mohamed I.Eldesouki Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Artificial Intelligence(AI)encompasses various domains such as Machine Learning(ML),Deep Learning(DL),and other cognitive technologies which have been widely applied in healthcare *** models are utilized in healthcare sector in which the machines are used to investigate and make decisions based on prediction and classification of input *** this motivation,the current study involves the design of Metaheuristic Optimization with Kernel Extreme Learning Machine for COVID-19 Prediction Model on Epidemiology Dataset,named MOKELM-CPED *** primary aim of the presented MOKELM-CPED model is to accomplish effectual COVID-19 classification outcomes using epidemiology *** the proposed MOKELM-CPED model,the data first undergoes pre-processing to transform the medical data into useful *** by,data classification process is performed by following Kernel Extreme Learning Machine(KELM)***,Symbiotic Organism Search(SOS)optimization algorithm is utilized to fine tune the KELM parameters which consequently helps in achieving high detection *** order to investigate the improved classifier outcomes of MOKELM-CPED model in an effectual manner,a comprehensive experimental analysis was conducted and the results were inspected under diverse *** outcome of the experiments infer the enhanced performance of the proposed method over recent approaches under distinct measures.

关键词： COVID-19 epidemiology dataset machine learning artificial intelligence metaheuristics healthcare

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Powered Design and Implementation for Classification of Missing Data in IoT Applications 1

引用

3rd International Conference on Mechanical and Energy Technologies, ICMET 2023

作者： Deepan, S. Suchitra, B. Arya, Namita Ghori, Md. Atheeq Sultan Bora, Ashim Mahender, Nagurla School of CS & AI SR University Telangana Warangal India IT & Cognitive System Tamilnadu Coimbatore India Galgotias University UP Greater Noida India Department of Computer Science and Engineering Telangana University Nizamabad Telangana State Nizamabad India Kampur College Assam Kampur India School of Computer Science and Artificial Intelligence SR University Telangana Warangal India

ISBN: (数字)9789819727162

ISBN: (纸本)9789819727155

Missing values are a common issue in machine learning, which has formed the foundation for data analysis and extraction. Several reasons can lead to missing values, such as missing entirely, missing at random, and not missing. However, before conducting any data analysis, it is imperative to tackle missing values since this might result in biased or inaccurate results. One common technique to increase the precision of IoT data classification is to combine many sensors with different data techniques. But all modalities are seldom available even for the evaluation results, and this lack of data creates serious obstacles to multimodal education. Inspired by recent developments in deep learning, we present a cross-neural network for the segmentation of the IoT Data Classification, which is created on data modalities not all obtainable during trials. We educate the framework in IoT Data Classification using a cost function specifically designed for imbalanced classes. We are giving the device an insufficiently full baseline dataset. Our technique extends beyond CNN training and the gathering of two CNNs trained in the missing modality by using temporal data, considering that they are not involved in the research process. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cost functions

来源：评论

学校读者我要写书评

暂无评论

Learning Pareto Set for Multi-Objective Continuous Robot Control

arXiv

引用

arXiv 2024年

作者： Shu, Tianye Shang, Ke Gong, Cheng Nan, Yang Ishibuchi, Hisao Department of Computer Science and Engineering Southern University of Science and Technology China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science City University of Hong Kong Hong Kong

For a control problem with multiple conflicting objectives, there exists a set of Pareto-optimal policies called the Pareto set instead of a single optimal policy. When a multi-objective control problem is continuous and complex, traditional multiobjective reinforcement learning (MORL) algorithms search for many Pareto-optimal deep policies to approximate the Pareto set, which is quite resource-consuming. In this paper, we propose a simple and resource-efficient MORL algorithm that learns a continuous representation of the Pareto set in a high-dimensional policy parameter space using a single hypernet. The learned hypernet can directly generate various well-trained policy networks for different user preferences. We compare our method with two state-of-the-art MORL algorithms on seven multi-objective continuous robot control problems. Experimental results show that our method achieves the best overall performance with the least training parameters. An interesting observation is that the Pareto set is well approximated by a curved line or surface in a highdimensional parameter space. This observation will provide insight for researchers to design new MORL algorithms. © 2024, CC BY.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Impact of Image Enhancement for Osteoporosis Detection Based on Deep Learning Algorithm 2

Impact of Image Enhancement for Osteoporosis Detection Based...

引用

2nd International Conference on computer system, Information Technology, and Electrical engineering, COSITE 2023

作者： Iman, Riza Dianil Roslidar, Roslidar Muchtar, Kahlil Amalia, Amalia Syaryadhi, Mohammad Mufti, Alfatirta Maulina, Novi Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia Graduate School of System Designs Tokyo Metropolitan University Tokyo Japan Faculty of Medicine Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (纸本)9798350343069

Osteoporosis can be defined as a degenerative disease with reduced bone mass and changes in bone architecture that can lead to bone fragility and the risk of fractures. This abnormality can be indicated by the bone density which in visual can be determined using X-Ray images. However, X-Ray images are susceptible to noise, while in image analysis image contrast affects deep learning abilities. Hence, the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm is used as a contrast enhancement technique in X-Ray images. This study aims to build a deep learning model using the CLAHE-enhanced image dataset with the ResNet-50 and ResNet-101 architectures. The model was built using two different datasets, namely the original image dataset and the CLAHE-enhanced image dataset. The result shows that the highest performance is given by the ResNet-101 model using the CLAHE-enhanced image dataset with an accuracy rate of 96%, precision of 95%, specificity of 95%, recall of 97% and an F1-score of 96%, respectively. By using the CLAHE algorithm, the resulting image has high contrast and looks better at displaying features in the image so as to produce better model performance. © 2023 IEEE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Perioperative Anesthesia Data: Visualization, Effects, Analysis with Artificial Intelligence 4

Perioperative Anesthesia Data: Visualization, Effects, Analy...

引用

4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Liu, Xiaoxiao McGrath, Sean Flanagan, Colin Lei, Yiming Zeng, Liaoyuan Electronic and Computer Engineering University of Limerick Limerick Ireland School of Mathematics and Information HeBei University HeBei China Mobile Digital Hospital System School of Electronics Peking University Ministry of Education Engineering Research Center Beijing China University of Electronic Science and Technology of China Department of Information and Communication Engineering Chengdu China

ISBN: (纸本)9798350360721

A comprehensive understanding of physiological data is essential for anesthesiologists to monitor and maintain the health of surgical patients. Understanding anesthetic signals is essential to improve over the past ten decades to comprehend a variety of diseases, ensure the safety of patients, and accelerate their recovery. We identified and summarized four themes of artificial intelligence (AI) research in anesthesia: applications of AI in anesthesiology, signals monitored in different anesthesia stages, commonly monitored signs, and data standardization. The effect of various AI technologies on data analysis was studied, and the types, functions, sensors, characteristics, and intelligent analysis of physiological signals monitored during anesthesia are presented in this article. AI influences anesthesia practices in many ways, including preoperative data evaluation, anesthetic depth monitoring, and postoperative event prediction. © 2024 IEEE.

关键词： Machine learning

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：