检索结果-内蒙古大学图书馆

On the integration of reinforcement learning and simulated annealing for the parallel batch scheduling problem with setups

引用

European Journal of Operational Research 2025年第2期326卷 220-233页

作者： Rolim, Gustavo Alencar Tomazella, Caio Paziani Nagano, Marcelo Seido Department of Production Engineering Sao Carlos School of Engineering University of Sao Paulo Sao Carlos Brazil Department of Applied Mathematics and Statistics Institute of Mathematical and Computer Sciences University of Sao Paulo Sao Carlos Brazil Department of Production Engineering Federal University of Sao Carlos Sao Carlos Brazil

Motivated by semiconductor applications, where wafer lots are grouped into families and processed on batch machines, this paper addresses a generalized unrelated parallel-batch scheduling problem. The goal is to minimize total completion time (flow time) while considering family- and machine-dependent setup times. We propose a mixed-integer programming formulation, establish a necessary condition for optimal schedules, and develop a polynomial-time heuristic for batching and sequencing. We also evaluate Q-Learning, a model-free reinforcement learning algorithm, for neighborhood selection within two Simulated Annealing-based metaheuristics: Stochastic Local Search (SLS) and Adaptive Large Neighborhood Search (ALNS). Results show that SLS and ALNS achieve better solutions and faster convergence compared to existing approaches. Finally, we conclude that while Q-Learning has the potential to improve solution quality in certain cases, it also increases the complexity of the algorithms, making them harder to configure and scale. © 2025 Elsevier B.V.

关键词： Simulated annealing

来源：评论

学校读者我要写书评

暂无评论

Exploiting Secure Multi-Hop Transmissions with NOMA Networks: Performance Analysis 6

Exploiting Secure Multi-Hop Transmissions with NOMA Networks...

引用

6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Pramitarini, Yushintia Yoga Perdana, Ridho Hendra Shim, Kyusung An, Beongku Graduate School Hongik University Dept. of Software and Communications Engineering Korea Republic of School of Computer Engineering and Applied Mathematics Hankyong National University Korea Republic of Hongik University Dept. of Software and Communications Engineering Korea Republic of

ISBN: (纸本)9798350344349

In this paper, we analyze the secrecy performance of secure multi-hop transmission for the NOMA system. More specifically, the multi-hop transmission can extend the coverage of wireless transmission. However, according to the principle of the wireless medium, an eavesdropper can overhear the legitimate users' transmission, which leads to serious security issues. In order to analyze the relationship between network parameters and secrecy outage probability, we derive exact closed-form expressions for the secrecy outage probability (SOP) of cell center and cell edge users, respectively. The numerical results show that the simulation and analysis results are tightly matched. Additionally, we investigate the impact of the number of hops and the distance on the secrecy performance. © 2024 IEEE.

关键词： Transmissions

来源：评论

学校读者我要写书评

暂无评论

Exploring the Impact of Carbon Emissions on Supply Chains of Imperfect Quality Items: A Game Theory Perspective 2nd

Exploring the Impact of Carbon Emissions on Supply Chains of...

引用

2nd Biennial International Symposium on Fluids and Thermal engineering, FLUTE 2023

作者： Mittal, Mandeep Dhanda, Anil Raghav, Tisha Phanden, Rakesh Kumar Department of Mathematics School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Mathematics Amity Institute of Applied Sciences Amity University Uttar Pradesh Noida India Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819731725

This research study proposes a cooperative and non-cooperative game theory strategy. The study’s objectives are to examine how carbon emissions impact the supply chain, especially in means of quality control and cost restriction, and to evaluate the effectiveness of game theory in addressing the issue. To gather and evaluate both quantitative and qualitative data for the study, a mixed-methods technique is used. The importance of reducing carbon emissions in supply chains is emphasized in the study, as are the benefits of using cooperative and non-cooperative game theory strategies to achieve this goal. The paper improves knowledge on supply chain management by offering an approach for simulating carbon emissions in a network of supply chains. The results of this study benefit supply chain management and assist academics, policymakers, and sustainability practitioners. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Game theory

来源：评论

学校读者我要写书评

暂无评论

The Impact of Predictive Maintenance on the Performance of Industrial Enterprises

引用

SN computer Science 2025年第1期6卷 1-34页

作者： Er-Ratby, Mohamed Kobi, Abdessamad Sadraoui, Youssef Kadiri, Moulay Saddik LaSTI Laboratory of Science and Engineering Techniques National School of Applied Sciences of Khouribga Sultan Moulay Slimane University Khouribga Morocco LARIS Angevin Laboratory for Systems Engineering Research Polytech Angers University of Angers Angers France LIPIM Laboratory of Process Engineering Computer Science and Mathematics National School of Applied Sciences of Khouribga Sultan Moulay Slimane University Khouribga Morocco

Optimizing maintenance and enhancing the performance of businesses are significant concerns in the modern industrial world. Predictive maintenance is emerging as an innovative approach to address these challenges, allowing companies to shift from corrective maintenance to preventive maintenance. Predictive maintenance relies on the use of advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) to collect and analyse real-time data from equipment. Through this predictive analysis, it becomes possible to identify early warning signals of failures, enabling the anticipation of potential issues and the proactive planning of maintenance interventions. In this study, we will thoroughly examine the impact of predictive maintenance on the performance of businesses. We will explore the benefits and opportunities it offers in terms of reducing downtime, optimizing maintenance costs, and enhancing productivity. We will also investigate the various technologies and methods used in the implementation of predictive maintenance, along with potential challenges and best practices for successful adoption. This research focuses on studying the application of predictive maintenance within a company using data science and machine learning methods. Predictive maintenance represents an innovative approach aimed at anticipating equipment failures by leveraging real-time collected data. Through the analysis of this data using sophisticated algorithms, it becomes possible to identify early signals of potential problems and implement preventive maintenance actions before breakdowns occur. This approach not only reduces unexpected downtime but also optimizes maintenance operations by avoiding unnecessary interventions and maximizing resource utilization. The ultimate goal is to improve equipment availability, optimize operational performance, and maximize the overall yield of the company. © The Author(s), under exclusive licence to S

关键词： Artificial intelligence (AI) and machine learning (ML) Data science Optimizing maintenance Performance indicators Predictive maintenance Production

来源：评论

学校读者我要写书评

暂无评论

An Unsupervised Voice Activity Detection Using Time-Frequency Features 1

引用

1st international conference on Machine Intelligence and computer Science Applications, ICMICSA 2022

作者： Ait Mait, Hind Aboutabit, Noureddine Laboratory of Process Engineering Computer Science and Mathematics National School of Applied Sciences Khouribga University Sultan Moulay Slimane Beni Mellal Morocco

ISBN: (数字)9783031293139

ISBN: (纸本)9783031288456

Voice Activity Detection (VAD) is a binary classification problem for separating speech segments from background silence or noise. Over time, many features for the VAD have been proposed. In our study, we applied two features: Short Time Energy (STE) and Spectral Centroid, which belong to temporal and frequency domains, respectively. The goal of applying this VAD method is to use the speech segment extracted as an input of a phonetic segmentation method of Arabic and Moroccan dialect speech signals. We evaluated the method on 400 sentences from the Arabphone corpus recorded in noisy and noiseless environments. The results showed promising accuracy of 85%, which is comparable to some other previously proposed methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

Boosting Adversarial Training with Learnable Distribution

引用

computers, Materials & Continua 2024年第3期78卷 3247-3265页

作者： Kai Chen Jinwei Wang James Msughter Adeke Guangjie Liu Yuewei Dai School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China Key Laboratory of Intelligent Support Technology for Complex Environments Ministry of EducationNanjing210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Nanjing Center for Applied Mathematics Nanjing211135China

In recent years,various adversarial defense methods have been proposed to improve the robustness of deep neural *** training is one of the most potent methods to defend against adversarial ***,the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial *** paper proposes a learnable distribution adversarial training method,aiming to construct the same distribution for training data utilizing the Gaussian mixture *** distribution centroid is built to classify samples and constrain the distribution of the sample *** natural and adversarial examples are pushed to the same distribution centroid to improve the accuracy and robustness of the *** proposed method generates adversarial examples to close the distribution gap between the natural and adversarial examples through an attack algorithm explicitly designed for adversarial *** algorithm gradually increases the accuracy and robustness of the model by scaling ***,the proposed method outputs the predicted labels and the distance between the sample and the distribution *** distribution characteristics of the samples can be utilized to detect adversarial cases that can potentially evade the model *** effectiveness of the proposed method is demonstrated through comprehensive experiments.

关键词： Adversarial training feature space learnable distribution distribution centroid

来源：评论

学校读者我要写书评

暂无评论

Analyzing Instagram Images to Predict Personality Traits 1

引用

1st international conference on Machine Intelligence and computer Science Applications, ICMICSA 2022

作者： El Bahy, Siham Aboutabit, Noureddine Hafidi, Imad Laboratory of Process Engineering Computer Science and Mathematics National School of Applied Sciences Khouribga University Sultan Moulay Slimane Beni Mellal Morocco

ISBN: (数字)9783031293139

ISBN: (纸本)9783031288456

Our posts on social media are a way of expressing ourselves and our inclinations. In this research, we focused on Instagram (a social media platform for sharing images) to study the relationship between the personality of users and the content of photos they post. We also predicted their personalities based on the relationship between them and their images. This study is the first work that focuses on the Moroccan population. We collected a database of 316 Instagram Moroccan users, larger than the other databases used in previous works. We asked the users to fill out a NEO PI form to extract a Big Five that we chose to express their personality. And then we downloaded the images of users and extracted from them three categories of features;visual features (9 colors and lighting), image content (objects, animals, faces, people, light sources, dark, buildings), and emotional features (anger, disgust, fear, happy, neutral, sad, surprise). A root mean square error (RMSE) was used to indicate prediction accuracy, which relates to the [1,5] score scale. We succeeded in outperforming the best results obtained in the previous works, especially in predicting extraversion and neuroticism traits. We got an RMSE value of 0.83 versus 0.90 for extraversion and 0.87 versus 0.89 for neuroticism. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Light sources

来源：评论

学校读者我要写书评

暂无评论

An Improved Active Machine Learning Query Strategy for Entity Matching Problem 1

引用

1st international conference on Machine Intelligence and computer Science Applications, ICMICSA 2022

作者： Jabrane, Mourad Hafidi, Imad Rochd, Yassir Laboratory of Process Engineering Computer Science and Mathematics National School of Applied Sciences Khouribga University Sultan Moulay Slimane Beni Mellal Morocco

ISBN: (数字)9783031293139

ISBN: (纸本)9783031288456

Entity matching (EM) is crucial step in data integration. Supervised machine learning (SML) approaches have attained the SOTA performance in EM. In real - world scenarios SML suffers from absence or lack of large labeled data for training. Active machine learning (AML) for EM minimize the number of training data required and tries to reduce the amount of Hand labeling by picking just the helpful pairs. ALL AML approaches use just one of the two criteria - informativeness or representativeness - for query selection, Which limit their efficacy. In this work, we propose a Combined Score Sampling (CSS) that combines informativeness and representativeness selection criteria. We evaluate the CSS using the benchmark e-commerce data-sets pair Abt-Buy and AML with ensemble learning as SML model and we demonstrate that it effectively addresses the issue. Comparing our strategy to SML, we demonstrate that it lead to overall enhanced F1 score and stability of the learnt models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data integration

来源：评论

学校读者我要写书评

暂无评论

An Online Model for Detecting Attacks in Wireless Sensor Networks 1

引用

1st international conference on Machine Intelligence and computer Science Applications, ICMICSA 2022

作者： Tabbaa, Hiba Hafidi, Imad Laboratory of Process Engineering Computer Science and Mathematics National School of Applied Sciences Khouribga University Sultan Moulay Slimane Beni Mellal Morocco

ISBN: (数字)9783031293139

ISBN: (纸本)9783031288456

The Internet of Things (IoT) has been employed in a variety of critical fields, including healthcare, geriatric surveillance, self-driving vehicles, and energy management. The most prevalent infrastructure for these applications is Wireless Sensor Networks (WSNs). Although WSNs have intriguing characteristics, the security of such networks is a major concern, especially for applications where confidentiality is extremely crucial. In order to set up WSNs securely, any type of intrusion should be identified before attackers potentially harm the network. However, research findings have demonstrated that existing approaches are ineffective, particularly in detecting attacks in real-time, mostly owing to the accumulation of enormous amounts of data through interconnected devices. Within this interpretation, our intention is to construct a robust Intrusion Detection System (IDS) for analyzing real-time network traffic of WSNs using online learning algorithm while taking into account the network’s resource constraints. To achieve this goal, we investigated the use of an online classifier, namely the Hoeffding Adaptive Tree (HAT), along with the selection of relevant attributes to distinguish four kinds of DoS attacks among normal network traffic: the attacks considered are Blackhole, Grayhole, Flooding, and Scheduling attacks. Among the experimental findings, we determined that utilizing the HAT classifier along with the Chi-squared method of feature selection, detection rate percentages were 86.75%, 80.02%, 94.92%, 99.12% and 99.03% respectively for Flooding, Scheduling, Grayhole, Blackhole and normal case attacks. With an overall accuracy of 99.03%. Based on these findings, it is indeed possible to infer that the HAT classifier is extremely beneficial for categorizing attacks, as it has managed to secure a high detection rate despite the existence of many threats. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

Network Traffic Synthesis and Simulation Framework for Cybersecurity Exercise Systems

引用

computers, Materials & Continua 2024年第9期80卷 3637-3653页

作者： Dong-Wook Kim Gun-Yoon Sin Kwangsoo Kim Jaesik Kang Sun-Young Im Myung-Mook Han Department of AI Software Gachon UniversitySeongnam-Si13120Republic of Korea School of Computer Engineering&Applied Mathematics Hankyong National UniversityPyeongtaek-Si17738Republic of Korea Cyber ElectronicWarfare Research and Development LIG Nex1Seongnam-Si13488Republic of Korea

In the rapidly evolving field of cybersecurity,the challenge of providing realistic exercise scenarios that accurately mimic real-world threats has become increasingly *** methods often fall short in capturing the dynamic and complex nature of modern cyber *** address this gap,we propose a comprehensive framework designed to create authentic network environments tailored for cybersecurity exercise *** framework leverages advanced simulation techniques to generate scenarios that mirror actual network conditions faced by professionals in the *** cornerstone of our approach is the use of a conditional tabular generative adversarial network(CTGAN),a sophisticated tool that synthesizes realistic synthetic network traffic by learning fromreal data *** technology allows us to handle technical components and sensitive information with high fidelity,ensuring that the synthetic data maintains statistical characteristics similar to those observed in real network *** meticulously analyzing the data collected from various network layers and translating these into structured tabular formats,our framework can generate network traffic that closely resembles that found in actual *** integral part of our process involves deploying this synthetic data within a simulated network environment,structured on software-defined networking(SDN)principles,to test and refine the traffic *** simulation not only facilitates a direct comparison between the synthetic and real traffic but also enables us to identify discrepancies and refine the accuracy of our *** initial findings indicate an error rate of approximately 29.28%between the synthetic and real traffic data,highlighting areas for further improvement and *** providing a diverse array of network scenarios through our framework,we aim to enhance the exercise systems used by cybersecurity *** not only improves their ability to r

关键词： Cybersecurity exercise synthetic network traffic generative adversarial network traffic generation software-defined networking

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：