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International conference on Communications, Circuits and Systems (ICCCAS)

作者： Yalan Ye Zhi-lin Zhang Shaozhi Wu Xiaobin Zhou Blind Source Separation Research Group School of Computer Science and Engineering University of Electronic Science and Technology ChengDu China School of Computer Science and Engineering University of Electronic Science and Technology ChengDu China

Recently, the fully-multiplicative orthogonal-group ICA (OgICA) neural algorithm has been proposed, which exploits the known principle of diagonalisation of a tensor of a warped network's outputs. Unfortunately, the algorithm is only able to separate sub-Gaussian source signals. To address this problem, the paper proposes an improved algorithm that adopts two nonlinearities and a flexible nonlinear model switching technique. The improved OgICA algorithm can instantaneously separate not only the mixture of pure sub-Gaussian source signals, but also the mixture of super-Gaussian and sub-Gaussian source signals. Besides, the algorithm has fast convergence speed and high separation performance. The validity and effectiveness of our proposed algorithm are confirmed through extensive computer simulations

关键词： Independent component analysis Source separation Computer science Switches Application software Tensile stress Convergence Computer simulation Blind source separation Iterative algorithms

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An Enhanced NN-based Load Frequency Control Design of MGs: A Fractional order Modeling Method

An Enhanced NN-based Load Frequency Control Design of MGs: A...

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Compatibility in Power Electronics (CPE)

作者： Vjatseslav Skiparev Komeil Nosrati Juri Belikov Aleksei Tepljakov Eduard Petlenkov Department of Software Science Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia

Developing accurate mathematical models for microgrid (MG) components is the initial step before implementing various load frequency control (LFC) strategies and analysis. In this regard, different high-order models associated with different nonlinearities have been included to increase the modeling accuracy resulted in a performance improvement in the LFC techniques. Nevertheless, these high-order nonlinear models pose some potential problems such as obstacles in the analytical description of the system and control problem along with its high computational complexity. In this light, the fractional order based models are deployed to effectively balance the model accuracy and analytical complexity. First, two fractional order components (energy storage system and fuel cell) are arranged in a controlled coordinated strategy to enhance the frequency stability. Then, two artificial neural network (ANN) controllers are deployed for each components in a multi-agent framework. To accomplish this step, a multi-agent stochastic reinforcement learning optimization is applied to train the two controllers. Test results on an isolated MG with fractional components validate the efficacy of the coordinated LFC strategy.

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A Secure Aware Routing Protocol for Efficient and Reliable FPANETS

A Secure Aware Routing Protocol for Efficient and Reliable F...

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software, Systems and Information Technology (SSITCON), International conference on

作者： P Arockia Mary D Maalini K Manivannan K Umamaheswari S. Balaji R Deepak Department of Information Technology V.S.B. Engineering College Karur India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

The use of Unmanned Aerial Vehicles (UAVs) has evolved significantly, with a current focus on utilizing multiple UAVs in teams to achieve high-level objectives. To support this evolution, new networking frameworks are needed for the highly mobile nodes like UAVs in fleets. These frameworks enable both indirect and direct communication between nodes within communication range. However, constructing an ad hoc network between UAVs presents challenges, with criteria that differ from traditional networks such as Mobile Ad-hoc Networks (MANETs) and aerial ad-hoc networks using the Flower Pollination Algorithm Protocol (FPAP). This study presents network models of UAVs, highlights open research questions, explores opportunities and future directions, and discusses the challenges of deploying UAVs as ad hoc relay nodes. The research aims to enhance data transmission security in FPANETs to reduce the risks of black hole attacks. An Artificial Neural Network (ANN) model achieves 98.97% detection accuracy, while cluster-based routing streamlines network architecture and reduces complexity. performance evaluations consider end-to-end latency, network throughput, hop count, and other network metrics. This paper highlights the effectiveness of these techniques in FPANET security and routing optimization.

关键词： software algorithms Flower pollination algorithm Artificial neural networks Routing Throughput software systems Routing protocols Ad hoc networks Security Vehicle dynamics

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A Machine Learning-Enhanced IoT Wearable Device for Monitoring and Providing Feedback on Stuttering

A Machine Learning-Enhanced IoT Wearable Device for Monitori...

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Computational Modelling, Simulation and optimization (ICCMSO), International conference on

作者： Hussain Suttarwala Ritesh Kumar Madhavi L. Deorankar Dikshendra Daulat Sarpate Pallavi Shingade Surender Kumar Sharma Vrushali More Uday Chandrakant Patkar Deepak K. Sharma Senior Software Engineer American Express Phoenix AZ USA Dept. of AI and Data Science Dr. Akhilesh Das Gupta Institute of Professional Studies New Delhi Delhi India Dept. of Electronics and Telecommunications Marathwada Mitramandal College of Engineering Pune Maharashtra India Dept. of AI and Data Science Zeal College of Engineering and Research Narhe Pune Maharashtra India Dept. of AI and Data Science D Y Patil College of Engineering Akurdi Pune India Dept. of Electrical Engineering Poornima University Jaipur India Dept. of AI and Data Science Genba Sopanrao Moze College of Engineering Balewadi Pune Maharashtra India Department of Computer Engineering Bharati Vidyapeeth’s College of Engineering Lavale Pune Dept. of AI and Data Science Savitribai Phule Pune University Pune Maharashtra India

ISBN: (数字)9798350361391

ISBN: (纸本)9798350361407

This study introduces an innovative IoT-enabled wearable device tailored for monitoring and providing feedback on stuttering, leveraging advanced machine learning algorithms to enhance precision and real-time responsiveness. The device integrates an array of hardware and software elements, including sensors, microcontrollers, and communication protocols, to accurately capture speech patterns and movements. Utilizing machine learning techniques such as Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), the device performs real-time analysis to identify stuttering instances. Detailed discussions cover the criteria for selecting machine learning models, the training and testing procedures, and the metrics used for performance evaluation. The prototype underwent extensive testing and validation, showcasing high accuracy, sensitivity, and specificity in differentiating stuttered from fluent speech. User feedback emphasized the ergonomic design, user-friendly interface, and real-time feedback features, highlighting its potential to significantly enhance stuttering therapy outcomes. Comparative analysis with existing solutions revealed the device’s superior performance, indicating strong potential for clinical application. The study discusses the implications for stuttering therapy, acknowledges the limitations, outlines future research directions, and underscores the device’s contributions to personalized and effective stuttering interventions. This research advances the field of assistive technologies in speech therapy and demonstrates the promise of IoT-based wearable devices integrated with machine learning for improving the quality of life for individuals with stuttering disorders.

关键词： performance evaluation Machine learning algorithms Accuracy Medical treatment Prototypes Machine learning Real-time systems

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非线性方程组求解器全局优化求解能力对比研究

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计算机应用与软件 2022年第10期39卷 1-10,24页

作者：程培澄程培聪王萌邵宇辰亓路宽北京工业大学材料科学与工程学院北京100124 北京交通大学计算机与信息技术学院北京100044 北京工业大学生命科学与生物工程学院北京100124 北京工业大学建筑工程学院北京100124

首次总结提出一套共14道低维但高难度非线性方程组案例题集,从效果与效率两方面对比测试了国内外目前认可度很高的5款优秀全局优化求解器Baron、Antigone、Couenne、Lingo和1stOpt,给出这5款求解器在非线性方程组全局优化求解能力方面... 详细信息

首次总结提出一套共14道低维但高难度非线性方程组案例题集,从效果与效率两方面对比测试了国内外目前认可度很高的5款优秀全局优化求解器Baron、Antigone、Couenne、Lingo和1stOpt,给出这5款求解器在非线性方程组全局优化求解能力方面的综合表现及排名,探讨了非线性方程组求解过程中“除法陷阱”和“冗余项影响”问题及解决方法,确定性优化算法的优缺点以及全局最优化求解器研发者的“卡耐基梅隆”及“跨行业”现象。

关键词：全局最优化非线性方程组优化求解器

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Image Clustering Based on Supervised Graph Regularized Discriminative Concept Factorization

Image Clustering Based on Supervised Graph Regularized Discr...

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International Joint conference on Neural Networks

作者： Xianzhong Long Yun Li School of Computer Science & Technology School of Software Nanjing University of Posts and Telecommunications Nanjing China

Concept Factorization (CF) divides a matrix into the product of three matrices. It is considered as one variant of Non-negative Matrix Factorization (NMF). The biggest difference between the two methods is that CF can be executed in a kernel space. Because of this characteristic, many schemes based on CF have been proposed in computer vision and pattern recognition fields. Recent studies have shown that high dimensional data is often located in a low dimensional manifold space, in order to improve the performance and reduce the storage space, how to find the mapping function is particularly important. In addition, the development of supervised learning methods show that label information is critical to enhance the model's ability. In this paper, a supervised graph regularized discriminative concept factorization (SGDCF) method is presented for image clustering. In the SGDCF, we make use of local manifold geometry structure and label information. The corresponding multiplicative update solutions and convergence verification are given. Clustering results on four image data sets reveal that the SGDCF outperforms the state-of-the-art algorithms in terms of accuracy and normalized mutual information.

关键词： Matrix decomposition Manifolds Laplace equations Kernel Symmetric matrices Clustering algorithms optimization

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Accelerating Communication for Distributed Training with Collaborative In-network Aggregation

Accelerating Communication for Distributed Training with Col...

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International conference on Communication Technology (ICCT)

作者： Long Luo Xixi Chen Shulin Yang Wenqi Fan HongFang Yu University of Electronic Science and Technology of China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

Distributed training (DT) has emerged as a crucial and popular practice for training high-quality machine learning (ML) models. However, the communication efficiency of gradient aggregation during the training process has become the primary performance bottleneck. Advanced programmable switches with in-network computing capabilities offer a promising solution to improve the communication efficiency of DT by offloading partial gradient aggregation tasks from servers to switches within the network. In this paper, we propose CINA (Collaborative In-network Aggregation), a novel approach that harnesses the in-network computing capabilities of programmable switches and allows multiple switches and servers to collaboratively perform gradient aggregation tasks, thereby improving the communication efficiency of distributed machine learning training. CINA aims to minimize the aggregation completion time by jointly optimizing the aggregation task assignment among switches and the gradient routing of aggregation requests. We formulate this joint optimization problem as an integer nonlinear programming problem under multi-dimensional resource constraints and design an efficient two-stage random rounding algorithm to compute satisfactory solutions rapidly. Extensive experimental results demonstrate that CINA significantly outperforms other state-of-the-art in-network gradient aggregation solutions, reducing the aggregation completion time by up to 75%.

关键词： Training Machine learning algorithms Simulation Collaboration Machine learning Life estimation Programming Routing Servers optimization

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Design and Evaluation of Agent Based Prioritized Dynamic Round Robin Scheduling Algorithm on Computational Grids

Design and Evaluation of Agent Based Prioritized Dynamic Rou...

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2012 AASRI conference on Computational Intelligence and Bioinformatics (CIB2012)

作者： Syed Nasir Mehmood Shah M Nordin B Zakaria Nazleeni Haron Ahmad Kamil Bin Mahmood Ken Naono Department of Computer and Information Sciences Universiti Teknologi PETRONAS Bandar Seri Iskandar

Grid computing enables sharing, selection and aggregation of computing resources for solving complex and large-scale scientific problems. Grid scheduling is playing a vital role for the efficient and effective execution of jobs on computational grids. Most of the scheduling algorithms do not consider user and system objectives at the same time. Therefore, in this paper we introduce a concept of fairness to scheduling and present a new agent based job scheduling algorithm called Agent based Prioritized Dynamic Round Robin(APDRR). APDRR is designed and developed by combining the best features of round robin and priority job scheduling algorithm using agent technology. APDRR is fair scheduling algorithm from a user point of view while also regarding the optimization criteria that are anticipated from the system perspective. This paper also presents the comparative performance analysis of our proposed APDRR along with other well known job scheduling algorithms, considering the performance metrics comprised of average waiting time, average turnaround time, average response time, total completion time, average bounded slowdown time and maximum job stretch time. performance evaluation of job scheduling algorithms has been carried out on a computational grid using real workload traces. Experimental evaluation confirmed that the proposed APDRR scheduling algorithm posses a high degree of optimality in performance, efficiency and scalability. This paper also includes a statistical analysis of real workload traces to present the nature and behavior of jobs.

关键词： Distributed systems Cluster Grid computing software agent Grid scheduling Workload modeling performance evaluation Simulation Load balancing Task synchronization Parallel processing

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Evaluating the performance of an advanced train dispatching system

Evaluating the performance of an advanced train dispatching ...

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International conference on Infrastructure Systems and Services: Developing 21st Century Infrastructure Networks (INFRA)

作者： Andrea D'Ariano Francesco Corman Ingo A. Hansen Delft University of Technology Postbus Delft GA The Netherlands

ISBN: (纸本)9781424468874

In the last years, real-time railway traffic optimization experienced an increasing interest due to the expected growth of traffic and the limited possibilities of enhancing the infrastructure, which ask for a more efficient use of resources and the application of more advanced decision support tools. This paper presents a computerized train dispatching system, called ROMA (Railway traffic optimization by Means of Alternative graphs), for supporting railway traffic controllers during operations. Innovative scheduling and rerouting algorithms have been developed in order to globally optimize disturbed railway traffic conditions. ROMA can anticipate the evolution of traffic, including the propagation of delays in a regional railway network, and can estimate the effects of different dispatching measures during a period of about 15 minutes ahead. Therefore, ROMA would enable traffic controllers to frequently perform incremental changes to the actual timetable to accommodate changes in traffic patterns due to disturbances, such as train delays and blocked tracks. An extensive computational study is carried out, based on a dispatching area of the Dutch railway network, to show the high potential of our train dispatching system as a support tool to improve punctuality.

关键词： Dispatching Rail transportation Communication system traffic control Delay estimation Application software Control systems Processor scheduling Scheduling algorithm Delay effects Propagation delay

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A Real-Time NetFlow-based Intrusion Detection System with Improved BBNN and high-Frequency Field Programmable Gate Arrays

A Real-Time NetFlow-based Intrusion Detection System with Im...

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IEEE International conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Quang Anh Tran Frank Jiang JIankun Hu Faculty of Information Technology Hanoi University Hanoi Vietnam School of Engineering University of New South Wales Canberra Australia

Future large-scale complex computing environments present challenges to the real-time intrusion detection systems (IDSs). In this paper, we design a prototype with hybrid software-enabled detection engine on the basis of our improved block-based neural network (BBNN), and integrate it with a high-frequency FPGA board to form a real-time intrusion detection system. The established prototype can seamlessly feed the large-scale NetFlow data obtained from Cisco routers directly into the improved BBNN based IDS. The corresponding BBNN structure and parameter settings have been improved and experimentally tested. Experimental performance comparisons have been conducted against four major schemes of Support Vector Machine (SVM) and Naive Bayes algorithm. The results show that the improved BBNN outperforms other algorithms with respect to the classification and detection performances. The false alarm rate is successfully reduced as low as 5.14% while the genuine detection rate 99.92% is still maintained.

关键词： Field programmable gate arrays Real time systems optimization Artificial neural networks Feature extraction Intrusion detection

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