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P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

Intelligent Automation & Soft Computing 2023年第1期35卷 553-566页

作者： Ayman Altameem Ramesh Chandra Poonia Ankit Kumar Linesh Raja Abdul Khader Jilani Saudagar Department of Computer Science and Engineering College of Applied Studies and Community ServicesKing Saud UniversityRiyadh11533Saudi Arabia Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Department of Computer Engineering and Applications GLA UniversityMathuraUPIndia Department of Computer Application Manipal University JaipurRajasthan303007India Information Systems Department Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Data clustering is crucial when it comes to data processing and *** new clustering method overcomes the challenge of evaluating and extracting data from big *** or categorical data can be *** clustering methods favor numerical data clustering and ignore categorical data *** recently,the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering ***,these algorithms could not use the concept of categorical data for *** that,suggestions for expanding traditional categorical data processing methods were *** addition to expansions,several new clustering methods and extensions have been proposed in recent *** is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster *** paper aims to modify the algo-rithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster *** parameterized ROCK algorithm is the name given to the modified algorithm(P-ROCK).The proposed modification makes the original algorithm moreflexible by using user-defined parameters.A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm.A comparison with the original ROCK algorithm is also *** results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%.The proposed P-ROCK algorithm has improved the runtime and is moreflexible and scalable.

关键词： ROCK K-means algorithm clustering approaches unsupervised learning K-histogram

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Intelligent Control of an Integrated Energy System: Low Carbon Modeling and Intelligent Optimization 6

Intelligent Control of an Integrated Energy System: Low Carb...

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6th International Conference on Electronic engineering and Informatics, EEI 2024

作者： Fu, Zezheng Lin, Wenting Yang, Degang College of Computer and Information Science Chongqing Normal University Chongqing401331 China Chongqing Engineering Research Center of Educational Big Data Intelligent Perception and Application Chongqing401331 China

ISBN: (纸本)9798350353594

An integrated energy system is a complex system that requires intelligent control to optimize its operation. This paper proposes an intelligent control method for integrated energy systems based on a low carbon model. The model integrates a variety of low carbon technologies such as carbon capture systems, demand response, carbon emissions trading, and clean energy generation. The effect of the proposed intelligent control methods on reducing system costs and carbon emissions is verified through a case study. The results show that the combination of the intelligent control method and the low carbon model leads to a significant reduction in system operating costs and carbon emissions. This provides an important reference for intelligent control of the existing integrated energy system from a low-carbon approach. © 2024 IEEE.

关键词： Carbon capture and utilization

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A comparison of the performance of graph breadth-first search algorithms based on OPENMP and CUDA

A comparison of the performance of graph breadth-first searc...

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computer and Information Processing Technology (ISCIPT), International Symposium on

作者： Yunfei Zhang Yonghao Xiao Institute of Computer Application Chinese Academy of Engineering Physics Mianyang China

ISBN: (数字)9798350388404

ISBN: (纸本)9798350388411

Graphs, as fundamental data structures, are indispensable for delineating intricate interrelations among entities, exhibiting utility across diverse domains within the realm of the information society. Traversal algorithms, including the pivotal breadth-first search, are keystones in the realm of graph theory, constituting essential elements within broader algorithmic frameworks. With the expansion of graph scale, the imperative for expedient processing of graph data becomes increasingly pronounced. This paper aims to meticulously examine the execution efficiency of three distinct breadth-first traversal methodologies across varying node magnitudes: the traditional queue-based sequential approach, CPU parallelization leveraging OPENMP and GPU parallelization harnessing CUDA. Each method possesses its distinctive array of advantages and disadvantages contingent upon the scale of nodes. Moreover, this study aims to delve into the nuanced effect of thread count on the efficacy of OPENMP-based CPU parallel traversal, elucidating an inversely proportional relationship in scenarios with fewer nodes. Finally, we investigate the influence of graph sparsity on the traversal methodologies, acknowledging its pivotal role in connectivity and traversal time.

关键词： 6G mobile communication Graphics processing units Parallel processing Search problems Graph theory Arrays

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Robust LiDAR-Camera Alignment over Point Cloud Density Variation

Robust LiDAR-Camera Alignment over Point Cloud Density Varia...

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Chinese Automation Congress (CAC)

作者： Angfan Zhu Tao Ma Institute of Computer Application China Academy of Engineering Physics Sichuan China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

For the first time, we raise and address the research problem of robust LiDAR-Camera alignment (LCA) over point cloud density variation, which is essentially important to the applications like autonomous driving and robot navigation. Our key idea is to generate the regular point cloud that holds the characteristics of density-insensitivity and evenness jointly for extracting robust and effective LCA feature. Our intuition lies in 2 main folders. That is, density-insensitivity aims to make the generated point cloud have a fixed number of points in local spaces. And, evenness requires that the points in local spaces can be evenly distributed to better recover the scene. The density-insensitivity and evenness ensure the generated point cloud can provide robust and effective information for LCA tasks. Specifi-cally, a 2D CNN model is used to achieve the density-insensitivity and evenness of generated regular point clouds. Additionally, a novel loss function is proposed to train our model without ground truth. The experiments on KITTI dataset verify superiority of our proposition. Our method can achieve comparable performance with state-of-the-arts LCA methods under original testing point clouds. As the point cloud density decreases, the performance of our method gradually outperforms them. When the point cloud density is very sparse, the performance advancement of our method is over 1.5 m / 0.4° on translation / rotation error with mis-alignment magnitudes of up to ±1.5m and ±20°.

关键词： Point cloud compression Translation Automation Accuracy Navigation Feature extraction Robots Autonomous vehicles Testing

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Research and Implementation of Log-Based Anomaly Detection Platform Based on ELK+Kafka

Research and Implementation of Log-Based Anomaly Detection P...

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International Conference on computer and Communications (ICCC)

作者： Shuai Xu Ziyang Meng Han Wang Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Anomaly detection is vitally essential to modern large-scale distributed systems. Over the years,many log-based anomaly detection methods have been proposed. Logs record the real-time running information of processes in distributed systems and show the running status of the processes. However, when the reliability issues that happen in distributed systems, operators often inspect the logs manually with multi-rule matching and multi-keyword search. Additionally, The increasing scale and complexity of distributed systems, make the volume of logs explode, which renders the infeasibility of manual inspection. To reduce manual effort, we provide a log-based anomaly detection platform, which is based on ELK (Elasticsearch, Logstash, Kibana), Kafka and deep learning-based methods. Our proposed platform is evaluated using two different publicly-available production log datasets, and our results demonstrate that it achieves the $F_{1}$ -score of 0.792 and 0.829, respectively, outperforming the manually inspecting log anomalies.

关键词： Training Learning systems Systems operation Noise Microservice architectures Production Manuals Real-time systems Reliability Anomaly detection

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A zeroing neurodynamics‐based location method for nodes in underwater acoustic sensor network

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 661-669页

作者： Shuqiao Wang Xiujuan Du Tiantai Deng College of Computer Qinghai Provincial Key Laboratory of loTQinghai Normal UniversityXiningChina The State Key Laboratory of Tibetan Intelligent Information Processing and Application XiningChina The Department of Electronics and Electrical Engineering The University of SheffieldSheffieldEnglandUnited Kingdom

Zeroing neurodynamics methodology,which dedicates to finding equilibrium points of equations,has been proven to be a powerful tool in the online solving of problems with considerable *** this paper,a method for underwater acoustic sensor network(UASN)localisation is proposed based on zeroing neurodynamics methodology to preferably locate moving underwater nodes.A zeroing neurodynamics model specifically designed for UASN localisation is constructed with rigorous theoretical analyses of its *** proposed zeroing neurodynamics model is compatible with some localisation algorithms,which can be utilised to eliminate error in non‐ideal situations,thus further improving its ***,the effectiveness and compatibility of the proposed zeroing neurodynamics model are substantiated by examples and computer simulations.

关键词： artificial neural network internet of things underwater acoustic sensor network

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Track-before-detect algorithm based optimized interactive multiple model particle filtering for tracking maneuvering weak target

Track-before-detect algorithm based optimized interactive mu...

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2024 International Conference on Image Processing and Artificial Intelligence, ICIPAl 2024

作者： Wu, Xiaojia Yang, Jinlong School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education Beijing100039 China

ISBN: (纸本)9781510681514

This paper proposes an optimized algorithm for tracking maneuvering weak radar targets in complex sea environments. In the proposal, we first preprocesses radar echo images using the fractional Fourier transform (FrFT) to reduce clutter interference. Then, Feedback factor and residual resampling are introduced to optimize the interactive multiple model particle filtering (IMMPF) algorithm to achieve effective tracking of maneuvering targets. Additionally, the optimized IMMPF is combined with track before detect algorithm (TBD) to suppress clutter and accumulate target trajectory. Simulation experiment results demonstrate that the proposed algorithm can effectively track targets compared to other TBD algorithms. © 2024 SPIE.

关键词： Monte Carlo methods

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Content-Based Book Recommender System Using Supervised Learning 2nd

Content-Based Book Recommender System Using Supervised Learn...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Rahul, Mayur Singh, Akhilesh Verma, Shekhar Shukla, Himanshu Yadav, Vikash Department of Computer Application School of Engineering and Technology CSJM University Uttar Pradesh Kanpur India Department of Technical Education Government Polytechnic Bighapur Unnao Uttar Pradesh Manpur India

ISBN: (纸本)9789819735938

Online shopping in India has already got more attention in the last few decades. The Indians are more interested in online shopping which has changed all the concepts of the market scenario. There are so many online shopping websites available nowadays like Flipkart, Amazon, Junglee, Snapdeal, etc. With the increase in the number of traders and buyers, efficient and effective techniques are used to manage the vast amount of data formed day by day. Recommender systems give a platform to filter out the given data and able to provide relevant information to the end users. The methods used in the recommender systems are content-based, collaborative-based, and demographic-based but there are some issues that fail in producing efficient information for the end users. Thus, it is very important to find the most important features that are capable of optimizing recommender systems. This paper proposed a content-based book recommender system using PCA and SVM with the help of two publicly available datasets like Amazon Book Reviews (AB) and BookCrossing (BX). The results of the proposed recommender system prove the superiority of the system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Recommender systems

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A Data Constraint Analysis Method for Full-Digital Simulation Test

A Data Constraint Analysis Method for Full-Digital Simulatio...

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IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Shasha Wu Shiyong Li Yanhong Peng Siyou Ma Fei Deng Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

At present, software testing is an effective approach to ensure and improve software quality. However, there is a lack of data dependency analysis methods for constructing full-digital test data scripts, which results in the problem that constructed test data scripts maybe hard to simulate the real-world mission scenarios of the weapon embedded software. To solve this problem, this paper clarifies the emphasis of data constraint analysis is sporadic data by analyzing the external data features of the weapon embedded software, and then proposes a data constraint analysis method based on profile and directed graph. The proposed method can complete the analysis of data constraints from coarse-grained to fine-grained efficiently. Moreover, according to the analysis results, full-digital testing test data script that can simulate the real-world mission scenarios of the software can be constructed. Experiment results have proven that the proposed method is effective and can improve analysis efficiency.

关键词： Software testing Weapons Directed graphs Software quality Software reliability Security Embedded software

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Software Defect Classification and Analysis Study

Software Defect Classification and Analysis Study

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Information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Liyuan Zheng Yang Xie Boyuan Hou Xin Xie Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

This study proposes a set of improved defect categorization criteria and analysis strategies based on a synthesis of a variety of commonly used software defect categorization methods, combined with the actual software development and testing process. By exploring the defect analysis process, this paper discusses the application of this standard in practice. Finally, basic methods and processes are proposed for software defect prevention, providing theoretical and practical guidance for quality management in the software development process.

关键词： Productivity Prevention and mitigation Standards organizations Organizations Reliability theory Software Software reliability

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