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State Feedback Stabilization of Large-Scale Logical Control Networks via Network Aggregation

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第12期66卷 6033-6040页

作者： Li, Haitao Liu, Yuna Wang, Shuling Niu, Ben Shandong Normal Univ Sch Math & Stat Jinan 250014 Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China

Finding a computationally tractable method to solve the feedback stabilization problem of large-scale logical control networks (LCNs) is a challenging issue. This article combines network aggregation and algebraic state-space representation (ASSR) to solve the state feedback stabilization problem of large-scale LCNs. First, the whole network of large-scale LCNs is divided into several small subnetworks via network aggregation. Second, the mode-dependent state feedback gain is explored for the stabilization of switched LCNs based on the ASSR method. Third, using the mode-dependent state feedback gain in each subnetwork, an effective result is proposed to design the state feedback gain for the stabilization of large-scale LCNs. Finally, two illustrative examples are given to demonstrate the effectiveness of the obtained new results.

关键词： State feedback Aerospace electronics partitioning algorithms Genetics Control design Computational modeling Biological system modeling Algebraic state-space representation (ASSR) logical control network (LCN) mode-dependent control network aggregation stabilization

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DRT: A Novel Tool for Data Archiving

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IEEE SOFTWARE 2021年第2期38卷 88-95页

作者： Williams, Kevin Li, Yan Olfman, Lorne Avalon Consulting Solut Inc Cerritos CA 90703 USA Claremont Grad Univ Ctr Informat Syst & Technol Claremont CA 91711 USA

This article describes a novel data retirement tool (DRT) that removes data without taking the system offline and suffering increased load. Compared to Structured Query Language Delete, the standard data deletion meth... 详细信息

关键词： File Organisation Query Processing SQL DRT Data Archiving Data Retirement Tool System Offline Suffering Increased Load Structured Query Language Delete Standard Data Deletion Method Deletion Time System Load Databases Structured Query Language Storage Management partitioning algorithms Synchronization Data Storage Systems System Performance Structured Data Archiving Database Maintenance Data Management Data Retirement Tool

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An Efficient Data Access Approach With Queue and Stack in Optimized Hybrid Join

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IEEE ACCESS 2021年 9卷 41261-41274页

作者： Aziz, Omer Anees, Tayyaba Mehmood, Erum Univ Management & Technol Sch Syst & Technol Lahore 54770 Pakistan

As rapid decision making in business organizations gain in popularity, the complexity and adaptability of extract, transform, and load (ETL) process of near real-time data warehousing has dramatically increased. The most important part of near real-time data warehouse is to feed new data from different data sources on near-real-time basis. However, this new data is not in the format of the data warehouse therefore, it needs to be transformed into the required format by using transformation algorithms which is essential part of ETL process. A semi-stream join algorithm is required to implement this transformation, for this purpose a HYBRIDJOIN (hybrid join) algorithm has been presented in the literature. However, major design issue with this algorithm is that it uses a single buffer to load the disk partitions and therefore, the algorithm has to wait until the next disk partition overwrites the exiting partition in the disk buffer. As the cost of loading disk partition into disk buffer is the major cost of overall algorithm processing cost, this leaves the performance of algorithm sub-optimal. Moreover, existing approaches only considering the oldest key join attributes for finding the matches with master data and maintaining the Queue of key join attribute. However, performance can be improved if recent and oldest attributes process in parallel. This article addresses the limitation of HYBRIDJOIN by presenting two optimized new algorithms named: Parallel-Hybrid Join (P-HYBRIDJOIN) and Hybrid Join with Queue and Stack (QaS-HYBRIDJOIN). Proposed algorithms aim to reduce major processing cost that is disk I/O as well as to increase number of matching stream tuples. Both of these algorithms perform significantly better in terms of throughput and number of matching tuples as compared to existing approaches. Performance analysis and cost model for proposed algorithms show the best performance using intermittent stream data under limited resources.

关键词： partitioning algorithms Real-time systems Data warehouses Loading Big Data Throughput Heuristic algorithms Near-real-time data warehouse semi-stream join optimized hybrid join queue and stack

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MT-ART: A Test Case Generation Method Based on Adaptive Random Testing and Metamorphic Relation

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IEEE TRANSACTIONS ON RELIABILITY 2021年第4期70卷 1397-1421页

作者： Hui, Zhan-wei Wang, Xiaojuan Huang, Song Yang, Sen Acad Mil Sci Inst Evaluat & Assessment Res Beijing 100091 Peoples R China Army Engn Univ PLA Software Testing & Evaluat Ctr Nanjing 210007 Peoples R China Army Engn Univ PLA Command Control Engn Inst Nanjing 210007 Peoples R China

Most of metamorphic testing (MT) research works focused on the generation and application of metamorphic relations (MRs). There is no clear conclusion about the relationship between test case generation methods and performance of MT. In this article, we introduce a novel method based on adaptive random testing (ART) and MR for MT test case generation. It proposes a family of algorithms for MT test cases generation, named as MT based ART (MT-ART). Three distances are measured to generate the next MT test case. In order to verify the performance of this method, series of experiments on four programs with different numbers of inputs are introduced. The results show that MT-ART performs better than other ART algorithms not only in test effectiveness, but also in test efficiency and test coverage. Based on this article, the following conclusions can be drawn: first, considering the effectiveness of MRs and test cases in MT may lead to better results. In this way, most of the existing research can be improved by this method. This is the most important contribute of our research. Second, not only the source test cases, but also the follow-up test cases can improve the performance of MT. Therefore, they should be considered together during the process of the next test case generation. Third, the average distance performs better than the max distance and the minimum distance in metamorphic test case selection.

关键词： Testing Subspace constraints Software partitioning algorithms Genetic algorithms Target tracking Software algorithms Adaptive random test metamorphic distance (MD) metamorphic relation (MR) metamorphic testing (MT) random test

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RCD: Radial Cell Decomposition Algorithm for Mobile Robot Path Planning

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IEEE ACCESS 2021年 9卷 149982-149992页

作者： Salama, Omnia A. A. Eltaib, Mohamed E. H. Mohamed, Hany Ahmed Salah, Omar Assiut Univ Dept Mech Engn Fac Engn Assiut 71516 Egypt Kafrelsheikh Univ Dept Mech Engn Fac Engn Kafrelsheikh 33511 Egypt Higher Technol Inst Dept Mech Engn 10th Of Ramadan City 44634 Egypt

Finding the optimum path for mobile robots is now an essential task as lots of autonomous mobile robots are widely used in factories, hospitals, farms, etc. Many path planning algorithms have been developed to finding the optimum path with the minimum processing time. The vertical cell decomposition algorithm (VCD) is one of the popular path planning algorithms. It is able to find a path in a very short time. In this paper, we present a new algorithm, called the Radial cell decomposition (RCD) algorithm, which can generate shorter paths and a slightly faster than VCD algorithm. Furthermore, the VCD algorithm cannot be applied directly to obstacles in special cases, like two vertices have the same x-coordinate;on the other hand, the RCD algorithm can be applied to these special cases directly. In addition to that, the RCD algorithm is very suitable for corridor environments, unlike the VCD algorithm. In this paper, the RCD algorithm is described and tested for both cluttered and corridor environments. Furthermore, Two different algorithms A*, and Vertical cell decomposition are compared to the RCD algorithm. Simulation results confirm the effectiveness of the RCD algorithm in terms of path length and processing time.

关键词： Path planning Mobile robots Planning partitioning algorithms Mechanical engineering Licenses Task analysis Path planning radial cell decomposition vertical cell decomposition

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Soft Overlapping Community Detection in Large-Scale Networks via Fast Fuzzy Modularity Maximization

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2021年第6期29卷 1533-1543页

作者： Yazdanparast, Sakineh Havens, Timothy C. Jamalabdollahi, Mohsen Michigan Technol Univ Dept Elect & Comp Engn Houghton MI 49931 USA

Soft overlapping clustering is one of the notable problems of community detection. Extensive research has been conducted to develop efficient methods for nonoverlapping and crisp-overlapping community detection in large-scale networks. In this article, fast fuzzy modularity maximization (FFMM) for soft overlapping community detection is proposed. FFMM exploits novel iterative equations to calculate the modularity gain associated with changing the fuzzy membership values of network vertices. The simplicity of the proposed scheme enables efficient modifications, reducing computational complexity to a linear function of the network size, and the number of communities. Moreover, to further reduce the complexity of FFMM for very large networks, multicycle FFMM (McFFMM) is proposed. The proposed McFFMM reduces complexity by breaking networks into multiple subnetworks and applying FFMM to detect their communities. Performance of the proposed techniques is demonstrated with real-world data and the Lancichinetti-Fortunato-Radicchi benchmark networks. Moreover, the performance of the proposed techniques is evaluated versus some state-of-the-art soft overlapping community detection approaches. Results show that the McFFMM produces a remarkable performance in terms of overlapping modularity with fuzzy memberships, computational time, number of detected overlapping nodes, and overlapping normalized mutual information.

关键词： Linear programming Mathematical model Computational complexity Image edge detection partitioning algorithms Benchmark testing Big data analysis community detection fuzzy membership fast fuzzy modularity maximization graph clustering large-scale networks soft overlapping clustering

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Search Trajectory Networks Applied to the Cyclic Bandwidth Sum Problem

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IEEE ACCESS 2021年 9卷 151266-151277页

作者： Narvaez-Teran, Valentina Ochoa, Gabriela Rodriguez-Tello, Eduardo Cinvestav Tamaulipas Tamps 87130 Vic Mexico Univ Stirling Comp Sci & Math Dept Stirling FK9 4LA Scotland

Search trajectory networks (STNs) were proposed as a tool to analyze the behavior of metaheuristics in relation to their exploration ability and the search space regions they traverse. The technique derives from the study of fitness landscapes using local optima networks (LONs). STNs are related to LONs in that both are built as graphs, modelling the transitions among solutions or group of solutions in the search space. The key difference is that STN nodes can represent solutions or groups of solutions that are not necessarily locally optimal. This work presents an STN-based study for a particular combinatorial optimization problem, the cyclic bandwidth sum minimization. STNs were employed to analyze the two leading algorithms for this problem: a memetic algorithm and a hyperheuristic memetic algorithm. We also propose a novel grouping method for STNs that can be generally applied to both continuous and combinatorial spaces.

关键词： Bandwidth Trajectory Search problems Heuristic algorithms Memetics partitioning algorithms Layout Search trajectory networks cyclic bandwidth sum problem hyperheuristics memetic algorithms hybridization

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DTransE: Distributed Translating Embedding for Knowledge Graph

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2021年第10期32卷 2509-2523页

作者： Song, Dandan Zhang, Feng Lu, Meiyan Yang, Sicheng Huang, Heyan Beijing Inst Technol Sch Comp Sci & Technol Engn Res Ctr High Volume Language Informat Proc & Beijing 100081 Peoples R China Renmin Univ China Key Lab Data Engn & Knowledge Engn MOE Beijing 100872 Peoples R China Renmin Univ China Sch Informat Beijing 100872 Peoples R China

Knowledge graphs play an important role in many applications, such as link prediction and question answering. Translating embedding for knowledge graphs is done with the aim of encoding structured information on entities and their rich relations in a low-dimensional embedding space. TransE is one of the most important methods in translation-based models, and uses translation invariance to implement translating embedding for knowledge graphs. In this line of work, translating embedding models represent the relation as a translation from the head entity to the tail entity and have achieved impressive results. Currently, the TransE model is only developed on single-node machines. Unfortunately, the computing and storage capacities of a single machine can easily reach their limits as knowledge graphs become larger and more complex, which limits the application scope of TransE. In order to solve this problem, we propose a distributed TransE method, known as DTransE, which can utilize distributed computing resources to calculate knowledge graph embeddings. However, building a distributed TransE is complicated and involves challenges of knowledge graph partitioning and computation. To solve these challenges, we provide a high-quality edge partitioning algorithm for the power-law graph by considering the high-degree and low-degree vertices with adaptive weights, which can balance the workload. By using the unactivated Gather-Apply-Scatter model on TransE, the processes periodically exchange messages in a loop. The irregular data distribution among the processes is also optimized to further accelerate communication. As far as we know, this is the first work on a distributed TransE method. We use link prediction to evaluate the DTransE in a distributed environment. Experiments show that, compared to the original TransE method, our proposed DTransE is, on average, 24.5 times faster with a minimum loss of accuracy;compared to the state-of-the-art parallel TransE implementation,

关键词： Computational modeling Training partitioning algorithms Mathematical model Parallel processing Memory management Prediction algorithms TransE distributed computing workload partitioning knowledge graph and representation

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Multi-View Clustering With Learned Bipartite Graph

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IEEE ACCESS 2021年 9卷 87952-87961页

作者： Li, Miaomiao Liang, Weixuan Liu, Xinwang Changsha Coll Dept Comp Changsha 410073 Peoples R China Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China

In this paper, we propose a late fusion multi-view clustering via a learned bipartite graph (MVC-LBG). Firstly, we obtain the base partition from each single view. Then, a consensus bipartite graph is constructed by these base partitions for final clustering results. Inspired by anchors used in spectral clustering on a bipartite graph, we introduce representative points that are updated during the iterating of MVC-LBG. Compared with early fusion multi-view clustering algorithms, MVC-LBG not only dramatically improves the clustering accuracy but also has lower computational complexity. By adopting the base partitions in a new way, the proposed algorithm achieves better performance than existing late fusion multi-view clustering algorithms. Extensive experiments on six data sets demonstrate the effects and efficiency of our algorithm. Finally, we extend MVC-LBG to large-scale datasets.

关键词： Clustering algorithms partitioning algorithms Kernel Optimization Bipartite graph Sparse matrices Matrix decomposition Late fusion multi-view clustering bipartite graph

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An Unsupervised Ensemble Clustering Approach for the Analysis of Student Behavioral Patterns

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IEEE ACCESS 2021年 9卷 7076-7091页

作者： Li, Xiaoyong Zhang, Yong Cheng, Huimin Zhou, Feifei Yin, Baocai Beijing Artificial Intelligence Inst Fac Informat Technol Beijing Key Lab Multimedia & Intelligent Software Beijing 100124 Peoples R China Beijing Univ Technol Informat Technol Support Ctr Beijing 100124 Peoples R China

Specialized services and management must understand students' behavioral patterns in a timely and accurate manner. Based on these patterns, we can make targeted rules, especially for unexpected patterns. To perform this type of work, a questionnaire method is usually used to collect data and analyze students' behavioral states. However, the effectiveness of this method is greatly influenced by the timeliness and validity of the feedback data. To address this problem, we propose an unsupervised ensemble clustering framework to use student behavioral data to discover behavioral patterns. Because the behavioral data produced by students on campus are available in real time without intentional bias, clustering analysis can be relatively efficient and reliable. The proposed framework extracts behavior features from the two perspectives of statistics and entropy and then combines density-based spatial clustering of applications with noise (DBSCAN) and k-means algorithms to discover behavioral patterns. To evaluate the performance of the proposed framework, we carry out experiments on six types of behavioral data produced by undergraduates in a university in Beijing and analyze the relationships between different behavioral patterns and students' grade point averages (GPAs). The results show that the framework can not only detect anomalous behavioral patterns but also find mainstream patterns. The findings from this research can assist student-related departments in providing better services and management, such as psychological consulting and academic guidance.

关键词： Clustering algorithms Feature extraction partitioning algorithms Entropy Trajectory Reliability Prediction algorithms Specialized services and management behavioral patterns ensemble clustering DBSCAN k-means

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