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Chinese Automation Congress (CAC)

作者： Su, Zhaolu Yu, Yang Duan, Mingming Wang, Mushu Electrificat Co CCCC First Highway Engn Co Ltd Beijing Peoples R China Shandong Jiaotong Univ Sch Informat Sci & Elect Engn Jinan Peoples R China

ISBN: (纸本)9781728176871

With the development of high-speed roads and increase of lighting, ventilation and monitoring devices, it is necessary to study a real-time and robust voltage stability monitoring method. In this paper, a new method of voltage monitoring is proposed, and this method relies on voltage dynamical entropy and its deviation. At the beginning of the paper, the voltage is symbolized by using a maximum-entropy-based clustering algorithm (MECA), and the MECA is put forward in this paper. Then, a dynamical pattern is constructed, and voltage dynamical entropy is calculated of a short-term signals. The entropy is used to describe the voltage fluctuation and voltage dynamical properties. Subsequently, the entropy and a voltage deviation consist of a voltage feature. And a fuzzy classifier is constructed and used for monitoring a voltage stability status. At last, a simulation is given to demonstrate feasibility and effectiveness of the proposed method.

关键词： voltage monitoring dynamical pattern clustering algorithm information entropy feature recognition

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Network embedding architecture using laplace regularization-non-negative matrix factorization for virtualization

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MICROPROCESSORS AND MICROSYSTEMS 2021年 81卷

作者： Sudhakar, Sengan Kanmani, P. Amudha, K. Raja, P. Vishnu Dubey, Anil Kumar Sulthana, A. Razia Subramaniyaswamy, V Priya, V Sree Sakthi Engn Coll Dept Comp Sci & Engn Coimbatore 641104 Tamil Nadu India SRM Inst Sci & Technol Dept Comp Sci Chennai 603203 Tamil Nadu India Kongunadu Coll Engn & Technol Dept Elect & Commun Engn Tiruchy 621215 Tamil Nadu India Kongu Engn Coll Dept Comp Sci & Engn Perundurai 638060 Tamil Nadu India ABES Engn Coll Dept Comp Sci & Engn Ghaziabad Ncr India Birla Inst Sci & Technol Pilani Dept Comp Sci & Engn Dubai Campus Dubai 45055 U Arab Emirates SASTRA Deemed Univ Sch Comp Thanjavur 613401 Tamil Nadu India Mahendra Inst Technol Dept Comp Sci & Engn Namakkal 637503 Tamil Nadu India

While modeling the applications for a problem in cloud computing, researchers and scientists frequently use graphs as abstractions. Graphs provide structural models that make it possible to analyze and understand how many separate systems act together. The omnipresence in cloud computing systems is increasing information networks. The graph embedding algorithms preserve the microscopic structure over the cloud, and many of them miss the mesoscopic structure of the networks. In this paper, asymmetric non-negative Laplace regularization for cloud platform and matrix factorization is implemented for network embedding. The proposed algorithm preserves the mesoscopic structure in cloud computing, the learned model from the Laplace, and matrix factorization. The embedded cloud network can be used for link prediction, vertex recommendation, node clustering. It is a scalable algorithm for higher proximity preserving along with community structure. The correctness and convergence are measures as performance parameters in the network. Based factorization is used for updating the rules. The experimental study shows that the proposed system is well-organized compared to the existing process in structure preservation in cloud computing.

关键词： Cloud computing Graph embedding network Homogenous network clustering algorithm Embedded System

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Filtering-Based Correlation Power Analysis (CPA) with Signal Envelopes Against Shuffling Methods 21st

Filtering-Based Correlation Power Analysis (CPA) with Signal...

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21st International World Conference on Information Security Applications (WISA)

作者： Jeon, Youngbae Yoon, Ji Won Korea Univ Sch Cybersecur Inst Cyber Secur & Privacy ICSP Seoul South Korea

ISBN: (纸本)9783030652999;9783030652982

The Correlation Power Analysis (CPA) is one of the powerful Side-Channel Analysis (SCA) methods to reveal the secret key using linear relationship between intermediate values and power consumption. To defense the analysis, many crypto-systems often embed the shuffling implementation which shuffles the order of operations to break the relationship between power consumption and processed information. Although the shuffling method increases the required number of power traces for deploying the CPA, it is still vulnerable if an attacker can classify or group the power traces by operations. In this work, we propose a new CPA technique by efficiently clustering the power traces using signal envelopes. We demonstrate theoretically reduced time complexity and tested our approach with the eight-shuffling AES implementations.

关键词： Side-Channel Analysis Correlation Power Analysis (CPA) Shuffling method Envelope clustering algorithm

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Cooperative trajectory planning for mother/child aircrafts in aerial recovery 3

Cooperative trajectory planning for mother/child aircrafts i...

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3rd International Conference on Unmanned Systems (ICUS)

作者： Liu, Yongbei Qi, Naiming Tang, Mengying Yao, Weiran Zhao, Jun Harbin Inst Technol Sch Astronaut Harbin Peoples R China Expace Technol Co Ltd Fourth Acad China Aerosp Sci & Ind Corp Wuhan Peoples R China

ISBN: (纸本)9781728180250

Aerial recovery technology for aircrafts plays a significant role in practical applications, which presents a challenging problem in cooperative trajectory planning. To solve the problem, a hierarchical trajectory planning architecture is proposed in this paper. Firstly, the waypoints of the mother aircraft trajectory are obtained based on the distribution of the child aircrafts by using a hierarchical clustering algorithm. A greedy algorithm is used to obtain the traverse sequence for the waypoints and the mother aircraft trajectory is generated based on the Dubins path. Secondly, a genetic algorithm is employed to optimize the recovery position and recovery time for each child aircraft. Lastly, the trajectory of each child aircraft is generated by using a double-stage trajectory generation method. Simulation results validate the effectiveness of the proposed method for cooperative trajectory planning in aerial recovery.

关键词： mother aircraft aerial recovery trajectory planning clustering algorithm

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clustering and supervised response for XACML policy evaluation and management

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KNOWLEDGE-BASED SYSTEMS 2020年 205卷

作者： Deng, Fan Yu, Zhenhua Zhang, Liyong Ge, Xiaodong Zhao, Ruiyu Li, Xiaotong Ma, Yuhao Yan, Yang Wen, Zhe Xian Univ Sci & Technol Sch Comp Sci & Technol Inst Syst Secur & Control Xian 710054 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

To meet the increasingly complex requirements in access control using XACML (eXtensible Access Control Markup Language), it is necessary for a policy decision engine to deal with large-scale policy sets and intensively abundant requests efficiently. A practical policy evaluation engine, namely CSRM, is proposed to tackle this problem. The PDP (Policy Decision Point) in traditional policy decision engines is replaced by a new component ESPDP (Efficient Searching Policy Decision Point). CK-means algorithm is studied in this paper to perform clustering among all policies in a policy set. ESPDP is adopted to construct a virtual mapping table on the basis of the result of the CK-means algorithm. The virtual mapping table stores the relationship between subject attributes and policies, such that the irrelevant polices are excluded when rule search is carried out. Besides, the rules in every policy are merged according to particular principles, thus saving storage space and greatly speeding up rule search. When responding to intensive requests, a supervised response method is applied to determine an optimal rule search order by analyzing the response to the requests in a short period. The experimental results on four practical datasets demonstrate that our proposed CSRM outperforms some classic and state-of-the-art methods when dealing with large-scale policy sets. With high practicality and wide applicability, CSRM effectively eliminates the bottlenecks of improving PDP evaluation performance, and can respond to requests efficiently when handling large-scale policy sets. (C) 2020 Elsevier B.V. All rights reserved.

关键词： clustering algorithm Large-scale policy sets Policy Decision Point (PDP) Supervised learning XACML

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Research on the Application of Curriculum Knowledge Point Recommendation algorithm Based on Learning Diagnosis Model 5

Research on the Application of Curriculum Knowledge Point Re...

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5th International Conference on Electromechanical Control Technology and Transportation (ICECTT)

作者： Gan, Baiqiang Zhang, Chi Guangzhou Nanyang Polytech Guangzhou 510900 Peoples R China Sun Yat Sen Univ Nanfang Coll Guangzhou 510900 Peoples R China

ISBN: (数字)9781728199283

ISBN: (纸本)9781728199283

Because the degree of mastering knowledge points in courses in traditional cognitive diagnostic models cannot be probabilistic, there are only two situations: mastery and non-mastery. Therefore, for the current research, the recommendations of knowledge points recommended by learners' learning behavior attributes are not fully considered to be insufficient, this paper proposes a curriculum knowledge point recommendation algorithm model based on learning diagnosis, the model comprehensively considers the learner's learning emotions, learner problem test conditions and knowledge point characteristics, and the film and television synthesis in the Chaoxing online teaching service platform the course learning data is tested to verify the effectiveness of the recommendation algorithm. The experimental results show that the effectiveness and accuracy of the recommendation algorithm model proposed in this paper can meet the learning needs of learners.

关键词： learning diagnosis model course knowledge points recommendation algorithm clustering algorithm collaborative filtering algorithm

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Energy-Efficient and QoS-Improved D2D Small Cell Deployment for Smart Grid

Energy-Efficient and QoS-Improved D2D Small Cell Deployment ...

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IEEE Global Communications Conference (GLOBECOM) on Advanced Technology for 5G Plus

作者： Chi, Hao Ran Domingues, M. Fatima Refaey, Ahmed Radwan, Ayman Inst Telecommunicacoes Aveiro Portugal Univ Aveiro Aveiro Portugal Manhattan Coll Dept Elect & Comp Engn Bronx NY 10471 USA

ISBN: (纸本)9781728182988

This paper proposes a device-to-device communication based small cell (SC) deployment scheme in Smart Grid, named as JODI-D2D, to achieve high QoS, high reliability, and high energy efficiency. SC deployment is formulated as a joint optimization of data rate maximization and interference minimization, serving the distributed smart grid user equipment (SLUE), while reducing the computation latency. The SC powering strategy is determined with the k-means clustering, which considers the features of SGUEs, endowed by both power and communication layers. Simulation results show that the proposed JODI-D2D achieves high QoS with improved data rate performance, sufficient computation latency and maximized energy efficiency for SG applications.

关键词： QoS enhancement small cells smart grid D2D clustering algorithm HetNet Reliability

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Using an algorithmic approach for grouping roles and sub-units

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JOURNAL OF ORGANIZATION DESIGN 2020年第1期9卷 1-19页

作者： Worren, Nicolay Christiansen, Tore Soldal, Kim Verner Norwegian Univ Life Sci NMBU Sch Econ & Business POB 5003 N-1432 As Norway Inkubator As Reconfig A 5 Postboks 19 N-1431 As Norway

A key task in organization design is to group elements (e.g., roles) into sub-units (e.g., teams or departments). This task is computationally challenging as one must take into consideration a potentially large number of interdependencies between the elements. It also requires data about work processes in the organization, which are rarely present. We have initiated a research program that aims at developing a tool-Reconfig-to improve grouping decisions. It first collects data from employees about their working relationships (i.e., interdependencies) and then uses a computer algorithm to cluster the data in the most optimal manner. The clustered solution represents the formal structure that minimizes coordination costs by grouping the most highly interdependent elements together. We describe the tool, report on two pilot applications, and discuss both the future potential and limitations of the approach.

关键词： Organization design clustering algorithm Design structure matrix (DSM) Interdependencies Software tool

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Study and Evaluation of Unsupervised algorithms Used in Network Anomaly Detection 4th

Study and Evaluation of Unsupervised Algorithms Used in Netw...

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Future Technologies Conference (FTC)

作者： Dromard, Juliette Owezarski, Philippe Univ Toulouse CNRS LAAS CNRS Toulouse France

ISBN: (纸本)9783030325237;9783030325220

Network anomalies are unusual traffic mainly induced by network attacks or network failures. Therefore it is important for network operators as end users to detect and diagnose them to protect their network. However, these anomalies keep changing in time, it is therefore important to propose detectors which can learn from the traffic and spot anomalies without relying on any previous knowledge. Unsupervised network anomaly detectors reach this goal by taking advantage of machine learning and statistical techniques to spot the anomalies. There exists many unsupervised network anomaly detectors in the literature. Each algorithm puts forward its good detection performance, therefore it is difficult to select one detector among the large set of available detectors. Therefore, this paper, presents an extensive study and assessment of a set of well known unsupervised network anomaly detectors, and underlines their strengths and weaknesses. This study overwhelms previous similar evaluation by considering for the comparison some new, original and of premier importance parameters as detection similarity, detectors sensitivity and curse of dimensionality, together with the classical detection performance, and execution time parameters.

关键词： Unsupervised network anomaly detection Outlier detection Subspace PCA method clustering algorithm Curse of dimensionality

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Intelligent Production and Detection Template of Outlier Dataset Using clustering 5

Intelligent Production and Detection Template of Outlier Dat...

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IEEE 5th Conference on Knowledge Based Engineering and Innovation (KBEI)

作者： Kiani, Rasoul Montazeri, Mojtaba Minaei-Bidgoli, Behrouz Iran Univ Sci & Technol Sch Comp Engn Tehran Iran

ISBN: (纸本)9781728108728

Outliers are data with anomalous behaviors to other datasets. There are three different types of outliers, namely point anomaly, collective anomaly, and conditional anomaly. Different density-, clustering-, distance-, and distribution-based methods are used to detect outliers. It is obvious that before testing detection algorithms, a dataset that encompasses different types of outliers is required. In this paper an intelligent clustering algorithm is presented to produce a dataset consisting of different outliers. The other important point in this paper is the probability of two uninvestigated types of collective data among datasets that the anomalies are called type I and II. Results show that the proposed algorithm is capable of producing a dataset including different types of outliers. This dataset can be used in all outlier detection techniques. In addition to detection of point anomalies, it can detect all collective anomalies.

关键词： outlier detection anomaly detection clustering algorithm dataset

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