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Risk Control of Energy Performance Fluctuation in Multi-Unit Housing for Weather Uncertainty

BUILDINGS 2023年第7期13卷

作者： Wang, Jiahe Mae, Masayuki Taniguchi, Keiichiro Univ Tokyo Grad Sch Engn Dept Architecture 7-3-1 Hongo Bunkyo Ku Tokyo 1138656 Japan

With the acceleration of urban development, the population density of urban cities has increased. As the spatial characteristics of multi-unit housing (MUH) perfectly fit this developmental trend and, simultaneously, have high energy efficiency, the number of MUHs has increased rapidly in recent decades. Although many studies have proposed high energy efficiency strategies, weather uncertainty leads to errors between the operational performance of building energy and simulated values. This study introduces a robust optimization framework that incorporates uncertainty considerations into the optimization process to suppress energy consumption fluctuations and improve the average building energy consumption performance. Neural networks are used to model the uncertainty of multiple weather elements as normal distributions for each hour, and the accuracy of the uncertainty model is validated by calculating the mean absolute percentage error (MAPE) between the mean values of the distribution and the measurement values, which ranges from 3% to 13%. The clustering algorithm is proposed to replace the sampling method to complete the sampling work from the normal distribution space of the weather elements to serve the subsequent optimization process. Compared with the traditional method, the sampling results of the clustering algorithm show better representativeness in the sample space. The robust optimization results show that the average energy consumption of the optimal scheme decreases by 13.4%, and the standard deviation decreases by approximately 17.2%, which means that the optimal scheme, generated by the robust optimization framework proposed in this study, has lower average energy consumption results and a more stable energy consumption performance in the face of weather uncertainty.

关键词： building performance gap robust optimization uncertain weather conditions deep learning clustering algorithm multi-unit housing

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Design of Computer Network Security Intrusion Prevention Strategy and Evaluation algorithm Analysis Technology

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Procedia Computer Science 2023年 228卷 1270-1276页

作者： Fengzheng Wang Shandong Transport Vocational College Weifang Shandong China

With the continuous improvement of global informatization, computer networks have basically reached complete popularization. In today's society, it has become a major application and provides corresponding services for various industries. Therefore, it is necessary to effectively protect various information in the network. Therefore, this article provided a detailed discussion on the problems existing in computer network information security. These issues included their own reasons, hacker intrusion, and spam. Based on this, this article explored computer network information security protection strategies such as deploying data encryption, timely patching system vulnerabilities, and installing firewalls and antivirus software. The experimental results showed that the network output values of the clustering algorithm were: 0.888 for level 1, 0.725 for level 2, 0.678 for level 3, 0.461 for level 4, and 0.211 for level 5. These were all within the scope.

关键词： Intrusion Prevention Strategy Computer Network Security Evaluation algorithm Analysis clustering algorithm

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RESEARCH ON DATA SECURITY DETECTION algorithm IN IOT BASED ON K-MEANS

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2021年第2期22卷 149-159页

作者： Zhu, Jianxing Huo, Lina Ansari, Mohd Dilshad Ikbal, Mohammad Asif XingTai Univ Coll Math & Informat Technol Xingtai 054001 Peoples R China CMR Coll Engn & Technol Dept Comp Sci & Engn Hyderabad India Lovely Profess Univ Dept Elect Engn Phagwara Punjab India

The development of the Internet of Things has prominently expanded the perception of human beings, but ensuing security issues have attracted people's attention. From the perspective of the relatively weak sensor network in the Internet of Things. Proposed method Aiming at the characteristics of diversification and heterogeneity of collected data in sensor networks, the data set is clustered and analyzed from the aspects of network delay and data flow to extract data characteristics. Then, according to the characteristics of different types of network attacks, a hybrid detection method for network attacks is established. An efficient data intrusion detection algorithm based on K-means clustering is proposed. This paper proposes a network node control method based on traffic constraints to improve the security level of the network. Simulation experiments show that compared with traditional password-based intrusion detection methods;the proposed method has a higher detection level and is suitable for data security protection in the Internet of Things. This paper proposes an efficient intrusion detection method for applications with Internet of Things.

关键词： Internet of things intrusion detection clustering algorithm network security

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A Text Mining Model Based on Improved Density clustering algorithm

A Text Mining Model Based on Improved Density Clustering Alg...

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4th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC)

作者： Chen Qi Lu Jianfeng Zhang Hao Tongji Univ CIMS Ctr Shanghai 200092 Peoples R China

ISBN: (纸本)9781467349338

The clustering algorithm based on density is widely used on text mining model, for example the DBSCAN(density-based spatial clustering of application with noise) algorithm. DBSCAN algorithm is sensitive in choose of parameters, it is hard to find suitable parameters. In this paper a method based on k-means algorithm is introduced to estimate the E neighborhood and minpts. Finally an example is given to show the effectiveness of this algorithm.

关键词： clustering algorithm text mining density clustering

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The potential of Wi-Fi data to estimate bus passenger mobility

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TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 2023年第1期192卷

作者： Lea, Fabre Caroline, Bayart Bonnel, Patrick Mony, Nicolas Univ Lumiere Lyon 2 Lab Amenagement Econ Transports 3 Rue Maurice Audin F-69120 Vaulx En Velin France Univ Lumiere Lyon 1 Lab Sci Actuarielles & Financieres 50 Ave Tony Garnier F-69007 Lyon France Explain 36 Bd Canuts F-69004 Lyon France

Last decades have been marked by deep socio-economic transformations, an uneven evolution of transport demand in main urban areas and the emergence of new and more sustainable modes of transportation (carpooling, self-services bicycles). These changes have strongly impacted the interaction between service supply and demand in the transport industry. In this context, passive data as Wi-Fi and Bluetooth become a key source of information to understand individual mobility behaviors and ensure the sustainable development of transport infrastructures. In this paper, we present a framework that uses disruptive technology to collect passive data in buses, continuously and at a lower cost than traditional mobility surveys. Previous research, conducted over a more limited spatial and temporal framework, uses filtering methods, which do not allow the results to be replicated. This study uses artificial intelligence to sort transmitted signals, get transit ridership and build Origin-Destination matrices. Its originality consists in providing a concrete, automatic and replicable method to transport operators. The comparison of the results with other data sources confirms the relevance of the presented algorithms in demand forecasting. Therefore, our findings provide interesting insights for data-driven decision making and service quality management in urban public transport.

关键词： Passive data analytics Wi-Fi sensors clustering algorithm Origin-Destination matrices Travel behavior Public transport demand

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ESC: An efficient synchronization-based clustering algorithm

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KNOWLEDGE-BASED SYSTEMS 2013年 40卷 111-122页

作者： Huang, Jianbin Sun, Heli Kang, Jianmei Qi, Junjie Deng, Hongbo Song, Qinbao Xidian Univ Sch Software Xian 710071 Peoples R China Xi An Jiao Tong Univ Dept Comp Sci & Technol Xian 710049 Peoples R China Univ Illinois Dept Comp Sci Urbana IL 61801 USA

clustering is an essential approach for detecting the intrinsic groups in data. An efficient clustering algorithm based on a generalized local synchronization model is proposed. It uses a novel stopping criterion of data synchronization to detect clusters prior to the perfect synchronization. Moreover, a density-biased sampling method is adopted to extract samples from the original data set. The clustering structure can be effectively revealed on the samples. As a result, the clustering efficiency is significantly improved. By using a cluster validity criterion, the proposed algorithm can find clusters of arbitrary number, shape, size and density as well as isolate noises in the vector data without any data distribution assumption. Extensive experiments on several synthetic and real-world data sets show that the proposed algorithm possesses high accuracy and it is more efficient than the state-of-the-art synchronization-based clustering method. (C) 2012 Elsevier B.V. All rights reserved.

关键词： clustering algorithm Dynamical synchronization model Neighborhood closure Density-biased sampling Cluster validity criterion

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Anomaly Detection in Student Activity in Solving Unique Programming Exercises: Motivated Students against Suspicious Ones

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DATA 2023年第8期8卷 129页

作者： Demidova, Liliya A. Sovietov, Peter N. Andrianova, Elena G. Demidova, Anna A. MIREA Russian Technol Univ Fed State Budget Educ Inst Higher Educ Inst Informat Technol 78 Vernadsky Ave Moscow 119454 Russia

This article presents a dataset containing messages from the Digital Teaching Assistant (DTA) system, which records the results from the automatic verification of students' solutions to unique programming exercises of 11 various types. These results are automatically generated by the system, which automates a massive Python programming course at MIREA-Russian Technological University (RTU MIREA). The DTA system is trained to distinguish between approaches to solve programming exercises, as well as to identify correct and incorrect solutions, using intelligent algorithms responsible for analyzing the source code in the DTA system using vector representations of programs based on Markov chains, calculating pairwise Jensen-Shannon distances for programs and using a hierarchical clustering algorithm to detect high-level approaches used by students in solving unique programming exercises. In the process of learning, each student must correctly solve 11 unique exercises in order to receive admission to the intermediate certification in the form of a test. In addition, a motivated student may try to find additional approaches to solve exercises they have already solved. At the same time, not all students are able or willing to solve the 11 unique exercises proposed to them;some will resort to outside help in solving all or part of the exercises. Since all information about the interactions of the students with the DTA system is recorded, it is possible to identify different types of students. First of all, the students can be classified into 2 classes: those who failed to solve 11 exercises and those who received admission to the intermediate certification in the form of a test, having solved the 11 unique exercises correctly. However, it is possible to identify classes of typical, motivated and suspicious students among the latter group based on the proposed dataset. The proposed dataset can be used to develop regression models that will predict outbursts of student activ

关键词： unique programming exercises tasks Python Digital Teaching Assistant anomalies detection typical students motivated students suspicious students clustering algorithm UMAP algorithm

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A clustering algorithm-based control chart for inhomogeneously distributed TFT-LCD processes

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2013年第18期51卷 5644-5657页

作者： Kang, Ji Hoon Kim, Seoung Bum Korea Univ Sch Ind Management Engn Seoul South Korea

Statistical process control techniques have been widely used to improve processes by reducing variations and defects. In the present paper, we propose a multivariate control chart technique based on a clustering algorithm that can effectively handle a situation in which the distribution of in-control observations is inhomogeneous. A simulation study was conducted to examine the characteristics of the proposed control chart and to compare them with Hotelling's T-2 multivariate control charts that are widely used in real-world processes. Moreover, an experiment with real data from the thin film transistor liquid crystal display (TFT-LCD) manufacturing process demonstrated the effectiveness and accuracy of the proposed control chart.

关键词： bootstrap clustering algorithm Hotelling's T-2 multivariate control chart TFT-LCD

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Research on Traffic Classification for Space-Ground Integrated Network Based on Feature clustering 2021

Research on Traffic Classification for Space-Ground Integrat...

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2021 The 9th International Conference on Information Technology: IoT and Smart City

作者： Lei Gu Zhongliang Deng Yicheng Liao Ke Wang Wenliang Lin Xiaoyi Yu Hao Liu Yang Liu Xiangfeng Kong School of Electronic Engineering Beijing University of Posts and Telecommunications China School of Information and Communication Engineering Beijing University of Posts and Telecommunications China

ISBN: (纸本)9781450384971

The space-ground integrated information network takes the space-based network as the main body, the ground network as the foundation and according to the type of service, it can access multiple users in real time and realize the communication of a large number of users. With the development of satellite communication and the popularization of 5G networks, the space-ground integrated network has received more and more attention. However, due to problems such as strong traffic bursts and time-varying topologies in the space-ground integrated information network, communication is easily interrupted. In order to make the traffic prediction more accurate, based on the feature clustering, this paper studies the traffic identification of the space-ground integrated network, and designs a traffic sensing algorithm based on the density peak clustering. According to traffic characteristics and applicable scenarios, analyze and process traffic perception from service and functional perspective. According to the functional requirements, this paper design the overall framework of the clustering algorithm. Finally, realize and compare the algorithm on OPNET simulation platform.

关键词： space-ground integrated information network clustering algorithm traffic awareness

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The identification of power IoT devices on differential constellation trajectory map 13

The identification of power IoT devices on differential cons...

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13th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)

作者： Li, Qinyuan Sun, Xin Lv, Pang Sun, Changhua State Grid Zhejiang Elect Power Co Ltd Res Inst Hangzhou 310014 Zhejiang Peoples R China

ISBN: (纸本)9781665438926

As a long-distance wireless transmission technology based on spread spectrum, LoRa is a hot research object in the field of power Internet of Things. To solve the problem of terminal authentication, this paper proposes a LoRa device identification method based on a differential constellation trajectory map, which skillfully transforms the radiofrequency fingerprint feature matching problem into the image processing problem. This method first obtains the constellation locus of the received signal. It performs differential processing and then USES the clustering algorithm to get the constellation locus's clustering center. It then calculates the Euclidean distance between the clustering centers of two devices to obtain the similarity between them and USES this as a basis for equipment identification. The experimental results show that the LoRa device recognition method based on differential constellation trajectory map can effectively identify five LoRa transmission modules and have a high recognition accuracy even in low SNR.

关键词： LoRa Radio frequency fingerprint Differential constellation trajectories clustering algorithm

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