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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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DYNAMIC IDENTIFICATION OF WEAR STATE BASED ON NONLINEAR PARAMETERS

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FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY 2019年第5期27卷

作者： Zuo, Xue Zhou, Yuankai Ma, Chenbo Fang, Haifeng Jiangsu Univ Sci & Technol Sch Mech Engn Zhenjiang 212003 Jiangsu Peoples R China China Univ Min & Technol Sch Mechatron Engn Xuzhou 221116 Jiangsu Peoples R China Nanjing Forestry Univ Sch Mech & Elect Engn Nanjing 210000 Jiangsu Peoples R China Jiangsu Univ Sci & Technol Suzhou Inst Technol Zhangjiagang 215600 Peoples R China

This paper presents a new methodology of wear state recognition by using fractal parameters, nuillifractal parameters and recurrence parameters. The relationship between these nonlinear parameters is analyzed. A nonlinear state point of worn surface is established by fractal dimension, average diagonal length and spectrum width. Further, a steady state sphere is obtained by the nonlinear state point and K-means clustering algorithm. Results show that fractal, multifractal and recurrence parameters characterize the worn surface from different perspectives. They should be used simultaneously to comprehensively characterize the integral structures, partial structures and internal structures of worn surface. The proposed nonlinear state point shows a variation process of concentration-stabilization-separation during the wear process. The wear states can be identified effectively by the relationship between nonlinear state points and steady state sphere.

关键词： Wear State Recognition Fractal Dimension Spectrum Width Average Diagonal Length clustering algorithm

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Blind speech source separation via nonlinear time-frequency masking

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Chinese Journal of Acoustics 2008年第3期27卷 203-214页

作者： XU Shun CHEN Shaorong LIU Yulin DSP Lab. Chongqing Communication College Chongqing 400035

Aim at the underdetermined convolutive mixture model, a blind speech source separation method based on nonlinear time-frequency masking was proposed, where the approximate W-disjoint orthogonality （W-DO） property among independent speech signals in time-frequency domain is utilized. In this method, the observation mixture signal from multimicrophones is normalized to be independent of frequency in the time-frequency domain at first, then the dynamic clustering algorithm is adopted to obtain the active source information in each time-frequency slot, a nonlinear function via deflection angle from the cluster center is selected for time-frequency masking, finally the blind separation of mixture speech signals can be achieved by inverse STFT （short-time Fourier transformation）. This method can not only solve the problem of frequency permutation which may be met in most classic frequency-domain blind separation techniques, but also suppress the spatial direction diffusion of the separation matrix. The simulation results demonstrate that the proposed separation method is better than the typical BLUES method, the signal-noise-ratio gain （SNRG） increases 1.58 dB averagely.

关键词： separation method blind separation Fourier transformation clustering algorithm simulation results nonlinear function deflection angle information in cluster center mixture model

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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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Data Mining Study of Chinese Herbal Fumigation and Washing in the Treatment of Perianal Eczema

International Core Journal of Engineering

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International Core Journal of Engineering 2022年第8期8卷 867-876页

作者： Jiyun Wu Yaping Cui Qiangqiang Gao Lu Lin Yan Ou Yanrong Zhan

Objective To explore the law of Chinese herbal fumigation and washing in the treatment of perianal eczema through data mining. Methods China National Knowledge Network (CNKI), VIP Chinese Journal Server (VIP), Wanfang Statistical Information Public Service network platform (Wanfang Statistics), National Biomedical Literature Research Information Service Network Management System (SinoMed) were retrieved from the database construction to May 2020 On the research paper on the treatment of perianal eczema by traditional Chinese medicine fumigating and washing. According to the prescriptions recorded in the collected articles, Microsoft Excel or WPS was used to establish a database to analyze the drug frequency, sexual taste and meridian return, etc. IBM SPSS Statistics 18.0 statistical software was used to analyze the data of association rules based on association rules and Apriori *** 286 articles were included, including 283 prescriptions, including 155 traditional Chinese medicines, 22 of which were more frequently used than 35. Sophora flavescens was the most frequently used drug. The medicinal taste of 155 is mainly bitter cold and simmering temperature, and mainly goes to the liver meridian. The efficacy classification is mainly heat-clearing medicine. In the association rule, the core drug pair of Sophora flavescens and Phellodendron phellodendri was the association rule (support = 73.85).Conclusion TCM fumigating and washing prescription drugs for perianal eczema are mainly heat-clearing drugs, accompanied by poison, insecticide and antipruritic drugs, antipruritic drugs, etc. Sophora flavescens, Phellodendron phellodendri, Cnidium fruit, Difuzi fruit, Fructus diorrhizae, rhizoma dioscoreae, parsnip and so on are frequently used.

关键词： Perianal Eczema Fumigation of Traditional Chinese Medicine Data Mining clustering algorithm

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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 103616-103616页

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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Improvement of Features Extraction Process and Classification of Cervical Cancer for the NeuralPap System

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Procedia Computer Science 2015年 60卷 750-759页

作者： Siti Noraini Sulaiman Nor Ashidi Mat-Isa Nor Hayati Othman Fadzil Ahmad Faculty of Electrical Engineering Universiti Teknologi MARA Penang Campus 13500 Permatang Pauh Penang MALAYSIA. Imaging and Intelligent System Research Team (ISRT) School of Electrical & Electronics Engineering Universiti Sains Malaysia Engineering Campus 14300 Nibong Tebal Penang MALAYSIA. Clinical Research Platform School of Medical Sciences Universiti Sains Malaysia 16150 Kubang Kerian Kelantan MALAYSIA

Cervical cancer has caused many deaths each year. Screening tests, such as Pap smear test used for the detection of the precancerous stage are able to avoid the occurrence of cervical cancer. However, the Pap smear test has several disadvantages such as less effective slides preparation and human error. Therefore, a computer-aided diagnosis system is introduced as a solution to the problem. One of the diagnostic systems that has been built is NeuralPap. However, the NeuralPap performance is limited by several constraints. This research proposed several new image processing algorithms to reduce these constraints. The Adaptive Fuzzy-k-Means (AFKM) clustering algorithm is proposed to replace the Moving k-Means (MKM) to segment Pap smear images into the nucleus, cytoplasm and background regions. Next, the feature extraction algorithm based on pseudo colouring called the Pseudo Colour Feature Extraction (PCFE) manual and Semi-Automatic PCFE are designed to replace the Region Growing Based Feature Extraction (RGBFE) which uses monochromatic images. This research is a step forward compared with the NeuralPap system by proposing the feature extraction algorithm for overlapping cells by combining the concept of colour space with Semi-Automatic PCFE algorithm. In addition, this research has also suggested the AFKM algorithm as a new centre positioning algorithm for the Radial Basis Function (RBF) and Hybrid RBF (HRBF) networks replacing the MKM algorithm. The entire proposed algorithm has been proven to produce better performance than the corresponding algorithm used in the NeuralPap. In addition, the combination of all algorithms has managed to increase the accuracy of the classification of cervical cancer to 76.35%, compared with 73.40% which is obtained from the previous NeuralPap system.

关键词： Diagnosis System Neural Network Region Growing Feature Extraction Cervical Cancer clustering algorithm

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Cluster-Based Quality-Aware Adaptive Data Compression for Streaming Data

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ACM JOURNAL OF DATA AND INFORMATION QUALITY 2017年第1期9卷 2-2页

作者： Basheer, Aseel Sha, Kewei Univ Baghdad Coll Sci Women Dept Comp Sci Baghdad Iraq Univ Houston Dept Comp Sci Houston TX 77058 USA Univ Houston Cyber Secur Inst Houston TX 77058 USA

Wireless sensor networks (WSNs) are widely applied in data collection applications. Energy efficiency is one of the most important design goals of WSNs. In this article, we examine the tradeoffs between the energy efficiency and the data quality. First, four attributes used to evaluate data quality are formally defined. Then, we propose a novel data compression algorithm, Quality-Aware Adaptive data Compression (QAAC), to reduce the amount of data communication to save energy. QAAC utilizes an adaptive clustering algorithm to build clusters from dataset;then a code for each cluster is generated and stored in a Huffman encoding tree. The encoding algorithm encodes the original dataset based on the Haffman encoding tree. An improvement algorithm is also designed to reduce the information loss when data are compressed. After the encoded data, the Huffman encoding tree and parameters used in the improvement algorithm have been received at the sink, a decompression algorithm is used to retrieve the approximation of the original dataset. The performance evaluation shows that QAAC is efficient and achieves a much higher compression ratio than lossy and lossless compression algorithms, while it has much smaller information loss than lossy compression algorithms.

关键词： Wireless sensor networks data quality adaptive clustering algorithm energy efficiency data compression

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Meshless numerical manifold method with novel subspace tracking and CSS locating techniques for slope stability analysis

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COMPUTERS AND GEOTECHNICS 2024年 166卷

作者： Cao, Xitailang Lin, Shan Liang, Zenglong Guo, Hongwei Zheng, Hong Beijing Univ Technol Key Lab Urban Secur & Disaster Engn Minist Educ Beijing 100124 Peoples R China Hong Kong Polytech Univ PolyU Dept Civil & Environm Engn Hung Hom Kowloon Hong Kong Peoples R China

The meshless numerical manifold method (MNMM) inherits the advantages of handling continuous and discontinuous problems uniformly, improving interpolation precision, and avoiding linear dependence issues. This paper refines the MNMM method for slope stability analysis to overcome the dependence and sensitivity of the strength reduction method of the finite element method on the number of finite element meshes and the algorithm for solving the nonlinear equations when calculating slopes with soft and weak interlayers. First, the strength reduction method (SRM) was incorporated into MNMM to create the numerical model called MNMM-SRM. Based on the MNMM-SRM, a complementary Mohr-Coulomb (M-C) control equation compliant with Koiter's rule of multi-yielding surfaces was established. In the principal stress space, the subspace tracking method was used to track the eigenvalues and eigenvectors of the stress tensor, thereby addressing the corner problem of M-C multi-yield surfaces. Combining the k-means clustering algorithm with wavelet transformation and employing the slope's total nodal displacement field as an input variable, an intelligent and efficient critical slip surface (CSS) automatic extraction method was developed. Three typical cases were used to validate the accuracy of the numerical model. This numerical model has a wide range of potential applications in slope stability research.

关键词： Meshless Numerical Manifold Method Strength reduction method Slope stability analysis clustering algorithm Wavelet transformation

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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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