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18th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)

作者： Hu, Fang Li, Liuhuan Huang, Xiaoyu Huang, Panpan Cheng, Liang Hubei Univ Chinese Med Coll Informat Engn Wuhan Hubei Peoples R China Univ West Florida Dept Math & Stat Pensacola FL 32514 USA Hubei Univ Chinese Med Coll Basic Med Wuhan Hubei Peoples R China

ISBN: (纸本)9781728128658

Recent research in machine learning has led to significant progress in various research fields. Especially, the knowledge discovery using this method in Traditional Chinese Medicine (TCM) has been becoming a hot topic. In this paper, we studied on the herb compatibility rule of insomnia using some machine learning approaches. We have extracted insomnia data set with 807 samples from the real-world Electronic Medical Records (EMRs). After cleaning and selecting the theme data referring to the prescriptions and their herbs, we constructed the herb network analysis model using the theory of complex network. In order to explore the hidden relationships among the herbs, we trained each herb node in network to obtain the herb embeddings using the Skip-Gram model in word embedding theory. After acquiring the vocabulary of herbs with the formation of vectors, we calculated the similarity among any two herb embeddings, and clustered these herb embeddings into seven communities using the spectral clustering (SC) algorithm. The experimental results shed light on that the methodologies used in this paper can objectively and effectively discover the relationships among herbs, and reveal the herb compatibility and herb clusters for clinical treatment research of insomnia.

关键词： Insomnia Core Herb Herb Community Word Embedding spectral clustering algorithm

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Decomposition Characteristics of SF6 and Component Features Extraction Under Negative DC Partial Discharge 21st

Decomposition Characteristics of SF6 and Component Features ...

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21st International Symposium on High Voltage Engineering (ISH)

作者： Zhang, Mingxuan Miao, Yulong Yao, Qiang Zeng, Fuping Tang, Ju Wuhan Univ Wuhan Peoples R China State Grid Chongqing Elect Power Co Elect Power Res Inst Chongqing Peoples R China

ISBN: (纸本)9783030316761;9783030316754

To obtain the decomposition characteristics of SF6 under different types of negative DC partial discharge, and then establish a fault diagnosis method based on decomposition component analysis, the author extracts four typical defects that may appear in SF6 gas-insulated electrical equipment, and obtains the decomposition characteristics caused by the four kinds of defects under negative DC. The differences of SF6 characteristic decomposition component concentration and concentration ratio in the four cases are analysed, and the results show that the SF6 gas will decompose under the negative DC-PD caused by the four defects and generate five stable decomposed components that are CF4, CO2, SO2F2, SOF2, and SO2. The concentration and concentration ratio of five gas both change with its specific regulation. spectral clustering algorithm is used to identify and diagnose the two feature quantities, which shows that the two types of characteristic parameters can effectively reflect the PD type, and the concentration ratio performs better.

关键词： Negative DC PD Decomposition component of SF6 spectral clustering algorithm

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Cost optimization model design of fresh food cold chain system in the context of big data

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BIG DATA RESEARCH 2024年 35卷

作者： Wang, Lei Liu, Guangjun Ahmad, Ibrar Hanjiang Normal Univ Sch Econ & Management Shiyan 442000 Hubei Peoples R China Wuchang Univ Technol Business Sch Wuhan 430223 Hubei Peoples R China Univ Peshawar Dept Comp Sci Peshawar Pakistan

The assessment of cold chain logistics for fresh products can be more precise with high-dimensional information data, providing valuable insights for the optimization of associated costs. Nonetheless, traditional data processing techniques fail to meet the processing efficiency required for such high-dimensional cold chain logistics data. Therefore, this paper proposes a spectral clustering algorithm based on the local standard deviation and optimized initial center, which comprehensively analyzes the fixed, transportation, refrigeration, and cargo damage costs of cold chain logistics. Additionally, this algorithm includes a variation operator based on clustering and introduces a large neighborhood search mechanism for optimizing the individual connectivity gene layer after selecting the gene layer site for variation. Simulation results demonstrate that the proposed algorithm exhibits better convergence in 15 iterations, reduces error rates, and significantly cuts down on the clustering process time. This ultimately leads to a reduction in the total cost of cold chain calculation.

关键词： Fresh cold chain spectral clustering algorithm Variation Cost optimization

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Probability model of sensitive similarity measures in information retrieval

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2020年第1期17卷

作者： Gu, Xiaolong Zhang, Jie Chongqing Technol & Business Univ Natl Res Base Intelligent Mfg Serv Chongqing 400067 Peoples R China Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing Peoples R China

In today's Internet age, a lot of data is stored and used, which is very important. In people's daily life, if these data are sorted, information retrieval technology will be used, and in information retrieval, some information retrieval inaccuracies often appear. Information retrieval model is an important framework and method for fast, complete, and accurate user information retrieval. With the rapid development of information technology, great changes have taken place in people's production and life. Various information network technologies are widely used in people's lives. The resulting flow of information shows explosive growth, information retrieval. User requirements are getting higher and higher. How to complete personalized information retrieval in a large amount of mixed information, so that retrieval technology can help us obtain effective retrieval results, has become a realistic problem worth exploring. In this article, the application of probability model based on sensitive similarity measure in information retrieval model is analyzed, and a similarity measure algorithm based on spectral clustering is proposed. By improving the similarity measure, the sensitivity problem of scale parameters is overcome and the retrieval precision is improved. In order to better reflect the superiority of the proposed algorithm, this article compares with ng-jordan-weiss (NJW) and deep sparse subspace clustering (DSSC) algorithms. The experimental results show that the proposed algorithm is superior to NJW and DSSC algorithms for different data sets in different evaluation indicators (Rand and F-measure).

关键词： Information retrieval network technology similarity measure probability model spectral clustering algorithm

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Learning hidden Markov models with unknown number of states

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2022年 594卷 127047-127047页

作者： Zheng, Jing Yu, Dongjie Zhu, Bin Tong, Changqing Hangzhou Dianzi Univ Coll Econ Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Sch Sci Hangzhou 310018 Peoples R China

This paper proposes a novel method for learning hidden Markov models (HMMs) with an unknown number of states based on a valuable feature set. The feature set is built using the hitting times of HMMs. Based on the feature set, we obtain a parameter estimation for HMMs by SVD and the clustering algorithm. The advantages of the proposed method are that it can accurately and automatically identify the number of hidden states, it is robust to misspecified emission distributions, it is less sensitive to initialization, and is asymptotically consistent. Numerical experiments show that the proposed method performs better than other methods when the observed time series is long enough. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Hidden Markov model spectral clustering algorithm The first hitting time

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Autoregressive networks

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 10688-10756页

作者： Binyan Jiang Jialiang Li Qiwei Yao Department of Applied Mathematics Hong Kong Polytechnic University Hong Kong China Department of Statistics and Applied Probability National University of Singapore Singapore Department of Statistics London School of Economics London

We propose a first-order autoregressive (i.e. AR(1)) model for dynamic network processes in which edges change over time while nodes remain unchanged. The model depicts the dynamic changes explicitly. It also facilitates simple and efficient statistical inference methods including a permutation test for diagnostic checking for the fitted network models. The proposed model can be applied to the network processes with various underlying structures but with independent edges. As an illustration, an AR(1) stochastic block model has been investigated in depth, which characterizes the latent communities by the transition probabilities over time. This leads to a new and more effective spectral clustering algorithm for identifying the latent communities. We have derived a finite sample condition under which the perfect recovery of the community structure can be achieved by the newly defined spectral clustering algorithm. Furthermore the inference for a change point is incorporated into the AR(1) stochastic block model to cater for possible structure changes. We have derived the explicit error rates for the maximum likelihood estimator of the change-point. Application with three real data sets illustrates both relevance and usefulness of the proposed AR(1) models and the associate inference methods.

关键词： AR(1) networks change point dynamic stochastic block model Hamming distance maximum likelihood estimation spectral clustering algorithm Yule-Walker equation

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On Herb Compatibility Rule of Insomnia Based on Machine Learning Approaches

On Herb Compatibility Rule of Insomnia Based on Machine Lear...

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第十八届分布式计算及其应用国际学术研讨会

作者： Fang Hu Liuhuan Li Xiaoyu Huang Panpan Huang Liang Cheng College of Information Engineering Hubei University of Chinese Medicine Department of Mathematics and Statistics University of West Florida College of Basic Medicine Hubei University of Chinese Medicine

Recent research in machine learning has led to significant progress in various research ***,the knowledge discovery using this method in Traditional Chinese Medicine(TCM) has been becoming a hot *** this paper,we studied on the herb compatibility rule of insomnia using some machine learning *** have extracted insomnia data set with 807 samples from the real-world Electronic Medical Records(EMRs).After cleaning and selecting the theme data referring to the prescriptions and their herbs,we constructed the herb network analysis model using the theory of complex *** order to explore the hidden relationships among the herbs,we trained each herb node in network to obtain the herb embeddings using the Skip-Gram model in word embedding *** acquiring the vocabulary of herbs with the formation of vectors,we calculated the similarity among any two herb embeddings,and clustered these herb embeddings into seven communities using the spectral clustering(SC) *** experimental results shed light on that the methodologies used in this paper can objectively and effectively discover the relationships among herbs,and reveal the herb compatibility and herb clusters for clinical treatment research of insomnia.

关键词： Insomnia Core Herb Herb Community Word Embedding spectral clustering algorithm

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clustering Wind Turbines for a Large Wind Farm Using spectral clustering Approach Based on Diffusion Mapping Theory

Clustering Wind Turbines for a Large Wind Farm Using Spectra...

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IEEE International Conference on Power System Technology

作者： Li LIN Ying CHEN Ningbo WANG North China Electric Power University Beijing Beijing CN State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China Gansu Electric Power Company Lanzhou China

Increasing number of wind farms connected to power system calls for a simplified equivalent model of wind farm so that it can be represented by only a few equivalent wind turbines for dynamic system studies. In order to aggregate wind turbines in complex terrain or irregular layout, this paper presents a new wind turbine clustering method based on spectral clustering algorithm. The clustering principle relies on the same or similar operating points of selected wind turbines. The proposed method is used to capture the similarity of characteristics of wind turbines by analyzing real-time data sets of wind turbines in a wind farm. Thus wind turbines of a wind farm can be clustered into different groups. The clustering method is demonstrated and tested in an actual wind power system. A good performance is achieved for all simulation scenarios while comparing the aggregated and detailed models.

关键词： clustering wind turbines Dynamic equivalence spectral clustering algorithm Wind farm Diffusion mapping theory

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Methodology for Real-Time Evaluation of Geographic Health Care Resource Allocation: Iwate Prefectural Hospitals

Methodology for Real-Time Evaluation of Geographic Health Ca...

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2021第五届经济，金融与管理科学国际会议

作者： Xinhe Li Kazunori Kawamura Graduate School of Information Sciences Tohoku University

The effective utilization and assessment of medical resources have become a common concern for scholars in various countries due to the impact of the COVID-19 pandemic. This study deals with a method to monitor medical resource allocation in real-time and verify the effectiveness of the proposed method with actual data. In this work, we selected Iwate Prefecture in northeastern Japan based on the geographical characteristics and social environment. By collecting data from the Japanese Ministry of Land, Infrastructure, Transport, and Tourism and Welfare(MLIT), we clustered population centers in Iwate Prefecture, and found the clustering centers in densely populated areas from the k-means algorithm. Subsequently, to compare the distribution of county-level medical resources across different secondary care areas, we selected the indicators of Iwate Prefectural Hospitals from the Hospital Intelligence Agency. We classified 19 prefectural hospitals in Iwate Prefecture into four different clusters using the spectral clustering algorithm. The clustering results revealed that all hospitals close to the " clustering centers in densely populated areas" were Iwate prefectural disaster stronghold hospitals. Moreover, we found that these hospitals performed well in operational indicators. Only the Ninohe prefectural hospital in the Ninohe medical area was found not located in a population center. However, it still performs well in terms of business indicators since the Ninohe medical area has a high proportion of public hospitals and the Ninohe prefectural hospital plays an important role. Hence, the government should fully consider geographical characteristics when considering hospital restructuring. We used a real data set to demonstrate the validity of the proposed technique, providing a theoretical basis for the government's healthcare policy.

关键词： Medical Resources Real-time Evaluation Method K-means algorithm spectral clustering algorithm Iwate Prefectural Hospitals

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How university students assess the planetary boundaries: A global empirical study

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Environmental Challenges 2023年 11卷

作者： Kleespies, Matthias Winfried Hahn-Klimroth, Max Dierkes, Paul Wilhelm Department of Bioscience Education and Zoo Biology Goethe University Frankfurt Germany Faculty of Computer Science TU Dortmund University Dortmund 44227 Germany

In order to effectively address global environmental problems, it is important that future decision-makers in society are aware of the safe operation space for humans, which is limited by the planetary boundaries. Until now, however, there has been a lack of international studies examining how the planet's boundaries are perceived. In this study, we investigated how students of environmental and sustainability studies in 35 countries (n = 4140) assess the planetary boundaries. Based on the rating, using spectral clustering, the 35 countries were assigned to five different clusters. Four indicators (Human Development Index, Legatum Prosperity Index, Natural Resources Income and Forest Area) were used to provide explanations for the clustering result. The indices allow a distinction between the clusters and provide initial explanations for the clustering. The results provide important insights for today's decision-makers, as possible measures for action in the individual countries can be derived from the findings. © 2023 The Author(s)

关键词： International survey Planetary boundaries spectral clustering algorithm University students

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