Study region: Dongqing Reservoir located in Guizhou, China. Study focus: Zoning the RWTF (reservoir water temperature field) is of great significance and is an effective way to analyze RWTF's nature. In practice, ...
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Study region: Dongqing Reservoir located in Guizhou, China. Study focus: Zoning the RWTF (reservoir water temperature field) is of great significance and is an effective way to analyze RWTF's nature. In practice, the conditions of RWTF fluctuate greatly as time goes on, which leads to the existing RWTF zoning methods can't give a steady zoning result. In consequence, this paper creates a kind of zoning method to study the properties of RWTF in Dongqing Reservoir, which has two main steps: firstly, numerical simulation is used to obtain the whole data of RWTF, and then the k-means clustering algorithm is executed based on the numerical simulation results. New hydrological insights for the region: This paper proved that the zoning method developed in this paper, which combined numerical simulation and unsupervised machine learning, can be effectively applied and divided the RWTF into four zones without using experimental parameters in Dongqing Reservoir. Moreover, on the base of the four zones' spatial borders, the influencing factors of water temperature in each zone of Dongqing Reservoir were able to be found, which would be of value in further research of RWTF.
Nowadays, the business market is more complicated and comprises many challenges;it became more competitive and surrounded by high-risk patterns. Seeking for new technologies and adopting innovation is becoming an impo...
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ISBN:
(纸本)9781728109039
Nowadays, the business market is more complicated and comprises many challenges;it became more competitive and surrounded by high-risk patterns. Seeking for new technologies and adopting innovation is becoming an important and crucial issue to eliminate the complexity of the decision-making process and failure probability. Decision support system (DSS) is a computerized system that encompasses mathematical and analytical models, knowledge base and a user interface to help managers for making better decisions. This research aims to develop a decision support system based on k-means clustering algorithm to detect the optimal store location through social network events. Also, this research explains how to extract data from one social network channel "Instagram" using the "Octoparse API" as a web data extraction tool. k-meansalgorithm identifies k-number of centroids, and allocates every data point to the nearest cluster. As a result, we analyzed 12754 posts started on the 1st of January 2019. Cleaned data are transformed using Minimax and k-meansalgorithms. As an output, we have got json format data file with centres which are placed on the map to provide a better understanding. The Result of this research is a visualized map pointed with places to define the optimal location of a specific store at the selected region. The practical value of this DSS tool is to help users to make a more valuable and accurate decision which lead to a decrease in the probability of ineffective business decision and minimize the business losses.
Considering the shortcomings of existing clusteringalgorithms in clustering quality, this paper proposes a load clustering research based on singular value decomposition and k-means clustering algorithm. First, eight...
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ISBN:
(数字)9781728167824
ISBN:
(纸本)9781728167824
Considering the shortcomings of existing clusteringalgorithms in clustering quality, this paper proposes a load clustering research based on singular value decomposition and k-means clustering algorithm. First, eight characteristic indexes of load are extracted, and the singular value is used to decompose the load characteristics of the user side. The solved singular value reflects the importance of this type of load characteristic. The load characteristics corresponding to the data with large singular value are taken as the main load characteristics to complete the dimensionality reduction of the data. Then, the clustering evaluation index SSE is used to compare the effects of direct clustering of load characteristics and clustering after singular value decomposition. The results show that the proposed method has better clustering effect on load characteristics. Finally, k-means clustering algorithm is used to cluster the load characteristics.
Computational fluid dynamics (CFD) modelling is a scientific tool to provide fluid dynamics and chemical simulation that facilitates understanding of the complex combustion phenomenon in engine studies. With the advan...
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Computational fluid dynamics (CFD) modelling is a scientific tool to provide fluid dynamics and chemical simulation that facilitates understanding of the complex combustion phenomenon in engine studies. With the advance of Machine Learning (ML) technology, the big data from CFD results can be intelligently recognized and classified, thus ease the data post-processing. This study proposed an integrated analysis that uses CFD simulation results of scalar distributions and k-means clustering algorithm to optimally partition engine combustion chamber into different zones. Therefore, the space of combustion chamber was automatically divided into light soot zones and heavy soot zones based on the clustering results on local equivalence ratio (ER) and temperature. Consequently, the surveys of soot mitigation by Reactivity Controlled Compression Ignition (RCCI) engines combustion mode were carried out as well as corresponding sooting tendency by CFD numerical study. The localized soot depositions in each zone under varied combustion boundaries were compared, hence improving the development of control strategy with numerical modellings and machine learning techniques.
Information based human resource management is a complete solution to enterprise human resource management through information technology. It is a new human resource management mode based on advanced software and high...
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ISBN:
(纸本)9781450387828
Information based human resource management is a complete solution to enterprise human resource management through information technology. It is a new human resource management mode based on advanced software and high-speed and large-capacity hardware. It can reduce costs, improve efficiency and improve employees through centralized information base, automatic processing of information, self-service, outsourcing and service sharing. The purpose of the service model. It is connected with the existing network technology of enterprises and institutions to ensure the synchronous development of the technological environment with the rapid development of human resources. Human resource management is to optimize the allocation of human resource management in a planned way according to the development needs of the organization. Through the recruitment, training, assessment, incentive and other aspects of the staff, the enthusiasm of the staff can be brought into play, so as to create maximum benefits for the organization. Human resources are the most important resources in today's society. How to optimize the allocation and efficient use of human resources management departments should fully consider the problem. In today's data and information technology developed today, enterprises and institutions should use the Internet, big data and other means to scientifically transform, optimize and innovate the human resources management of institutions, and expand more new paths.
The severity of ill effects (SEV) index is based on the limited meta-analysis of previous peer reviewed reports and consultations, and described as a function of duration of exposure to turbid conditions in fisheries ...
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The severity of ill effects (SEV) index is based on the limited meta-analysis of previous peer reviewed reports and consultations, and described as a function of duration of exposure to turbid conditions in fisheries or fish life stages by fish adapted to life in clear water ecosystems. In this study, the performance of classification by SEV index was investigated using the k-means clustering algorithm. This study is based on 303 tests undertaken on aquatic ecosystem quality over a wide range of sediment concentrations (1-50,000 mg SS/L) and durations of exposure (1-35,000 h). Training and testing data includes concentration of suspended sediment, duration of exposure, species and life stages as the input variables and the SEV index for fish as the output variable. Results indicate that the k-means clustering algorithm, as an efficient novel approach with an acceptable range of error, can be used successfully for improving the performance of classification by SEV index.
clustering has been widely applied in interpreting the underlying patterns in microarray gene expression profiles, and many clusteringalgorithms have been devised for the same. k-means is one of the popular algorithm...
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clustering has been widely applied in interpreting the underlying patterns in microarray gene expression profiles, and many clusteringalgorithms have been devised for the same. k-means is one of the popular algorithms for gene data clustering due to its simplicity and computational efficiency. But, k-meansalgorithm is highly sensitive to the choice of initial cluster centers. Thus, the algorithm easily gets trapped with local optimum if the initial centers are chosen randomly. This paper proposes a deterministic initialization algorithm for k-means (Dk-means) by exploring a set of probable centers through a constrained bi-partitioning approach. The proposed algorithm is compared with classical k-means with random initialization and improved k-means variants such as k-means++ and MinMax algorithms. It is also compared with three deterministic initialization methods. Experimental analysis on gene expression datasets demonstrates that Dk-means achieves improved results in terms of faster and stable convergence, and better cluster quality as compared to other algorithms.
This paper aims to provide an insight into the roles of the different types of airports in China by improved k-means clustering algorithm. The first part of the work analyzed the characteristics of Chinese airline net...
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ISBN:
(纸本)9783030364052;9783030364045
This paper aims to provide an insight into the roles of the different types of airports in China by improved k-means clustering algorithm. The first part of the work analyzed the characteristics of Chinese airline network and pointed out that the key to construct hub-and-spoke airline network is determining the function of each airport. The index system of airport function orientation was established from airport operation index, airport hinterland index and airport growth index. The airports in China were classified into four classes by the k-means clustering algorithm. In order to improve reliability of clusteringalgorithm, a formula was used to normalize the value of each index, and the airports were clustered by improved k-means clustering algorithm. The algorithm was simulated by the MATLAB and the clustered results show the airports have obvious hierarchy.
Anomaly detection in specific datasets involves the detection of circumstances that are common in a homogeneous data. When looking at network traffic data, it is generally difficult to determine the type of attack wit...
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Anomaly detection in specific datasets involves the detection of circumstances that are common in a homogeneous data. When looking at network traffic data, it is generally difficult to determine the type of attack without proper analysis and this holds true when simply viewing a record of network traffic with thousands of internet users to detect malicious activity. However, there are different types of datasets in light of the way they record or acquire data and facts. The paper aims to compare and analyse multiple datasets including NSL-kDD and MAWI by using k-means clustering algorithm. Specifically, the paper analyses the blind-Spots of the datasets and evaluates the most suitable dataset for k-means clustering algorithm. This paper's quantitative data analysis results are helpful in evaluating weaknesses of each dataset of traffic data, when using k-means clustering algorithm.
Energy storage refers to a series of related technologies that achieve energy reserve and release by some ways when needed. Energy storage technology plays an important role in peak shaving and valley filling, improvi...
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ISBN:
(纸本)9781728119731
Energy storage refers to a series of related technologies that achieve energy reserve and release by some ways when needed. Energy storage technology plays an important role in peak shaving and valley filling, improving the equipment utilization efficiency, enhancing the system safety and so on. It has great significances to realize the transformation of energy structure and the change of electricity production or consumption patterns for promoting the overall development of energy industry through the research of user-side energy storage technology. In this paper, we analyze the historical power consumption data of industrial users by k-means clustering algorithm to design a user type automatic evaluation model, which can assist large industrial users to judge whether industrial users are suitable for energy storage construction or not, it can help enterprises save manpower and financial resources. In addition, we design a method to calculate the potential value of users, which can reflect the degree of suitability for equipping energy storage device. Simulation results show that the proposed model can correctly obtain the users' typical load curve and power usage type through clustering historical power load data.
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