Efficient registration of multi-view point sets by K-means clustering

Zhu, Jihua; Jiang, Zhuangde; Evangelidis, Georgios; Zhang, Changqing; Pang, Shanmin; Li, Zhongyu

doi:10.1016/j.ins.2019.03.024

Cited by 73 publications

(27 citation statements)

References 30 publications

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“…For example, fuzzy k-means clustering first randomly selects a number of cluster centers, all data points are given a certain degree of fuzzy membership to the cluster centers, and then iteratively modify the clustering center continuously. In the iterative process, the optimization goal is to minimize the distance between all data points and each cluster center and to take the weighted sum of the minimum membership degrees [36]. After determining the number of fuzzy subsets for each attribute, calculate the membership of each fuzzy subset.…”

Section: A Data Fuzzificationmentioning

confidence: 99%

Weighted Fuzzy Production Rule Extraction Using Modified Harmony Search Algorithm and BP Neural Network Framework

Zhou

et al. 2020

IEEE Access

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Compared with rules in the form of 'IF-THEN,' weighted fuzzy production rules (WFPRs) have more robust knowledge expression capabilities, but weighted fuzzy production rules are more difficult to obtain. The weighted fuzzy production rules obtained using traditional neural network methods have shortcomings, such as insufficient precision and insufficient knowledge extraction. Focusing on the mentioned shortages, a modified weighted fuzzy production rules extraction approach is proposed by combining the modified harmony search algorithm, and neural network. The method consists of three main stages. First, a global optimal adaptive harmony search algorithm (AGOHS) is proposed to overcome the traditional harmony search algorithm's existing poor adaptive ability. Then, the AGOHS algorithm is used to optimize the neural network's initial weights to improve the neural network's training efficiency. Finally, extract the WFPRs with IF-THEN from the trained neural network and give the corresponding fuzzy reasoning. Through the WFPRs extraction experiments using IRIS and PIMA data sets reveal the proposed rule extraction framework has some apparent highlights, such as high accuracy, the smaller number of generated rules, and low redundancy.

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Section: A Data Fuzzificationmentioning

confidence: 99%

Weighted Fuzzy Production Rule Extraction Using Modified Harmony Search Algorithm and BP Neural Network Framework

Zhou

et al. 2020

IEEE Access

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“…It is worth to highlight that a maximum number of 300 iterations with 15 re-runs were used. The initialization was set at random whilst the class centroids were estimated in order to normalize the observations from the initially aligned points to stimulate the efficacy as well as the effectiveness of the clustering (22) . Consequently, every single point is assigned to a particular cluster whilst each cluster centroid is updated simultaneously.…”

Section: The K-means Clustering Algorithmmentioning

confidence: 99%

Predicting students academic performance from wellness status markers using machine learning techniques

Musa¹

2020

IJST

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Background: A high level of wellness is vital to produce a well-balanced and competent graduate. Despite the importance of wellness to the attainment of high productivity, limited attention is directed towards predicting the academic performance of students with regards to wellness markers. This study aims to ascertain the association between wellness and academic achievement of undergraduate students. Methods: A total of 250 undergraduate students drawn from various faculties in one of the public universities in Malaysia participated in the study. The wellness Lifestyle inventory which evaluates an initial rating of a person's present attempt to remain healthy and assessed nine major areas namely; health-related fitness, nutrition, avoiding chemical dependency, stress management, personal hygiene and health, disease prevention, emotional well-being, personal safety and environmental health protection was used as a tool for determining the wellness status of the students whilst their CGPA was utilized as a measure for their academic achievement. K-means clustering analysis was used to group the students into high grades students (HGS) and low grades students (LGS) through their CGPA. A Logistic Regression Model (LR) is developed to classify the students based on their wellness status markers. Findings: An excellent classification accuracy of 99 to 100 % was obtained from the LR model for both training and testing, respectively. Moreover, analysis of variance demonstrated that the HGS and LGS differ in their effort to stay healthy with respect to certain markers p < 0.05. Applications: To ensure better academic grades, some wellness status elements need to be accentuated amongst undergraduate students.

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“…Reference [22] gives the typical curves of cooling, heating, and electrical load and solar radiation in different seasons to illustrate these uncertainties, which does not make cluster analysis or scenario reduction. Furthermore, there are many frequently used scenario-clustering methods applied in the energy field, such as K-mean [23,24], Fuzzy c-mean [25,26], K-harmonic means [27,28], and K-shape [29]. However, there is a problem with these conventional clustering methods; that is, the number of expected clustering scenarios is given subjectively before clustering analysis but not objectively explaining why such clustering is the best through reasonable arguments.…”

Section: Literature Reviewmentioning

confidence: 99%

Low Carbon Scheduling Optimization of Flexible Integrated Energy System Considering CVaR and Energy Efficiency

Liu

Nie

2019

Sustainability

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With the rapid transformation of energy structures, the Integrated Energy System (IES) has developed rapidly. It can meet the complementary needs of various energy sources such as cold, thermal, and electricity in industrial parks; can realize multi-energy complements and centralized energy supplies; and can further improve the use efficiency of energy. However, with the extensive access of renewable energy, the uncertainty and intermittentness of renewable energy power generation will greatly reduce the use efficiency of renewable energy and the supply flexibility of IES so as to increase the operational risk of the system operator. With the goal of minimum sum of the system-operating cost and the carbon-emission penalty cost, this paper analyzes the combined supply of cooling, heating, and power (CCHP) influence on system efficiency, compared with the traditional IES. The flexible modified IES realizes the decoupling of cooling, thermal, and electricity; enhances the flexibility of the IES in a variety of energy supply; at the same time, improves the use efficiency of multi-energy; and reasonably avoids the occurrence of energy loss and resource waste. With the aim of reducing the risk that the access of renewable energy may bring to the IES, this paper introduces the fuzzy c-mean-clustering comprehensive quality (FCM-CCQ) algorithm, which is a novel method superior to the general clustering method and performs cluster analysis on the output scenarios of wind power and photovoltaic. Meanwhile, conditional value at risk (CVaR) theory is added to control the system operation risk, which is rarely applied in the field of IES optimization. The model is simulated in a numerical example, and the results demonstrate that the availability and applicability of the presented model are verified. In addition, the carbon dioxide emission of the traditional operation mode; thermoelectric decoupling operation mode; and cooling, thermal, and electricity decoupling operation mode of the IES decrease successively. The system flexibility is greatly enhanced, and the energy-use rate of the system is improved as a whole. Finally, IES, after its flexible transformation, significantly achieve energy conservation, emission reduction, and environmental protection.

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Efficient registration of multi-view point sets by K-means clustering

Cited by 73 publications

References 30 publications

Weighted Fuzzy Production Rule Extraction Using Modified Harmony Search Algorithm and BP Neural Network Framework

Weighted Fuzzy Production Rule Extraction Using Modified Harmony Search Algorithm and BP Neural Network Framework

Predicting students academic performance from wellness status markers using machine learning techniques

Low Carbon Scheduling Optimization of Flexible Integrated Energy System Considering CVaR and Energy Efficiency

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