Load Profile Extraction by Mean-Shift Clustering with Sample Pearson Correlation Coefficient Distance

Kim, Nakyoung; Park, Sangdon; Lee, Joohyung; Choi, Jun Kyun

doi:10.3390/en11092397

Cited by 16 publications

(11 citation statements)

References 26 publications

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“…where, I is the gray value of the pixel; R is the red component; G is the green component, and B is the blue component. The Mean-Shift algorithm, which has been widely used in clustering, is essentially an iterative search algorithm [20]. In this paper, the gray values of pixels were used as the data samples, and the Mean-Shift algorithm was applied to cluster the pixels.…”

Section: Extraction Of Suspected Abnormal Heating Regions Based On Mean-shift Algorithmmentioning

confidence: 99%

Autonomous Analysis of Infrared Images for Condition Diagnosis of HV Cable Accessories

Mu¹,

Xü

Xia³

et al. 2021

Energies

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Infrared thermography has been used as a key means for the identification of overheating defects in power cable accessories. At present, analysis of thermal imaging pictures relies on human visual inspections, which is time-consuming and laborious and requires engineering expertise. In order to realize intelligent, autonomous recognition of infrared images taken from electrical equipment, previous studies reported preliminary work in preprocessing of infrared images and in the extraction of key feature parameters, which were then used to train neural networks. However, the key features required manual selection, and previous reports showed no practical implementations. In this contribution, an autonomous diagnosis method, which is based on the Faster RCNN network and the Mean-Shift algorithm, is proposed. Firstly, the Faster RCNN network is trained to implement the autonomous identification and positioning of the objects to be diagnosed in the infrared images. Then, the Mean-Shift algorithm is used for image segmentation to extract the area of overheating. Next, the parameters determining the temperature of the overheating parts of cable accessories are calculated, based on which the diagnosis are then made by following the relevant cable condition assessment criteria. Case studies are carried out in the paper, and results show that the cable accessories and their overheating regions can be located and assessed at different camera angles and under various background conditions via the autonomous processing and diagnosis methods proposed in the paper.

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Section: Extraction Of Suspected Abnormal Heating Regions Based On Mean-shift Algorithmmentioning

confidence: 99%

Autonomous Analysis of Infrared Images for Condition Diagnosis of HV Cable Accessories

Mu¹,

Xü

Xia³

et al. 2021

Energies

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“…The food similarity filter means the filter of a similarity coefficient between food products in each one of 25 clusters. As a similarity coefficient, Pearson correlation coefficient (also known as Pearson's r) is used [24]. A Pearson's r ranges from −1 to +1.…”

Section: Knowledge Base-based Healthcare For Improving Dietary Habitsmentioning

confidence: 99%

Hybrid clustering based health decision-making for improving dietary habits

Baek

Kim

Chun

et al. 2019

THC

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BACKGROUND: Humans supply a variety of nutrients to their body in dietary life, which are directly related to health. Chronic diseases are long accumulated in the body on account of heredity or living habits, and draw attention as a main issue in the era of disease-controlled longevity. Therefore, it is essential to make health care continuously through the improvement in dietary habits. OBJECTIVE: By recommending alternative food products whose diet and nutrition structure is similar to that of the food products positively influencing users' health conditions, it is possible to satisfy user's health and preference. METHOD: We used the hybrid clustering based food recommendation method that uses chronic disease based clustering, diet and nutrition ontology, diet and nutrition knowledge base. Active users are classified into the chronic disease based cluster that has the nearest euclidean distance. According to the classified clusters, food products are recommended to users, and similar food products are also recommended with the use of food clustering and knowledge base. Food products are clustered with the uses of k-means algorithm and food and nutrient data system. Based on the created food clusters and food preference data, diet and nutrition knowledge base is generated. It is composed of food cluster filter, food similarity filter, universal preference filter, and user feedback filter. The universal preference filter represents the similarity weight between diet and nutrition, and user preference. The user feedback filter has the similarity weight between active user preference and diet and nutrition. They continue to be updated through associated feedback. RESULT: The proposed health decision-making method takes into account each user's health condition so that the method has more precision than an existing recommendation method. In addition, the proposed method brings about better evaluation results than a general user-by-user health context information based recommendation method. CONCLUSION: By recommending the food products related to users' chronic diseases through the proposed hybrid clustering, it is possible to help out their healthcare. In addition, by letting users receive satisfying feedback flexibly, it is possible to improve their dietary habits.

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“…In statistics, the Pearson correlation coefficient is widely used to measure the linear correlation between two variables X and Y [20,21]. It has a value between −1 and +1, where −1 is total negative linear correlation, 0 is no linear correlation, and +1 is total positive linear correlation.…”

Section: Feature Selectionmentioning

confidence: 99%

Short-Term Photovoltaic Power Output Prediction Based on k-Fold Cross-Validation and an Ensemble Model

2019

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Short-term photovoltaic power forecasting is of great significance for improving the operation of power systems and increasing the penetration of photovoltaic power. To improve the accuracy of short-term photovoltaic power forecasting, an ensemble-model-based short-term photovoltaic power prediction method is proposed. Firstly, the quartile method is used to process raw data, and the Pearson coefficient method is utilized to assess multiple features affecting the short-term photovoltaic power output. Secondly, the structure of the ensemble model is constructed, and a k-fold cross-validation method is used to train the submodels. The prediction results of each submodel are merged. Finally, the validity of the proposed approach is verified using an actual data set from State Power Investment Corporation Limited. The simulation results show that the quartile method can find outliers which contributes to processing the raw data and improving the accuracy of the model. The k-fold cross-validation method can effectively improve the generalization ability of the model, and the ensemble model can achieve higher prediction accuracy than a single model.

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Load Profile Extraction by Mean-Shift Clustering with Sample Pearson Correlation Coefficient Distance

Cited by 16 publications

References 26 publications

Autonomous Analysis of Infrared Images for Condition Diagnosis of HV Cable Accessories

Autonomous Analysis of Infrared Images for Condition Diagnosis of HV Cable Accessories

Hybrid clustering based health decision-making for improving dietary habits

Short-Term Photovoltaic Power Output Prediction Based on k-Fold Cross-Validation and an Ensemble Model

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