An enhanced ISODATA algorithm for recognizing multiple electric appliances from the aggregated power consumption dataset

Li, Mingchao; Han, Shuai; Shi, Jonathan

doi:10.1016/j.enbuild.2017.02.006

Cited by 25 publications

(9 citation statements)

References 20 publications

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“…In order to prove the effectiveness and superiority of the clustering method based on OPTICS algorithm proposed in this paper when dealing with the mural data, we choose other four clustering algorithms to compare with OPTICS, including K-means 41 , K-means++ 42 , ISODATA 43 and Mean Shift (MS) 44 . In order to demonstrate the clustering result of different methods, we selected the data after SAE feature extraction as test data to be clustered.…”

Section: Resultsmentioning

confidence: 99%

OPTICS-based Unsupervised Method for Flaking Degree Evaluation on the Murals in Mogao Grottoes

Sun

Wang

et al. 2018

Sci Rep

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In recent years, the preventive protection and restoration work of the murals in Mogao Grottoes has received extensive attention. Due to the fragility and detachment of the murals, it is necessary to study non-contact disease detection and prevention methods. In this paper, we propose an unsupervised method to accurately predict the degree of mural flaking diseases in Mogao Grottoes. The hyperspectral image (HSI) is captured by V10-PS hyperspectral camera. The proposed method includes three main steps: (1) extract the spectral features of the HSI by Principal Component Analysis (PCA) and Sparse Auto-Encoder (SAE) respectively; (2) cluster the extracted features by the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm based on the density; (3) calculate the distance between the cluster core point and the other points in the feature space and visualize the final classification result. Different from other existing hyperspectral classification works, the research proposed in this paper is the degree detection of flaking of murals. Since the degree of flaking is continuous and the work is conducted without any supervision information, the entire workflow is complex and challenging. The experimental results show the effectiveness of our method.

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Section: Resultsmentioning

confidence: 99%

OPTICS-based Unsupervised Method for Flaking Degree Evaluation on the Murals in Mogao Grottoes

Sun

Wang

et al. 2018

Sci Rep

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“…The meter recorded the power consumed by the family every minute for February 2011, and a total of 40,320 data points (= 60 min × 24 h × 28 d) were recorded for the entire month. The time in a cycle (from one idle time period to the next idle time period) was divided into four segments: startup, stable operation, shutdown, and idle time [10], as shown in Figure 2. In this paper, we first discuss a method of preprocessing large monitored power data where the noise and mutation was deleted.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…After decades of research, many methods have been applied to pattern recognition of NILM. Some representative methods are K-means, K-nearest neighbor, enhanced ISODATA and artificial neural network [8][9][10][11]. These methods provide some ideas for identifying the operating mode of electrical appliances, but when there is a large number of home appliances, the recognition results are often not ideal.…”

mentioning

confidence: 99%

Multi-Pattern Data Mining and Recognition of Primary Electric Appliances from Single Non-Intrusive Load Monitoring Data

Han

et al. 2019

Energies

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The electric power industry is an essential part of the energy industry as it strengthens the monitoring and control management of household electricity for the construction of an economic power system. In this paper, a non-intrusive affinity propagation (AP) clustering algorithm is improved according to the factor graph model and the belief propagation theory. The energy data of non-intrusive monitoring consists of the actual energy consumption data of each electronic appliance. The experimental results show that this improved algorithm identifies the basic and combined class of home appliances. According to the possibility of conversion between different classes, the combination of classes is broken down into different basic classes. This method provides the basis for power management companies to allocate electricity scientifically and rationally.Energies 2019, 12, 992 2 of 20 power of different electrical appliances. The power company can obtain the usual information about the user's electricity, to make a scientific and reasonable decision. This method can reduce the cost of installation and reduce the level of interference in measurement [6]. Commonly used clustering methods for identifying appliances include the following: Hart first proposed non-intrusive household appliance load monitoring and analyzed the algorithm and characteristics, in this case, the essence of this method is to decompose the aggregated load data of household appliance [7]. After decades of research, many methods have been applied to pattern recognition of NILM. Some representative methods are K-means, K-nearest neighbor, enhanced ISODATA and artificial neural network [8][9][10][11]. These methods provide some ideas for identifying the operating mode of electrical appliances, but when there is a large number of home appliances, the recognition results are often not ideal. Frey and Dueck first proposed a standard AP clustering algorithm to solve the clustering problem [12]. The standard AP clustering algorithm can be derived from the factor graph model and the belief propagation theory [12,13]. The factor graph model, proposed by Kschischang and Frey, is a graph description method where the global function is decomposed into the product of the local function [14]. The theory of belief propagation was proposed by Pearl and is a kind of message transfer algorithm for inference in the graph model [15]. Since its publication, AP clustering algorithm has been applied in many fields because of its unique clustering characteristics.

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“…The K-means algorithm, as a classical clustering algorithm, is famous for its simplicity and strong clustering ability [33]. As mentioned in Section 3.4.2, we set the value of K as three to divide the binarization spectrograms into three clusters, meaning that there is a major cluster that contained the most binarization spectrograms, and the other two clusters represented two extremes that differed from the major cluster.…”

Section: Clustering Based On the Modified K-means Algorithmmentioning

confidence: 99%

A Deep Learning Based Method for the Non-Destructive Measuring of Rock Strength through Hammering Sound

Han

et al. 2019

Applied Sciences

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Hammering rocks of different strengths can make different sounds. Geological engineers often use this method to approximate the strengths of rocks in geology surveys. This method is quick and convenient but subjective. Inspired by this problem, we present a new, non-destructive method for measuring the surface strengths of rocks based on deep neural network (DNN) and spectrogram analysis. All the hammering sounds are transformed into spectrograms firstly, and a clustering algorithm is presented to filter out the outliers of the spectrograms automatically. One of the most advanced image classification DNN, the Inception-ResNet-v2, is then re-trained with the spectrograms. The results show that the training accurate is up to 94.5%. Following this, three regression algorithms, including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) are adopted to fit the relationship between the outputs of the DNN and the strength values. The tests show that KNN has the highest fitting accuracy, and SVM has the strongest generalization ability. The strengths (represented by rebound values) of almost all the samples can be predicted within an error of [−5, 5]. Overall, the proposed method has great potential in supporting the implementation of efficient rock strength measurement methods in the field.

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An enhanced ISODATA algorithm for recognizing multiple electric appliances from the aggregated power consumption dataset

Cited by 25 publications

References 20 publications

OPTICS-based Unsupervised Method for Flaking Degree Evaluation on the Murals in Mogao Grottoes

OPTICS-based Unsupervised Method for Flaking Degree Evaluation on the Murals in Mogao Grottoes

Multi-Pattern Data Mining and Recognition of Primary Electric Appliances from Single Non-Intrusive Load Monitoring Data

A Deep Learning Based Method for the Non-Destructive Measuring of Rock Strength through Hammering Sound

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