Transport mode detection in urban areas with the help of mobile phones is not anymore, a challenging problem. Most studies via machine and deep learning models using GPS and inertial mobile sensors can distinguish dif...
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ISBN:
(纸本)9781665455305
Transport mode detection in urban areas with the help of mobile phones is not anymore, a challenging problem. Most studies via machine and deep learning models using GPS and inertial mobile sensors can distinguish different modes with various prediction accuracies. This paper presents a new way to count the number of stations in metro trips using a magnetometer sensor where GPS, internet, and wireless positioning are unavailable. The primary source for this investigation was recorded via mobile magnetometer sensor of metro riders. We first find contextual features that can effectively recognize acceleration state according to the 3D magnetometer data and then classification with a k-means unsupervised method into different classes. Finally, we present a station counter algorithm to count the number of metro stations in metro-based trips. Results from experiments in Rome and Stockholm metro systems show that our final algorithm can count the number of stations with an accuracy of 86 % where there is no internet, GPS, and WiFi access.
The wellbore flow analysis of optical fiber vibration signal depends on distributed optical fiber logging. Distributed optical fiber logging technology identifies the fluid in the well through distributed optical fibe...
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The wellbore flow analysis of optical fiber vibration signal depends on distributed optical fiber logging. Distributed optical fiber logging technology identifies the fluid in the well through distributed optical fiber acoustic sensor (DAS) and distributed optical fiber temperature sensor (DTS). Distributed optical fiber sensor has the advantages of small underground interference, high efficiency and low cost. In this paper, the wellhead data extracted by the distributed optical fiber acoustic sensor is used to calculate the upper bound of the fluid sound frequency band in the pipe by nonlinear least squares fitting. The k-means clustering algorithm is used to cluster the optical fiber vibration signals in the low frequency band. According to the clustering results, the ratio of the optical fiber signal eigenvalues of each production layers is obtained, and the trend of the ratio of the optical fiber signal eigenvalues of each production layers is judged to be close to the trend of the water absorption intensity. Compared with traditional acoustic logging, the wellbore flow analysis using distributed optical fiber acoustic sensor can quickly determine the production contribution of each layer and the change of fluid phase state in the production cycle. Combined with traditional production logging technology, distributed optical fiber logging shows its reliability and accuracy in data collection, logging interpretation and production application. Starting from the principle of distributed optical fiber acoustic sensing technology, this paper briefly expounds the properties of distributed optical fiber acoustic sensor and the principle of injection profile logging, systematically introduces the processing of distributed optical fiber acoustic data, and emphatically introduces the accuracy of k-means clustering algorithm for analyzing distributed optical fiber acoustic signal and qualitative judgment of production layer, which provides a new idea for judging the accura
This study investigates to evaluate feasibility of k-means clustering algorithm in order to improve effectiveness of the results recommended by RICEST Journal Finder System. More than 15,000 papers published in filed ...
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This study investigates to evaluate feasibility of k-means clustering algorithm in order to improve effectiveness of the results recommended by RICEST Journal Finder System. More than 15,000 papers published in filed of engineering journals during 2013-2017 were collected from their websites. Their titles, abstracts and keywords were extracted, normalized and processed in order to form the test body. According to the number of papers collected, using Cochran's formula, 400 papers completely relevant to the subject of each journal were randomly and proportionally selected and entered the system as queries in order to receive the journals recommended by the system before and after k-means clustering algorithm and the results were recorded. Finally, effectiveness of the system results was determined at each stage by leave-one-out cross validation method based on precision at k top ranked results. Also, opinions of subject reviewers on relevance of the target journal were investigated through a questionnaire. Results showed that before data clustering, only 40% of target journal was recommended at the first 3 ranks. But after k-means clustering algorithm, in more than 80% of searches, the target journal was retrieved at the first 3 ranks. Also, effectiveness of the recommendations, according to 210 subject reviewers, after k-means clustering algorithm, showed that more than 80% of the recommended journals are completely relevant to the given paper. According to the study results, data clustering can significantly increase effectiveness of the results recommended by journal recommender systems.
With the acceleration of globalization, English plays an increasingly important role in international communication. Therefore, the improvement of English teaching quality has become an important issue that educators ...
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With the continuous development of science and technology, the amount of data generated in life is increasing, and big data technology has now penetrated into all walks of life. This paper focuses on the application o...
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In order to quantitatively analyze regional economy through scientific method, the differences of economic development in different regions are revealed. This paper uses k-means clustering algorithm to divide regional...
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In order to quantitatively analyze regional economy through scientific method, the differences of economic development in different regions are revealed. This paper uses k-means clustering algorithm to divide regional economic data into several groups, and each group represents a region with similar economic development level. On this basis, the index system of regional economic development level including a number of economic indicators is constructed, and the accuracy and consistency of data are ensured through data collection and pre-processing. The experimental results show that the k-means clustering algorithm can effectively divide the regional economy into several groups with different development levels, and the regions within each group have significant similarities in economic development, while there are obvious differences between different groups.
The prediction and recommendation of financial stocks are of great values. This study mainly analyzed the application of k-means clustering algorithm in stock forecasting and recommendation. Firstly, it introduced the...
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The prediction and recommendation of financial stocks are of great values. This study mainly analyzed the application of k-means clustering algorithm in stock forecasting and recommendation. Firstly, it introduced the k-meansalgorithm briefly and analyzed its advantages and disadvantages. Then, the k-meansalgorithm was optimized by introducing artificial fish swarm algorithm (AFSA) to obtain kAFSA. Then 100 stocks of listed companies were taken as the research subject and predicted by kAFSA designed in this study. The prediction results were verified through closing price, price earning ratio, earnings per share and return on net assets. The results showed that there were obvious differences between A and B stocks divided by kAFSA, and the differences of B stocks were significantly larger than those of A stocks. It shows that 100 stocks are well divided into high performance stocks and poor performance stocks through clustering, which provides a good reference for investors to invest in stocks and is worth of further application.
In modern society, the scientific symbiotic relationship of aesthetic community space is crucial for the development of cities. This article aimed to explore the symbiotic relationship between aesthetic community spac...
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In modern society, the scientific symbiotic relationship of aesthetic community space is crucial for the development of cities. This article aimed to explore the symbiotic relationship between aesthetic community space and science, and analyze this relationship based on the k-means clustering algorithm. The article analyzed the spatial characteristics of different types of aesthetic communities, as well as the commonalities and differences between aesthetic communities and science, including the complementary relationship between the two. The k-means clustering algorithm is integrated to classify community spatial features, thereby analyzing and optimizing the relationship between the two. The experiment selected a sample of an aesthetic community and divided it into three different types of spatial features: architectural area, landscape greening area, and other activity areas. This article also compared the clustering results of the k-meansalgorithm with the k-meansalgorithm based on three-dimensional grid space. The results showed that the k-meansalgorithm optimized by the principle of three-dimensional grid space had stronger clustering performance. In the experiment, four different types of clusteringalgorithms and multiple evaluation indicators were also selected for testing. The data showed that in the clustering of spatial features in building areas, the k-means average clustering error score was controlled at around 13-14 under different missing ratios;the minimum average clustering error score of the characteristic data of the landscape greening area was 12.1. The average clustering error score in spatial feature clustering of other activity areas reached a minimum value of 23.5 when the missing ratio was 10%, which was lower than other algorithms. The overall experimental results indicate that the fused k-means clustering algorithm can effectively partition the aesthetic community space and optimize scientific symbiotic relationships.
Lamb waves are widely used in the localization and quantification of structural damage for plate-type structure. The propagation velocity and dispersion characteristics of Lamb waves are very important calculation par...
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ISBN:
(纸本)9781665403153
Lamb waves are widely used in the localization and quantification of structural damage for plate-type structure. The propagation velocity and dispersion characteristics of Lamb waves are very important calculation parameters. The method to estimate the dispersion curves of Lamb waves is proposed based on k-means clustering algorithm. Three piezoelectric transducers with different center frequencies experimentally transmit the Lamb wave signals, and laser vibrometer is used to receive signals at different intervals in the direction of wave propagation. The phase velocity of lamb waves is estimated by many pairs of adjacent signals peaks. Use Euclidean distance as the index of data evaluation to find the data center, which is the phase velocity to be solved. And the dispersion curves are further estimated and inverted. The proposed method has been experimentally verified in isotropic-aluminum plate. The relative error of the actual phase velocity of lamb waves compared with all measured clustering centers is less than 1%, and the overall average relative error is less than 0.4%.
Spectral ratio methods have been widely used in evaluation of nonlinear seismic site response. Nevertheless, it remains inefficient and subjective to identify stations with nonlinear site response according to empiric...
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Spectral ratio methods have been widely used in evaluation of nonlinear seismic site response. Nevertheless, it remains inefficient and subjective to identify stations with nonlinear site response according to empirical threshold values of spectral ratio nonlinear degree indicators. This study, which was the first to apply the machine learning clusteringalgorithm to address this problem, used the September 6, 2018 M(w)6.6 Hokkaido Iburi-Tobu earthquake (Japan) as an example. First, we calculated the surface/borehole and horizontal/vertical spectral ratios using strong ground motion data recorded by kik-net vertical array and k-NET stations, respectively. The degree of nonlinear site response (DNL) and percentage of nonlinear site response (PNL) were computed using the difference between the strong motion of the mainshock and weak aftershocks as the reference for linear site response. Then, the k-means clustering algorithm was incorporated in the identification of nonlinear site response using the DNL, PNL, strength of ground motion (PGA) and site condition (V-S20 or V-S30) as explanatory variables. After careful multicollinearity diagnosis and confirmation of the optimum clustering number, we successfully classified the stations into two clusters with nonlinear and linear site responses. Overall, the clustering results were found in good agreement with the classification results based on empirical thresholds of several nonlinear indicators. For the stations identified with nonlinear site response, the reduction of amplification and frequency shift could be observed from the spectral ratio curves regarding the ground motions in the mainshock and the reference weak aftershocks, demonstrating typical nonlinearity response characteristics. Furthermore, a comprehensive indicator of nonlinear site response occurrence probability (NLscore) was obtained from a linear weighted combination of the normalized variables (PGA, V-S30/V-S20, DNL and PNL). The NLscore ranking of th
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