Browsing by Author "Chen, Chien-Cheng"

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Crystal Quality of 3C-SiC Influenced by the Diffusion Step in the Modified Four-Step Method
(2010)
Chen, Wei-Yu
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Wang, Wei-Lin
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Liu, Jui-Min
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Chen, Chien-Cheng
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Hwang, Jenn-Chang
The atomic arrangement and bonding characteristics of void-free 3C-SiC/Si(100) grown by the modified four-step method are presented. Without the diffusion step, Si-C bonds are partially formed in the as-carburized layer on Si(100). The ratio of C-C bonds to Si-C bonds is about 7:3, which can be lowered to about 1:9 after the diffusion step at 1350 degrees C for 5 min or at 1300 degrees C for 7 min according to C 1s core level spectra. The residual C-C bonds cannot be removed, which is associated with an irregular atomic arrangement (amorphous) located either at the 3C-SiC/Si(100) interface or at the intersection of twin boundaries in the 3C-SiC buffer layer based on the lattice image taken by transmission electron microscope. The diffusion step helps the formation of Si-C bonds more completely and results in a SiC buffer layer of high quality formed on Si(100) before the growth step. However, twins and stacking faults still appear in the 3C-SiC buffer layer after the diffusion step. The formation mechanism of the 3C-SiC buffer layer is proposed and discussed.
Exploring Communication Behaviors of Users to Target Potential Users in Mobile Social Networks
(2017-09-01)
Chen, Chien-Cheng
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Hsu, Kuo-Wei
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Peng, Wen-Chih
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資訊工程學系
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Department of Computer Science
In mobile communication services, users can communicate with each other over different telecommunication carriers. For telecom operators, how to acquire and retain users is a significant and practical task. Note that telecom operators only have their own customer profiles. For the users from other telecom operators, their information is sparse. Thus, given a set of communication logs, the main theme of our work is to identify the potential users who will possibly join the target services in the near future. Since only a limited amount of information is available, one challenging issue is how to extract features from the communication logs. In this article, we propose a Communication-Based Feature Generation (CBFG) framework that extracts features and builds models to infer the potential users. Explicitly, we construct a heterogeneous information network from the communication logs of users. Then, we extract the explicit features, which refer to those calling features of users, from the potential users' interaction behaviors in the heterogeneous information network. Moreover, from the calling behaviors of users, one could extract the possible community structures of users. Based on the community structures, we further extract the implicit features of users. In light of both explicit and implicit features, we propose an information-gain-based method to select the effective features. According to the features selected, we utilize three popular classifiers (i.e., AdaBoost, Random Forest, and SVM) to build models to target the potential users. In addition, we have designed a sampling approach to extract training data for classifiers. To evaluate our methods, we have conducted experiments on a real dataset. The results of our experiments show that the features extracted by our proposed method can be effective for targeting the potential users.
Inferring Potential Users in Mobile Social Networks
(2014)
Hsu, Tsung-Hao
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Chen, Chien-Cheng
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Chiang, Meng-Fen
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Hsu, Kuo-Wei
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Peng, Wen-Chih
In mobile social networks, users can communicate with each other over different telecom operators. Thus, for telecom operators, how to attract new customers is a significant issue. The work of churn prediction is to determine whether a customer would leave soon. Differing from churn prediction, our work is to find those users who are likely to join target services from the competitors in the near future, where these users are called potential users. To infer potential users, we propose a framework including feature extraction, feature selection, and classifier learning to solve the problem. First, we construct a heterogeneous information network from the call detail records of users. Then, we extract the explicit features from potential users\' interaction behavior in the heterogeneous information network. Moreover, because users are influenced by their community, we extract community-based implicit features of potential users. After feature extraction, we explore the Information Gain to select the effective features. We use the effective explicit and implicit features to learn potential user classifiers, and use the classifiers to determine the potential users. Finally, we conduct experiments on real datasets. The results of our experiments show that the features extracted by our proposed method can improve the accuracy of inferring potential users.
Magnitude-tunable sub-THz shear phonons in a non-polar GaN multiple-quantum-well p-i-n diode
(2012-05-14)
Chen, Chien-Cheng
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Huang, Huei-Min
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Lu, Tien-Chang
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Kuo, Hao-Chung
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Sun, Chi-Kuang
Coherent transverse acoustic phonons are optically generated and detected through the piezoelectric coupling between the build-in electric fields and shear strains of a non-polar GaN multiple quantum wells embedded in a p-n junction. By optical transient transmission change measurement, the phonon frequency is observed to be 0.4 THz which corresponds to a wavelength of 12.5 nm, the periodicity of the multiple quantum wells, and the estimated phonon velocity corresponds to the transverse acoustic phonon velocity in GaN. Moreover, we can magnify the driving amplitude of the generated shear phonons by increasing the reverse bias of the p-i-n diode. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4718524]
Mining and clustering mobility evolution patterns from social media for urban informatics
(2016-05)
Chen, Chien-Cheng
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Chiang, Meng-Fen
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Peng, Wen-Chih
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交大名義發表
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National Chiao Tung University
In this paper, given a set of check-in data, we aim at discovering representative daily movement behavior of users in a city. For example, daily movement behavior on a weekday may show users moving from one to another spatial region associated with time information. Since check-in data contain both spatial and temporal information, we propose a mobility evolution pattern to capture the daily movement behavior of users in a city. Furthermore, given a set of daily mobility evolution patterns, we formulate their similarity distances and then discover representative mobility evolution patterns via the clustering process. Representative mobility evolution patterns are able to infer major movement behavior in a city, which could bring some valuable knowledge for urban planning. Specifically, mobility evolution patterns consist of segments with the spatial region distribution and the corresponding time interval. To measure good segmentation from a set of check-in data, we formulate the problem of mining evolution patterns as a compression problem. In particular, we compute the representation length of the patterns based on the Minimum Description Length principle. Since the number of daily mobility evolution patterns is huge, we further cluster the daily mobility evolution patterns into groups and discover representative patterns. Note that we use the concept of locality-sensitive hashing to accelerate the cluster performance. To evaluate our proposed algorithms, we conducted experiments on the Gowalla and Brightkite datasets, and the experimental results show the effectiveness and efficiency of our proposed algorithms.
Mining Mobility Evolution from Check-in Datasets
(2014-01-01)
Chiang, Meng-Fen
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Chen, Chien-Cheng
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Peng, Wen-Chih
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Yu, Philip S.
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交大名義發表
The advances in location-acquisition and smart phone technologies have led to a myriad of location-based social media. Therefore, analyzing the increasing amount of spatio-temporal data emerges as an important topic. Most studies on geographic data mining focus on exploring static mobility patterns. As the amount of incoming data streams increases, revealing the temporal aspect of user mobility patterns is worth investigating. This paper targets on mining user mobility patterns over time (referred to as mobility evolution) from streams of check-in records. Intuitively, at each time slot, a mobility pattern indicates spatial regions where users stay. Therefore, given a set of time slots, mobility evolution refers a sequence of spatial regions at each time slot. Note that nearby time slots may have similar spatial region distribution. Thus, given check-in datasets, we use the idea of data compression to obtain a sequence of representative segments, where each representative segment captures spatial region distribution at the corresponding time interval. To measure the quality of a segmentation result, we propose a representation cost function based on the Minimum Description Length (MDL) principle. In addition, because deriving the sequence of segments incurs expensive computational cost, we propose a family of greedy algorithms for segmentation to serve diverse requirements: efficient compression, informative compression, and cost-effective compression. Besides, to handle the massive amount of incoming check-in data, we also propose an incremental compression approach to incrementally update the mobility evolution. We conduct experiments on Foursquare datasets to demonstrate both the effectiveness and efficiency of our proposed algorithms.
Mining Spatial-Temporal Semantic Trajectory Patterns from Raw Trajectories
(2015)
Chen, Chien-Cheng
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Kuo, Chia-Hsiang
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Peng, Wen-Chih
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交大名義發表
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National Chiao Tung University
With the development of GPS and the popularity of smart phones and wearable devices, users can easily log their daily trajectories. Prior works have elaborated on mining trajectory patterns from raw trajectories. However, trajectory patterns do not have explicit time information or semantic information. To enrich trajectory patterns, we propose STS-TPs (standing for Spatial-Temporal Semantic Trajectory Patterns) which refer to the moving patterns with spatial, temporal, and semantic attributes. Given a set of user trajectories, we aim at mining STS-TPs. Explicitly, we extract the three attributes from raw trajectories, and convert these trajectories into semantic trajectory sequences. Given a set of such semantic trajectory sequences, STS-TPs could be viewed as sequential patterns with multiple attributes. To fully explore the efficiency of PrefixSpan on sequential pattern mining, we propose a PrefixSpan-based algorithm (abbreviated as PS) to discover STS-TPs. Note that the input for PrefixSpan is a set of sequences consisting of items. However, each item of semantic trajectory sequences contains three attributes, and we need to further transform these sequences into symbolized sequences before using PrefixSpan. Therefore, we propose two algorithms of Sequence Symbolization (SS) and Advanced Sequence Symbolization (ASS) to achieve this purpose. In light of STS-TPs, we further propose query tasks to predict users\' behaviors. To evaluate our proposed algorithms, we conducted experiments on the real datasets of Google Location History, and the experimental results show the effectiveness and efficiency of our proposed algorithms.
Trajectory Pattern Mining: Exploring Semantic and Time Information
(2016-01-01)
Chen, Chien-Cheng
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Chiang, Meng-Fen
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交大名義發表
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National Chiao Tung University
With the development of GPS and the popularity of smart phones and wearable devices, users can easily log their daily trajectories. Prior works have elaborated on mining trajectory patterns from raw trajectories. Trajectory patterns consist of hot regions and the sequential relationships among them, where hot regions refer the spatial regions with a higher density of data points. Note that some hot regions do not have any meaning for users. Moreover, trajectory patterns do not have explicit time information or semantic information. To enrich trajectory patterns, we propose semantic trajectory patterns which are referred to as the moving patterns with spatial, temporal, and semantic attributes. Given a user trajectory, we aim at mining frequent semantic trajectory patterns. Explicitly, we extract the three attributes from a raw trajectory, and convert it into a semantic mobility sequence. Given such a semantic mobility sequence, we propose two algorithms to discover frequent semantic trajectory patterns. The first algorithm, MB (standing for matching-based algorithm), is a naive method to find frequent semantic trajectory patterns. It generates all possible patterns and extracts the occurrence of the patterns from the semantic mobility sequence. The second algorithm, PS (standing for PrefixSpan-based algorithm), is developed to efficiently mine semantic trajectory patterns. Due to the good efficiency of PrefixSpan, algorithm PS will fully utilize the advantage of PrefixSpan. Since the semantic mobility sequence contains three attributes, we need to further transform it into a raw sequence before using algorithm PrefixSpan. Therefore, we propose the SS algorithm (standing for sequence symbolization algorithm) to achieve this purpose. To evaluate our proposed algorithms, we conducted experiments on the real datasets of Google Location History, and the experimental results show the effectiveness and efficiency of our proposed algorithms.