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한국정보처리학회> JIPS(Journal of Information Processing Systems)

JIPS(Journal of Information Processing Systems) update

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  • : 1976-913x
  • : 2092-805X
  • : International journal of information processing systems(~2007)→Journal of information processing system(2008~)

수록정보
수록범위 : 1권1호(2005)~15권5호(2019) |수록논문 수 : 752
JIPS(Journal of Information Processing Systems)
15권5호(2019년 10월) 수록논문
최근 권호 논문
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KCI등재 SCOPUS

1Learning Algorithms in AI System and Services

저자 : Young-sik Jeong , Jong Hyuk Park

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 15권 5호 발행 연도 : 2019 페이지 : pp. 1029-1035 (7 pages)

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In recent years, artificial intelligence (AI) services have become one of the most essential parts to extend human capabilities in various fields such as face recognition for security, weather prediction, and so on. Various learning algorithms for existing AI services are utilized, such as classification, regression, and deep learning, to increase accuracy and efficiency for humans. Nonetheless, these services face many challenges such as fake news spread on social media, stock selection, and volatility delay in stock prediction systems and inaccurate moviebased recommendation systems. In this paper, various algorithms are presented to mitigate these issues in different systems and services. Convolutional neural network algorithms are used for detecting fake news in Korean language with a Word-Embedded model. It is based on k-clique and data mining and increased accuracy in personalized recommendation-based services stock selection and volatility delay in stock prediction. Other algorithms like multi-level fusion processing address problems of lack of real-time database.

KCI등재 SCOPUS

2Privacy-Preservation Using Group Signature for Incentive Mechanisms in Mobile Crowd Sensing

저자 : Mihui Kim , Younghee Park , Pankaj Balasaheb Dighe

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 15권 5호 발행 연도 : 2019 페이지 : pp. 1036-1054 (19 pages)

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Recently, concomitant with a surge in numbers of Internet of Things (IoT) devices with various sensors, mobile crowdsensing (MCS) has provided a new business model for IoT. For example, a person can share road traffic pictures taken with their smartphone via a cloud computing system and the MCS data can provide benefits to other consumers. In this service model, to encourage people to actively engage in sensing activities and to voluntarily share their sensing data, providing appropriate incentives is very important. However, the sensing data from personal devices can be sensitive to privacy, and thus the privacy issue can suppress data sharing. Therefore, the development of an appropriate privacy protection system is essential for successful MCS. In this study, we address this problem due to the conflicting objectives of privacy preservation and incentive payment. We propose a privacy-preserving mechanism that protects identity and location privacy of sensing users through an on-demand incentive payment and group signatures methods. Subsequently, we apply the proposed mechanism to one example of MCS―an intelligent parking system―and demonstrate the feasibility and efficiency of our mechanism through emulation.

KCI등재 SCOPUS

3Community Discovery in Weighted Networks Based on the Similarity of Common Neighbors

저자 : Miaomiao Liu , Jingfeng Guo , Jing Chen

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 15권 5호 발행 연도 : 2019 페이지 : pp. 1055-1067 (13 pages)

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In view of the deficiencies of existing weighted similarity indexes, a hierarchical clustering method initializeexpand- merge (IEM) is proposed based on the similarity of common neighbors for community discovery in weighted networks. Firstly, the similarity of the node pair is defined based on the attributes of their common neighbors. Secondly, the most closely related nodes are fast clustered according to their similarity to form initial communities and expand the communities. Finally, communities are merged through maximizing the modularity so as to optimize division results. Experiments are carried out on many weighted networks, which have verified the effectiveness of the proposed algorithm. And results show that IEM is superior to weighted common neighbor (CN), weighted Adamic-Adar (AA) and weighted resources allocation (RA) when using the weighted modularity as evaluation index. Moreover, the proposed algorithm can achieve more reasonable community division for weighted networks compared with cluster-recluster-merge-algorithm (CRMA) algorithm.

KCI등재 SCOPUS

4Lung Sound Classification Using Hjorth Descriptor Measurement on Wavelet Sub-bands

저자 : Achmad Rizal , Risanuri Hidayat , Hanung Adi Nugroho

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 15권 5호 발행 연도 : 2019 페이지 : pp. 1068-1081 (14 pages)

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Signal complexity is one point of view to analyze the biological signal. It arises as a result of the physiological signal produced by biological systems. Signal complexity can be used as a method in extracting the feature for a biological signal to differentiate a pathological signal from a normal signal. In this research, Hjorth descriptors, one of the signal complexity measurement techniques, were measured on signal sub-band as the features for lung sounds classification. Lung sound signal was decomposed using two wavelet analyses: discrete wavelet transform (DWT) and wavelet packet decomposition (WPD). Meanwhile, multi-layer perceptron and N-fold cross-validation were used in the classification stage. Using DWT, the highest accuracy was obtained at 97.98%, while using WPD, the highest one was found at 98.99%. This result was found better than the multiscale Hjorth descriptor as in previous studies.

KCI등재 SCOPUS

5An Improved Fast Camera Calibration Method for Mobile Terminals

저자 : Fang-li Guan , Ai-jun Xu , Guang-yu Jiang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 15권 5호 발행 연도 : 2019 페이지 : pp. 1082-1095 (14 pages)

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Camera calibration is an important part of machine vision and close-range photogrammetry. Since current calibration methods fail to obtain ideal internal and external camera parameters with limited computing resources on mobile terminals efficiently, this paper proposes an improved fast camera calibration method for mobile terminals. Based on traditional camera calibration method, the new method introduces two-order radial distortion and tangential distortion models to establish the camera model with nonlinear distortion items. Meanwhile, the nonlinear least square L-M algorithm is used to optimize parameters iteration, the new method can quickly obtain high-precise internal and external camera parameters. The experimental results show that the new method improves the efficiency and precision of camera calibration. Terminals simulation experiment on PC indicates that the time consuming of parameter iteration reduced from 0.220 seconds to 0.063 seconds (0.234 seconds on mobile terminals) and the average reprojection error reduced from 0.25 pixel to 0.15 pixel. Therefore, the new method is an ideal mobile terminals camera calibration method which can expand the application range of 3D reconstruction and close-range photogrammetry technology on mobile terminals.

KCI등재 SCOPUS

6An Ontology-Based Labeling of Influential Topics Using Topic Network Analysis

저자 : Hyon Hee Kim , Hey Young Rhee

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 15권 5호 발행 연도 : 2019 페이지 : pp. 1096-1107 (12 pages)

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In this paper, we present an ontology-based approach to labeling influential topics of scientific articles. First, to look for influential topics from scientific article, topic modeling is performed, and then social network analysis is applied to the selected topic models. Abstracts of research papers related to data mining published over the 20 years from 1995 to 2015 are collected and analyzed in this research. Second, to interpret and to explain selected influential topics, the UniDM ontology is constructed from Wikipedia and serves as concept hierarchies of topic models. Our experimental results show that the subjects of data management and queries are identified in the most interrelated topic among other topics, which is followed by that of recommender systems and text mining. Also, the subjects of recommender systems and context-aware systems belong to the most influential topic, and the subject of k-nearest neighbor classifier belongs to the closest topic to other topics. The proposed framework provides a general model for interpreting topics in topic models, which plays an important role in overcoming ambiguous and arbitrary interpretation of topics in topic modeling.

KCI등재 SCOPUS

7Image Denoising via Fast and Fuzzy Non-local Means Algorithm

저자 : Junrui Lv , Xuegang Luo

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 15권 5호 발행 연도 : 2019 페이지 : pp. 1108-1118 (11 pages)

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Non-local means (NLM) algorithm is an effective and successful denoising method, but it is computationally heavy. To deal with this obstacle, we propose a novel NLM algorithm with fuzzy metric (FM-NLM) for image denoising in this paper. A new feature metric of visual features with fuzzy metric is utilized to measure the similarity between image pixels in the presence of Gaussian noise. Similarity measures of luminance and structure information are calculated using a fuzzy metric. A smooth kernel is constructed with the proposed fuzzy metric instead of the Gaussian weighted L2 norm kernel. The fuzzy metric and smooth kernel computationally simplify the NLM algorithm and avoid the filter parameters. Meanwhile, the proposed FMNLM using visual structure preferably preserves the original undistorted image structures. The performance of the improved method is visually and quantitatively comparable with or better than that of the current state-ofthe- art NLM-based denoising algorithms.

KCI등재 SCOPUS

8Fake News Detection Using Deep Learning

저자 : Dong-ho Lee , Yu-ri Kim , Hyeong-jun Kim , Seung-myun Park , Yu-jun Yang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 15권 5호 발행 연도 : 2019 페이지 : pp. 1119-1130 (12 pages)

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With the wide spread of Social Network Services (SNS), fake news―which is a way of disguising false information as legitimate media―has become a big social issue. This paper proposes a deep learning architecture for detecting fake news that is written in Korean. Previous works proposed appropriate fake news detection models for English, but Korean has two issues that cannot apply existing models: Korean can be expressed in shorter sentences than English even with the same meaning; therefore, it is difficult to operate a deep neural network because of the feature scarcity for deep learning. Difficulty in semantic analysis due to morpheme ambiguity. We worked to resolve these issues by implementing a system using various convolutional neural network-based deep learning architectures and “Fasttext” which is a word-embedding model learned by syllable unit. After training and testing its implementation, we could achieve meaningful accuracy for classification of the body and context discrepancies, but the accuracy was low for classification of the headline and body discrepancies.

KCI등재 SCOPUS

9Research on the Variable Rate Spraying System Based on Canopy Volume Measurement

저자 : Kaiqun Hu , Xin Feng

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 15권 5호 발행 연도 : 2019 페이지 : pp. 1131-1140 (10 pages)

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Characteristics of fruit tree canopies are important target information for adjusting the pesticide application rate in variable rate spraying in orchards. Therefore, the target detection of the canopy characteristics is very important. In this study, a canopy volume measurement method for peach trees was presented and a variable rate spraying system based on canopy volume measurement was developed using the ultrasonic sensing, one of the most effective target detection method. Ten ultrasonic sensors and two flow control units were mounted on the orchard air-assisted sprayer. The ultrasonic sensors were used to detect the canopy diameters and the flow controls were used to modify the flow rate of the nozzles in real time. Two treatments were established: a constant application rate of 300 Lha-1 was set as the control treatment for the comparison with the variable rate application at a 0.095 Lm-3 canopy. The tracer deposition at different parts of peach trees and the tracer losses to the ground (between rows and within rows) were analyzed in detail under constant rate and variable rate application. The results showed that there were no significant differences between two treatments in the liquid distribution and the capability to reach the inner parts of the crop canopies.

KCI등재 SCOPUS

10Personalized Movie Recommendation System Combining Data Mining with the k-Clique Method

저자 : Phonexay Vilakone , Khamphaphone Xinchang , Doo-soon Park

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 15권 5호 발행 연도 : 2019 페이지 : pp. 1141-1155 (15 pages)

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Today, most approaches used in the recommendation system provide correct data prediction similar to the data that users need. The method that researchers are paying attention and apply as a model in the recommendation system is the communities' detection in the big social network. The outputted result of this approach is effective in improving the exactness. Therefore, in this paper, the personalized movie recommendation system that combines data mining for the k-clique method is proposed as the best exactness data to the users. The proposed approach was compared with the existing approaches like k-clique, collaborative filtering, and collaborative filtering using k-nearest neighbor. The outputted result guarantees that the proposed method gives significant exactness data compared to the existing approach. In the experiment, the MovieLens data were used as practice and test data.

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