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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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KIPS Transactions on Software and Data Engineering
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Korea Information Processing Society
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Volume & Issues
Volume 3, Issue 12 - Dec 2014
Volume 3, Issue 11 - Nov 2014
Volume 3, Issue 10 - Oct 2014
Volume 3, Issue 9 - Sep 2014
Volume 3, Issue 8 - Aug 2014
Volume 3, Issue 7 - Jul 2014
Volume 3, Issue 6 - Jun 2014
Volume 3, Issue 5 - May 2014
Volume 3, Issue 4 - Apr 2014
Volume 3, Issue 3 - Mar 2014
Volume 3, Issue 2 - Feb 2014
Volume 3, Issue 1 - Jan 2014
Selecting the target year
A Measuring Model of Risk Impact on The App Development Project in The Social App Manufacturing Environment
Baek, Jung Hee ; Lim, Young Hwan ;
KIPS Transactions on Software and Data Engineering, volume 3, issue 9, 2014, Pages 335~340
DOI : 10.3745/KTSDE.2014.3.9.335
Crowd Sourcing-based Social App Manufacturing environment, a small app development project by a team of anonymous virtual performed without the constraints of time and space, and manage it for the app development process need to be automated method. Virtual teams with anonymity is a feature of the Social App Manufacturing, is an important factor that increases the uncertainty of whether the completion of the project or reduction in visibility of the progress of the project. In this study, as one of how to manage the project of Social App Manufacturing environment, the impact of risk that can be used to quantitatively measure the impact of the risk of delay in development has on the project also proposes a measurement model. Effects of risk and type of the impact of risks associated with delays in the work schedule also define the characteristic function, measurement model that has been proposed, suggest the degree of influence measurement equation of risk of the project in accordance with the progressive. The advantage of this model, the project manager is able to ensure the visibility of the progress of the project. In addition, identify the project risk of work delays, and to take precautions.
Unspecified Event Detection System Based on Contextual Location Name on Twitter
Oh, Pyeonghwa ; Yim, Junyeob ; Yoon, Jinyoung ; Hwang, Byung-Yeon ;
KIPS Transactions on Software and Data Engineering, volume 3, issue 9, 2014, Pages 341~348
DOI : 10.3745/KTSDE.2014.3.9.341
The advance in web accessibility with dissemination of smart phones gives rise to rapid increment of users on social network platforms. Many research projects are in progress to detect events using Twitter because it has a powerful influence on the dissemination of information with its open networks, and it is the representative service which generates more than 500 million Tweets a day in average; however, existing studies to detect events has been used TFIDF algorithm without any consideration of the various conditions of tweets. In addition, some of them detected predefined events. In this paper, we propose the RTFIDF VT algorithm which is a modified algorithm of TFIDF by reflecting features of Twitter. We also verified the optimal section of TF and DF for detecting events through the experiment. Finally, we suggest a system that extracts result-sets of places and related keywords at the given specific time using the RTFIDF VT algorithm and validated section of TF and DF.
Performance Comparison of DW System Tajo Based on Hadoop and Relational DBMS
Liu, Chen ; Ko, Junghyun ; Yeo, Jeongmo ;
KIPS Transactions on Software and Data Engineering, volume 3, issue 9, 2014, Pages 349~354
DOI : 10.3745/KTSDE.2014.3.9.349
Since Hadoop which is the Big-data processing platform was announced, SQL-on-Hadoop is the spotlight as the technique to analyze data using SQL on Hadoop. Tajo created by Korean programmers has recently been promoted to Top-Level-Project status by the Apache in April and has been paid attention all around world. Despite a sensible change caused by Hadoop's appearance in DW market, researches of those performance is insufficient. Thus, this study has been conducted to help choose a DW solution based on SQL-on-Hadoop as progressing the test on comparison analysis of RDBMS and Tajo. It has shown that Tajo based on Hadoop is more superior than RDBMS if it is used with accurate strategy. In addition, open-source project Tajo is expected not only to achieve improvements in technique due to active participation of many developers but also to be in charge of an important role of DW in the filed of data analysis.
Time Series Analysis of Patent Keywords for Forecasting Emerging Technology
Kim, Jong-Chan ; Lee, Joon-Hyuck ; Kim, Gab-Jo ; Park, Sang-Sung ; Jang, Dong-Sick ;
KIPS Transactions on Software and Data Engineering, volume 3, issue 9, 2014, Pages 355~360
DOI : 10.3745/KTSDE.2014.3.9.355
Forecasting of emerging technology plays important roles in business strategy and R&D investment. There are various ways for technology forecasting including patent analysis. Qualitative analysis methods through experts' evaluations and opinions have been mainly used for technology forecasting using patents. However qualitative methods do not assure objectivity of analysis results and requires high cost and long time. To make up for the weaknesses, we are able to analyze patent data quantitatively and statistically by using text mining technique. In this paper, we suggest a new method of technology forecasting using text mining and ARIMA analysis.
Real-Time Vehicle License Plate Recognition System Using Adaptive Heuristic Segmentation Algorithm
Jin, Moon Yong ; Park, Jong Bin ; Lee, Dong Suk ; Park, Dong Sun ;
KIPS Transactions on Software and Data Engineering, volume 3, issue 9, 2014, Pages 361~368
DOI : 10.3745/KTSDE.2014.3.9.361
The LPR(License plate recognition) system has been developed to efficient control for complex traffic environment and currently be used in many places. However, because of light, noise, background changes, environmental changes, damaged plate, it only works limited environment, so it is difficult to use in real-time. This paper presents a heuristic segmentation algorithm for robust to noise and illumination changes and introduce a real-time license plate recognition system using it. In first step, We detect the plate utilized Haar-like feature and Adaboost. This method is possible to rapid detection used integral image and cascade structure. Second step, we determine the type of license plate with adaptive histogram equalization, bilateral filtering for denoise and segment accurate character based on adaptive threshold, pixel projection and associated with the prior knowledge. The last step is character recognition that used histogram of oriented gradients (HOG) and multi-layer perceptron(MLP) for number recognition and support vector machine(SVM) for number and Korean character classifier respectively. The experimental results show license plate detection rate of 94.29%, license plate false alarm rate of 2.94%. In character segmentation method, character hit rate is 97.23% and character false alarm rate is 1.37%. And in character recognition, the average character recognition rate is 98.38%. Total average running time in our proposed method is 140ms. It is possible to be real-time system with efficiency and robustness.
Local Path Generation Method for Unmanned Autonomous Vehicles Using Reinforcement Learning
Kim, Moon Jong ; Choi, Ki Chang ; Oh, Byong Hwa ; Yang, Ji Hoon ;
KIPS Transactions on Software and Data Engineering, volume 3, issue 9, 2014, Pages 369~374
DOI : 10.3745/KTSDE.2014.3.9.369
Path generation methods are required for safe and efficient driving in unmanned autonomous vehicles. There are two kinds of paths: global and local. A global path consists of all the way points including the source and the destination. A local path is the trajectory that a vehicle needs to follow from a way point to the next in the global path. In this paper, we propose a novel method for local path generation through machine learning, with an effective curve function used for initializing the trajectory. First, reinforcement learning is applied to a set of candidate paths to produce the best trajectory with maximal reward. Then the optimal steering angle with respect to the trajectory is determined by training an artificial neural network. Our method outperformed existing approaches and successfully found quality paths in various experimental settings, including the cases with obstacles.
Affine Invariant Local Descriptors for Face Recognition
Gao, Yongbin ; Lee, Hyo Jong ;
KIPS Transactions on Software and Data Engineering, volume 3, issue 9, 2014, Pages 375~380
DOI : 10.3745/KTSDE.2014.3.9.375
Under controlled environment, such as fixed viewpoints or consistent illumination, the performance of face recognition is usually high enough to be acceptable nowadays. Face recognition is, however, a still challenging task in real world. SIFT(Scale Invariant Feature Transformation) algorithm is scale and rotation invariant, which is powerful only in the case of small viewpoint changes. However, it often fails when viewpoint of faces changes in wide range. In this paper, we use Affine SIFT (Scale Invariant Feature Transformation; ASIFT) to detect affine invariant local descriptors for face recognition under wide viewpoint changes. The ASIFT is an extension of SIFT algorithm to solve this weakness. In our scheme, ASIFT is applied only to gallery face, while SIFT algorithm is applied to probe face. ASIFT generates a series of different viewpoints using affine transformation. Therefore, the ASIFT allows viewpoint differences between gallery face and probe face. Experiment results showed our framework achieved higher recognition accuracy than the original SIFT algorithm on FERET database.
Swear Word Detection and Unknown Word Classification for Automatic English Writing Assessment
Lee, Gyoung ; Kim, Sung Gwon ; Lee, Kong Joo ;
KIPS Transactions on Software and Data Engineering, volume 3, issue 9, 2014, Pages 381~388
DOI : 10.3745/KTSDE.2014.3.9.381
In this paper, we deal with implementation issues of an unknown word classifier for middle-school level English writing test. We define the type of unknown words occurred in English text and discuss the detection process for unknown words. Also, we define the type of swear words occurred in students's English writings, and suggest how to handle this type of words. We implement an unknown word classifier with a swear detection module for developing an automatic English writing scoring system. By experiments with actual test data, we evaluate the accuracy of the unknown word classifier as well as the swear detection module.