• KIPS Transactions on Software and Data Engineering
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• v.2 no.8
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• pp.565-570
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• 2013

Previous researches on mining sequential patterns mainly focused on discovering patterns from the point-based event. Interval events with a time interval occur in the real world that have the start and end point. Existing interval pattern mining methods that discover relationships among interval events based on the Allen operators have some problems. These are that interval patterns having three or more interval events can be interpreted as several meanings. In this paper, we propose the I_TPrefixSpan algorithm, which is an efficient sequence pattern mining technique for removing ambiguity in the Interval Patterns Mining. The proposed algorithm generates event sequences that have no ambiguity. Therefore, the size of generated candidate set can be minimized by searching sequential pattern mining entries that exist only in the event sequence. The performance evaluation shows that the proposed method is more efficient than existing methods.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.3
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• pp.220-227
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• 2017

Technology development is leading to the invention of more sophisticated electronics appliances that require long battery life. Therefore, saving power is a major concern in current-day scenarios. A notable source of power dissipation in sequential structures of integrated circuits is due to the continuous switching of high-frequency clock signals, which do not carry any information, and hence, their switching is eliminated by a method called clock gating. In this paper, we have incorporated a recent clock-gating style named Leakage Control Transistor (LECTOR)-based clock gating to drive a multi-stage sequential architectures, and we focus on its performance under three different process corners (fast-fast, slow-slow, typical-typical) through Monte Carlo simulation at 18 GHz clock with 90 nm technology. This gating is found to be one of the best gated approaches for multi-stage architectures in terms of total power consumption.

• Journal of Information Technology Applications and Management
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• v.11 no.2
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• pp.29-43
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• 2004

As the development of an information technique, gradually, mobile device is going to be miniaturized and operates at high speed. By such the requirements, the devices using a flash memory as a storage media are increasing. The flash memory consumes low power, is a small size, and has a fast access time like the main memory. But the flash memory must erase for recording and the erase cycle is limited. JFFS is a representative filesystem which reflects the characteristics of the flash memory. JFFS to be consisted of LSF structure, writes new data to the flash memory in sequential, which is not related to a file size. Mounting a filesystem or an error recovery is achieved through the sequential approach. Therefore, the mounting delay time is happened according to the file system size. This paper proposes a MJFFS to use a multi-checkpoint information to manage a mass flash file system efficiently. A MJFFS, which improves JFFS, divides a flash memory into the block for suitable to the block device, and stores file information of a checkpoint structure at fixed interval. Therefore mounting and error recovery processing reduce efficiently a number of filesystem access by collecting a smaller checkpoint information than capacity of actual files. A MJFFS will be suitable to a mobile device owing to accomplish fast mounting and error recovery using advantage of log foundation filesystem and overcoming defect of JFFS.

• Transactions of the Korean Society of Automotive Engineers
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• v.6 no.3
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• pp.115-122
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• 1998

For digital engine control timings, such as ignition, are based on the crank shaft angle. Therefore, it is very important that the angle of the crank shaft can be detected with accuracy for optimal ignition timing. Sequential multi-point injection(MPI) systems that have independent injection events for each cylinder, are used to inject an accurate quantity of fuel, and to cope with varying engine status promptly. In this study the distributorless ignition timing. A crankshaft position sensor has been installed such that it generates a number of pulses per crankshaft revolution to permit accurate detection of the crank shaft angle. An event detecting algorithm has been developed, which detects the crank shaft pulses generated by the position sensor, and the software outputs the required control signals at given crank angle values. We clarified that the hardware method is the best way to increase the performance of the control system, because the event detecting duration T(1+2)max becomes zero.

• The Journal of Korean Physical Therapy
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• v.22 no.5
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• pp.109-115
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• 2010

Purpose: This study was to delineate the structural change of neural pathway after sequential motor learning using diffusion tensor imaging (DTI). Methods: The participants were 16 healthy subjects, which were divided by training (n=8) and control (n=8) group. The task for the training was the Serial Reaction Time Task (SRTT) which was designed by Superlab program. When the 'asterisk' shows up in the 4 partition spaces on the monitor, the subject presses the correct response button as soon as possible. The training group participated in the training program of motor learning with SRTT composed of 24 digits pattern in one hour per daily through 10 days during 2 weeks. Results: In the behavioral results the training group showed significant changes in the increase of response number and the reduction of response time than those of the control group. There was significant difference in the left inferior frontal area in the fractional anisotropy (FA) map of the training group in DTI analysis. Conclusion: Motor sequential learning as like SRTT may be needed to the learning of language and visuospatial processing and may be induced for the experience-dependent structural plasticity during short period.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.10a
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• pp.100-105
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• 2008

EPCglobal은 기업 간의 물류 활동 촉진과 글로벌 유통물류 시스템 구축을 위하여 EPCglobal Architecture Framework을 제시 하였다. EPCglobal Architecture Framework의 한 구성 요소인 EPCIS(Electronic Product Code Information Services)는 EPC, 시간, 위치와 같은 물류 관련 정보에 대해 저장 및 검색 서비스를 제공한다. EPCIS는 단발성 질의(poll)와 연속 질의(subscribe) 검색 서비스를 제공한다. EPCIS의 연속 질의는 시스템 자동화 및 재고 관리, 공급망 관리를 위해 다양한 응용에서 활용이 가능하다. 일반적으로 연속 질의 처리를 위해서는 등록된 연속 질의와 입력된 데이터를 순차적으로 비교하는 Sequential Matching 기법을 사용한다. Sequential Matching기법은 등록된 연속 질의 수가 증가 할 경우 많은 부하를 발생 시키고, 이로 인해 시스템 처리 지연이 발생한다. 본 논문에서는 EPCIS의 시공간 EPC 데이터의 연속질의 처리 성능 향상을 위해 선택률 기반 효율적인 연속질의 처리 기법을 제안한다. 13차원의 도메인을 여러 개의 질의 색인으로 구성하고, 등록된 질의 정보를 기반으로 선택률을 계산한다. 선택률에 의해 변경되는 동적 질의 실행 계획을 제안함으로써, EPCIS에서 시공간 EPC 데이터의 연속질의 처리에 대해 평균 60%의 성능이 향상이 가능하도록 하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.466-471
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• 2006

IAn important problem of pattern recognition is to extract or select feature set, which is included in the pre-processing stage. In order to extract feature set, Principal component analysis has been usually used and SFS(Sequential Forward Selection) and SBS(Sequential Backward Selection) have been used as a feature selection method. This paper applies genetic algorithm which is a popular method for nonlinear optimization problem to the feature selection problem. So, we call it Genetic Algorithm Feature Selection(GAFS) and this algorithm is compared to other methods in the performance aspect.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1390-1403
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• 2016

Facial expression recognition (FER) plays a very significant role in computer vision, pattern recognition, and image processing applications such as human computer interaction as it provides sufficient information about emotions of people. For video-based facial expression recognition, depth cameras can be better candidates over RGB cameras as a person's face cannot be easily recognized from distance-based depth videos hence depth cameras also resolve some privacy issues that can arise using RGB faces. A good FER system is very much reliant on the extraction of robust features as well as recognition engine. In this work, an efficient novel approach is proposed to recognize some facial expressions from time-sequential depth videos. First of all, efficient Local Binary Pattern (LBP) features are obtained from the time-sequential depth faces that are further classified by Generalized Discriminant Analysis (GDA) to make the features more robust and finally, the LBP-GDA features are fed into Hidden Markov Models (HMMs) to train and recognize different facial expressions successfully. The depth information-based proposed facial expression recognition approach is compared to the conventional approaches such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA) where the proposed one outperforms others by obtaining better recognition rates.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.2
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• pp.233-237
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• 2010

The recent advance of sensor networks and ubiquitous techniques allow collecting and analyzing of the data which overcome the limitation imposed by time and space in real-time for making decisions. Also, analysis and prediction of collected data can support useful and necessary information to users. The collected data in sensor networks environment is the stream data which has continuous, unlimited and sequential properties. Because of the continuous, unlimited and large volume properties of stream data, managing stream data is difficult. And the stream data needs dynamic processing method because of the memory constraint and access limitation. Accordingly, we analyze correlation stream data using principal component analysis. And using result of analysis, it helps users for making decisions.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.1
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• pp.1-8
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• 2018

It is not easy to learn simple expressions of moving picture data since it contains noise and a lot of information in addition to time-based information. In this study, we propose a similarity representation method and a deep learning method between sequential data which can express such video data abstractly and simpler. This is to learn and obtain a function that allow them to have maximum information when interpreting the degree of similarity between image data vectors constituting a moving picture. Through the actual data, it is confirmed that the proposed method shows better classification performance than the existing moving image classification methods.