• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.127-133
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• 2021

A recent study of people physically falling focused on analyzing the motions of the falls using a recurrent neural network (RNN) and a deep learning approach to get good results from detecting 2D human poses from a single color image. In this paper, we investigate a detection method for estimating the position of the head and shoulder keypoints and the acceleration of positional change using the skeletal keypoints information extracted using PoseNet from an image obtained with a low-cost 2D RGB camera, increasing the accuracy of judgments about the falls. In particular, we propose a fall detection method based on the characteristics of post-fall posture in the fall motion-analysis method. A public data set was used to extract human skeletal features, and as a result of an experiment to find a feature extraction method that can achieve high classification accuracy, the proposed method showed a 99.8% success rate in detecting falls more effectively than a conventional, primitive skeletal data-use method.

• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.525-529
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• 2022

In the eLoran system, the Loran Data Channel (LDC) is used to provide precise timing and positioning. The LDC message can be modulated with the Eurofix method, which modulates the transmission time of the 3rd-8th pulse not used for navigation, and the 9th pulse method, which modulates data using the 9th additional pulse after the existing 8 Loran pulses. In this paper, we analyzed the reception performance of the LDC message transmitted from the eLoran transmitter according to the modulation method. The eLoran testbed transmitter in Incheon was set to transmit LDC messages simultaneously with the 9th pulse modulation method and the Eurofix modulation method. Then, the LDC messages stored in the databases of the eLoran differential stations in Incheon and Pyeongtaek were analyzed in terms of the message reception rate according to the modulation method. Using the navigation aid management ship Inseong No. 1, the range of LDC message reception of actual sea users near Incheon Port was also analyzed. The results of this study are expected to be utilized in the full operational capability service after the eLoran pilot service.

• Journal of the Korean Society of Earth Science Education
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• v.13 no.3
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• pp.284-296
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• 2020

This study investigates the effect of SSI debate on first-year high school student's opinions about environmental issue, their judgment grounds, and solutions to regional environmental problems. The SSI debate was about white heron habitats near the village where students live. As the main data of the study, environmental perception questionnaires, and students' workbook including open-ended questions were collected before and after class. The environmental perception questionnaire was analyzed by descriptive statistics, and the response of the open-ended questions was analyzed through inductive qualitative research methods. First, the results of this study shows that the SSI debate has a statistically significant impact on students' environmental attitude. Second, a majority of students agreed on the idea that villagers should drive the birds out of town and they did not change their after the discussion class. However, after the discussion class, students' solutions about the issue were changed in a way that more short-term, feasible, concrete, and less time-consuming solutions to the problem. Based on the results of this study, this study implies that SSI issue debate using local problem should be used more often in science classroom so the students recognize local SSI and improve real world problem solving skills.

• Journal of the Korean earth science society
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• v.42 no.1
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• pp.76-87
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• 2021

In this study, temporal trend of researches in earthquake with groundwater level, water quality, radon, remote sensing, electrical resistivity, gravity, and geomagnetism was searched from 2001 to 2020, using the journals indexed in Web of Science, and the number of articles published in international journals was counted in relation to the occurrences of earthquakes (≥Mw 5.0, ≥Mw 6.0, ≥Mw 7.0, ≥Mw 8.0, and ≥Mw 9.0). The number of articles shows an increasing trend over the studied period. This is explained by that studies on earthquake precursor and seismic monitoring becomes active in various fields with integrated data analysis through the development of remote sensing technology, progress of measurement equipment, and big data. According to Mann-Kendall and Sen's tests, gravity-related articles exhibit an increasing trend of 1.30 articles/yr, radon-related articles (0.60 articles/yr), groundwater-related articles (0.70 articles/yr), electrical resistivity-related articles (0.25 articles/yr), and remote-sensing-related articles (0.67 articles/yr). By cross-correlation analysis of the number of articles in each field with removing trend effect and the number of earthquakes of ≥Mw 5.0, ≥Mw 6.0, ≥Mw 7.0, ≥Mw 8.0, and ≥Mw 9.0, radon and remote sensing fields exhibit a high cross-correlation with a delay time of one year. In addition, large-scale earthquakes such as the 2004 and 2005 Sumatra earthquake, the 2008 Sichuan earthquake, the 2010 Haiti earthquake, and the 2010 Chile earthquake are estimated to be related with the increase in the number of articles in the corresponding periods.

• Journal of KIISE:Software and Applications
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• v.26 no.8
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• pp.974-987
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• 1999

이동 컴퓨팅 환경에서 자주 접근하는 데이터에 대한 캐싱은 무선 채널의 좁은 대역폭에서 경쟁을 줄일 수 있는 유용한 기술이다. 그러나, 트랜잭션 캐시 일관성을 지원하는 전통적인 클라이언트/서버 전략은 클라이언트와 서버간에 많은 양의 통신을 필요로 하기 때문에 이동 클라이언트/서버 컴퓨팅 환경에서는 적절하지 않다. 본 논문에서는 브로드캐스트-기반 캐시 무효화 정책을 사용하면서 트랜잭션 캐시 일관성을 지원하는 OCC-UTS (Optimistic Concurrency Control with Update TimeStamp) 프로토콜을 제안한다. 접근한 데이터에 대한 일관성 검사 및 완료 프로토콜은 캐시 무효화 과정의 내부 과정으로 완전 분산 형태로 효율적으로 구현되며, 일관성 체크의 대부분이 이동 클라이언트에서 수행된다. 또한, 분석 모델에 기반한 성능 비교를 통해, 본 논문에서 제안하는 OCC-UTS 프로토콜이 다른 경쟁 프로토콜보다 높은 트랜잭션 처리율을 얻으며, 데이터 항목을 자주 접근하면 할수록 지역 캐시를 사용하는 OCC-UTS 프로토콜이 더 효율적임을 보인다. 이동 클라이언트의 접속 단절에 대해서는 무효화 브로드캐스트 윈도우를 크게 하여 접속 단절에 적절히 대처할 수 있다.Abstract In a mobile computing environment, caching of frequently accessed data has been shown to be a useful technique for reducing contention on the narrow bandwidth of the wireless channels. However, the traditional client/server strategies for supporting transactional cache consistency that require extensive communications between a client and a server are not appropriate in a mobile client/server computing environment. In this paper, we propose a new protocol, called OCC-UTS (Optimisitic Concurrency Control with Update TimeStamp), to support transactional cache consistency in a mobile client/server computing environment by utilizing the broadcast-based solutions for the problem of invalidating caches. The consistency check on accessed data and the commitment protocol are implemented in a truly distributed fashion as an integral part of cache invalidation process, with most burden of consistency check being downloaded to mobile clients. Also, our experiments based on an analytical model substantiate the basic idea and study the performance characteristics. Experimental results show that OCC-UTS protocol without local cache outperforms other competitor protocol, and the more frequent a mobile client accesses data items the more efficient OCC-UTS protocol with local cache is. With respect to disconnection, the tolerance to disconnection is improved if the invalidation broadcast window size is extended.

• KIISE Transactions on Computing Practices
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• v.22 no.4
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• pp.189-194
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• 2016

A trajectory is the motion path of a moving object. The advances in IT have made it possible to collect an immeasurable amount of various type of trajectory data from a moving object using location detection devices like GPS. The trajectories of moving objects are widely used in many different fields of research, including the geographic information system (GIS) field. In the GIS field, several attempts have been made to automatically generate digital maps of roads by using the vehicle trajectory data. To achieve this goal, the method to cluster the trajectories on the same road is needed. Usually, the $Fr{\acute{e}}chet$ distance measure is used to calculate the distance between a pair of trajectories. However, the $Fr{\acute{e}}chet$ distance measure requires prolonged calculation time for a large amount of trajectories. In this paper, we presented a fast heuristic algorithm to distinguish whether the trajectories are in close distance or not using the discrete $Fr{\acute{e}}chet$ distance measure. This algorithm trades the accuracy of the resulting distance with decreased calculation time. By experiments, we showed that the algorithm could distinguish between the trajectory within 10 meters and the distant trajectory with 95% accuracy and, at worst, 65% of calculation reduction, as compared with the discrete $Fr{\acute{e}}chet$ distance.

• Korean Journal of Clinical Laboratory Science
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• v.48 no.4
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• pp.348-354
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• 2016

Data stored in hospital information systems has a great potential to improve adequacy assessment and quality management. Moreover, an establishment of a data warehouse has been known to improve quality management and to offer help to clinicians. This study constructed a data mart that can be used to analyze antibiotic usage as a part of systematic and effective data analysis of infection control information. Metadata was designed by using the XML DTD method after selecting components and evaluation measures for infection control. OLAP-a multidimensional analysis tool-for antibiotic usage analysis was developed by building a data mart through modeling. Experimental data were obtained from data on antibiotic usage at a university hospital in Cheonan area for one month in July of 1997. The major components of infection control metadata were antibiotic resistance information, antibiotic usage information, infection information, laboratory test information, patient information, and infection related costs. Among them, a data mart was constructed by designing a database to apply antibiotic usage information to a star schema. In addition, OLAP was demonstrated by calculating the statistics of antibiotic usage for one month. This study reports the development of a data mart on antibiotic usage for infection control through the implementation of XML and OLAP techniques. Building a conceptual, structured data mart would allow for a rapid delivery and diverse analysis of infection control information.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.167-171
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• 2007

Application areas of Knowledge Discovery in Database (KDD) have been expanded into many R&D management processes including technology trends analysis, forecasting and evaluation etc. Established research field such as informetrics (or scientometrics) has recently fully utilized techniques or methods of KDD. Various systems have been developed to support works of analyzing large-scale R&D related databases such as patent DB or bibliographic DB by a few researchers or institutions. But extant systems have some problems for korean users to use. Their prices is not cheap, korean language process not available, and user's demands not reflected. To solve these problems, Korea Institute of Science and Technology Information (KISTI) developed stand-alone type information analysis system named as KnowledgeMatrix. KnowledgeMatrix system offer various functions to analyze retrieved data set from databases. Knowledge Matrix main operation unit is composed of user-defined lists and matrix generation, cluster analysis, visualization, data pre-processing. KnowledgeMatrix show better performances and offer more various functions than extant systems.

• Journal of KIISE
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• v.41 no.9
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• pp.666-673
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• 2014

This paper proposes a scalable face recognition method for unconstrained face databases, and shows a simple experimental result. Existing face recognition research usually has focused on improving the recognition rate in a constrained environment where illumination, face alignment, facial expression, and background is controlled. Therefore, it cannot be applied in unconstrained face databases. The proposed system is face feature extraction algorithm for unconstrained face recognition. First of all, we extract the area that represent the important features(landmarks) in the face, like the eyes, nose, and mouth. Each landmark is represented by a high-dimensional LBP(Local Binary Pattern) histogram feature vector. The multi-scale LBP histogram vector corresponding to a single landmark, becomes a low-dimensional face feature vector through the feature reduction process, PCA(Principal Component Analysis) and LDA(Linear Discriminant Analysis). We use the Rank acquisition method and Precision at k(p@k) performance verification method for verifying the face recognition performance of the low-dimensional face feature by the proposed algorithm. To generate the experimental results of face recognition we used the FERET, LFW and PubFig83 database. The face recognition system using the proposed algorithm showed a better classification performance over the existing methods.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.68-74
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• 2008

Application areas of Knowledge Discovery in Database(KDD) have been expanded to many R&D management processes including technology trends analysis, forecasting and evaluation etc. Established research field such as informetrics (or scientometrics) has utilized techniques or methods of KDD. Various systems have been developed to support works of analyzing large-scale R&D related databases such as patent DB or bibliographic DB by a few researchers or institutions. But extant systems have some problems for korean users to use. Their prices is not moderate, korean language processing is impossible, and user's demands not reflected. To solve these problems, Korea Institute of Science and Technology Information(KISTI) developed stand-alone type information analysis system named as KnowledgeMatrix. KnowledgeMatrix system offer various functions to analyze retrieved data set from databases. KnowledgeMatrix's main operation unit is composed of user-defined lists and matrix generation, cluster analysis, visualization, data pre-processing. Matrix generation unit help extract information items which will be analyzed, and calculate occurrence, co-occurrence, proximity of the items. Cluster analysis unit enable matrix data to be clustered by hierarchical or non-hierarchical clustering methods and present tree-type structure of clustered data. Visualization unit offer various methods such as chart, FDP, strategic diagram and PFNet. Data pre-processing unit consists of data import editor, string editor, thesaurus editor, grouping method, field-refining methods and sub-dataset generation methods. KnowledgeMatrix show better performances and offer more various functions than extant systems.