• 한국철도학회논문집
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• 제7권4호
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• pp.406-411
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• 2004

BOM(Bill of Materials) is a listing or description of raw materials, parts, and assemblies that define a product. In order to evaluate the performance of proposed BOM management system, which is very important to maintenance information system of urban transit. We develop component based BOM data and rule-set to design data structure that is mutually independent and integrated efficiently. It divides data whit management interface using component technology. The component based master BOM have advantage in database size and flexibility. Flexibility is measured as the number of updating records in accordance with added new product or engineering change. In database size, component based BOM is the best. we develop master BOM management system in web environment.

• Journal of the Korean Data and Information Science Society
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• 제26권4호
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• pp.957-970
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• 2015

이 연구는 일반선형모형에 이원변량분석과 위계적회귀분석을 적용하여 한국남자프로농구 경기기록 (2014-2015 정규리그 270경기)을 분석하였다. 이원변량분석 결과, 3점슛시도에서 승패와 홈원정 집단간에 상호작용효과가 통계적으로 유의하게 나타났다. 이 외에 변인들 (2점슛시도, 어시스트, 속공, 선수교체)은 모두 승패 집단간에는 통계적으로 유의한 차이가 나타났고, 홈원정경기 간에는 유의한 차이가 없게 나타났다. 위계적회귀분석 결과, 어시스트는 3점슛시도가 총득점에 미치는 영향에 대해 통계적으로 유의한 조절변수로 나타났으며, 속공은 어시스트가 2점슛성공에 미치는 영향에 대해 유의한 조절변수로 나타났다. 반면 어시스트는 2점슛시도가 총득점에 미치는 영향에, 그리고 속공은 어시스트가 3점슛성공에 미치는 영향에 유의한 조절효과가 없는 것으로 나타났다. 마지막으로 선수교체는 2점슛시도, 3점슛시도 그리고 어시스트가 총득점에 미치는 영향에 통계적으로 유의한 조절효과가 없는 것으로 나타났다.

• Asian-Australasian Journal of Animal Sciences
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• 제13권10호
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• pp.1347-1352
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• 2000

The objectives of the study were to examine non-genetic factors that influence somatic cell counts in dairy cattle and to estimate the genetic parameters of somatic cell counts. A total of 34, 097-test day somatic cell count records were obtained from the Zimbabwe Dairy Services Association (ZDSA). The data were from 5, 615 Holstein daughters of 390 sires and 2, 541 dams tested between May 1994 and December 1998. First lactation cows contributed 22, 147 records to the data set, while 11, 950 records were from second and later parity cows. The model for analysis included fixed effects of month of calving, year of calving, stage of lactation, calving interval and test date. Milk yield and age on test day were fitted in the model as covariates. The additive genetic effects pertaining to cows, sires and dams and the residual error were the random effects. The Average Information Restricted Maximum Likelihood algorithm was used for analysis. The heritability of somatic cell scores was low at $0.027{\pm}0.013$ for parity one cows and $0.087{\pm}0.031$ for parity two and above. Repeatability estimates were $0.22{\pm}0.01$ and $0.30{\pm}0.01$ for the two lactation groups, respectively. Genetic and phenotypic correlations between the somatic cell scores and test day milk production were small and negative. It seems that there is no genetic link between somatic cell counts and milk yield in Holstein cattle in Zimbabwe. The results also seem to indicate that somatic cell count is a trait that is mainly governed by environmental factors.

• 한국운동역학회지
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• 제25권1호
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• pp.1-10
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• 2015

Objective : The purpose of this study was to find some kinetic variable's relationships between personal records and low records in female high jump. Methods : Collected data of the subjects(N=8, ages: $25.5{\pm}1.85$, height: $173{\pm}5.83$, mass: $54.75{\pm}6.36$ personal record: $1.71{\pm}0.04$, low record: $1.62{\pm}0.03$) were used for the last three strides and take-off phase. Five video cameras set in 30frames/s were used for recording. After digitizing motion, the Direct Linear Transformation(DLT) technique was employed to obtain 3-D position coordinates. The kinematic and kinetic factors of distance, velocity, angle, impulse, jerk variables were calculated. A paired t-test was applied for the difference of variables between personal records and lower records and for correlation with performances and variables. The significance level was accepted at p<.05. Results : There was no relationship between pattern of stride and performance. However, rate of change of velocity was related with cental of mass height(CMH) at peak point(PP). Knee, hip, backward lean, foot plant, approach and take off angle showed no difference between best record and low record. Vertical impulse momentum also showed no difference between performances. Conclusion : According to a t-test result, there were significant differences in CMH at PP and jerk at touch down between best record and low record.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권1호
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• pp.90-113
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• 2021

Electronic Records Management System (ERMS) is a computer program or set of applications that is utilized for keeping up to date records along with their storage. ERMS has been extensively utilized for enhancing the performance of academic institutions. The system assists in the planning and decision-making processes, which in turn enhances the competencies. However, although ERMS is significant in supporting the process of decision-making, the majority of organizations have failed to take an initiative to implement it, taking into account that are some implementing it without an appropriate framework, and thus resulted in the practice which does not meet the accepted standard. Therefore, this study identifies the factors influencing the adoption of ERMS among employees of HLI in Yemen and the role of such adoption in the decision-making process, using the Unified Theory of Acceptance and Use of Technology (UTAUT) along with Technology, Organization and Environment (TOE) as the underpinning theories. The study conducts a cross-sectional survey with a questionnaire as the technique for data collection, distributed to 364 participants in various Yemeni public Higher Learning Institutions (HLI). Using AMOS as a statistical method, the findings revealed there are significant and positive relationships between technology factors (effort expectancy, performance expectancy, IT infrastructure and security), organizational factors (top management support, financial support, training, and policy),environmental factors (competitiveness pressure, facilitating conditions and trust) and behavioral intention to adopt ERMS, which in return has a significant relationship with the process of decision-making in HLI. The study also presents a variety of theoretical and empirical contributions that enrich the body of knowledge in the field of technology adoption and the electronic record's domain.

• 에너지공학
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• 제25권3호
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• pp.149-156
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• 2016

본고의 목적은 국내 수송부문 에너지절약 및 에너지효율 향상 정책효과 분석 및 평가를 위한 기초 DB의 구축범위를 도출하는 것이다. 국내외 사례분석에 기초하여 도출한 대분류 항목은 에너지소비, 에너지원단위, 이산화탄소 또는 온실가스 배출량, 경제지표, 수송량/수송실적, 자동차 관련 기초자료 등이다. 전문가 의견조사에 의해 에너지 소비, 수송량/수송실적, 에너지효율/에너지원단위, 자동차, 에너지경제, 에너지환경 등 대분류 도출 하에, 하위 항목으로 세분하여, 각 구성항목에 대한 세부 분류에 대한 정보를 반영할 수 있는 분류지표로 설정하였다.

• Structural Engineering and Mechanics
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• 제17권2호
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• pp.215-244
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• 2004

There is an ever-increasing demand for assessment of earthquake effects on transportation structures, emphasised by the crippling consequences of recent earthquakes hitting developed countries reliant on road transportation. In this work, vulnerability functions for RC bridges are derived analytically using advanced material characterisation, high quality earthquake records and adaptive inelastic dynamic analysis techniques. Four limit states are employed, all based on deformational quantities, in line with recent development of deformation-based seismic assessment. The analytically-derived vulnerability functions are then compared to a data set comprising observational damage data from the Northridge (California 1994) and Hyogo-ken Nanbu (Kobe 1995) earthquakes. The good agreement gives some confidence in the derived formulation that is recommended for use in seismic risk assessment. Furthermore, by varying the dimensions of the prototype bridge used in the study, and the span lengths supported by piers, three more bridges are obtained with different overstrength ratios (ratio of design-to-available base shear). The process of derivation of vulnerability functions is repeated and the ensuing relationships compared. The results point towards the feasibility of deriving scaling factors that may be used to obtain the set of vulnerability functions for a bridge with the knowledge of a 'generic' function and the overstrength ratio. It is demonstrated that this simple procedure gives satisfactory results for the case considered and may be used in the future to facilitate the process of deriving analytical vulnerability functions for classes of bridges once a generic relationship is established.

• 한국컴퓨터정보학회논문지
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• 제24권6호
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• pp.1-9
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• 2019

Hard disks, which have long been used as secondary storage in computing systems, are increasingly being replaced by solid state drives (SSDs), due to their relatively fast data input / output speeds and small, light weight. SSDs that use NAND flash memory as a storage medium are significantly different from hard disks in terms of physical operation and internal operation. In particular, there is a feature that data overwrite can not be performed, which causes erase operation before writing. In order to solve this problem, a hot data for frequently updating a data for a specific page is distinguished from a cold data for a relatively non-hot data. Hot data identification helps to improve overall performance by identifying and managing hot data separately. Among the various hot data identification methods known so far, there is a technique of recording consecutive write requests by using a Bloom filter and judging the values by hot data. However, the Bloom filter technique has a problem that a new bit array must be generated every time a set of items is changed. In addition, since it is judged based on a continuous write request, it is possible to make a wrong judgment. In this paper, we propose a method using a counting filter for accurate hot data verification. The proposed method examines consecutive write requests. It also records the number of times consecutive write requests occur. The proposed method enables more accurate hot data verification.

• 한국컴퓨터정보학회논문지
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• 제22권7호
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• pp.55-62
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• 2017

Recently, there has been an increasing need for the sharing of microdata containing information regarding an individual entity. As microdata usually contains sensitive information on an individual, releasing it directly for public use may violate existing privacy requirements. Thus, to avoid the privacy problems that occur through the release of microdata for public use, extensive studies have been conducted in the area of privacy-preserving data publishing (PPDP). The k-anonymity algorithm, which is the most popular method, guarantees that, for each record, there are at least k-1 other records included in the released data that have the same values for a set of quasi-identifier attributes. Given an original table, the corresponding k-anonymous table is obtained by generalizing each record in the table into an indistinguishable group, called the equivalent class, by replacing the specific values of the quasi-identifier attributes with more general values. However, query processing over the anonymized data is a very challenging task, due to generalized attribute values. In particular, the problem becomes more challenging with an equi-join query (which is the most common type of query in data analysis tasks) over k-anonymous tables, since with the generalized attribute values, it is hard to determine whether two records can be joinable. Thus, to address this challenge, in this paper, we develop a novel scheme that is able to effectively perform an equi-join between k-anonymous tables. The experiment results show that, through the proposed method, significant gains in accuracy over using a naive scheme can be achieved.

• 생물정신의학
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• 제27권1호
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• pp.18-26
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• 2020

Objectives The aim was to find effective vectorization and classification models to predict a psychiatric diagnosis from text-based medical records. Methods Electronic medical records (n = 494) of present illness were collected retrospectively in inpatient admission notes with three diagnoses of major depressive disorder, type 1 bipolar disorder, and schizophrenia. Data were split into 400 training data and 94 independent validation data. Data were vectorized by two different models such as term frequency-inverse document frequency (TF-IDF) and Doc2vec. Machine learning models for classification including stochastic gradient descent, logistic regression, support vector classification, and deep learning (DL) were applied to predict three psychiatric diagnoses. Five-fold cross-validation was used to find an effective model. Metrics such as accuracy, precision, recall, and F1-score were measured for comparison between the models. Results Five-fold cross-validation in training data showed DL model with Doc2vec was the most effective model to predict the diagnosis (accuracy = 0.87, F1-score = 0.87). However, these metrics have been reduced in independent test data set with final working DL models (accuracy = 0.79, F1-score = 0.79), while the model of logistic regression and support vector machine with Doc2vec showed slightly better performance (accuracy = 0.80, F1-score = 0.80) than the DL models with Doc2vec and others with TF-IDF. Conclusions The current results suggest that the vectorization may have more impact on the performance of classification than the machine learning model. However, data set had a number of limitations including small sample size, imbalance among the category, and its generalizability. With this regard, the need for research with multi-sites and large samples is suggested to improve the machine learning models.