• Journal of Advanced Information Technology and Convergence
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• v.10 no.2
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• pp.167-175
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• 2020

In this paper, we propose a traffic accident detection on vehicle-mounted camera. In the proposed method, the minimum bounding box coordinates the central coordinates on the bird's eye view and motion vectors of each vehicle object, and ego-motions of the vehicle equipped with dash-cam are extracted from the dash-cam video. By using extracted 4 kinds features as the input of Bi-LSTM (bidirectional LSTM), the accident probability (score) is predicted. To investigate the effect of each input feature on the probability of an accident, we analyze the performance of the detection the case of using a single feature input and the case of using a combination of features as input, respectively. And in these two cases, different detection models are defined and used. Bi-LSTM is used as a cascade, especially when a combination of the features is used as input. The proposed method shows 76.1% precision and 75.6% recall, which is superior to our previous work.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.177-191
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• 2022

This paper addresses the use of machine learning methods for causal estimation of treatment effects from observational data. Even though conducting randomized experimental trials is a gold standard to reveal potential causal relationships, observational study is another rich source for investigation of exposure effects, for example, in the research of comparative effectiveness and safety of treatments, where the causal effect can be identified if covariates contain all confounding variables. In this context, statistical regression models for the expected outcome and the probability of treatment are often imposed, which can be combined in a clever way to yield more efficient and robust causal estimators. Recently, targeted maximum likelihood estimation and causal random forest is proposed and extensively studied for the use of data-adaptive regression in estimation of causal inference parameters. Machine learning methods are a natural choice in these settings to improve the quality of the final estimate of the treatment effect. We explore how we can adapt the design and training of several machine learning algorithms for causal inference and study their finite-sample performance through simulation experiments under various scenarios. Application to the percutaneous coronary intervention (PCI) data shows that these adaptations can improve simple linear regression-based methods.

• The KIPS Transactions:PartD
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• v.10D no.5
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• pp.829-836
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• 2003

Software Reliability Growth Model that have been studied variously. But measurement of correct parameter of this model is not easy. Specially, estimation of correct model about failure data must be establish and estimation of parameter can consist exactly. To get correct testing, we calculate the normal score and describe the normal probability plot. Use the normal probability plot, we estimate the distribution for failure data. In this paper, we estimate the software reliability growth model for through the normal probability plot. In this research, we applies software reliability growth model through distribution characteristics of failure data. If we see plot, we determine the software reliability growth model, we can make sure superior in model's performance estimation.

• Journal of Veterinary Clinics
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• v.33 no.4
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• pp.200-204
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• 2016

This study investigated whether presynchronization with GnRH 6 days before initiation of the Ovsynch program improved reproductive outcomes in dairy cows. Additionally, postponement of initiation of the Ovsynch program for cows during the metestrus phase by 5 days was investigated to determine if it improved reproductive outcomes. To accomplish this, 941 Holstein dairy cows with unknown estrous cycle were randomly allocated into an Ovsynch group (n = 768; $100{\mu}g$ gonadorelin [a GnRH analogue], $500{\mu}g$ of cloprostenol [$PGF_{2{\alpha}}$ analogue] seven days later, $100{\mu}g$ gonadorelin 56 h later and timed artificial insemination [AI] 16 h after) and a G6-Ovsynch (n = 173) that received $100{\mu}g$ GnRH followed by the Ovsynch program 6 days later. Additionally, 272 dairy cows with known estrous cycle (metestrus stage) received the Ovsynch 5 days later (Day 5-Ovsynch group, n = 272). The odds ratio (OR) for pregnancy was analyzed by logistic regression using the LOGISTIC procedure in SAS. The treatment group (p < 0.001) and AI season (p < 0.05) significantly affected the probability of pregnancy, whereas farm, cow parity, calving to AI interval, and body condition score had no affect (p > 0.05). The Day 5-Ovsynch group had a higher probability of pregnancy (OR: 1.71) than the Ovsynch group, while that of the G6-Ovsynch group was intermediate (p > 0.05). Cows inseminated during winter had a higher OR (1.39) than those inseminated during spring. Overall, additional GnRH treatment 6 days before the Ovsynch did not improve reproductive outcomes, whereas postponement of the initiation of Ovsynch by 5 days for cows during metestrus improved reproductive outcomes.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.33 no.3
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• pp.317-331
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• 2023

Objectives: Construction workers are exposed to various hazardous substances simultaneously. However, little is known about the exposure hazards in construction industry. This study was aimed at identifying the risk of exposure hazards among construction workers. Methods: The expert survey (n=29) was conducted, including construction industry health managers (n=11) and work environment monitoring experts (n=18), on exposure probability, intensity and risk of hazardous substances by construction occupations Results: The exposure hazards of 30 construction occupations were identified and summarized through a literature review and expert survey. The most prevalent hazards were in order of noise, awkward posture, heat/cold, crystalline silica, cement/concrete dust, metal fumes, and volatile organic compounds. The hazards with highest risk score(over seven points) at construction occupations were noise(formwork carpenter, concrete finisher, rebar worker, demolition worker, driller/rock blaster), hazardous rays(welder), heat/cold (earthworks, formwork carpenter, rebar worker, concrete placer, scaffolder), awkward posture(bricklayer, caulker/tile setter, rebar worker) and heavy lifting(bricklayer, rebar worker). Among construction workers, the job types with the highest risk of exposure to carcinogens, and in which occupational cancer has been reported, were in order of stonemason, concrete finisher, rock blaster, welder, insulation installer, painter, scaffolder, plant worker and earthworks in order Conclusions: Systematic research and discussion on occupational disease among construction workers and its various hazardous factors are needed to establish job exposure matrix for facilitating standard for promptly processing the workers' compensation.

• Journal of Korea Water Resources Association
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• v.52 no.7
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• pp.475-482
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• 2019

Subseasonal-to-Seasonal (S2S) prediction information which have 2 weeks to 2 months lead time are expected to be used through many parts of industry fields, but utilizability is not reached to expectation because of lower predictability than weather forecast and mid- /long-term forecast. In this study, we used multi-layer perceptron (MLP) which is one of machine learning technique that was built for regression training in order to improve predictability of S2S precipitation data at South Korea through post-processing. Hindcast information of ECMWF was used for MLP training and the original data were compared with trained outputs based on dichotomous forecast technique. As a result, Bias score, accuracy, and Critical Success Index (CSI) of trained output were improved on average by 59.7%, 124.3% and 88.5%, respectively. Probability of detection (POD) score was decreased on average by 9.5% and the reason was analyzed that ECMWF's model excessively predicted precipitation days. In this study, we confirmed that predictability of ECMWF's S2S information can be improved by post-processing using MLP even the predictability of original data was low. The results of this study can be used to increase the capability of S2S information in water resource and agricultural fields.

• Therapeutic Science for Rehabilitation
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• v.12 no.3
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• pp.61-77
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• 2023

Objective : This study analyzed the correlation between cognitive function and oral health-related quality of life (OHQoL). Methods : Demographic and clinical characteristics were extracted and utilized for subjects aged 45 years or older who participated in the 8th Korean Longitudinal Study on Aging in 2020. The dependent variable was the Geriatric Oral Health Assessment Index, and the independent variable was the level of cognitive function classified by the Mini-Mental State Examination scores. The analysis method used inverse probability of treatment weighting (IPTW). Then, the association between cognitive function and OHQoL was analyzed by multiple regression analysis. Results : Among the participants, 4,367 (71.40%) had normal cognition, 1,155 (18.89%) had moderate cognitive impairment, and 594 (9.71%) had severe cognitive impairment. As a result of analysis by applying IPTW, there was a negative correlation between the cognitive function group and OHQoL (normal vs. moderate: β = -2.534, p < .0001; normal vs. severe: β = -2.452, p < .0001). Conclusion : After propensity score matching, mild cognitive impairment showed a more negative association than severe cognitive impairment. Therefore, patients with cognitive impairment require oral health management education to improve OHQoL regardless of the level of cognitive impairment.

• Journal of the Korean Society of Radiology
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• v.83 no.5
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• pp.1081-1089
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• 2022

Purpose To compare the computerized Greulich-Pyle based bone age with elbow bone age. Materials and Methods A total of 2126 patients (1525 girls; 601 boys) whose elbow bone age was within the evaluable range by the Sauvegrain method, and who simultaneously underwent hand radiography, were enrolled in the study. The 1st-bone age and VUNO score of the hand were evaluated using VUNOMed-BoneAge software. The correlation between the hand and elbow bone age was analyzed according to the child's gender and the probability of 1st-bone age. Results The correlation between VUNO score and elbow bone age (r = 0.898) was higher than the correlation between 1st-bone age and elbow bone age (r = 0.879). Moreover, the VUNO score showed a better correlation with the elbow bone age in patients with a 1st-bone age probability of less than 70%, or in girls. Elbow bone age was more advanced compared to hand bone age, and this difference increased until the middle of puberty and gradually decreased in the latter half. Conclusion The computerized Greulich-Pyle based hand bone age showed a significant correlation with the elbow bone age at puberty. However, since the elbow bone age tends to advance faster than the hand bone age, caution is required while judging the bone age during puberty.

• Annual Conference on Human and Language Technology
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• 2014.10a
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• pp.95-99
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• 2014

본 논문에서는 최근 급속히 증가하여 사회적 이슈가 되고 있는 SMS 스팸 필터링을 위한 듀얼 SMS 스팸필터링 기법을 제안한다. 지속적으로 증가하고 새롭게 변형되는 SMS 문자 필터링을 위해서는 패턴 및 스팸 단어 사전을 통한 필터링은 많은 수작업을 요구하여 부적합하다. 그리하여 기계 학습을 이용한 자동화 시스템 구축이 요구되고 있으며, 효과적인 기계 학습을 위해서는 자질 선택과 자질의 가중치 책정 방법이 중요하다. 하지만 SMS 문자 특성상 문장들이 짧기 때문에 출현하는 자질의 수가 적어 분류의 어려움을 겪게 된다. 이 같은 문제를 개선하기 위하여 본 논문에서는 슬라이딩 윈도우 기반 N-gram 확장을 통해 자질을 확장하고, 확장된 자질로 그래프를 구축하여 얕은 구조적 특징을 표현한다. 학습 데이터에 출현한 N-gram 자질을 정점(Vertex)으로, 자질의 출현 빈도를 그래프의 간선(Edge)의 가중치로 설정하여 햄(HAM)과 스팸(SPAM) 그래프를 각각 구성한다. 이렇게 구성된 그래프를 바탕으로 노드의 중요도와 간선의 가중치를 활용하여 최종적인 자질의 가중치를 결정한다. 입력 문자가 도착하면 스팸과 햄의 그래프를 각각 이용하여 입력 문자의 2개의 자질 벡터(Vector)를 생성한다. 생성된 자질 벡터를 지지 벡터 기계(Support Vector Machine)를 이용하여 각 SVM 확률 값(Probability Score)을 얻어 스팸 여부를 결정한다. 3가지의 실험환경에서 바이그램 자질과 이진 가중치를 사용한 기본 시스템보다 F1-Score의 약 최대 2.7%, 최소 0.5%까지 향상되었으며, 결과적으로 평균 약 1.35%의 성능 향상을 얻을 수 있었다.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.647-656
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• 2020

This study empirically examines the effect of ownership structure on bank diversification and risk-taking behavior. The population of this study is based on all commercial banks listed in Bangladesh. Thirty-two conventional commercial banks were randomly selected from thirty-three conventional banks for this study. Data was collected from the annual reports of the concerned banks from 2000 to 2017. To analyze the data, we had applied the two-stage least squares (2SLS) estimator. The results of the analysis show that ownership structure i.e. managerial ownership, institutional ownership, general public ownership, and ownership concentration have a significant negative impact on bank diversification. On the other hand, institutional ownership, managerial ownership, and general public ownership have a significant positive impact on Z-score, and ownership concentration has an insignificant but positive impact on the Z-score of banks in Bangladesh. Therefore, the study opposes the benefits of diversification and promotes ownership structure which is capable of ensuring better financial stability by reducing the probability of risk. The policy-makers especially, Bangladesh banks should evaluate the fact of this study to issue guidelines on corporate governance, bank diversification, and risk-taking behavior of commercial banks.