• Management & Information Systems Review
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• v.37 no.4
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• pp.1-20
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• 2018

This study aimed to examine earnings quality of firms selected as Global Champs project which has been promoted by the government since 2013 to support small and medium sized enterprises, for the screening year(t-1) and selected year(t). Earing quality is measured as the value of discretionary accruals estimated by Dechow et al.(1995) adjusted Jones model and Kothari et al.(2005) model, respectively. I analyze the differences of earning quality between the Global Champ firms and the paired firms selected through criteria of the similar total assets and the same industry in the screening year and the selected year. This study is motivated by the needs of measurement of the performance of the Project from the accounting transparent point of view. As the results of this study, major findings are summarized as follows. Firstly the earnings quality of the selected firms was lower than that of the paired firms. This can be explained as a result of motivation of earnings management by companies eager to meet the requirements to be selected for the Project. Secondly, in the selected year, the earnings quality was proved to improve, comparing to the screening year. This can be explained by the efforts of companies to reinforce management innovation and transparent management, which in turn led to positive effects on the earnings quality. These findings were found to be consistent in the additional analyses, where the earning quality of the reconstructed sample with only selected companies was compared for the screening year and the selected year, based on the year before the screening year(t-2).

• Journal of KIISE:Software and Applications
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• v.36 no.7
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• pp.548-559
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• 2009

The cascade face detector learned by Adaboost algorithm, which was proposed by Viola and Jones, is state of the art face detector due to its great speed and accuracy. In spite of its great performance, it still suffers from false alarms, and more computation is required to reduce them. In this paper, we want to reduce false alarms with less computation using facial color. Using facial color information, proposed face detection model scans sub-window efficiently and adapts a fast face/non-face classifier at the first stage of cascade face detector. This makes face detection faster and reduces false alarms. For facial color filtering, we define a facial color membership function, and facial color filtering image is obtained using that. An integral image is calculated from facial color filtering image. Using this integral image, its density of subwindow could be obtained very fast. The proposed scanning method skips over sub-windows that do not contain possible faces based on this density. And the face/non-face classifier at the first stage of cascade detector rejects a non-face quickly. By experiment, we show that the proposed face detection model reduces false alarms and is faster than the original cascade face detector.

• Management & Information Systems Review
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• v.35 no.4
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• pp.169-184
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• 2016

Deferred tax assets (liability) in a company's financial statements are to reflect the temporary difference between taxable income and accounting income and therefore can provide useful information as a proxy for discretionary accruals. In addition, deferred tax assets allow a company to manage its earnings by reviewing the feasibility of the assets' recognition. As such, this study focused on deferred tax assets to examine their relationship with discretionary accruals, which were measured by a modified Jones model (Dechow et al. 1995), and investigated the impact of audit quality on this relationship. In order to control for the effects of tax rate change and measurement credibility, deferred tax assets of 2,670 non-financial firms from 2009 to 2010 were collected as samples for the study. The results of the empirical analysis are as follows. First, the samples as a whole indicated that deferred tax assets have a negative relationship with discretionary accruals in a general sense, but a high-quality audit did not reveal a significant relationship between them. Second, the 1,379 samples with negative discretionary accruals did not reveal a significant relationship between deferred tax assets and discretionary accruals; however, the result showed a significant negative relationship under a high-quality audit. These findings suggest that in the case of negative discretionary accruals, a high-quality audit restricts an earnings management technique that utilizes deferred tax assets and that the assets can be a useful tool for detecting discretionary accruals. The present study is meaningful in that, unlike previous research, it combined the two contrasting roles of deferred tax assets-that of an earnings management detector and an earnings management tool-to examine their general relationship. The study also suggested that audit quality could influence the usefulness of deferred tax assets in providing information on discretionary accruals.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1150-1158
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• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

• Journal of Korean Academy of Nursing
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• v.6 no.1
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• pp.80-90
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• 1976

정신과 영역의 환자를 위해 간호원의 역활을 필요로 한 이래 여러 가지 간호의 개념으로 간호원의 역할이 변화되어오고 있다. 정신과 환자의 안전만이 가장 큰 치료의 중심일 때는 병동열쇠의 위엄에 곁따라 보호관리에만 치중해 왔으며 정신의학에서 약물요법, 전기요법의 치료과정이 생기면서 간호원의 역할 변화 및 지식의 요구를 필요로 하게 되었으며, 환경과 개인의 밀접한 관계를 중시해오면서 치료적 환경속으로 환자의 인간적 치료가 강조되었을 때 의사소통과 대인관계의 인적 환경으로써 또한 간호원의 역활이 중요시 되어왔다. 이런 관점에서 치료적 환경에 대한 정확한 이해는 간호행위과정의 불완전을 제거하며 보다 활발한 정신과 환자간호에 기여하는 일 일 것이다. DR. Bartom은 병실 환경이 비생산적이고 비 치료적일때 성격의 변화는 물론 행동적 특성의 변화까지 가져올 수 있다고 말했다. 즉 무감동적이고, 무조건적 순종이 있으며 솔선하여 행하는 행위가 줄고 장래 계획에 대한 자극이 줄어들고 될대로 되어 가는 상태 그 자체에 머물러 있어 인간의 특징적 의미와 가치를 상실하게 된다는 것이다. 정신과 병실은 잠정적 체류지로 보아야 하겠고 이 체류지에서의 영향이 환자에게 보다 유익하게 끼칠려면 간호원이 지속적으로 치료적 분위기를 유지해야 할 것이다. 치료적 입장으로서의 간호의 활동 초점은 대인관계에서 환자의 의식수준과 자아관련 수준에서의 취급이 무의식 수준에서의 탐구조사보다 바람직하다. 치료적 가치로써 치료적 환경의 이론적 근거를 DR. Sullivan 은 인간의 상호관련 문제에 두고 있다. 즉 상호작용이 존재하는 환경은 어떠한 곳이든 성격에 영향이 있고 이 성격은 대인관계의 복잡성으로부터 결코 떨어질 수 없다는 얘기다. 자아구성 또한 환경의 영향을 받는데 Cumming은 병동환경과 자아구성 재동기간에 밀접성을 시사한바 있다. Visher와 O'sullivan은 정신과적 치료중에서 일상생활에서 경험되어지는 의사소통과 대인관계속에서 학습되어지는 여러 가지가 있기 때문에 매일의 활동획이 치료적 방향으로 계획되어 져야 한다고 말했다. Maxwell Jones 또한 치료적 환경의 유용한 가동은 전 직원의 기여에 있으며 이는 정신건강을 최적으로 올려 줄 것이다. 라고 말했다. 이러한 상황에서 간호원은 의미 없이 환자의 감정 욕구를 깨닫지 못하고 감정지지를 주지 못하며 정서적 긴장을 예방하지 못한 체 환자와의 관계를 유지한다면 현대간호의 개념에서 이탈되어지고 발달되어지지 못한 미숙아 현상이 유지 될 것이다. 보다 바람직한 치료적 환경 유지는 간호로써 환자에게 기여해 주는 일이다. 간호의 역활과 더불어 전문적 태도는 따뜻하고 포용성 있게 그리고 융통성 있게 대함은 물론 간호인 자신의 "자기이용"을 깊이 그리고 치료적으로 이용할 것을 깨달아야 할 것이다. 즉 정신과 병실에서의 간호원 존재 자제가 환자에게 미치는 영향도 고려해야 한다는 것이다. 덧붙여 환자를 위한 일주일 병동 행사표를 Model로 제시하였고 그 안에서의 간호원의 역활을 약술하였다.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.18
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• pp.7539-7545
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• 2014

Background: It has been proved that participating in exercise improves colorectal cancer patients' prognosis. This study is to identify barriers to exercise in Korean colorectal cancer patients and survivors. Materials and Methods: A total of 427 colorectal cancer patients and survivors from different stages and medical status completed a self-administered questionnaire that surveyed their barriers to exercise and exercise participation. Results: The greatest perceived exercise barriers for the sampled population as a whole were fatigue, low level of physical fitness, and poor health. Those under 60-years old reported lack of time (p=0.008), whereas those over 60 reported low level of physical fitness (p=0.014) as greater exercise barriers than their counterparts. Women reported fatigue as a greater barrier than men (p<0.001). Those who were receiving treatment rated poor health (p=0.0005) and cancer-related factors as greater exercise barriers compared to those who were not receiving treatment. A multivariate model found that other demographic and medical status were not potential factors that may affect exercise participation. Further, for those who were not participating in physical activity, tendency to be physically inactive (p<0.001) and lack of exercise skill (p<0.001) were highly significant barriers, compared to those who were participating in physical activity. Also, for those who were not meeting ACSM guidelines, cancer-related exercise barriers were additionally reported (p<0.001), compared to those who were. Conclusions: Our study suggests that fatigue, low level of physical fitness, and poor health are most reported exercise barriers for Korean colorectal cancer survivors and there are differences in exercise barriers by age, sex, treatment status, and physical activity level. Therefore, support for cancer patients should be provided considering these variables to increase exercise participation.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.805-809
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• 2007

최근 도시의 발달은 하상공간에 대한 이용도를 높이는 방향으로 개발이 진행되어가는 추세이며, 하상도로 및 하상주차장의 이용은 이제 도시 내에서 이용 가능한 마지막 여유 공간으로 인식될 정도로 그 의존도가 높아져가고 있다. 그러나 하상공간의 활용도가 높아져갈수록 도시홍수의 발생으로 인한 대피문제가 발생하게 되고 돌발홍수로 인하여 하상도로의 차단 혹은 하상 주차장에 주차된 차량의 소거가 늦어지는 경우 고스란히 피해를 보게 되는 등 그 부작용도 계속 증가되고 있다. 도시홍수의 특성을 살펴보면 국지성 돌발 강우에 의한 유량의 급격한 증가와 짧은 유하시간, 작은 유역면적 등에 의하여 주요 예보지점까지의 도달시간이 매우 짧아 수문학적 홍수예측 모형을 이용하여 홍수예측 업무를 수행하는데 선행시간을 충분히 확보할 수 없다는 단점을 지니고 있다. 이에 따라 본 연구에서는 기존의 하천시스템에 대한 설계 등을 목적으로 하여 모형의 적용을 통한 시뮬레이션 기법을 적용하고 이를 통하여 홍수 예경보를 발령하기에는 선행시간의 확보(대피시간의 확보)라는 측면에서 상당한 어려움을 지닐 수 있으므로 시시각각으로 측정되는 실시간 수위측정 자료 및 실시간 강우자료를 이용하여 모형의 수행과정을 생략하고 하천의 수위변동을 직접 예측하고 대피할 수 있는 시나리오 기반의 수문모형을 개발하였다. SPSS를 사용한 통계학적 모형을 대전광역시 3대 하천에 대하여 적용한 결과 예측자료가 실측자료를 고수위 및 저수위 부근에서 정확히 모의하지 못하는 경향이 나타났으나 경계 및 위험수위를 설정하고 이를 넘어가는 시점에 대한 예측을 하는 홍수경보 시점 예측에는 효율적인 적용성을 나타내었다.씬 간편하면서도 정확도가 높아서, 환경방사성 스트론튬의 정량분석에 적절히 사용될 수 있다.e form of Jones matrix, which allows a new interpretation in the conversion efficiency of the thin-film optical waveguides.있다는 장점이 있었다. 따라서 소아에서 복막투석도관 수술 시 복강경적 방법을 이용하는 것이 효율적인 복막 투석을 위해 유용하다고 생각된다.상부 방광천자에 비해 민감도 59.5%(25/42), 특이도 86.6%(13/15)였고 위양성률 13.3%(2/15), 위음성률 40.5%(17/42) 로 정확도가 낮았다. 결론 : 소변을 가리지 못하는 영유아에서 요로 감염을 진단하기 위해서는 도뇨관 채뇨에 비해 초음파 감시하 치골상부 방광천자가 정확하고 안전한 채뇨법으로 권장되어야 한다고 생각한다.應裝置) 및 운용(運用)에 별다른 어려움이 없고, 내열성(耐熱性)이 강(强)하므로 쉬운 조건하(條件下)에서 경제적(經濟的)으로 공업적(工業的) 이용(利用)에 유리(有利)하다고 판단(判斷)되어진다.reatinine은 함량이 적었다. 관능검사결과(官能檢査結果) 자가소화(自家消化)시킨 크릴간장은 효소(酵素)처리한 것이나 재래식 콩간장에 비하여 품질 면에서 손색이 없고 저장성(貯藏性)이 좋은 크릴간장을 제조(製造)할 수 있다는 결론을 얻었다.이 있음을 확인할 수 있었다.에 착안하여 침전시 슬러지층과 상등액의 온도차를 측정하여 대사열량의 발생량을 측정하고 슬러지의 활성을 측정할 수 있는 방법을 개발하였다.enin과 Rhaponticin의 작용(作用)에 의(依)한 것이며,

• Journal of the Korea Society of Computer and Information
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• v.25 no.5
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• pp.47-56
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• 2020

The stock index is used not only as an economic indicator for a country, but also as an indicator for investment judgment, which is why research into predicting the stock index is ongoing. The task of predicting the stock price index involves technical, basic, and psychological factors, and it is also necessary to consider complex factors for prediction accuracy. Therefore, it is necessary to study the model for predicting the stock price index by selecting and reflecting technical and auxiliary factors that affect the fluctuation of the stock price according to the stock price. Most of the existing studies related to this are forecasting studies that use news information or macroeconomic indicators that create market fluctuations, or reflect only a few combinations of indicators. In this paper, this we propose to present an effective combination of the news information sentiment analysis and various macroeconomic indicators in order to predict the US Dow Jones Index. After Crawling more than 93,000 business news from the New York Times for two years, the sentiment results analyzed using the latest natural language processing techniques BERT and NLTK, along with five macroeconomic indicators, gold prices, oil prices, and five foreign exchange rates affecting the US economy Combination was applied to the prediction algorithm LSTM, which is known to be the most suitable for combining numeric and text information. As a result of experimenting with various combinations, the combination of DJI, NLTK, BERT, OIL, GOLD, and EURUSD in the DJI index prediction yielded the smallest MSE value.

• Physical Therapy Korea
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• v.14 no.3
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• pp.48-56
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• 2007

Environmental Enrichment (EE) alone is not capable of enhancing the fine digit and the forelimb functions. Therefore, we applied modified constraint-induced movement therapy (mCIMT) under the influence of EE to assess its effect on promoting improved forelimb sensorimotor functions. Focal ischemic brain injury was produced in Sprague-Dawley rats (60 rats, $250{\pm}50$ g) through middle cerebral artery occlusion (MCAO). Before MCAO induction, all rats were trained in modified limb placing tests and reaching tasks for 1 week. Then they were randomly divided into three groups: Group I: application of standard environment (SE) after MCAO induction (n=20), Group II: application of EE after MCAO induction (n=20), Group III: MCAO+EE, mCIMT and task-oriented training that was initiated at 10th day after MCAO induction (n=20). We also applied mCIMT (between 9 AM and 5 PM/daily) which included restraining the forelimb ipsilateral to the lesion using the 'Jones & Schallert' method. We assessed the change of modified limb placing, single pellet reaching test and the immunoreactivity of BDNF by immunohistochemistry (pre, 1st, 5th, 10th and 20th day). Group I showed no improved outcome, whereas group II and III significantly improved on the use of the forelimb and the immunoreactivity. The qualitative analysis of the skilled reaching test, of group III showed the greatest improvement in the fine digit and the forelimb function. These results suggest that EE combined with mCIMT is more functional in promoting enhanced fine digit and forelimb functional movements.