• KDI Journal of Economic Policy
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• 제26권2호
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• pp.3-62
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• 2004

한국의 자연실업률에 대한 기존 연구들은 대부분 한 가지의 추정방법에 의존하고 있어 연구 간에 상이하게 나타나는 추정결과를 평가할 근거가 없는 상황이다. 따라서 본고에서는 이를 감안하여 순수 시계열방법, 축약형 모형을 이용한 방법, 구조모형을 이용한 방법 등 다양한 추정방법을 검토하여 추정방법 간 상대적인 장단점을 비교하고 이를 기반으로 한국의 자연실업률을 추정하고자 하였다. 또한 본 논문에서는 추정결과의 신뢰구간을 몬테카를로 적분(Monte Carlo integration)방법을 이용하여 추정함으로써 추정결과의 정확성에 대한 평가 근거를 제시하였다. 축약형 모형의 하나인 다변수 비관측인자모형이 여타 추정방법에 비해 상대적으로 장점을 지니고 있는 것으로 평가되었으나 추정결과가 모형설정오류에 민감하다는 점을 고려하여 모형설정에 세심한 주의를 기울일 필요가 제기되었으며, 순수 시계열방법이나 구조 벡터자기회귀모형도 나름대로의 장점이 있으므로 특정방법을 이용한 결과에 의존하기보다는 여러 추정방법에 의한 추정결과에서 공통적으로 발견되는 부분에 기반을 두어 자연실업률을 추론하는 것이 바람직하다고 사료된다. 추정방법에 따라 다소 차이가 있지만, 한국의 자연실업률은 1979~87년 동안 평균 3.7~4.0% 수준에서 1988~97년 기간 동안 평균 2.6~3.2% 수준으로 하락하였으나, 외환위기를 거치며 4.0~5.3% 수준까지 상승하였다가 이후 하락하는 추세를 지속하고 있는 것으로 나타났다. 또한 대부분의 추정결과에서 최근에 실제실업률이 자연실업률에 근접해 있으나 실업률 갭이 상승하고 있는 것으로 나타나 최근 비교적 높은 수준에 머무르고 있는 실업률이 외환위기 이후 자연실업률의 상승이라는 구조적 변화와 경기침체라는 경기순환적 요인에 함께 영향 받고 있을 가능성을 시사하였다.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1245-1245
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• 2001

Food adulteration is a serious consumer fraud and a matter of concern to food processors and regulatory agencies. A range of analytical methods have been investigated to facilitate the detection of adulterated or mis-labelled foods & food ingredients but most of these require sophisticated equipment, highly-qualified staff and are time-consuming. Regulatory authorities and the food industry require a screening technique which will facilitate fast and relatively inexpensive monitoring of food products with a high level of accuracy. Near infrared spectroscopy has been investigated for its potential in a number of authenticity issues including meat speciation (McElhinney, Downey & Fearn (1999) JNIRS, 7(3), 145-154; Downey, McElhinney & Fearn (2000). Appl. Spectrosc. 54(6), 894-899). This report describes further analysis of these spectral sets using a hierarchical approach and binary decisions solved using logistic regression. The sample set comprised 230 homogenized meat samples i. e. chicken (55), turkey (54), pork (55), beef (32) and lamb (34) purchased locally as whole cuts of meat over a 10-12 week period. NIR reflectance spectra were recorded over the wavelength range 400-2498nm at 2nm intervals on a NIR Systems 6500 scanning monochromator. The problem was defined as a series of binary decisions i. e. is the meat red or white\ulcorner is the red meat beef or lamb\ulcorner, is the white meat pork or poultry\ulcorner etc. Each of these decisions was made using an individual binary logistic model based on scores derived from principal component or partial least squares (PLS1 and PLS2) analysis. The results obtained were equal to or better than previous reports using factorial discriminant analysis, K-nearest neighbours and PLS2 regression. This new approach using a combination of exploratory and logistic analyses also appears to have advantages of transparency and the use of inherent structure in the spectral data. Additionally, it allows for the use of different data transforms and multivariate regression techniques at each decision step.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1152-1152
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• 2001

Food adulteration is a serious consumer fraud and a matter of concern to food processors and regulatory agencies. A range of analytical methods have been investigated to facilitate the detection of adulterated or mis-labelled foods & food ingredients but most of these require sophisticated equipment, highly-qualified staff and are time-consuming. Regulatory authorities and the food industry require a screening technique which will facilitate fast and relatively inexpensive monitoring of food products with a high level of accuracy. Near infrared spectroscopy has been investigated for its potential in a number of authenticity issues including meat speciation (McElhinney, Downey & Fearn (1999) JNIRS, 7(3), 145 154; Downey, McElhinney & Fearn (2000). Appl. Spectrosc. 54(6), 894-899). This report describes further analysis of these spectral sets using a hierarchical approach and binary decisions solved using logistic regression. The sample set comprised 230 homogenized meat samples i. e. chicken (55), turkey (54), pork (55), beef (32) and lamb (34) purchased locally as whole cuts of meat over a 10-12 week period. NIR reflectance spectra were recorded over the wavelength range 400-2498nm at 2nm intervals on a NIR Systems 6500 scanning monochromator. The problem was defined as a series of binary decisions i. e. is the meat red or white\ulcorner is the red meat beef or lamb\ulcorner, is the white meat pork or poultry\ulcorner etc. Each of these decisions was made using an individual binary logistic model based on scores derived from principal component or partial least squares (PLS1 and PLS2) analysis. The results obtained were equal to or better than previous reports using factorial discriminant analysis, K-nearest neighbours and PLS2 regression. This new approach using a combination of exploratory and logistic analyses also appears to have advantages of transparency and the use of inherent structure in the spectral data. Additionally, it allows for the use of different data transforms and multivariate regression techniques at each decision step.

• Radiation Oncology Journal
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• 제32권3호
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• pp.179-186
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• 2014

Purpose: To study the long-term outcomes and tolerance in our patients who received dose escalated radiotherapy in the early salvage post-prostatectomy setting. Materials and Methods: The medical records of 54 consecutive patients who underwent radical prostatectomy subsequently followed by salvage radiation therapy (SRT) to the prostate bed between 2003-2010 were analyzed. Patients included were required to have a pre-radiation prostate specific antigen level (PSA) of 2 ng/mL or less. The median SRT dose was 70.2 Gy. Biochemical failure after salvage radiation was defined as a PSA level >0.2 ng/mL. Biochemical control and survival endpoints were analyzed using the Kaplan-Meier method. Univariate and multivariate Cox regression analysis were used to identify the potential impact of confounding factors on outcomes. Results: The median pre-SRT PSA was 0.45 ng/mL and the median follow-up time was 71 months. The 4- and 7-year actuarial biochemical control rates were 75.7% and 63.2%, respectively. The actuarial 4- and 7-year distant metastasis-free survival was 93.7% and 87.0%, respectively, and the actuarial 7-year prostate cancer specific survival was 94.9%. Grade 3 late genitourinary toxicity developed in 14 patients (25.9%), while grade 4 late genitourinary toxicity developed in 2 patients (3.7%). Grade 3 late gastrointestinal toxicity developed in 1 patient (1.9%), and grade 4 late gastrointestinal toxicity developed in 1 patient (1.9%). Conclusion: In this series with long-term follow-up, early SRT provided outcomes and toxicity profiles similar to those reported from the three major randomized trials studying adjuvant radiation therapy.

• Radiation Oncology Journal
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• 제31권4호
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• pp.234-238
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• 2013

Purpose: Esophageal tolerance is needed to guide the safe administration of stereotactic radiosurgery (SRS). We evaluated comprehensive dose-volume parameters of acute esophageal toxicity in patients with spinal metastasis treated with SRS. Materials and Methods: From May 2008 to May 2011, 30 cases in 27 patients with spinal metastasis received single fraction SRS to targets neighboring esophagus. Endpoints evaluated include length (mm), volume (mL), maximal dose (Gy), and series of dose-volume thresholds from the dose-volume histogram (volume of the organ treated beyond a threshold dose). Results: The median time from the start of irradiation to development of esophageal toxicity was 2 weeks (range, 1 to 12 weeks). Six events of grade 1 esophageal toxicity occurred. No grade 2 or higher events were observed. $V_{15}$ of external surface of esophagus was found to predict acute esophageal toxicity revealed by multivariate analysis (odds radio = 1.272, p = 0.047). Conclusion: In patients with spinal metastasis who received SRS for palliation of symptoms, the threshold dose-volume parameter associated with acute esophageal toxicity was found to be $V_{15}$ of external surface of esophagus. Restrict $V_{15}$ to external surface of esophagus as low as possible might be safe and feasible in radiosurgery.

• 통상정보연구
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• 제16권3호
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• pp.73-96
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• 2014

본 연구는 우리나라 수출 상위 5개 품목 중 하나인 자동차 수출을 대상으로, 승용차 브랜드별 단기 수출수요에 영향을 미치는 이론적 잠재요인을 발굴 및 설계하여 이론적 수출수요예측모델을 개발하고, 다변량시계열분석 기반의 VAR(Vector Auto Regressive)모형을 이용한 실증분석을 통해 개별상품과 시장특성이 반영된 단기수출수요예측모델을 검정하고자 하였다. 따라서 미국에 수출되고 있는 우리나라 소형 승용차 2개 브랜드(엑센트, 아반떼)에 대해 VAR모형을 이용한 분기단위 단기수요예측모델을 개발하고, 브랜드별 예측모델을 통해 산출된 t+1분기 시점의 예측값과 실제 판매된 판매대수를 대상기간을 1분기씩 달리하여 비교평가 하였다. 그 결과 엑센트와 아반떼의 RMSE %는 각각 4.3%와 20.0%로 났으며, 일평균 판매량을 기준으로 보았을 때 엑센트는 3.9일에 해당하고 아반떼는 18.4일에 해당하는 물량임을 알 수 있었다. 따라서 본 연구의 단기수출수요예측모델은 예측력과 검정시점별 일관성 측면에서 활용성이 높은 것으로 평가할 수 있었다.

• Journal of the Korean Data and Information Science Society
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• 제25권6호
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• pp.1449-1466
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• 2014

본 논문에서는 기초자산의 선물을 이용하는 헷지 전략을 연구하였다. 최적헷지비율을 구하기 위한 전통적인 방법으로 회귀분석이 사용되고 있으나, 현물과 선물 사이에 존재하는 장기균형관계와 금융 시계열 자료의 분산에 존재하는 변동성 군집현상 등의 특징을 설명하지 못하는 한계가 있다. 이를 극복하기 위해 코스피200 지수와 선물 자료에 대해 평균모형으로 벡터오차수정모형을 적합하고, 분산모형으로 다변량 GARCH 모형을 적합하여 분산-공분산 행렬을 추정하고, 이를 통해 최적헷지비율을 구하는 방법을 연구하였다. 실증분석 결과에 의하면 시장이 안정적일 때에는 회귀분석을 사용해도 큰 차이가 없지만, 시장이 불안정해지고 변동성이 커지는 구간에서는 벡터오차수정모형과 다변량 GARCH 모형을 이용하는 경우에 헷지성과가 월등히 좋아지는 결과를 얻을 수 있었다.

• International Journal of Aeronautical and Space Sciences
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• 제13권1호
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• pp.14-26
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• 2012

The only extraterrestrial life detection experiments ever conducted were the three which were components of the 1976 Viking Mission to Mars. Of these, only the Labeled Release experiment obtained a clearly positive response. In this experiment $^{14}C$ radiolabeled nutrient was added to the Mars soil samples. Active soils exhibited rapid, substantial gas release. The gas was probably $CO_2$ and, possibly, other radiocarbon-containing gases. We have applied complexity analysis to the Viking LR data. Measures of mathematical complexity permit deep analysis of data structure along continua including signal vs. noise, entropy vs.negentropy, periodicity vs. aperiodicity, order vs. disorder etc. We have employed seven complexity variables, all derived from LR data, to show that Viking LR active responses can be distinguished from controls via cluster analysis and other multivariate techniques. Furthermore, Martian LR active response data cluster with known biological time series while the control data cluster with purely physical measures. We conclude that the complexity pattern seen in active experiments strongly suggests biology while the different pattern in the control responses is more likely to be non-biological. Control responses that exhibit relatively low initial order rapidly devolve into near-random noise, while the active experiments exhibit higher initial order which decays only slowly. This suggests a robust biological response. These analyses support the interpretation that the Viking LR experiment did detect extant microbial life on Mars.

• 품질경영학회지
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• 제49권4호
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• pp.581-594
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• 2021

Purpose: The purpose of this study is to compare machine learning models for anomaly detection of mechanical facility equipment and suggest an anomaly detection system for mechanical facility equipment in subway stations. It helps to predict failures and plan the maintenance of facility. Ultimately it aims to improve the quality of facility equipment. Methods: The data collected from Daejeon Metropolitan Rapid Transit Corporation was used in this experiment. The experiment was performed using Python, Scikit-learn, tensorflow 2.0 for preprocessing and machine learning. Also it was conducted in two failure states of the equipment. We compared and analyzed five unsupervised machine learning models focused on model Long Short-Term Memory Variational Autoencoder(LSTM-VAE). Results: In both experiments, change in vibration and current data was observed when there is a defect. When the rotating body failure was happened, the magnitude of vibration has increased but current has decreased. In situation of axis alignment failure, both of vibration and current have increased. In addition, model LSTM-VAE showed superior accuracy than the other four base-line models. Conclusion: According to the results, model LSTM-VAE showed outstanding performance with more than 97% of accuracy in the experiments. Thus, the quality of mechanical facility equipment will be improved if the proposed anomaly detection system is established with this model used.

• Smart Structures and Systems
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• 제29권1호
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• pp.117-127
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• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.