Recently, most software development uses object-oriented method. The core of object-oriented method is class, and encaptualition and inheritance structure based on class improves development efficiency. However, if the method is encapsulated, it can be developed more effectively. In this paper, we examine the differences and effects from existing methods when encapsulating a method by applying the Template Method Pattern.

해양사고 예방을 위해서는 사고의 원인과 결과에 대한 분석 및 진단뿐만 아니라, 사고의 발생 패턴과 변화 추이를 예측함으로 써 정량적 위험도를 제시할 필요성이 있다. 선박교통과 관련된 해양사고 예측은 선박의 충돌위험도 분석 및 항해 경로 탐색 등 선박교통 의 흐름에 관한 연구가 주로 수행되었으며, 해양사고의 발생 패턴에 대한 분석은 전통적인 통계 분석에 따라 제시되었다. 본 연구에서는 해양사고 통계 자료 중 선박교통관련 사고의 월별, 시간대별 발생 현황 데이터를 활용하여 해양사고 발생 예측 모델을 제시하고자 한다. 국내 해양사고 발생 현황 중 월별, 시간대별 데이터 집계가 가능한 1998년부터 2021년까지의 통계자료 중 선박교통 관련 데이터를 분류하 여 정형 시계열 데이터로 변환하였으며, 대표적인 인공지능 모델인 순환 신경망 기반 장단기 기억 신경망을 통하여 예측 모델을 구축하 였다. 검증데이터를 통하여 모델의 성능을 검증한 결과 RMSE는 초기 신경망 모델에서 월별 52.5471, 시간대별 126.5893으로 나타났으며, 관측값으로 신경망 모델을 업데이트한 결과 RMSE는 월별 31.3680, 시간대별 36.3967로 개선되었다. 본 연구에서 제안한 신경망 모델을 기 반으로 다양한 해양사고의 특징 데이터를 학습하여 해양사고 발생 패턴을 예측할 수 있을 것이다. 향후 해양사고 발생 위험의 정량적 제 시와 지역기반의 위험지도 개발 등에 관한 추가 연구가 필요하다.

This study was performed to develop a Kdiet-index based on the definitions and characteristics of traditional Korean food. The Korean Food Pattern Index (Kdiet-index) for diseases was applied using dietary data from the 2017 National Health and Nutrition Examination Survey (KNHANES) and the association between Kdiet-index and metabolic disease was confirmed. The study subjects were 1,971 Korean adults aged >40, and the components were classified according to 14 criteria used to establish the Kdiet-index. Main ingredients up to the top 3 were designated using dish names, ingredients, and contents using KNHANES dietary data. Kdiet-index was classified using scores of ≤3 points, 4-7 points, and ≥8 points and total Kdietindex were calculated by summing dietary scores. Correlations between Kdiet-index and obesity, hypertension, hyperlipidemia, and diabetes were analyzed. The odds ratio (OR) and 95% confidence interval (CI) for obesity were 0.531, 0.385~0.732 and for elevated cholesterol (≥240 mg/dL) indices were 0.471, 0.282~0.788, respectively, which showed a significant decreased in the risk for each disease for Kdiet-index of ≥8 points. This study confirms that metabolically related clinical results improved significantly as Kdiet-index increased and that higher Korean food pattern indices are associated with lower risks of metabolic disease

본 연구는 2019년 5월부터 2021년 5월까지 무인센서카메라를 이용하여 설악산과 지리산 국립공원에 서식하는 다람 쥐(Tamias sibiricus)의 연중 관찰 양상과 행동 패턴을 파악하기 위해 실시하였다. 주차별 관찰빈도를 지표로 하여 관찰빈도의 연중 변화를 파악하였고 봄과 여름, 가을의 시간별 관찰빈도를 이용하여 계절별 일일 행동 패턴을 분석하였 다. 다람쥐의 일일 행동 패턴은 지역 및 계절에 따른 차이 없이 주행성인 것으로 나타났다. 두 지역에 서식하는 다람쥐는 연중 관찰 기간에 차이가 있었다. 설악산에서 18~45주차에, 지리산에서는 7~48주차에 다람쥐가 관찰되었으며, 이는 동면시기에 영향을 받은 것으로 판단된다. 두 지역에서 다람쥐는 가을에 가장 많이 관찰되었다. 계절에 따른 다람쥐의 개체군 동태 및 활동량 변화가 무인센서카메라 관찰빈도에 반영되는 것으로 판단된다. 무인센서카메라의 관찰빈도는 간접적인 지표로서 대상종의 서식 밀도와 활동량을 구분해낼 수 없다는 한계점이 존재하지만, 방법이 쉽고 조사자에 따른 편차가 적어 효율적인 모니터링 방법으로 제시될 수 있을 것으로 판단된다.

장관은 대통령의 국정 운영 파트너이자, 주요 행정부처의 수장이다. 이 에 본 연구는 민주화 이후 역대 장관의 충원 패턴에 대해 살펴보고, 덧 붙여 정부 운영의 안정성을 위해 장관의 재임 기간에 영향을 미치는 요 인을 전문성 요인과 대통령-장관 연계 요인에 주목하여 경험적으로 분석 하였다. 민주화 이후 장관직을 역임한 총 489명을 대상으로 데이터를 구 축하였고, 분석결과는 다음과 같다. 첫째, 충원 패턴을 살펴본 결과 개인 적 특성 요인에 있어서는 남성이 다수였고, 평균 연령은 약 58세였으며, 학력은 높은 수준이었다. 둘째, 전문성 요인에 있어서 장관들은 대체로 전공 및 경력 등에 있어서 소속 부처와 일치된 전문성을 보유하고 있었 다. 셋째, 대통령-장관 연계 요인과 관련하여 정실주의적 요인으로는 출 신 학교보다는 출신 지역에 있어서 대통령과의 연관성이 다소 나타났으 며, 엽관주의 요인으로 동일 정당 활동 여부가 장관 인선에 영향을 미쳤 을 가능성이 존재한다는 것을 확인하였다. 넷째, 장관의 평균 재임 기간 은 약 1년 3개월로 다른 국가에 비해 짧은 수준이었다. 마지막으로, 장 관의 재임 기간에 영향을 미치는 요인을 회귀분석한 결과, 장관의 전문 성 요인의 영향력이 가장 크게 나타났다.

The water consumption data of individual consumers must be analyzed and forecast to establish an effective water demand management plan. A k-mean cluster model that can monitor water use characteristics based on hourly water consumption data measured using automated meter reading devices and demographic factors is developed in this study. In addition, the quantification model that can estimate the daily water consumption is developed. K-mean cluster analysis based on the four clusters shows that the average silhouette coefficient is 0.63, also the silhouette coefficients of each cluster exceed 0.60, thereby verifying the high reliability of the cluster analysis. Furthermore, the clusters are clearly classified based on water usage and water usage patterns. The correlation coefficients of four quantification models for estimating water consumption exceed 0.74, confirming that the models can accurately simulate the investigated demographic data. The statistical significance of the models is considered reasonable, hence, they are applicable to the actual field. Because the use of automated smart water meters has become increasingly popular in recent year, water consumption has been metered remotely in many areas. The proposed methodology and the results obtained in this study are expected to facilitate improvements in the usability of smart water meters in the future.

The collapse of reinforced concrete (RC) frame buildings is mainly caused by the failure of columns. To prevent brittle failure of RC column, numerous studies have been conducted on the seismic performance of strengthened RC columns. Concrete jacketing method, which is one of the retrofitting method of RC members, can enhance strength and stiffness of original RC column with enlarged section and provide uniformly distributed lateral load capacity throughout the structure. The experimental studies have been conducted by many researchers to analyze seismic performance of seismic strengthened RC column. However, structures which have plan and vertical irregularities shows torsional behavior, and therefore it causes large deformation on RC column when subjected to seismic load. Thus, test results from concentric cyclic loading can be overestimated comparing to eccentric cyclic test results, In this paper, two kinds of eccentric loading pattern was suggested to analyze structural performance of RC columns, which are strengthened by concrete jacketing method with new details in jacketed section. Based on the results, it is concluded that specimens strengthened with new concrete jacketing method increased 830% of maximum load, 150% of maximum displacement and changed the failure modes of non-strengthened RC columns.

원형점토대토기에서 삼각형점토대토기로의 변천과정에 있어 가장 중요한 변화는 단연 구연부의 형 태와 제작기법이다. 지금까지 단면형태를 중심으로 다양한 연구가 이루어져 왔지만, 점토대토기 변천 의 요인에 주목한 연구는 많지 않다. 본고에서는 점토대토기의 구연부에 내포된 제작규범과 상징적 의미에 주목하여 구연부의 변천이 일어나게 된 요인을 추론하고자 하였다. 연구 대상은 사천 방지리유적에서 출토된 점토대토기로, 구연부의 제작기법을 분석하여 점토대토 기 제작패턴의 개념을 설정하고 단면형태와 내면형태, 지두흔과 정면흔의 조합을 통해 제작패턴을 분류하였다. 제작패턴은 점토대토기 구연부가 어떠한 의도에서 어떠한 방식으로 제작되었는지를 보 여주는 것으로, 점토대토기 구연부의 성격을 파악하고 점토대토기의 변화를 이해하는 토대가 된다. 이를 통해 도출된 제작의도에 범형의 개념을 적용하여 분석한 결과, 원형점토대토기와 삼각형점토대 토기의 제작은 유기적이며, 점토대토기의 변화는 구연부 제작방식의 변화로 인해 발생하는 것임을 알 수 있었다. 점토대토기의 구연부는 제작자, 나아가 집단의 정체성이 표현되는 부분으로, 일종의 상징적인 의미 가 부여되어 있다. 원형점토대토기는 점토대의 형태를 원형으로 유지하려는 제작규범 하에서 제작되 었으며, 삼각형점토대토기로의 변화 요인은 이러한 제작규범의 약화로, 제작규범의 약화는 점토대토 기 구연부에 대한 제작집단의 관념과 구연부 제작의 편의성에 대한 인식이 맞물려 일어난 것이다. 결 국 원형에서 삼각형으로의 변화는 외래계 요소의 영향이나 이주민에 의한 결과가 아니라 동일 집단 내에서의 변화라 볼 수 있다.

For clothing items that combine the top with the bottom, such as a jumper suit, it is necessary to design original forms to develop clothes in various styles that can satisfy aesthetic and functional needs. Therefore, to determine the original forms of women’s jumper suits, this study aimed to analyze the patterns of women’s jumper suits being sold in the market comparatively and evaluate their fitness. For the study method, this researcher obtained five types of women’s jumper suits from local brands and compared the patterns’ measured sizes, then conducted exterior evaluation on the surplus according to five different movements and analyzed the values of distortion of clothing pressure comparatively. According to the results, in the basic posture, brands A and C received favorable results, while in the other movements, brand B, which had the biggest surplus on the bottom, was evaluated favorably. In conclusion, in the patterns of women’s jumper suits, a bigger surplus is added to either the crotch length or crotch girth rather than the top length, and the top and the bottom tend to have a different surplus according to the design. In addition, based on the values of body surface lengths according to the range of movements, this study suggests that an additional surplus be added to the length.

이 연구는 전염병의 잠재적 확산 가능성이 높은 지역의 탐색을 목적으로, 코로나19 전후의 버스 네트워크 클러스터의 시공간적 변화를 분석한다. 분석방법으로는 Getis와 Ord의  통계를 공간 네트워크로 확장 및 적용한  통계 값을 사용하였다. 이 과정은 서울시 전체 버스 네트워크의 개별 흐름에 대해 각각 적용되기 때문에 대규모 연산을 위해 병렬컴퓨팅 방식을 적용한 슈퍼컴퓨터를 사용하였다. 연구 결과, 첫째, 코로나19 이후 버스 네트워크가 일부 흐름으로 집중된 경향을 보였다. 둘째, 코로나19이 후의 버스 흐름은 주거지, 농업지로의 이동은 증가하고 상업지역, 교통지역으로의 이동은 감소했음을 확인하였다. 셋째, 중심업무 지구 중 여의도 방면의 클러스터, 구로디지털단지역 방면의 클러스터와 달리, 강남일대는 코로나19 전후의 유의미한 변화가 나타나 지 않았다. 이 연구는 국내에서 처음으로 코로나19전후의 버스 네트워크 클러스터를 확인하고 변화 특징을 제시한다는 의미가 있다.

PURPOSES : This aim of this study is to develop a model for predicting road surface temperature using an LSTM network to predict road surface temperature associated with road icing. METHODS : A long short-term memory (LSTM) neural network suitable for time-series data with time correlation is used in the analysis. Moreover, an optimal neural network architecture is designed via hyperparameter search and verification using learning and validation data. Finally, the generalization performance is evaluated based on the RMSE using unseen data as test data. RESULTS : The results show that the predicted data are similar to the actual road surface temperature patterns , and that the network appears to be generalized. CONCLUSIONS : The LSTM model improves the accuracy and generalization of road surface temperature prediction, as compared with other machine learning models.

In this study, controlling pattern gap was induced by deformation of the stretchable substrate. The pre-fabricated pattern was made by depositing an Au(gold) on the Si substrate, and PDMS(Polydimethylsiloxane) was used for the stretchable substrate in consideration of surface energy, formability, and viscoelastic characteristics. For uniform deformation of PDMS, it is manufactured in the form of a tensile specimen using a molding method. An external force is applied to the deformable substrate in the uni-axial direction using a self-manufactured JIG, and the Au thin film pattern is transferred to the substrate under tensile state. After that, the external force is removed, and the PDMS specimen is recovered to its initial state. At this time, it can be seen that the pre-fabricated pattern gap is shortened from the initial size due to the viscoelastic properties of the PDMS. As a result, it was characterized to deformation rate of the pattern gap according to elongation rate of PDMS.

This study aimed to analyze adult men’s body sizes and shapes and suggest size specifications to provide preliminary data to academia and industries. A total of 814 adult men aged 30-44 were selected from the 7th Size Korea data, and 55 direct upper body measurement and calculation items were analyzed using SPSS 25.0. In individual Individual differences, thickness, circumference, and width were high, and height and length were low. Height above the waist base line and shoulder dimension decreased in early 40s age group, while height below the waist base line declined as age increased. In addition, buttocks shape changes were found in early 40s age group. According to factor analysis, ‘upper body and upper-extremity horizontal size’, ‘torso height and upper extremity length’, ‘shoulder dimension’, ‘upper body length’ and ‘shoulder angle’ were derived. Using clustering analysis, four different body types were classified: i) big abdomen with flat chest, ii) slender with big, raised shoulders, iii) dwarfish with small, droopy shoulders, and iv) obese with large shoulders. ‘Slender with big, raised shoulders’ was a typical body shape among men aged 30-44. In older participants, the ‘big abdomen with flat chest’ ratio was low, while ‘obese with large shoulders’ was more common. This study proposed size specifications by body type considering the above characteristics.

Depression is one of the most important psychiatric disorders worldwide. Most depression-related data mining and machine learning studies have been conducted to predict the presence of depression or to derive individual risk factors. However, since depression is caused by a combination of various factors, it is necessary to identify the complex relationship between the factors in order to establish effective anti-depression and management measures. In this study, we propose a methodology for identifying and interpreting patterns of depression expressions using the method of deriving random forest rules, where the random forest rule consists of the condition for the manifestation of the depressive pattern and the prediction result of depression when the condition is met. The analysis was carried out by subdividing into 4 groups in consideration of the different depressive patterns according to gender and age. Depression rules derived by the proposed methodology were validated by comparing them with the results of previous studies. Also, through the AUC comparison test, the depression diagnosis performance of the derived rules was evaluated, and it was not different from the performance of the existing PHQ-9 summing method. The significance of this study can be found in that it enabled the interpretation of the complex relationship between depressive factors beyond the existing studies that focused on prediction and deduction of major factors.

As mechatronic systems have various, complex functions and require high performance, automatic fault detection is necessary for secure operation in manufacturing processes. For conducting automatic and real-time fault detection in modern mechatronic systems, multiple sensor signals are collected by internet of things technologies. Since traditional statistical control charts or machine learning approaches show significant results with unified and solid density models under normal operating states but they have limitations with scattered signal models under normal states, many pattern extraction and matching approaches have been paid attention. Signal discretization-based pattern extraction methods are one of popular signal analyses, which reduce the size of the given datasets as much as possible as well as highlight significant and inherent signal behaviors. Since general pattern extraction methods are usually conducted with a fixed size of time segmentation, they can easily cut off significant behaviors, and consequently the performance of the extracted fault patterns will be reduced. In this regard, adjustable time segmentation is proposed to extract much meaningful fault patterns in multiple sensor signals. By considering inflection points of signals, we determine the optimal cut-points of time segments in each sensor signal. In addition, to clarify the inflection points, we apply Savitzky-golay filter to the original datasets. To validate and verify the performance of the proposed segmentation, the dataset collected from an aircraft engine (provided by NASA prognostics center) is used to fault pattern extraction. As a result, the proposed adjustable time segmentation shows better performance in fault pattern extraction.