• 소성∙가공
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• 제29권2호
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• pp.89-96
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• 2020

The finite element method has been widely applied in the sheet metal forming process. However, the finite element method is computationally expensive and time consuming. In order to tackle this problem, surrogate modeling methods have been proposed. An artificial neural network (ANN) is one such surrogate model and has been well studied over the past decades. However, when it comes to ANN with two or more layers, so called deep neural networks (DNN), there is distinct a lack of research. We chose to use DNNs our surrogate model to predict the behavior of sheet metal in the deep drawing process. Thickness variation is selected as an output of the DNN in order to evaluate workpiece feasibility. Input variables of the DNN are radius of die, die corner and blank holder force. Finite element analysis was conducted to obtain data for surrogate model construction and testing. Sampling points were determined by full factorial, latin hyper cube and monte carlo methods. We investigated the performance of the DNN according to its structure, number of nodes and number of layers, then it was compared with a radial basis function surrogate model using various sampling methods and numbers. The results show that our DNN could be used as an efficient surrogate model for the deep drawing process.

• The Journal of Korean Physical Therapy
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• 제15권4호
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• pp.112-128
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• 2003

Since areas of pain and dysfunction of musculoskeletal typically suffered by the patients with back disorders spread all over the body, WBIP(GCM Program) for the primary treatment and management is required. The purpose of this study is to analyze if WBIP(GCM Program) based on the hyper/hypomobility pattern of Four Body Types can identify the effective treatment of back disorders and the effect on the postural balanced restoration of the spine and extremities. Non-specific back disorder is still a major reason for sick leave. And moreover, its been reported that there was often recurrence to the patients whose symptom had been diminished. As a WBIP(GCM Program) based on kinematic chain patterns of Four Body Types, this study gave a new information on the effective diagnosis, treatment and management of non-specific back disorders. 337 patients above the twenty-five years old with the non-specific back disorders at the hospital and oriental medical clinics at Kyungnam and Busan areas in South Korea from August 24th, 2000 to Feb 23rd, 2001 have randomly been assigned to four experimental groups such as Whole Body Intervention Program Group, Physical Therapy Group like modality treatments, Acupuncture-Treatment Group, and Placebo Control Group. According to intervention program applied to the each four group for three times per week(twelve times per 4weeks), as the time-series methods, we compared and evaluated the body status of the pretest with that of post treatment completion of four week, three month, and six month, respectively. As the analytical method of measurement, our researchers used the Moire Interferometry Unit and Postural Kit that could measure the postural balance of spine and extremities. The collection of data was performed in the designated hospital and oriental medical clinics. For the analysis of the data, the SPSS 10.0 package program was used. X2-test has been taken in order to compare and analyze characteristics and GPES of the patients in four experimental groups. Repeated Measure ANOVA and Tukey post hoc test has been adopted in order to compare the effects of the balanced restoration of the spine and extremities among four Groups categorized for this study. Statistical significance was accepted at the 0.05 level of confidence The effect of the balanced restoration on the spine and extremities of the patients with non-specific back disorders has been proved in all of the Groups. As for the restoration degree, however, WBIP(GCM Program) Group produced the highest effectiveness in terms of the fact that it had a dense moire in comparison with the other three Groups and that the Moires of both sides had the same level by the time(p<0.01). WBIP(GCM Program) based on four tilting types of scapular and ilium and hyper/hypomobility pattern took a higher effect on the balanced restoration of the spine and extremities through a whole body as well as the treatment of back disorders than the other three Groups which the usual remedy without classification of body type had been applied to.

• 한국융합학회논문지
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• 제12권8호
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• pp.31-37
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• 2021

산업의 발전으로 도시화로 인해 건물의 규모가 커지면서, 건물의 공기 정화 및 쾌적한 실내 환경을 유지의 필요성 또한 증가하고 있다. 냉동 시스템의 모니터링 기술의 발전으로 건물 내에 발생하는 전력 소모량을 관리할 수 있게 되었다. 특히 상업용 건물에서 발생하는 전력 소모량 중 약 40%가 냉동 시스템에서 일어난다. 따라서 본 연구 냉동시스템 고장진단 알고리즘을 개발하기 위해서 냉동시스템의 구조를 이해하고, 냉동 시스템의 운영과정에서 발생하는 데이터를 수집 분석하여 다양한 유형과 심각도를 가지는 고장 상황을 조기에 신속하게 탐지 분류하고자 하였다. 특히 분류가 어려운 고장 유형들의 분류 정확도를 향상시키기 위하여 3단계 진단 및 분류 알고리즘을 개발하여 제안하였다. 다수의 실험과 초모수 (hyper parameter) 최적화 과정을 거쳐 각 단계에 적합한 분류 모형으로 SVM과 LGBM에 기반 한 모형을 제시하였다. 본 연구에서는 고장에 영향을 미치는 특성을 최대한 보존하면서, 선행연구에서 어려움을 겪었던 냉매 관련 고장을 포함한 모든 고장 유형을 우수한 결과로 도출하였다.

• 공학논문집
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• 제3권1호
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• pp.7-20
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• 1998

우리의 경제성장과 국민소득의 증가와 더불어 자가용승용차가 증가함으로서 자동차 대중화시대에 접어 들었지만 아직까지도 교통안전과 질서에 대한 의식이 성숙하지 못함에 따라 교통안전 사고 등의 문제를 야기시키고 있다. 따라서 웹기반 어린이 교통 질서 및 안전 교육 시스템의 개발은 교통 질서 및 안전 교육의 중요성과 필요성을 홍보하고 교육 함으로서 교통 안전 사고를 예방하는데 목적을 두고 있다. 이 시스템 개발이 이루어진 논문 내용은 다음과 같다. 교통 안전 교육에 필요한 텍스트, 이미지, 동영상 데이터 확보 및 디지타이징과 계층적 관계 확립, 정보간 관계성 분석 및 정보간 하이퍼 링크 구조설계, 시소러스 구축 및 시소러스 기반 정보검색 엔진 설계 및 구현, 교통 질서 및 안전 교육을 위한 데이터베이스 스키마 설계 및 구현과 사용자 중심의 GUI 구축등이다.

• 대한원격탐사학회지
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• 제26권2호
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• pp.251-262
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• 2010

GOCI(Geostationary Ocean Color Imager) 표준자료의 지속적인 품질관리를 위해서는 위성 운용기간 중 궤도상 복사보정, 대기보정 단계를 거쳐야 되며 해수환경 분석 알고리즘에 대한 검보정도 지속적으로 이루어져야 한다. GOCI의 복사, 대기, 해양환경 자료에 대한 검보정은 부이나 고정 플랫폼을 이용한 수온, 염분, 해수 광특성, 형광, 및 탁도 관측과, 주기적으로 해양환경 자료 수집을 통하여 실시한다. 이를 위하여 동중국해에 위치하고 있는 이어도 종합해양과학기지에 설치된 광학 관측 장비와 현장 관측의 복사자료를 상호 비교해 보았으며, GOCI 표준자료의 검정에 앞서 SeaWiFS 복사량과 비교하여 검정하였다. 해수출 광량은 현장관측에서 얻어진 광과 광량과는 약간의 차이를 보였지만, 흡광영역이 매우 잘 일치하고 있으며 스펙트럴 이동은 없는 것으로 판단된다. 이어도 종합해양과학기지의 분광측정기와 SeaWiFS의 전 밴드에서 얻어진 해수출 광량을 비교한 결과 평균 25% 정도의 에러가 발생했지만, 대기보정 밴드를 제외하면 절대오차가 11% 정도로 상당히 낮아진다. 이것은 SeaWiFS 표준 대기보정 방법의 문제점으로 GOCI 검보정 연구에서 고려되어 보완 되어야 할 것으로 판단된다. 이와 더불어 독도 지역의 표준 관측치(Reference Target Site) 구축을 통한 검보정 연구를 위하여, 독도 주변 해수의 광 특성과 해양환경 자료는 2009년 8월과 2009년 10월 2차례에 걸쳐서 현장관측을 실시하였다. 독도 주변 해역의 해양 광 특성은 원격반사도의 스펙트럼형태를 기준으로 Case-1 Water 성향이 강한 해수에서 나타나는 특성과 매우 유사하였다. 식물플랑크톤, 부유물질, 용존유기물의 흡광계수 스펙트럼의 형태들은 대체적으로 각 성분별 흡광 스펙트럼 특성을 잘 보여주었다. 또한 MODIS Aqua로부터 산출된 엽록소 농도와 현장관측을 통한 검증에서 위성자료 값들은 잘 일치한다. 위와 같이 현재 진행되고 있는 GOCI 검보정 연구를 통해서 복사, 대기, 해양환경 알고리즘에 대한 문제점이 도출되었고, 차후 검보정 계획에 반영하여 이 부분들에 대한 개선 및 보완이 이루어질 것으로 판단된다.

• 산업경영시스템학회지
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• 제40권1호
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• pp.57-64
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• 2017

In order to reduce damages to major railroad components, which have the potential to cause interruptions to railroad services and safety accidents and to generate unnecessary maintenance costs, the development of rolling stock maintenance technology is switching from preventive maintenance based on the inspection period to predictive maintenance technology, led by advanced countries. Furthermore, to enhance trust in accordance with the speedup of system and reduce maintenances cost simultaneously, the demand for fault diagnosis and prognostic health management technology is increasing. The objective of this paper is to propose a highly reliable learning model using various machine learning algorithms that can be applied to critical rolling stock components. This paper presents a model for railway rolling stock component fault diagnosis and conducts a mechanical failure diagnosis of motor components by applying the machine learning technique in order to ensure efficient maintenance support along with a data preprocessing plan for component fault diagnosis. This paper first defines a failure diagnosis model for rolling stock components. Function-based algorithms ANFIS and SMO were used as machine learning techniques for generating the failure diagnosis model. Two tree-based algorithms, RadomForest and CART, were also employed. In order to evaluate the performance of the algorithms to be used for diagnosing failures in motors as a critical railroad component, an experiment was carried out on 2 data sets with different classes (includes 6 classes and 3 class levels). According to the results of the experiment, the random forest algorithm, a tree-based machine learning technique, showed the best performance.

• Wind and Structures
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• 제36권6호
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• pp.393-404
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• 2023

Skewness and kurtosis are important higher-order statistics for simulating non-Gaussian wind pressure series on low-rise buildings, but their predictions are less studied in comparison with those of the low order statistics as mean and rms. The distribution gradients of skewness and kurtosis on roofs are evidently higher than those of mean and rms, which increases their prediction difficulty. The conventional artificial neural networks (ANNs) used for predicting mean and rms show unsatisfactory accuracy in predicting skewness and kurtosis owing to the limited capacity of shallow learning of ANNs. In this work, the deep neural networks (DNNs) model with the ability of deep learning is introduced to predict the skewness and kurtosis on a low-rise building. For obtaining the optimal generalization of the DNNs model, the hyper parameters are automatically determined by Bayesian Optimization (BO). Moreover, for providing a benchmark for future studies on predicting higher order statistics, the data sets for training and testing the DNNs model are extracted from the internationally open NIST-UWO database, and the prediction errors of all taps are comprehensively quantified by various error metrices. The results show that the prediction accuracy in this study is apparently better than that in the literature, since the correlation coefficient between the predicted and experimental results is 0.99 and 0.75 in this paper and the literature respectively. In the untrained cornering wind direction, the distributions of skewness and kurtosis are well captured by DNNs on the whole building including the roof corner with strong non-normality, and the correlation coefficients between the predicted and experimental results are 0.99 and 0.95 for skewness and kurtosis respectively.

• ETRI Journal
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• 제9권1호
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• pp.84-96
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• 1987

The equality relation is very important in mechanical theorem proving procedures. A proposed inference rule called RHU-resolution is intended to extend the hyperparamodulation[23, 9] by introducing a bidirectional proof search that simultaneously employs a forward reasoning and a backward reasoning, and generalize it by incorporating beneflts of extended hyper steps with a preprocessing process, that includes a subsumption check in an equality graph and a high level planning. The forward reasoning in RHU-resolution may replace the role of the function substitution link.[9] That is, RHU-deduction without the function substitution link gets a proof. In order to control explosive generation of positive equalities by the forward reasoning, we haue put some restrictions on input clauses and k-pd links, and also have included a control strategy for a positive-positive linkage, like the set-of-support concept, A linking path between two end terms can be found by simple checking of linked unifiability using the concept of a linked unification. We tried to prevent redundant resolvents from generating by preprocessing using a subsumption check in the subsumption based eauality graph(SPD-Graph)so that the search space for possible RHU-resolution may be reduced.

• 한국의류산업학회지
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• 제15권1호
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• pp.95-102
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• 2013

In order to understand meaningful customer experience in social media, this study profiles customer engagement by exposing the essential brand experience rooms in hyper-reality contexts. This study selects Snuggie as a target brand as it uses multiple contact points, including social media, to provide meaningful experience to customers. With their unique marketing strategy, Snuggie became a popular brand among the U. S. customers beyond just a wearable blanket. Upon analyzing a total of 364 customer reviews about Snuggie in Amazon.com, five experience rooms were exposed; "Physical artifacts" and "customer involvement" are influential experience rooms which signify interactions between products and customers, while "intangible artifacts", "technology" and "customer placement" reflect a lower degree of experiential engagement. This approach suggests a theoretical foundation in understanding the customer engagement concepts by the means of brand experience dimensions in social media. The ability to create compelling engagement in social media depends on the successful facilitation of relationships and information, which lead to a creative, communicative and interactive experience.

• 대한한방내과학회지
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• 제11권1호
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• pp.109-120
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• 1990

In order to investigate the therapeutic effects on blood pressure and hyper cholesteremia, aqueous extract of Chin Gan Sik Pung Tang were studied. The result of the total cholesterol contents in serum and blood pressure of each group were as follows, 1. The aqueous extract of Chin Gan Sik Pung Tang inhibited increased Total cholesterol in serum of rabbits administrated with cholesterol rich diet. 2. Blood pressure manifested gradual response by the fall of 4, 3, 9.2, 19.9 percent in proportion to the administration of 10, 30, 100 mg/kg of Chin Gan Sik pung Tang, respectively 3. Administration of Chin Gan Sik Pung Tang to the rabbit pretreated with Vagotomy, Atropine and Regitine did not show any significant difference in the blood pressure, compare with that of the control group. 4. Administration of Chin Gan Sik Pung Tang to the rabbit pretreated with propranolol show significant difference in the blood pressure, compare with that of the control group. From the above results, it is suggested that Chin Gam Sik Pung Tang has the action on adrenergic ${\beta}-receptor$ and can he used therapeutic effect on the hypertension, and inhibit the increase of Total Cholesterol contents in serum.