• 한국지반공학회논문집
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• 제15권5호
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• pp.111-124
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• 1999

중요구조물에 인접하여 지하굴착을 수행할 경우에 지반변형을 정확히 예측하여 피해를 최소화하는 것이 중요하다. 본 논문에서는 대규모의 지하수조에 인접하여 연약점성토 내에서 굴착이 수행될 때, 지반거동을 예측하기 위하여 지반조사와 실내토질실험과 함께 유한요소해석이 실시되었다. 이러한 예측과 현장계측을 통하여 흙막이벽체와 인접구조물의 거동 및 안정성이 검토되었다 지반변형에 대한 계측 및 예측결과의 비교에서 굴착공정 및 지하수위 강하를 해석시에 고려하는 것이 중요하다는 것을 보여주었다. 향후 더 좋은 예측을 위해서는 해석방법의 개선이 요구되었다.

• 소성∙가공
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• 제9권5호
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• pp.453-462
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• 2000

Tailor-welded blanks are used for forming of automobile structural skin components. The main objective of this study is to achieve weight and cost reduction in manufacturing of components. For successful application of tailor-welded blanks, design of initial welded blanks and prediction of the welding line movement are critical. The utilization of the backward tracing scheme of the finite element method shows to be desirable in design of initial welded blanks for net-shape production and in prediction of the welding line movement. First the design of the initial blank in forming of welded thick sheet with isotropy is tried, and it appears successful in obtaining a net-shape stamping product. Based on the first trial approach, the backward tracing scheme is applied to anisotropic tailored blanks. The welding line movement is also discussed.

• International journal of advanced smart convergence
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• 제8권2호
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• pp.132-139
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• 2019

We address the problem about forecasting the direction of stock price movement in the Korea market. Recently, the deep neural network is popularly applied in this area of research. In deep neural network systems, proper parameter initialization reduces training time and improves the performance of the model. Therefore, in our study, we propose a novel parameter initialization technique and apply this technique for the stock price movement prediction model. Specifically, we design a framework which consists of two models: a base model and a main prediction model. The base model constructed with LSTM is trained by using the large data which is generated by a large amount of the stock data to achieve optimal parameters. The main prediction model with the same architecture as the base model uses the optimal parameter initialization. Thus, the main prediction model is trained by only using the data of the given stock. Moreover, the stock price movements can be affected by other related information in the stock market. For this reason, we conducted our research with two types of inputs. The first type is the stock features, and the second type is a combination of the stock features and the Korea Composite Stock Price Index (KOSPI) features. Empirical results conducted on the top five stocks in the KOSPI list in terms of market capitalization indicate that our approaches achieve better predictive accuracy and F1-score comparing to other baseline models.

• 대한산업공학회지
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• 제31권4호
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• pp.334-340
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• 2005

A mouse is an important input device that is used in most of all computer works. A mouse control time prediction model was proposed in this study. Especially, the model described the time of mouse control that made a cursor to move within path constraints. The model was developed by a laboratory experiment. Cursor movement times were measured in 36 task conditions; 3 levels of path length, 3 levels of path width and 4 levels of target's width. 12 subjects participated in all conditions. The time of cursor movement with path constraints could be better explained by the combination of Fitts' law with steering law($r^2=0.947$) than by the other models; Fitts' law($r^2=0.740$), Steering law($r^2=0.633$) and Crossman's model($r^2=0.897$). The proposed model is expected to be used in menu design or computer game design.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2015년도 춘계학술대회
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• pp.113-115
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• 2015

본 논문에서는 Delay Tolerant Networks(DTNs)에서 노드의 속성 정보 변화율을 이용한 이동 예측 알고리즘인 WRC(Weighted Rate Control)알고리즘을 제안한다. 기존 DTN에서 예측기반 라우팅 기법은 노드의 이전 속성 정보를 이용하여 목적 노드와 연결성이 높은 노드를 중계 노드로 선정하여 통신한다. 따라서 이동 노드는 유동적이므로 노드의 이후 속성 정보를 반영하지 않는 예측 기법은 신뢰성이 낮아진다. 본 논문에서는 이전 속성 정보로부터 이후 속성정보까지의 시간에 따른 변화율과 속성의 가중치 정보를 이용하여 노드의 이동 경로를 예측하는 WRC알고리즘을 제안한다. 본 논문에서 제안하는 알고리즘은 노드의 속성 정보 중 노드의 속도와 방향성을 근사한 후, 변화율을 분석하고 이로부터 제안된 가중치를 이용하여 노드의 이동 경로를 예측하는 알고리즘이다. 주어진 모의실험 환경에서 노드의 이동 경로 예측을 통해 중계 노드를 선정하여 라우팅 함으로써 네트워크 오버헤드와 전송 지연 시간이 감소함을 보여주고 있다.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 1999년도 춘계학술대회논문집
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• pp.49-52
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• 1999

Tailored-welded blanks are used for forming of automobile structural and skin components. Its main objective is to achieve weight and production cost reduction in manufacturing of the components. For successful application of tailored-welded blanks design of initial welded blanks and prediction of welding line movement are critical. Here the utilization of the backward tracing scheme of the finite element method shows to be desirable in design of initial welded blanks for net-shape production and in prediction of the welding line movement. First the design of initial blank in forming of welded thick sheet with isotropy is tried and it appears successful in obtaining a net-shape stamping product. Based in the first approach the backward tracing scheme is applied to anisotropic tailored blank. The welding line movement is also discussed.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2016년도 춘계학술대회
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• pp.425-426
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• 2016

본 논문에서는 Delay Tolerant Networks(DTNs)에서 노드의 속성 정보를 Markov Chain으로 분석하여 노드의 이동 경로를 예측하는 알고리즘을 제안한다. 기존 DTN에서 예측기반 라우팅 기법은 노드가 미리 정해진 스케줄에 따라 이동하거나 노드 간 접촉정보와 같은 추가 정보가 필요하다. 이러한 네트워크에서는 추가적인 정보가 없는 경우 노드의 신뢰성이 낮아진다. 본 논문에서 제안하는 알고리즘은 노드의 속성 정보 중 노드의 속도와 방향성을 상태로 맵핑한 후, Markov chain을 이용하여 확률전이 매트릭스를 생성하여 노드의 이동 경로를 예측하는 알고리즘이다. 주어진 모의실험 환경에서 노드의 이동 경로 예측을 통해 중계 노드를 선정하여 라우팅 함으로써 메시지 전송률이 증가하고 전송 지연 시간이 감소함을 보여주고 있다.

• 국제초고층학회논문집
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• 제6권1호
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• pp.11-19
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• 2017

High-rise buildings move during construction due to time-dependent material properties of concrete (creep and shrinkage), construction sequences, and structural shapes. The building movements, including vertical and horizontal displacements, result from the sum of axial and lateral deformation of vertical members at each level. In addition to the vertical shortenings, the lateral movement induced by differential shortening can have adverse effects on the construction tolerance and serviceability of non-structural elements such as elevators and curtain walls. In this study a construction stage analysis method is developed to predict lateral movement induced by shortening, including the effect of creep and shrinkage. The algorithm of construction stage analysis is combined with the FE analysis program. It is then applied to predict lateral movement of a 58-story reinforced concrete building that was constructed in Kuala Lumpur, Malaysia. Gravity induced lateral movement of this building is predicted by the construction stage analysis. A field three-dimensional laser scanning survey is carried out to verify the prediction results, and satisfactory agreement is obtained.

• Journal of Communications and Networks
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• 제17권6호
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• pp.568-581
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• 2015

Predicting locations of users with portable devices such as IP phones, smart-phones, iPads and iPods in public wireless local area networks (WLANs) plays a crucial role in location management and network resource allocation. Many techniques in machine learning and data mining, such as sequential pattern mining and clustering, have been widely used. However, these approaches have two deficiencies. First, because they are based on profiles of individual mobility behaviors, a sequential pattern technique may fail to predict new users or users with movement on novel paths. Second, using similar mobility behaviors in a cluster for predicting the movement of users may cause significant degradation in accuracy owing to indistinguishable regular movement and random movement. In this paper, we propose a novel fusion technique that utilizes mobility rules discovered from multiple similar users by combining clustering and sequential pattern mining. The proposed technique with two algorithms, named the clustering-based-sequential-pattern-mining (CSPM) and sequential-pattern-mining-based-clustering (SPMC), can deal with the lack of information in a personal profile and avoid some noise due to random movements by users. Experimental results show that our approach outperforms existing approaches in terms of efficiency and prediction accuracy.

• 한국컴퓨터정보학회논문지
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• 제22권11호
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• pp.97-104
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• 2017

Location prediction has been successfully utilized to provide high quality of location-based services to customers in many applications. In its usual form, the conventional type of location prediction is to predict future locations based on user's past movement history. However, as location prediction needs are expanded into much complicated cases, it becomes necessary quite frequently to make inference on the locations that target user visited in the past. Typical cases include the identification of locations that infectious disease carriers may have visited before, and crime suspects may have dropped by on a certain day at a specific time-band. Therefore, primary goal of this study is to predict locations that users visited in the past. Information used for this purpose include user's demographic information and movement histories. Data mining classifiers such as Bayesian network, neural network, support vector machine, decision tree were adopted to analyze 6868 contextual dataset and compare classifiers' performance. Results show that general Bayesian network is the most robust classifier.