• Journal of the Korean Geotechnical Society
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• v.15 no.5
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• pp.111-124
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• 1999

When deep excavation adjacent to an existing structure is performed, it is very important to minimize damage on the structure through the prediction of ground movement. In this paper, finite element analysis was performed to predict the ground movement, based on the data from site investigation and laboratory tests, when deep excavation close to a buried water tank was carried out in soft clay ground. The movement and stabilities of the soil-cement wall(SCW) and the adjacent structure were checked using the results of the analysis and the field measurement. The comparison between the measured and the predicted ground movements showed the significance of the excavation procedure and lowering of water level in the analytical model. In the future, it is needed to improve the prediction method for better estimation of the ground movement.

• Transactions of Materials Processing
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• v.9 no.5
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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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• v.8 no.2
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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.

• Journal of Korean Institute of Industrial Engineers
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• v.31 no.4
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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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.113-115
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• 2015

In this paper, we proposed an algorithm based on movement prediction using rate of change of the attribute information of nodes what is called WRC(Weighted Rate Control) in delay tolerant networks(DTNs). Existing DTN routing algorithms based on movement prediction communicate by selecting relay nodes increasing connectivity with destination node. Thus, because the mobile nodes are in flux, the prediction algorithms that do not reflect the newest attribute information of node decrease reliability. In this paper, proposed algorithm approximate speed and direction of attribute information of node and analysis rate of change of attribute information of node. Then, it predict movement path of node using proposed weight. As the result, proposed algorithm show that network overhead and transmission delay time decreased by predicting movement path of node.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1999.03b
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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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.425-426
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• 2016

In this paper, we proposed an algorithm based on movement prediction using Markov chain in delay tolerant networks(DTNs). The existing prediction algorithms require additional information such as a node's schedule and connectivity between nodes. However, network reliability is lowered when additional information is unknown. To solve this problem, we proposed an algorithm for predicting a movement path of the node by using Markov chain. The proposed algorithm maps speed and direction for a node into state, and predict movement path of the node using transition probability matrix generated by Markov chain. As the result, proposed algorithm show that the proposed algorithms has competitive delivery ratio but with less average latency.

• International Journal of High-Rise Buildings
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• v.6 no.1
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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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• v.17 no.6
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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.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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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.