• Journal of the Korea Computer Graphics Society
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• v.23 no.1
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• pp.33-38
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• 2017

In this paper, we suggest a method to simulate high-quality iguana animation by using low-quality motion capture data. Iguana motion data captured using a small number of markers cannot express its movement precisely, and even with a realistic skin mesh, it shows unnatural movement because of limited degrees of freedom. In order to solve this problem, we propose to simulate a natural and flexible movement by applying a soft-body simulation technique which models the movement of an iguana according to muscle forces and body's elastic forces. We construct a motion graph from the motion capture data to describe the iguana's various movements, and utilize it to select appropriate movements when the iguana moves. A target point on a terrain is set from the user's input, and a graph path is planned based on it. As a result, the input movement of iguana walking on a flat ground transforms to a movement that is adapted in an online manner to the irregular heights of the terrain. Such a movement is used to calculate the ideal muscle lengths that are needed for soft-body simulation. Lastly, a tetrahedral mesh of the iguana is physically simulated to adapt to various situations by applying a soft-body simulation technique.

• Korean Journal of Construction Engineering and Management
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• v.22 no.6
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• pp.107-119
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• 2021

Recently, interest in remodeling apartment houses has been increasing due to problems such as a lack of parking spaces for old apartment houses. However, no method was suggested to predict the construction period of the apartment remodeling project. Unlike general apartment new construction, apartment remodeling construction involves demolition or reinforcement work, so a realistic remodeling construction period calculation plan differentiated from the existing construction period should be proposed. Therefore, this study intends to present a model for deriving the construction period of the underground parking lot of the apartment remodeling construction. Each construction period was calculated based on 19 activities of underground parking lot remodeling work through review of previous studies and expert advice. Activity's workload data and productivity data were derived to calculate the construction period, and the number of inputs and equipment inputs by Activity were determined to correct the productivity data. The construction period of Activity was calculated using the derived data, and the criteria for calculating the overlapping period for each Activity were presented to enable realistic construction period and scheduled schedule. As a result of predicting the accuracy of the construction period through the verification of the case complex, it is expected that it will be possible to predict the approximate construction period of the underground parking lot of the apartment remodeling construction in the future.

• Korean Chemical Engineering Research
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• v.57 no.1
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• pp.78-84
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• 2019

Recently, bio-butanol is being promoted as environmentally friendly sustainable energy. However, some problems are still obstacle for commercialization of bio-butanol: the development of cheap biomass and enhancement of fermentation ratio and preparation of economical separation process for fermented products. In the conventional ABE biobutanol fermentation process, organic acids with acetone, butanol, and ethanol are produced. Therefore, it is necessary to study phase equilibrium data and mixture properties for the design and operation of separation process. However, there is lack of design data for organic acids except acetic acid contained system. In this study, therefore, binary solid-liquid equilibria (SLE) and mixture properties: the excess molar volumes ($V^E$), molar refraction deviation (${\Delta}R$) and deviation of viscosity (${\Delta}v$) at 298.15 for $C_3-C_6$ organic acid were reported. The experimental SLE data were correlated with the NRTL and UNIQUAC activity coefficient model with less than 0.5 K of root mean square deviation (RMSD). In addition, $V^E$, ${\Delta}R$ and ${\Delta}v$ for the same binary systems were satisfactorily fitted using the Redlich-Kister polynomial with less than ca. 0.004 standard deviation.

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.315-324
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• 2011

In Web environment, a flood of spam causes serious social problems such as personal information leak, monetary loss from fishing and distribution of harmful contents. Moreover, types and techniques of spam distribution which must be controlled are varying as days go by. The learning based spam classification method using Bag-of-Words model is the most widely used method until now. However, this method is vulnerable to anti-spam avoidance techniques, which recent spams commonly have, because it classifies spam documents utilizing only keyword occurrence information from classification model training process. In this paper, we propose a spam document detection method using a characteristic of repeating words occurring in spam documents as a solution of anti-spam avoidance techniques. Recently, most spam documents have a trend of repeating key phrases that are designed to spread, and this trend can be used as a measure in classifying spam documents. In this paper, we define six variables, which represent a characteristic of word repetition, and use those variables as a feature set for constructing a classification model. The effectiveness of proposed method is evaluated by an experiment with blog posts and E-mail data. The result of experiment shows that the proposed method outperforms other approaches.

• Smart Media Journal
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• v.10 no.3
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• pp.39-47
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• 2021

Enhanced index tracking is a problem of optimizing the objective function to generate returns above the index based on the index tracking that follows the market return. In order to avoid problems such as large transaction costs and illiquidity, we used a method of constructing a portfolio by selecting only some of the stocks included in the index. Commonly used enhanced index tracking methods tried to find the optimal portfolio with only one objective function in all tested periods, but it is almost impossible to find the ultimate strategy that always works well in the volatile financial market. In addition, it is important to improve generalization performance beyond optimizing the objective function for training data due to the nature of the financial market, where statistical characteristics change significantly over time, but existing methods have a limitation in that there is no direct discussion for this. In order to solve these problems, this paper proposes ensemble learning that composes a portfolio by combining several objective functions and a 3-stage portfolio selection algorithm that can select a portfolio by applying criteria other than the objective function to the training data. The proposed method in an experiment using the S&P500 index shows Sharpe ratio that is 27% higher than the index and the existing methods, showing that the 3-stage portfolio selection algorithm and ensemble learning are effective in selecting an enhanced index portfolio.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.49-57
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• 2022

Currently, the problem of food shortage is emerging in our society due to climate problems and an increase population in the world. As a solution to this problem, we propose a multi-remote control smart farm that combines artificial intelligence (AI) and information and communication technology (ICT) technologies. The proposed smart farm integrates ICT technology to remotely control and manage crops without restrictions in space and time, and to multi-control the growing environment of crops. In addition, using Arduino and deep-learning technology, a smart farm capable of multiple control through a smart-phone application (APP) was proposed, and Ai technology with various data securing and diagnosis functions while observing crop growth in real-time was included. Various sensors in the smart farm are controlled by using the Arduino, and the data values of the sensors are stored in the built database, so that the user can check the stored data with the APP. For multiple control for multiple crops, each LED, COOLING FAN, and WATER PUMP for two or more growing environments were applied so that the user could control it conveniently. And by implementing an APP that diagnoses the growth stage through the Tensor-Flow framework using deep-learning technology, we developed an application that helps users to easily diagnose the growth status of the current crop.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used