• Journal of KIISE:Computing Practices and Letters
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• v.9 no.3
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• pp.322-331
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• 2003

As the complexity of a 3D game is increased by various factors of the game scenario, it has a problem for controlling the interrelation of the game objects. Therefore, a game system has a necessity of the coordination of the responses of the game objects. Also, it is necessary to control the behaviors of animations of the game objects in terms of the game scenario. To produce realistic game simulations, a system has to include a structure for designing the interactions among the game objects. This paper presents a method that designs the dynamic control mechanism for the interaction of the game objects in the game scenario. For the method, we suggest a game agent system as a framework that is based on intelligent agents who can make decisions using specific rules. Game agent systems are used in order to manage environment data, to simulate the game objects, to control interactions among game objects, and to support visual authoring interface that ran define a various interrelations of the game objects. These techniques can process the autonomy level of the game objects and the associated collision avoidance method, etc. Also, it is possible to make the coherent decision-making ability of the game objects about a change of the scene. In this paper, the rule-based behavior control was designed to guide the simulation of the game objects. The rules are pre-defined by the user using visual interface for designing their interaction. The Agent State Decision Network, which is composed of the visual elements, is able to pass the information and infers the current state of the game objects. All of such methods can monitor and check a variation of motion state between game objects in real time. Finally, we present a validation of the control method together with a simple case-study example. In this paper, we design and implement the supervised classification systems for high resolution satellite images. The systems support various interfaces and statistical data of training samples so that we can select the most effective training data. In addition, the efficient extension of new classification algorithms and satellite image formats are applied easily through the modularized systems. The classifiers are considered the characteristics of spectral bands from the selected training data. They provide various supervised classification algorithms which include Parallelepiped, Minimum distance, Mahalanobis distance, Maximum likelihood and Fuzzy theory. We used IKONOS images for the input and verified the systems for the classification of high resolution satellite images.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.21-44
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• 2018

In recent years, the rapid development of internet technology and the popularization of smart devices have resulted in massive amounts of text data. Those text data were produced and distributed through various media platforms such as World Wide Web, Internet news feeds, microblog, and social media. However, this enormous amount of easily obtained information is lack of organization. Therefore, this problem has raised the interest of many researchers in order to manage this huge amount of information. Further, this problem also required professionals that are capable of classifying relevant information and hence text classification is introduced. Text classification is a challenging task in modern data analysis, which it needs to assign a text document into one or more predefined categories or classes. In text classification field, there are different kinds of techniques available such as K-Nearest Neighbor, Naïve Bayes Algorithm, Support Vector Machine, Decision Tree, and Artificial Neural Network. However, while dealing with huge amount of text data, model performance and accuracy becomes a challenge. According to the type of words used in the corpus and type of features created for classification, the performance of a text classification model can be varied. Most of the attempts are been made based on proposing a new algorithm or modifying an existing algorithm. This kind of research can be said already reached their certain limitations for further improvements. In this study, aside from proposing a new algorithm or modifying the algorithm, we focus on searching a way to modify the use of data. It is widely known that classifier performance is influenced by the quality of training data upon which this classifier is built. The real world datasets in most of the time contain noise, or in other words noisy data, these can actually affect the decision made by the classifiers built from these data. In this study, we consider that the data from different domains, which is heterogeneous data might have the characteristics of noise which can be utilized in the classification process. In order to build the classifier, machine learning algorithm is performed based on the assumption that the characteristics of training data and target data are the same or very similar to each other. However, in the case of unstructured data such as text, the features are determined according to the vocabularies included in the document. If the viewpoints of the learning data and target data are different, the features may be appearing different between these two data. In this study, we attempt to improve the classification accuracy by strengthening the robustness of the document classifier through artificially injecting the noise into the process of constructing the document classifier. With data coming from various kind of sources, these data are likely formatted differently. These cause difficulties for traditional machine learning algorithms because they are not developed to recognize different type of data representation at one time and to put them together in same generalization. Therefore, in order to utilize heterogeneous data in the learning process of document classifier, we apply semi-supervised learning in our study. However, unlabeled data might have the possibility to degrade the performance of the document classifier. Therefore, we further proposed a method called Rule Selection-Based Ensemble Semi-Supervised Learning Algorithm (RSESLA) to select only the documents that contributing to the accuracy improvement of the classifier. RSESLA creates multiple views by manipulating the features using different types of classification models and different types of heterogeneous data. The most confident classification rules will be selected and applied for the final decision making. In this paper, three different types of real-world data sources were used, which are news, twitter and blogs.

• Cartoon and Animation Studies
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• s.47
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• pp.1-29
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• 2017

360-degree virtual reality has been a technology that has been available for a long time and has been actively promoted worldwide in recent years due to development of devices such as HMD (Head Mounted Display) and development of hardware for controlling and executing images of virtual reality. The production of the 360 degree VR requires a different mode of production than the traditional video production, and the matters to be considered for the user have begun to appear. Since the virtual reality image is aimed at a platform that requires enthusiasm, presence and interaction, it is necessary to have a suitable cinematography. In VR, users can freely enjoy the world created by the director and have the advantage of being able to concentrate on his interests during playing the image. However, the director had to develope and install the device what the observer could concentrate on the narrative progression and images to be delivered. Among the various methods of transmitting images, the director can use the composition of the short. In this paper, we will study how to effectively apply the technique of directing through the composition of this shot to 360 degrees virtual reality. Currently, there are no killer contents that are still dominant in the world, including inside and outside the country. In this situation, the potential of virtual reality is recognized and various images are produced. So the way of production follows the traditional image production method, and the shot composition is the same. However, in the 360 degree virtual reality, the use of the long take or blocking technique of the conventional third person view point is used as the main production configuration, and the limit of the short configuration is felt. In addition, while the viewer can interactively view the 360-degree screen using the HMD tracking, the configuration of the shot and the connection of the shot are absolutely dependent on the director like the existing cinematography. In this study, I tried to study whether the viewer can freely change the cinematography such as the composition of the shot at a user's desired time using the feature of interaction of the VR image. To do this, 3D animation was created using a game tool called Unreal Engine to construct an interactive image. Using visual scripting of Unreal Engine called blueprint, we create a device that distinguishes the true and false condition of a condition with a trigger node, which makes a variety of shorts. Through this, various direction techniques are developed and related research is expected, and it is expected to help the development of 360 degree VR image.

• Korean Chemical Engineering Research
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• v.55 no.3
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• pp.322-331
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• 2017

District heating was first introduced in Korea in 1985. As the service life of the underground thermal piping network has increased for more than 30 years, the maintenance of the underground thermal pipe has become an important issue. A variety of complex technologies are required for periodic inspection and operation management for the maintenance of the aged thermal piping network. Especially, it is required to develop a model that can be used for decision making in order to derive optimal maintenance and replacement point from the economic viewpoint in the field. In this study, the analysis was carried out based on the repair history and accident data at the operation of the thermal pipe network of five districts in the Korea District Heating Corporation. A failure probability model was developed by introducing statistical techniques of qualitative analysis and binomial logistic regression analysis. As a result of qualitative analysis of maintenance history and accident data, the most important cause of pipeline damage was construction erosion, corrosion of pipe and bad material accounted for about 82%. In the statistical model analysis, by setting the separation point of the classification to 0.25, the accuracy of the thermal pipe breakage and non-breakage classification improved to 73.5%. In order to establish the failure probability model, the fitness of the model was verified through the Hosmer and Lemeshow test, the independent test of the independent variables, and the Chi-Square test of the model. According to the results of analysis of the risk of thermal pipe network damage, the highest probability of failure was analyzed as the thermal pipeline constructed by the F construction company in the reducer pipe of less than 250mm, which is more than 10 years on the Seoul area motorway in winter. The results of this study can be used to prioritize maintenance, preventive inspection, and replacement of thermal piping systems. In addition, it will be possible to reduce the frequency of thermal pipeline damage and to use it more aggressively to manage thermal piping network by establishing and coping with accident prevention plan in advance such as inspection and maintenance.

• Asia pacific journal of information systems
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• v.18 no.2
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• pp.95-114
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• 2008

Recent interests in data mining result from the expansion of the amount of business data and the growing business needs for extracting valuable knowledge from the data and then utilizing it for decision making process. In particular, recent advances in association rule mining techniques enable us to acquire knowledge concerning sales patterns among individual items from the voluminous transactional data. Certainly, one of the major purposes of association rule mining is to utilize acquired knowledge in providing marketing strategies such as cross-selling, sales promotion, and shelf-space allocation. In spite of the potential applicability of association rule mining, unfortunately, it is not often the case that the marketing mix acquired from data mining leads to the realized profit. The main difficulty of mining-based profit realization can be found in the fact that tremendous numbers of patterns are discovered by the association rule mining. Due to the many patterns, data mining experts should perform additional mining of the results of initial mining in order to extract only actionable and profitable knowledge, which exhausts much time and costs. In the literature, a number of interestingness measures have been devised for estimating discovered patterns. Most of the measures can be directly calculated from what is known as a contingency table, which summarizes the sales frequencies of exclusive items or itemsets. A contingency table can provide brief insights into the relationship between two or more itemsets of concern. However, it is important to note that some useful information concerning sales transactions may be lost when a contingency table is constructed. For instance, information regarding the size of each market basket(i.e., the number of items in each transaction) cannot be described in a contingency table. It is natural that a larger basket has a tendency to consist of more sales patterns. Therefore, if two itemsets are sold together in a very large basket, it can be expected that the basket contains two or more patterns and that the two itemsets belong to mutually different patterns. Therefore, we should classify frequent itemset into two categories, inter-pattern co-occurrence and intra-pattern co-occurrence, and investigate the effect of the market basket size on the two categories. This notion implies that any interestingness measures for association rules should consider not only the total frequency of target itemsets but also the size of each basket. There have been many attempts on analyzing various interestingness measures in the literature. Most of them have conducted qualitative comparison among various measures. The studies proposed desirable properties of interestingness measures and then surveyed how many properties are obeyed by each measure. However, relatively few attentions have been made on evaluating how well the patterns discovered by each measure are regarded to be valuable in the real world. In this paper, attempts are made to propose two notions regarding association rule measures. First, a quantitative criterion for estimating accuracy of association rule measures is presented. According to this criterion, a measure can be considered to be accurate if it assigns high scores to meaningful patterns that actually exist and low scores to arbitrary patterns that co-occur by coincidence. Next, complementary measures are presented to improve the accuracy of traditional association rule measures. By adopting the factor of market basket size, the devised measures attempt to discriminate the co-occurrence of itemsets in a small basket from another co-occurrence in a large basket. Intensive computer simulations under various workloads were performed in order to analyze the accuracy of various interestingness measures including traditional measures and the proposed measures.

• Korean journal of food and cookery science
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• v.15 no.3
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• pp.203-209
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• 1999

The effect of several hydrocolloids on the rheological behavior of wheat flour was investigated. The influence of the selected hydrocolloids (alginate, carrageenan, CMC, guar, locustbean and xanthan) on wheat flour was tested by using two different techniques; amylograph and texture analyzer. In order to have a general overview of their effects hydrocolloids were chosen from different sources implying a broad diversity of chemical structures. The hydrocolloid addition decreased the brightness(L) but increased yellowness(b). The interaction between hydrocolloid and flour produces a slight modification of the amylogram parameters, being the most clearly affected parameter breakdown, which is increased by carrageenan, guar and xanthan. Hardness and cutting force were augmented by hydrocolloid addition, while springeness was decreased except guar and locustbean. In summary, when looking for the improvement of the noodle texture, guar, locustbean are the best candidate additives due to their effects on pasting and texture properties. These hydrocolloids increase the hardness, cutting force, gumness, chew-ness, so were thought to increase the eating quality. So, each tested hydrocolloid affected in a different way the rheological properties of wheat flour, the results obtained are important for the appropriate use of these hydrocolloid as ingredients in the noodle making process.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.252-252
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• 2017

Nowadays water shortage is becoming one of the biggest problems in the Korea. Many different methods are developed for conservation of water. Soil water management has become the most indispensable factor for augmenting the crop productivity especially on soybean (Glycine max L.) because of their high susceptibility to both water stress and water logging at various growth stages. The farmers have been using irrigation techniques through manual control which farmers irrigate lands at regular intervals. Automatic irrigation systems are convenient, especially for those who need to travel. If automatic irrigation systems are installed and programmed properly, they can even save you money and help in water conservation. Automatic irrigation systems can be programmed to provide automatic irrigation to the plants which helps in saving money and water and to discharge more precise amounts of water in a targeted area, which promotes water conservation. The objective of this study was to determine the possible effect of automatic irrigation systems based on soil moisture on soybean growth. This experiment was conducted on an upland field with sandy loam soils in Department of Southern Area Crop, NICS, RDA. The study had three different irrigation methods; sprinkle irrigation (SI), surface drip irrigation (SDI) and fountain irrigation (FI). SI was installed at spacing of $7{\times}7m$ and $1.8m^3/hr$ as square for per irrigation plot, a lateral pipe of SDI was laid down to 1.2 m row spacing with $2.3L\;h^{-1}$ discharge rate, the distance between laterals was 20 cm spacing between drippers and FI was laid down in 3m interval as square for per irrigation plot. Soybean (Daewon) cultivar was sown in the June $20^{th}$, 2016, planted in 2 rows of apart in 1.2 m wide rows and distance between hills was 20 cm. All agronomic practices were done as the recommended cultivation. This automatic irrigation system had valves to turn irrigation on/off easily by automated controller, solenoids and moisture sensor which were set the reference level as available soil moisture levels of 30% at 10cm depth. The efficiency of applied irrigation was obtained by dividing the total water stored in the effective root zone to the applied irrigation water. Results showed that seasonal applied irrigation water amounts were $60.4ton\;10a^{-1}$ (SI), $47.3ton\;10a^{-1}$ (SDI) and $92.6 ton\;10a^{-1}$ (FI), respectively. The most significant advantage of SDI system was that water was supplied near the root zone of plants drip by drip. This system saved a large quantity of water by 27.5% and 95.6% compared to SI, FI system. The average soybean yield was significantly affected by different irrigation methods. The soybean yield by different irrigation methods were $309.7kg\;10a^{-1}$ from SDI $282.2kg\;10a^{-1}$ from SI, $289.4kg\;10a^{-1}$ from FI, and $206.3kg\;10a^{-1}$ from control, respectively. SDI resulted in increase of soybean yield by 50.1%, 7.0% 9.8% compared to non-irrigation (control), FI and SI, respectively. Therefore, the automatic irrigation system supplied water only when the soil moisture in the soil went below the reference. Due to the direct transfer of water to the roots water conservation took place and also helped to maintain the moisture to soil ratio at the root zone constant. Thus the system is efficient and compatible to changing environment. The automatic irrigation system provides with several benefits and can operate with less manpower. In conclusion, improving automatic irrigation system can contribute greatly to reducing production costs of crops and making the industry more competitive and sustainable.

• Journal of the Korean Society of Food Science and Nutrition
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• v.42 no.6
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• pp.924-932
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• 2013

This study investigated the antioxidant activities of processed Deoduck (Codonopsis lanceolata) extracts treated through high-pressure extraction and steaming with fermentation. The antioxidant activities were determined for DPPH and ABTS radical-scavenging activity, SOD-like activity, ferric reducing antioxidant power (FRAP), and $Fe^{2+}$ chelating. Total phenolic and flavonoid contents were also measured. Among eight Deoduck extracts, the S5FDW extract had the highest total phenolic and flavonoid content, 73.9 mg GAE/g and 50.9 mg QUE/g, respectively. The S5FDW extract had the highest DPPH radical-scavenging activity (27%) at a 1.0 mg/mL concentration. The ABTS radical-scavenging activity was highest for S5FDW extract (82.1%) at a 10 mg/mL concentration. The HFDE extract showed the highest SOD-like activity (29.7%) at a 1.0 mg/mL concentration. FRAP was highest in S5FDW extract (140.8 ${\mu}M$) at a 1.0 mg/mL concentration. The DE extract showed the highest $Fe^{2+}$ chelating (46%) at a 1.0 mg/mL concentration. The phenolic and flavonoid contents significantly correlated with the antioxidant activity of several processed Deoduck extracts and was higher in the processed Deoduck extracts compared to the raw Deoduck extracts. Therefore, processing techniques can be useful methods for making Deoduck a more potent and natural antioxidant.

• Korean Journal of Remote Sensing
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• v.35 no.6_4
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• pp.1319-1326
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• 2019

The KARI(Korea Aerospace Research Institute) operates the government satellite information application consultation to cope with ever-increasing demand for satellite images in the public sector, and carries out various support projects including the generation and provision of mosaic images on the Korean Peninsula every year to enhance user convenience and promote the use of satellite images. In particular, the government has wanted to increase the utilization of mosaic images on the Korean Peninsula and seek to classify and update mosaic images so that users can use them in their businesses easily. However, it is necessary to test and verify whether the classification results of the mosaic images can be utilized in the field since the original spectral information is distorted during pan-sharpening and color balancing, and there is a limitation that only R, G, and B bands are provided. Therefore, in this study, the reliability of the classification result of the mosaic image was compared to the result of KOMPSAT-3 image. The study found that the accuracy of the classification result of KOMPSAT-3 image was between 81~86% (overall accuracy is about 85%), while the accuracy of the classification result of mosaic image was between 69~72% (overall accuracy is about 72%). This phenomenon is interpreted not only because of the distortion of the original spectral information through pan-sharpening and mosaic processes, but also because NDVI and NDWI information were extracted from KOMPSAT-3 image rather than from the mosaic image, as only three color bands(R, G, B) were provided. Although it is deemed inadequate to distribute classification results extracted from mosaic images at present, it is believed that it will be necessary to explore ways to minimize the distortion of spectral information when making mosaic images and to develop classification techniques suitable for mosaic images as well as the provision of NIR band information. In addition, it is expected that the utilization of images with limited spectral information could be increased in the future if related research continues, such as the comparative analysis of classification results by geomorphological characteristics and the development of machine learning methods for image classification by objects of interest.