• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.777-784
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• 2006

This paper proposes a c-fuzzy model tree using partial least square method to predict the Chlorophyll-a concentration in each zone. First, cluster centers are calculated by fuzzy clustering method using all input and output attributes. And then, each internal node is produced according to fuzzy membership values between centers and input attributes. Linear models are constructed by partial least square method considering input-output pairs remained in each internal node. The expansion of internal node is determined by comparing errors calculated in parent node with ones in child node, respectively. On the other hands, prediction is performed with a linear model haying the highest fuzzy membership value between input attributes and cluster centers in leaf nodes. To show the effectiveness of the proposed method, we have applied our method to water quality data set measured at several stations. Under various experiments, our proposed method shows better performance than conventional least square based model tree method.

• The Journal of Information Systems
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• v.30 no.2
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• pp.147-163
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• 2021

Purpose The study aims to predict real estate prices by utilizing regional characteristics. Since real estate has the characteristic of immobility, the characteristics of a region have a great influence on the price of real estate. In addition, real estate prices are closely related to economic development and are a major concern for policy makers and investors. Accurate house price forecasting is necessary to prepare for the impact of house price fluctuations. To improve the performance of our predictive models, we applied LSTM, a widely used deep learning technique for predicting time series data. Design/methodology/approach This study used time series data on real estate prices provided by the Ministry of Land, Infrastructure and Transport. For time series data preprocessing, HP filters were applied to decompose trends and SOM was used to cluster regions with similar price directions. To build a real estate price prediction model, SVR and LSTM were applied, and the prices of regions classified into similar clusters by SOM were used as input variables. Findings The clustering results showed that the region of the same cluster was geographically close, and it was possible to confirm the characteristics of being classified as the same cluster even if there was a price level and a similar industry group. As a result of predicting real estate prices in 1, 2, and 3 months, LSTM showed better predictive performance than SVR, and LSTM showed better predictive performance in long-term forecasting 3 months later than in 1-month short-term forecasting.

• Journal of the Korean earth science society
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• v.45 no.1
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• pp.37-47
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• 2024

In the summer of 2018, the Korea-Japan (KJ) region experienced an extremely severe and prolonged heatwave. This study examines the GloSea6 model's prediction performance for the 2018 KJ heatwave event and investigates how its prediction skill is related to large-scale circulation patterns identified by the k-means clustering method. Cluster 1 pattern is characterized by a KJ high-pressure anomaly, Cluster 2 pattern is distinguished by an Eastern European high-pressure anomaly, and Cluster 3 pattern is associated with a Pacific-Japan pattern-like anomaly. By analyzing the spatial correlation coefficients between these three identified circulation patterns and GloSea6 predictions, we assessed the contribution of each circulation pattern to the heatwave lifecycle. Our results show that the Eastern European high-pressure pattern, in particular, plays a significant role in predicting the evolution of the development and peak phases of the 2018 KJ heatwave approximately two weeks in advance. Furthermore, this study suggests that an accurate representation of large-scale atmospheric circulations in upstream regions is a key factor in seasonal forecast models for improving the predictability of extreme weather events, such as the 2018 KJ heatwave.

• The Journal of Bigdata
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• v.8 no.2
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• pp.45-54
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• 2023

Seoul's public bicycle system, 'Ttareungyi,' introduced in 2015, has achieved an annual ridership of 40 million in 2022. Similarly, electric scooters, a type of personal mobility device, surpassed one million riders in 2020 due to various sharing platforms. However, the major roadways for these new transportation, bicycle lanes, are notably insufficient compared to other forms of transport. Hence, this study proposes an optimal location selection method for bicycle lanes in Seoul to prevent accidents and enhance bicycle ride safety. The location selection process prioritizes road safety concerning bicycle accident risk. Using regression models, high-risk areas for bicycle accidents are identified. Cluster analysis categorizes these areas into six clusters, each suggesting suitable types of bicycle lanes based on cluster-specific characteristics. We hope that this study will contribute to the improvement of Seoul's transportation environment, including the expansion of dedicated bicycle lanes and lanes for personal mobility devices.

• Journal of the Korean Academic Society of Industrial Cluster
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• v.1 no.1
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• pp.67-86
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• 2007

Wonju Medical Equipment Industry, despite of its short history, poor sales and weak manpower and so on, have shown remarkable outcomes in a relatively short period. At the end of 2007, totally 79 enterprises (only 4.6% of whole enterprises in Korea) made 10% of the nationwide production and 15% of the nationwide exports with an annual average growth rate of 66.7%, contributing domestic medical equipment industry tremendously. In addition, many leading medical equipment enterprises in various fields already moved or plan to move to Wonju, accelerating Wonju Medical Equipment Cluster. Wonju Medical Equipment Industry Cluster now enters into the growth stage, getting out of the initial business setup stage. Especially, the nomination of Wonju cluster project from the government accelerates networking (e.g. the development of the universal parts, the establishment of the mutual collaboration model among enterprises, and the mutual marketing), making a rapid growth in Wonju Medical Equipment Industry. Wonju Medical Equipment Industry Cluster revealed positive outcomes despite of the weakness in investment size and infra-structure comparing with the other medical industry cluster in the advanced country, while many domestic enterprises pursued their own growth models and thus failed to promote the international competitive power. Wonju Medical Equipment Industry has been developed rapidly. However, there are many challenging problems to support enterprises: small R&D investment and thus weak technology power, difficulties in recruiting R&D engineers, and poor marketing capabilities, financial infrastructure & policies, and network architecture. In order to develop a world-competitive medical equipment industry cluster at Wonju, the complement of infrastructures, the technology innovation, the mutual marketing, and the network expansion to support enterprises are further required. Wonju' s experiences in developing medical equipment industry so far suggest that our own flexible cluster model considering the industry structure and maturity for different regions should be developed, and specific action plans from the local and central governments based on their systematic strategies for industry development should be implemented in order to build world-competitive industry clusters in Korea.

• Journal of Korean Tunnelling and Underground Space Association
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• v.14 no.4
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• pp.293-308
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• 2012

The shear behavior at the particle/surface interface such as rock joint can determine the mechanical behavior of whole structure. Therefore, a fundamental understanding of the mechanisms governing its behavior and accurately estimation of the interface strength is essential. In this paper, PFC, a numerical analysis program of discrete element method was used to investigate the effects of the surface roughness on interface strength. The surface roughness was characterized by smooth, intermediate, and rough surface, respectively. In order to investigate the effects of particle shape and crushing on particle/surface interface behavior, one ball, clump, and cluster models were created and their results were compared. The shape of particle was characterized by circle, triangle, square, and rectangle, respectively. The results showed that as the surface roughness increases, interface strength and friction angle increase and the void ratio increases. The one ball model with smooth surface shows lower interface strength and friction angle than the clump model with irregular surface. In addition, a cluster model has less interface strength and friction angle than the clump model. The failure envelope of the cluster model shows non-linear characteristic. From these findings, it is verified that the surface roughness and particle shape effect on the particle/surface interface shear behavior.

• Journal of the Korean Geographical Society
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• v.30 no.2
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• pp.120-131
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• 1995

To analyze mobility patterns, this study used three Constraint (Capability Constraint, Coupling Constraint, Authority Constraint) models which were proposed in Dr. Hagerstrand's Time-space theory. This paper shows that three constraint models have some effects upon mobility by age. In this study, Capability Constraint means a certain special constraint that is what we can't do during proceeding basic natural urges like sleep, fare, etc. Coupling constraint is a physical one. Each person limits the action range for staying on a special place in special time. For instance, students have to stay in school so that they have mobility constraints. Authority Constraint is a social one. When we use urban facilities or traffic, we may be controlled by mobility sphere by an agreement or a social position. It is social agreement that the opening hour of a store, the time table of mass-transportation and a social positional control that the personal income, the standard of education. In this study it has been in a process of determination of the cluster number that degree of influences a social constraint to mobility. Considering the mobility constraint of characteristics of space divides urban and rural, people in urban area have higher mobility rate than in rural area. Resuets of determination of the cluster, show similar mobility pattern. People in urban area are connected verity of mobility which related to urban space structures with determination of cluste-number. That is to say, mobility patterns can be changed by space charactcristics. Constraints by sex and age are also social constraints and they are influenced by mobility patterns. For instance, females at the age of twenties have similar mobility pattern to the same age male but they have sudden changes after thirty's age. Male entertains a similar pattern without restriction of age. That is to say, management by sex as a social constraint affects mobility. To establish more realistic traffie policy, mobility formation should be reflected to the space in a view of social-behavioral science. To embody this, some problems should be investigated as follows. 1. As a problem of methodology, if sufficient samples ensured, we could subdivide clusters and could open up a new method of analyzing the mobility clusters by using the neuro-network. 2. Extracting actions connected with mobility and finding life cycle which is classified by daily cluste-characteristics, suitable counterproposal could be presented to the traific policy.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.2
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• pp.85-92
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• 2015

This study was conducted to analyze the relationship among climatic factors and radial growth of Pinus koraiensis in South Korea. To determine climate-growth relationships, cluster analysis was applied to group climatically similar surveyed regions, and dendroclimatological model was developed to predict radial growth for each climate group under the RCP 4.5 and RCP 8.5 scenarios for greenhouse gases. The dendroclimatological models were developed through climatic variables and standardized residual chronology for each climatic cluster of P. koraiensis. 2 to 4 climatic variables were used in the models ($R^2$ values between 0.35~0.49). For each of the climatic clusters for Pinus koraiensis, the growth simulations obtained from two RCP climate-change scenarios were used for growth prediction. The radial growth of the Clusters 2 and 3, which grow at high elevation, tend to increase. In contrast, Cluster 1, which grows at low elevation, tends to decrease with a large difference. Thus, the growth of Pinus koraiensis, which is a boreal species, could increase along with increasing temperature up to a certain point.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.3
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• pp.579-586
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• 2018

With the beginning of the new era of bigdata, information extraction or prediction are an important research area. Here, we present the acquisition of semi-automatically curated large-scale biological database and the prediction of enzyme reaction annotation for analyzing the pharmacological activities of drugs. Because the xenobiotic metabolism of pharmaceutical drugs by cellular enzymes is an important aspect of pharmacology and medicine. In this study, we apply and analyze similarity models to predict bimolecular reactions between human enzymes and their corresponding substrates. Thirteen models select to reflect the characteristics of each cluster in the similarity model. These models compare based on sensitivity and AUC. Among the evaluation models, the Simpson coefficient model showed the best performance in predicting the reactivity between the enzymes. The whole similarity model implement as a web service. The proposed model can respond dynamically to the addition of reaction information, which will contribute to the shortening of new drug development time and cost reduction.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.125-130
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• 2020

At the end of 1997, the volatility of the exchange rate intensified as the nation's exchange rate system was converted into a free-floating exchange rate system. As a result, managing the exchange rate is becoming a very important task, and the need for forecasting the exchange rate is growing. The exchange rate prediction model using the existing exchange rate prediction method, statistical technique, cannot find a nonlinear pattern of the time series variable, and it is difficult to analyze the time series with the variability cluster phenomenon. And as the number of variables to be analyzed increases, the number of parameters to be estimated increases, and it is not easy to interpret the meaning of the estimated coefficients. Accordingly, the exchange rate prediction model using artificial neural network, rather than statistical technique, is presented. Using DNN, which is the basis of deep learning among artificial neural networks, and LSTM, a recurrent neural network model, the number of hidden layers, neurons, and activation function changes of each model found the optimal exchange rate prediction model. The study found that although there were model differences, LSTM models performed better than DNN models and performed best when the activation function was Tanh.