• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.257-264
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• 2003

In the context of a dynamic trading environment, the ultimate goal of the financial forecasting system is to optimize a specific trading objective. This paper proposes a two-phase (extraction and filtering) stock trading system that aims at maximizing the rates of returns. Extraction of stocks is performed by searching specific time-series patterns described by a combination of values of technical indicators. In the filtering phase, several rules are applied to the extracted sets of stocks to select stocks to be actually traded. The filtering rules are automatically induced from past data. From a large database of daily stock prices, the values of technical indicators are calculated. They are used to make the extraction patterns, and the distributions of the discretization intervals of the values are calculated for both positive and negative data sets. We assumed that the values in the intervals of distinctive distribution may contribute to the prediction of future trend of stocks, so the rules for filtering stocks are automatically induced from the data in those intervals. We show the rates of returns when using our trading system outperform the market average. These results mean rule induction method using distributional differences is useful.

• Journal of Korean Society of Transportation
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• v.27 no.5
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• pp.209-221
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• 2009

Travel time estimation under given traffic conditions is important for providing drivers with travel time prediction information. But the present expressway travel time estimation process cannot calculate a reliable travel time. The objective of this study is to estimate the path travel time spent in a through lane between origin tollgates and destination tollgates on an expressway as a prerequisite result to offer reliable prediction information. Useful and abundant toll collection system (TCS) data were used. When estimating the path travel time, the path travel time is estimated combining the link travel time obtained through a preprocessing process. In the case of a lack of TCS data, the TCS travel time for previous intervals is referenced using the linear interpolation method after analyzing the increase pattern for the travel time. When the TCS data are absent over a long-term period, the dynamic travel time using the VDS time space diagram is estimated. The travel time estimated by the model proposed can be validated statistically when compared to the travel time obtained from vehicles traveling the path directly. The results show that the proposed model can be utilized for estimating a reliable travel time for a long-distance path in which there are a variaty of travel times from the same departure time, the intervals are large and the change in the representative travel time is irregular for a short period.

• The Journal of Fisheries Business Administration
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• v.51 no.2
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• pp.51-69
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• 2020

The purpose of this study is to construct an outlook model that is consistent with the "Fisheries Outlook" monthly published by the Fisheries Outlook Center of the Korea Maritime Institute(KMI). In particular, it was designed as a partial equilibrium model limited to abalone items, but a model was constructed with a dynamic ecological equation model(DEEM) system taking into account biological breeding and shipping time. The results of this study are significant in that they can be used as basic data for model development of various items in the future. In this study, due to the limitation of monthly data, the market equilibrium price was calculated by using the recursive model construction method to be calculated directly as an inverse demand. A model was built in the form of a structural equation model that can explain economic causality rather than a conventional time series analysis model. The research results and implications are as follows. As a result of the estimation of the amount of young seashells planting, it was estimated that the coefficient of the amount of young seashells planting from the previous year was estimated to be 0.82 so that there was no significant difference in the amount of young seashells planting this year and last year. It is also meant to be nurtured for a long time after aquaculture license and limited aquaculture area(edge style) and implantation. The economic factor, the coefficient of price from last year was estimated at 0.47. In the case of breeding quantity, it was estimated that the longer the breeding period, the larger the coefficient of breeding quantity in the previous period. It was analyzed that the impact of shipments on the breeding volume increased. In the case of shipments, the coefficient of production price was estimated unelastically. As the period of rearing increased, the estimation coefficient decreased. Such result indicates that the expected price, which is an economic factor variable and that had less influence on the intention to shipments. In addition, the elasticity of the breeding quantity was estimated more unelastically as the breeding period increased. This is also correlated with the relative coefficient size of the expected price. The abalone supply and demand forecast model developed in this study is significant in that it reduces the prediction error than the existing model using the ecological equation modeling system and the economic causal model. However, there are limitations in establishing a system of simultaneous equations that can be linked to production and consumption between industries and items. This is left as a future research project.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.238-252
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• 2009

Due to the rapid increase of available contents via the convergence of broadcasting and internet, the efficient access to personally preferred contents has become an important issue. In this paper, for recommendation scheme for TV programs using a collaborative filtering technique is studied. For recommendation of user preferred TV programs, our proposed recommendation scheme consists of offline and online computation. About offline computation, we propose reasoning implicitly each user's preference in TV programs in terms of program contents, genres and channels, and propose clustering users based on each user's preferences in terms of genres and channels by dynamic fuzzy clustering method. After an active user logs in, to recommend TV programs to the user with high accuracy, the online computation includes pulling similar users to an active user by similarity measure based on the standard preference list of active user and filtering-out of the watched TV programs of the similar users, which do not exist in EPG and ranking of the remaining TV programs by proposed rank model. Especially, in this paper, the BM (Best Match) algorithm is extended to make the recommended TV programs be ranked by taking into account user's preferences. The experimental results show that the proposed scheme with the extended BM model yields 62.1% of prediction accuracy in top five recommendations for the TV watching history of 2,441 people.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.2
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• pp.38-50
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• 2008

The site-specific seismic responses and corresponding seismic hazards are influenced mainly by the subsurface geologic and geotechnical dynamic characteristics. To estimate reliably the seismic responses in this study, a geotechnical information system (GTIS) within GIS framework was developed by introducing new concepts, which consist of the extended area containing the study area and the additional site visit for acquiring surface geo-knowledge data. The GIS-based GTIS was built for Gyeongju area, which has records of abundant historical seismic hazards reflecting the high potential of future earthquakes. At the study area, Gyeongju, intensive site investigations and pre-existing geotechnical data collections were performed and the site visits were additionally carried out for assessing geotechnical characteristics and shear wave velocity ($V_S$) representing dynamic property. Within the GTIS for Gyeongju area, the spatially distributed geotechnical layers and $V_S$ in the entire study area were reliably predicted from the site investigation data using the geostatistical kriging method. Based on the spatial geotechnical layers and $V_S$ predicted within the GTIS, a seismic zoning map on site period ($T_G$) from which the site-specific seismic responses according to the site effects can be estimated was created across the study area of Gyeongju. The spatial $T_G$ map at Gyeongju indicated seismic vulnerability of two- to five-storied buildings. In this study, the seismic zonation based on $T_G$ within the GIS-based GTIS was presented as regional efficient strategy for seismic hazard prediction and mitigation.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Composites Research
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• v.30 no.2
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• pp.138-144
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• 2017

Damage sensing of polymer composite films consisting of poly(dicyclopentadiene) p-DCPD and carbon nanotube (CNT) was studied experimentally. Only up to 1st ring-opening polymerization occurred with the addition of CNT, which made the modified film electrically conductive, while interfering with polymerization. The interfacial adhesion of composite films with varying CNT concentration was evaluated by measuring the wettability using the static contact angle method. 0.5 wt% CNT/p-DCPD was determined to be the optimal condition via electrical dispersion method and tensile test. Dynamic fatigue test was conducted to evaluate the durability of the films by measuring the change in electrical resistance. For the initial three cycles, the change in electrical resistance pattern was similar to the tensile stress-strain curve. The CNT/p-DCPD film was attached to an epoxy matrix to demonstrate its utilization as a sensor for fracture behavior. At the onset of epoxy fracture, electrical resistance showed a drastic increase, which indicated adhesive fracture between sensor and matrix. It leads to prediction of crack and fracture of matrix.

• Journal of Korea Water Resources Association
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• v.47 no.8
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• pp.671-684
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• 2014

Change point analysis is a efficient tool to understand the fundamental information in hydro-meteorological data such as rainfall, discharge, temperature etc. Especially, this fundamental information to change points to future rainfall data identified by reasonable detection skills can affect the prediction of flood and drought occurrence because well detected change points provide a key to resolve the non-stationary or inhomogeneous problem by climate change. Therefore, in this study, the comparative study to assess the performance of the 3 change point detection skills, cumulative sum (CUSUM) method, Bayesian change point (BCP) method, and segmentation by dynamic programming (DP) was performed. After assessment of the performance of the proposed detection skills using the 3 types of the synthetic series, the 2 reasonable detection skills were applied to the observed and future rainfall data at the 5 rainfall gauges in South Korea. Finally, it was suggested that BCP (with 0.9 posterior probability) could be best detection skill and DP could be reasonably recommended through the comparative study. Also it was suggested that BCP (with 0.9 posterior probability) and DP detection skills to find some change points could be reasonable at the North-eastern part in South Korea. In future, the results in this study can be efficiently used to resolve the non-stationary problems in hydrological modeling considering inhomogeneity or nonstationarity.

• Journal of the Korean Geotechnical Society
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• v.38 no.4
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• pp.59-70
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• 2022

There is a growing interest in evaluating earthquake damage and determining disaster prevention measures due to the magnitude 5.8 earthquake in Pohang, Korea. Since the liquefaction phenomena occurred extensively in the residential area as a result of the earthquake, there was a demand for research on liquefaction phenomenon evaluation and liquefaction disaster prediction. Liquefaction is defined as a phenomenon where the strength of the ground is completely lost due to a sudden increase in excess pore water pressure caused due to large dynamic stress, such as an earthquake, acting on loose sand particles in a short period of time. The liquefaction potential index, which can identify the occurrence of liquefaction and predict the risk of liquefaction in a targeted area, can be used to create a liquefaction hazard map. However, since liquefaction assessment using existing field testing is predicated on a single borehole liquefaction assessment, there has been a representative issue for the whole targeted area. Spatial interpolation and geographic information systems can help to solve this issue to some extent. Therefore, in order to solve the representative problem of geotechnical information, this research uses the kriging method, one of the geostatistical spatial interpolation techniques, and constructs a geotechnical information database for liquefaction and spatial interpolation. Additionally, the liquefaction hazard map was created for each return period using the constructed geotechnical information database. Cross validation was used to confirm the accuracy of this liquefaction hazard map.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.594-602
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• 2023

To date, research on heavy floor impact noise has mainly been conducted. The reason is that in the case of lightweight floor impact noise, sufficient performance could be secured with only the floating floor structure and floor finishing materials. In the case of heavy floor impact noise in a floating floor structure, the reduction performance can be predicted to some extent by measuring the dynamic elasticity of the floor cushioning material. However, with the recent introduction of the post-measurement system, various floor structures are being developed. In particular, many non-floating floor structures that do not use cushioning materials are being developed. In floor structures where cushioning materials are not used, the finishing material will have a significant impact on lightweight floor impact noise. However, research on floor finishing materials is currently lacking. In this study, as a basic research on the development of various floor finishing materials for effective reduction of lightweight floor impact noise, various materials used as floor finishing materials for apartment complexes were selected, the sound insulation performance of lightweight floor impact noise was measured in an actual laboratory, and vibration characteristics were identified through simple experiments. The purpose was to confirm the predictability of light floor impact noise.