• Asia pacific journal of information systems
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• v.17 no.4
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• pp.187-206
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• 2007

The development of computer and information technology has been combined with the information superhighway internet infrastructure, so information widely spreads not only in special fields but also in the daily lives of people. Information ubiquity influences the traditional way of transaction, and leads a new E-commerce which distinguishes from the existing E-commerce. Not only goods as physical but also service as non-physical come into E-commerce. As the scale of E-Commerce is being enlarged as well. It keeps people from finding information they want. Recommender systems are now becoming the main tools for E-Commerce to mitigate the information overload. Recommender systems can be defined as systems for suggesting some Items(goods or service) considering customers' interests or tastes. They are being used by E-commerce web sites to suggest products to their customers who want to find something for them and to provide them with information to help them decide which to purchase. There are several approaches of recommending goods to customer in recommender system but in this study, the main subject is focused on collaborative filtering technique. This study presents a possibility of pre-evaluation for the prediction performance of customer's preference in collaborative filtering before the process of customer's preference prediction. Pre-evaluation for the prediction performance of each customer having low performance is classified by using the statistical features of ratings rated by each customer is conducted before the prediction process. In this study, MovieLens 100K dataset is used to analyze the accuracy of classification. The classification criteria are set by using the training sets divided 80% from the 100K dataset. In the process of classification, the customers are divided into two groups, classified group and non classified group. To compare the prediction performance of classified group and non classified group, the prediction process runs the 20% test set through the Neighborhood Based Collaborative Filtering Algorithm and Correspondence Mean Algorithm. The prediction errors from those prediction algorithm are allocated to each customer and compared with each user's error. Research hypothesis : Two research hypotheses are formulated in this study to test the accuracy of the classification criterion as follows. Hypothesis 1: The estimation accuracy of groups classified according to the standard deviation of each user's ratings has significant difference. To test the Hypothesis 1, the standard deviation is calculated for each user in training set which is divided 80% from MovieLens 100K dataset. Four groups are classified according to the quartile of the each user's standard deviations. It is compared to test the estimation errors of each group which results from test set are significantly different. Hypothesis 2: The estimation accuracy of groups that are classified according to the distribution of each user's ratings have significant differences. To test the Hypothesis 2, the distributions of each user's ratings are compared with the distribution of ratings of all customers in training set which is divided 80% from MovieLens 100K dataset. It assumes that the customers whose ratings' distribution are different from that of all customers would have low performance, so six types of different distributions are set to be compared. The test groups are classified into fit group or non-fit group according to the each type of different distribution assumed. The degrees in accordance with each type of distribution and each customer's distributions are tested by the test of ${\chi}^2$ goodness-of-fit and classified two groups for testing the difference of the mean of errors. Also, the degree of goodness-of-fit with the distribution of each user's ratings and the average distribution of the ratings in the training set are closely related to the prediction errors from those prediction algorithms. Through this study, the customers who have lower performance of prediction than the rest in the system are classified by those two criteria, which are set by statistical features of customers ratings in the training set, before the prediction process.

• Journal of Korean Society of Steel Construction
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• v.13 no.5
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• pp.567-575
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• 2001

In order to consider statistical properties of probability variables used in the structural analysis the conventional approach using the safety factor based on past experience usually estimated the safety of a structure Also the real structures could only be analyzed with the error in estimation of loads material characters and the dimensions of the members. But the errors should be considered systematically in the structural analysis Safety of structure could not precisely be appraised by the traditional structural design concept Recently new aproach based on the probability concept has been applied to the assessment of structural safety using the reliability concept Thus the computer program by the Probabilitstic FEM is developed by incorporating the probabilistic concept into the conventional FEM method. This paper estimated for the reliability of a plane stress structure by Advanced First-Order Second Moment method using von Mises, Tresca and Mohr-Coulomb failure criterions. Verification of the reliability index and failure probability of attained by the Monte Carlo Simulation method with the von Mises criterion were same as PFEM, but the Monte Carlo Simulation were very time-consuming. The variance of member thickness and load could influence the reliability and failure probability most sensitively among the design variables from the results of the parameter analysis. The proper failure criterion according to characteristic of materials must be used for safe design.

• Journal of Korean Society on Water Environment
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• v.25 no.3
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• pp.441-451
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• 2009

To evaluate the soil erosion best management practices, many computer models has been utilized over the years. Among those, the USLE and SWAT models have been widely used. These models estimate the soil erosion from the field using empirically-based USLE/MULSE in it. However, these models are not good enough to estimate soil erosion from highland agricultural watershed where severe storm events are causing soil erosion and muddy water issues at the receiving watersheds. Thus, physically-based WEPP watershed version was applied to a watershed, located at Jawoon-ri, Gangwon with very detailed rainfall data, rather than daily rainfall data. Then it was validated with measured sediment data collected at the sediment settling ponds and through overland flow. In this study, very detailed rainfall data, crop management data, soil data reflecting soil reconditioned for higher crop production were used in the WEPP runs. The $R^2$ and the EI for runoff comparisons were 0.88 and 0.91, respectively. For sediment comparisons, the $R^2$ and the EI values were 0.95 and 0.91. Since the WEPP provides higher accuracies in predicting runoff and sediment yield from the study watershed, various slope scenarios (2%, 3%, 5.5%, 8%, 10%, 13%, 15%, 18%, 20%, 23%, 25%, 28%, 30%) were made and simulated sediment yield values were analyzed to develop appropriate soil erosion management practices. It was found that soil erosion increase linearly with increase in slope of the field in the watershed. However, the soil erosion increases dramatically with the slope of 20% or greater. Therefore special care should be taken for the agricultural field with slope greater than 20%. As shown in this study, the WEPP watershed version is suitable model to predict soil erosion where torrential rainfall events are causing significant amount of soil loss from the field and it can also be used to develop site-specific best management practices.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.3
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• pp.170-176
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• 2004

This study was carried out to establish a spatial decision support system for evaluating climatic aspects of a given geographic location in complex terrains with respect to the quality apple production. Monthly climate data from S6 synoptic stations across South Korea were collected for 1971-2000. A digital elevation model (DEM) with a 10-m cell spacing was used to spatially interpolate daily maximum and minimum temperatures based on relevant topoclimatological models applied to Jangsoo county in Korea. For daily minimum temperature, a spatial interpolation scheme accommodating the potential influences of cold air accumulation and the temperature inversion was used. For daily maximum temperature estimation, a spatial interpolation model loaded with the overheating index was used. Freezing risk in January was estimated under the recurrence intervals of 30 years. Frost risk at bud-burst and blossom was also estimated. Fruit quality was evaluated for soluble solids, anthocyanin content, Hunter L and A values, and LID ratio, which were expressed as empirical functions of temperature based on long-term field observations. AU themes were prepared as ArcGlS Grids with a 10-m cell spacing. Analysis showed that 11 percent of the whole land area of Jangsoo county might be suitable for quality 'Fuji' apple production. A computer program (MAPLE) was written to help utilize the results in decision-making for site-selection of new orchards in this region.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1706-1709
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• 2010

Sewerage Treatment Plants(STPs) are complexes systems in which a range of physical, chemical and biological processes occur. Since Activated Sludge Model(ASM) No.1 was published, a number of new mathematical models for simulating biological processes have been developed. However, these models have disadvantages in cost and simplicity due to the laboriousness and tediousness of their procedures. One of the major difficulties of these mathematical model based tools is that the field-operators mostly don't have the time or the computer-science skills to handle there models, so it mainly remains on experts or special engineers. In order to solve these situations and help the field-operators, the $KM^2BM$(K-water & More-M Mass Balance Model) based on the dynamic-mass balance model was developed. This paper presents $KM^2BM$ as a simulation tools for STPs design and optimization. This model considers the most important microbial behavioral processes taking place in a STPs to maximize potential applicability without increasing neither model parameter estimation nor wastewater characterization efforts.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1C
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• pp.103-112
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• 2010

In this paper, we propose a coarse and fine frequency synchronization method which is suitable for the 3GPP(3rd Generation Partnership Project) LTE(Long Term Evolution) FDD(Frequency Division Duplexing) / TDD(Time Division Duplexing) dual mode system. In general, PSS(Primary Synchronization Signal) correlation based estimation method and CP(Cyclic Prefix) correlation based tracking loop are applied for coarse and fine frequency synchronization in 3GPP LTE OFDMA(Orthogonal Frequency Division Multiple Access) system, respectively. However, the conventional coarse frequency synchronization method has performance degradation caused by fading channel and squaring loss. Also, the conventional fine frequency synchronization method cannot guarantee stable operation in TDD mode because of signal power difference between uplink and downlink subframe. Therefore, in this paper, we propose enhanced coarse and fine frequency synchronization methods which can estimate more accurately in multi-path fading channel and high speed channel environments and has stable operation for TDD frame structure, respectively. By computer simulation, we show that the proposed methods outperform the conventional methods, and verify that the proposed frequency synchronization method can guarantee stable operation in 3GPP LTE FDD/TDD dual mode downlink receiver.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.17 no.4
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• pp.291-298
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• 1984

The extracted hagfish (Eptatretus burgeri) flesh lipid was separated into following fractions by column chromatography on Bio-beads SX-2 and Sephadex LH-20 prior to gab chromatographic analysis of their fatty acid compositions: polar lipid, triglyceride and free fatty acid. The major fatty acids of total lipid and triglyceride in hagfish were $C_{16:0},\;C_{16:1},\;and\;C_{18:1}$. The ratio of $C_{18:0}/C_{18:1}$ in the total lipid and triglyceride of hagfish was 0.1. The polar lipid of the hagfish muscle was mainly composed of phosphatidyl choline ($65.5\%$) and phosphatidyl ethanolamine ($28.0\%$). The triglyceride obtained was fractionated into four fractions by HPLC on the basis of partition numbers. Both the fatty acid composition and triglyceride composition on the basis of the total carbon number in the acyl chains of the triglyceride were analysed by the GLC. From the information obtained on triglyceride compositions based on the total carbon number by GLC and the partition number by HPLC and fatty acid composition by GLC, the combination of fatty acid in each triglycerides was estimated. A computer was used for estimation of the fatty acid combination in the triglyceride because hagfish lipid triglyceride was composed of various kinds of fatty acids. Fortyfour kinds of triglyceride were estimated. The major triglycerides in hagfish flesh lipid were found to those of ($1{\times}C_{16:0},\;2{\times}C_{18:1};\;13.5\%$), ($1{\times}C_{16:0},\;1{\times}C_{18:0},\;1{\times}C_{18:1};\;7.2\%$), ($1{\times}C_{16:1},\;2{\times}C_{18:1};\;5.4\%$), ($2{\times}C_{16:0},\;1{\times}C_{22:5};\;5.2\%$), ($1{\times}C_{14:0},\;2{\times}C_{18:1};\;4.5\%$), ($2{\times}C_{18:1},\;1{\times}C_{22:5};\;3.6\%$), ($1{\times}C_{14:0},\;1{\times}C_{18:0},\;1{\times}C_{18:1};\;2.7\%$) and ($1{\times}C_{14:0},\;1{\times}C_{16:0},\;1{\times}C_{18:2};\;2.2\%$).

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.819-829
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• 2020

This empirical research is aimed at testing the relationship of the big five personality traits namely openness to experience, extraversion, consciousness, agreeableness, neuroticism, and risk aversion with the investment intention of individual investors belonging to Balochistan, Pakistan. The primary data is collected through a self-administered questionnaire (a structured form that consists of a series of closed-ended and open-ended questions) from a sample of 397 active individual investors belonging to different districts of the province. The data is empirically analyzed by applying the Partial Least Square (PLS) path modeling technique by using the estimation package available in Smart-PLS. The findings of this study suggest that all the variables are statistically significant with investors' investment intention with risk aversion as the strongest predictor. Moreover, openness to experience, extraversion, consciousness, agreeableness, and risk are significantly and positively related to an investor's investment intention, whereas neuroticism is negatively related to an investor's investment intention. The results extended by this study can be used by financial planners and investment bankers to channelize the available financial resources in diversified portfolios. The results will help financial planners to make available diverse investment alternatives for investors in Balochistan, thus catering to their unique needs. Academia must offer courses on contemporary finance paradigm based on behavioral finance to enable future business graduates to make wise financial decisions.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.2
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• pp.93-98
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• 2016

For accurate precipitation forecasts the choice of weather factors and prediction method is very important. Recently, machine learning has been widely used for forecasting precipitation, and artificial neural network, one of machine learning techniques, showed good performance. In this paper, we suggest a new method for forecasting precipitation using DBN, one of deep learning techniques. DBN has an advantage that initial weights are set by unsupervised learning, so this compensates for the defects of artificial neural networks. We used past precipitation, temperature, and the parameters of the sun and moon's motion as features for forecasting precipitation. The dataset consists of observation data which had been measured for 40 years from AWS in Seoul. Experiments were based on 8-fold cross validation. As a result of estimation, we got probabilities of test dataset, so threshold was used for the decision of precipitation. CSI and Bias were used for indicating the precision of precipitation. Our experimental results showed that DBN performed better than MLP.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.4 no.3
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• pp.126-133
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• 2003

This paper is related to a morphological segmentation method for extracting the moving object in video sequence using global motion compensation and two-dimensional spatio-temporal entropic thresholding. First, global motion compensation is performed with camera panning vector estimated in the hierarchical pyramid structure constructed by wavelet transform. Secondly, the regions with high possibility to include the moving object between two consecutive frames are extracted block by block from the global motion compensated image using two-dimensional spatio-temporal entropic thresholding. Afterwards, the LUT classifying each block into one among changed block, uncertain block, stationary block according to the results classified by two-dimensional spatio-temporal entropic thresholding is made out. Next, by adaptively selecting the initial search layer and the search range referring to the LUT, the proposed HBMA can effectively carry out fast motion estimation and extract object-included region in the hierarchical pyramid structure. Finally, after we define the thresholded gradient image in the object-included region, and apply the morphological segmentation method to the object-included region pixel by pixel and extract the moving object included in video sequence. As shown in the results of computer simulation, the proposed method provides relatively good segmentation results for moving object and specially comes up with reasonable segmentation results in the edge areas with lower contrast.