• Journal of Intelligence and Information Systems
• /
• v.18 no.4
• /
• pp.43-57
• /
• 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
• /
• v.21 no.3
• /
• pp.79-99
• /
• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of Intelligence and Information Systems
• /
• v.21 no.2
• /
• pp.1-18
• /
• 2015

Collaborative filtering(CF) algorithm has been popularly used for recommender systems in both academic and practical applications. A general CF system compares users based on how similar they are, and creates recommendation results with the items favored by other people with similar tastes. Thus, it is very important for CF to measure the similarities between users because the recommendation quality depends on it. In most cases, users' explicit numeric ratings of items(i.e. quantitative information) have only been used to calculate the similarities between users in CF. However, several studies indicated that qualitative information such as user's reviews on the items may contribute to measure these similarities more accurately. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's reviews can be regarded as the informative source for identifying user's preference with accuracy. Under this background, this study proposes a new hybrid recommender system that combines with users' review mining. Our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and his/her text reviews on the items when calculating similarities between users. In specific, our system creates not only user-item rating matrix, but also user-item review term matrix. Then, it calculates rating similarity and review similarity from each matrix, and calculates the final user-to-user similarity based on these two similarities(i.e. rating and review similarities). As the methods for calculating review similarity between users, we proposed two alternatives - one is to use the frequency of the commonly used terms, and the other one is to use the sum of the importance weights of the commonly used terms in users' review. In the case of the importance weights of terms, we proposed the use of average TF-IDF(Term Frequency - Inverse Document Frequency) weights. To validate the applicability of the proposed system, we applied it to the implementation of a recommender system for smartphone applications (hereafter, app). At present, over a million apps are offered in each app stores operated by Google and Apple. Due to this information overload, users have difficulty in selecting proper apps that they really want. Furthermore, app store operators like Google and Apple have cumulated huge amount of users' reviews on apps until now. Thus, we chose smartphone app stores as the application domain of our system. In order to collect the experimental data set, we built and operated a Web-based data collection system for about two weeks. As a result, we could obtain 1,246 valid responses(ratings and reviews) from 78 users. The experimental system was implemented using Microsoft Visual Basic for Applications(VBA) and SAS Text Miner. And, to avoid distortion due to human intervention, we did not adopt any refining works by human during the user's review mining process. To examine the effectiveness of the proposed system, we compared its performance to the performance of conventional CF system. The performances of recommender systems were evaluated by using average MAE(mean absolute error). The experimental results showed that our proposed system(MAE = 0.7867 ~ 0.7881) slightly outperformed a conventional CF system(MAE = 0.7939). Also, they showed that the calculation of review similarity between users based on the TF-IDF weights(MAE = 0.7867) leaded to better recommendation accuracy than the calculation based on the frequency of the commonly used terms in reviews(MAE = 0.7881). The results from paired samples t-test presented that our proposed system with review similarity calculation using the frequency of the commonly used terms outperformed conventional CF system with 10% statistical significance level. Our study sheds a light on the application of users' review information for facilitating electronic commerce by recommending proper items to users.

• Journal of Intelligence and Information Systems
• /
• v.17 no.4
• /
• pp.241-254
• /
• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.28 no.6
• /
• pp.256-268
• /
• 2016

This article reviews the papers published in the Korean Journal of Air-Conditioning and Refrigeration Engineering during 2015. It is intended to understand the status of current research in the areas of heating, cooling, ventilation, sanitation, and indoor environments of buildings and plant facilities. Conclusions are as follows. (1) The research works on the thermal and fluid engineering were carried out in the areas of flow, heat and mass transfer, cooling and heating, and air-conditioning, the renewable energy system and the flow inside building rooms. Research issues dealing with air-conditioning machines and fire and exhausting smoke were reduced. CFD seems to be spreading to more research areas. (2) Research works on heat transfer area were carried out in the categories of heat transfer characteristics, pool boiling and condensing heat transfer and industrial heat exchangers. Researches on heat transfer characteristics included the economic analysis of GHG emission, micro channel heat exchanger, effect of rib angle on thermal performance, the airside performance of fin-and-tube heat exchangers, theoretical analysis of a rotary heat exchanger, heat exchanger in a cryogenic environment, the performance of a cross-flow-type, indirect evaporative cooler made of paper/plastic film. In the area of pool boiling and condensing, the bubble jet loop heat pipe was studied. In the area of industrial heat exchangers, researches were performed on fin-tube heat exchanger, KSTAR PFC and vacuum vessel at baking phase, the performance of small-sized dehumidification rotor, design of gas-injection port of an asymmetric scroll compressor, effect of slot discharge-angle change on exhaust efficiency of range hood system with air curtain. (3) In the field of refrigeration, various studies were carried in the categories of refrigeration cycle, alternative refrigeration/energy system, system control. In the refrigeration cycle category, a cold-climate heat pump system, $CO_2$ cascade systems, ejector cycles and a PCM-based continuous heating system were investigated. In the alternative refrigeration/energy system category, a polymer adsorption heat pump, an alcohol absorption heat pump and a desiccant-based hybrid refrigeration system were investigated. In the system control category, turbo-refrigerator capacity controls and an absorption chiller fault diagnostics were investigated. (4) In building mechanical system research fields, eighteen studies were reported for achieving effective design of the mechanical systems, and also for maximizing the energy efficiency of buildings. The topics of the studies included energy performance, HVAC system, ventilation, and renewable energies, piping in the buildings. Proposed designs, performance tests using numerical methods and experiments provide useful information and key data which can improve the energy efficiency of the buildings. (5) The field of architectural environment was mostly focused on indoor environment and building energy. The main researches of indoor environment were related to the user and location awareness technology applied dimming lighting control system, the lighting performance evaluation for light-shelves, the improvement evaluation of air quality through analysis of ventilation efficiency and the evaluation of airtightness of sliding and LS window systems. The subjects of building energy were worked on the energy saving estimation of existing buildings, the developing model to predict heating energy usage in domestic city area and the performance evaluation of cooling applied with economizer control. The studies were also performed related to the experimental measurement of weight variation and thermal conductivity in polyurethane foam, the development of flame spread prevention system for sandwich panels, the utilization of heat from waste-incineration facility in large-scale horticultural facilities.

• Journal of Intelligence and Information Systems
• /
• v.14 no.2
• /
• pp.83-102
• /
• 2008

Ubiquitous computing requires timely supply of contextual information in order to upgrade decision quality. In this sense, this study is aimed at proposing a multi-dynamic decision support system for highly ill-structured problems. Especially, it is very important for decision makers in the ubiquitous computing to coordinate conflicts among local goals and global goal harmoniously. The proposed Multi-Dynamic Decision Support System (MDDSS) is basically composed of both central structure and distributed structure, in which central structure supports multi objects decision making and distributed structure supports individual decision making. Its hybrid architecture consists of decision processor, multi-agent controller and intelligent knowledge management processor. Decision processor provides decision support using contexts which come from individual agents. Multi-agent controller coordinates tension among multi agents to resolve conflicts among them. Meanwhile, intelligent knowledge management processor manages knowledge to support decision making such as rules, knowledge, cases and so on. To prove the validity of the proposed MDDSS, we applied it to an u-fulfillment problem system in which many kinds of decision makers exist trying to satisfy their own objectives, and timely adjustment of action strategy is required. Therefore, the u-fulfillment problem is a highly ill-structured problem. We proved its effectiveness with the aid of multi-agent simulation comprising 60 customers and 10 vehicles under three experimental modes.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2012.08a
• /
• pp.184-184
• /
• 2012

Recently, InGaN/GaN multi-quantum well grown on GaN pyramid structures have attracted much attention due to their hybrid characteristics of quantum well, quantum wire, and quantum dot. This gives us broad band emission which will be useful for phosphor-free white light emitting diode. On the other hand, by using quantum dot emission on top of the pyramid, site selective single photon source could be realized. However, these structures still have several limitations for the single photon source. For instance, the quantum efficiency of quantum dot emission should be improved further. As detection systems have limited numerical aperture, collection efficiency is also important issue. It has been known that micro-cavities can be utilized to modify the radiative decay rate and to control the radiation pattern of quantum dot. Researchers have also been interested in nano-cavities using localized surface plasmon. Although the plasmonic cavities have small quality factor due to high loss of metal, it could have small mode volume because plasmonic wavelength is much smaller than the wavelength in the dielectric cavities. In this work, we used localized surface plasmon to improve efficiency of InGaN qunatum dot as a single photon emitter. We could easily get the localized surface plasmon mode after deposit the metal thin film because lnGaN/GaN multi quantum well has the pyramidal geometry. With numerical simulation (i.e., Finite Difference Time Domain method), we observed highly enhanced decay rate and modified radiation pattern. To confirm these localized surface plasmon effect experimentally, we deposited metal thin films on InGaN/GaN pyramid structures using e-beam deposition. Then, photoluminescence and time-resolved photoluminescence were carried out to measure the improvement of radiative decay rate (Purcell factor). By carrying out cathodoluminescence (CL) experiments, spatial-resolved CL images could also be obtained. As we mentioned before, collection efficiency is also important issue to make an efficient single photon emitter. To confirm the radiation pattern of quantum dot, Fourier optics system was used to capture the angular property of emission. We believe that highly focused localized surface plasmon around site-selective InGaN quantum dot could be a feasible single photon emitter.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.48 no.6
• /
• pp.8-17
• /
• 2011

Recently, fusion camera systems that consist of depth sensors and color cameras have been widely developed with the advent of a new type of sensor, time-of-flight (TOF) depth sensor. The physical limitation of depth sensors usually generates low resolution images compared to corresponding color images. Therefore, the pre-processing module, such as camera calibration, three dimensional warping, and hole filling, is necessary to generate the high resolution depth map that is placed in the image plane of the color image. However, the result of the pre-processing step is usually inaccurate due to errors from the camera calibration and the depth measurement. Therefore, in this paper, we present a depth map upsampling method robust these errors. First, the confidence of the measured depth value is estimated by the interrelation between the color image and the pre-upsampled depth map. Then, the detailed depth map can be generated by the modified kernel regression method which exclude depth values having low confidence. Our proposed algorithm guarantees the high quality result in the presence of the camera calibration errors. Experimental comparison with other data fusion techniques shows the superiority of our proposed method.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.38 no.4
• /
• pp.579-586
• /
• 2018

Short-term prediction of travel speed has been widely studied using data-driven non-parametric techniques. There is, however, a lack of research on the prediction aimed at urban areas due to their complex dynamics stemming from traffic signals and intersections. The purpose of this study is to develop a hybrid approach combining ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) for predicting urban travel speed. The EEMD decomposes the time-series data of travel speed into intrinsic mode functions (IMFs) and residue. The decomposed IMFs represent local characteristics of time-scale components and they are predicted using an ANN, respectively. The IMFs can be predicted more accurately than their original travel speed since they mitigate the complexity of the original data such as non-linearity, non-stationarity, and oscillation. The predicted IMFs are summed up to represent the predicted travel speed. To evaluate the proposed method, the travel speed data from the dedicated short range communication (DSRC) in Daegu City are used. Performance evaluations are conducted targeting on the links that are particularly hard to predict. The results show the developed model has the mean absolute error rate of 10.41% in the normal condition and 25.35% in the break down for the 15-min-ahead prediction, respectively, and it outperforms the simple ANN model. The developed model contributes to the provision of the reliable traffic information in urban transportation management systems.

• Genomics & Informatics
• /
• v.10 no.1
• /
• pp.1-8
• /
• 2012

Recently, the technologies of DNA sequence variation and gene expression profiling have been used widely as approaches in the expertise of genome biology and genetics. The application to genome study has been particularly developed with the introduction of the nextgeneration DNA sequencer (NGS) Roche/454 and Illumina/ Solexa systems, along with bioinformation analysis technologies of whole-genome $de$ $novo$ assembly, expression profiling, DNA variation discovery, and genotyping. Both massive whole-genome shotgun paired-end sequencing and mate paired-end sequencing data are important steps for constructing $de$ $novo$ assembly of novel genome sequencing data. It is necessary to have DNA sequence information from a multiplatform NGS with at least $2{\times}$ and $30{\times}$ depth sequence of genome coverage using Roche/454 and Illumina/Solexa, respectively, for effective an way of de novo assembly. Massive shortlength reading data from the Illumina/Solexa system is enough to discover DNA variation, resulting in reducing the cost of DNA sequencing. Whole-genome expression profile data are useful to approach genome system biology with quantification of expressed RNAs from a wholegenome transcriptome, depending on the tissue samples. The hybrid mRNA sequences from Rohce/454 and Illumina/Solexa are more powerful to find novel genes through $de$ $novo$ assembly in any whole-genome sequenced species. The $20{\times}$ and $50{\times}$ coverage of the estimated transcriptome sequences using Roche/454 and Illumina/Solexa, respectively, is effective to create novel expressed reference sequences. However, only an average $30{\times}$ coverage of a transcriptome with short read sequences of Illumina/Solexa is enough to check expression quantification, compared to the reference expressed sequence tag sequence.