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

• KSBB Journal
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• v.23 no.1
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• pp.90-95
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• 2008

In this study, a combined process of sequential anaerobic-aerobic digestion (SAAD), fluidized-bed bioreactor (FBBR), and ultrafiltration (UF) for the treatment of small scale food waste leachate was developed and evaluated. The SAAD process was tested for performance and stability by subjecting leachate from food waste to a two-phase anaerobic digestion. The main process used FBBR composed of aerators for oxygen supply and fluidization, three 5 ton reaction chambers containing an aerobic mesophilic microorganism immobilized in PE (polyethylene), and a sedimentation chamber. The HRTs (hydraulic retention time) of the combined SAAD-FBBR-UF process were 30, 7, and 1 day, and the operation temperature was set to the optimal one for microbial growth. The pilot process maintained its performance even when the CODcr of input leachate fluctuated largely. During the operation, average CODcr, TKN, TP, and salt of the effluent were 1,207mg/L, 100mg/L, 50 mg/L, and 0.01 %, which corresponded to the removal efficiencies of 99.4%, 98.6%, 89.6%, and 98.5%, respectively. These results show that the developed process is able to manage high concentration leachate from food waste and remove CODcr, TKN, TP, and salt effectively.

• Journal of Oral Medicine and Pain
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• v.30 no.2
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• pp.269-276
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• 2005

This in vitro study evaluated the influence of a flowable composite resin on the tensile bond strength of resin to enamel and dentin treated with Er:YAG laser and diamond bur. 96 Buccal enamel and mid-coronal dentin were laser-irradiated using an Er:YAG laser and treated with diamond bur. Each groups(48) were divided two small groups depends on acid-etching procedure. Light-cure flowable resin(Metafil Flo) and self-cure resin(Clearfil FII New Bond) were used in this study. After surface etching with 37% phosphoric acid and the application of an adhesive system, specimens were prepared with a hybrid composite resin. After 24hours storage in distilled water at 37$^{\circ}C$, all samples were submitted to the tensile bond strength evaluation, using a universal testing machine(Z020, Zwick, Germany). The obtained results were as follows: 1. TBS of acid-etching group were higher than those of non-etching group in both enamel and dentin treated with Er:YAG laser and diamond bur. Laser 'conditioning' was clearly less effective than acid-etching. Moreover, acid etching lased enamel and dentin significantly improved the microTBS of M-Flo. 2. In enamel, TBS of laser-irradiated group were lower than those of bur-prepared group. However, in flowable resin subgroup, there were not differed those between two groups in dentin. 3. In laser-treated group, TBS of flowable composite resin were higher than those of self-curing resin in dentin, however, there was no difference in enamel. From this study, we can conclude that the self- and light-cure composite resin bonded significantly less effective to lased than to bur-cut enamel and dentin, and that acid-etch procedure remains mandatory even after laser ablation. We suggest that Er:YAG laser was useful for preparing dentin cavity with flowable resin filling.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.1-10
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• 2013

RFID(Radio Frequency Identification) system is non-contact identification technology. A basic RFID system consists of a reader, and a set of tags. RFID tags can be divided into active and passive tags. Active tags with power source allows their own operation execution and passive tags are small and low-cost. So passive tags are more suitable for distribution industry than active tags. A reader processes the information receiving from tags. RFID system achieves a fast identification of multiple tags using radio frequency. RFID systems has been applied into a variety of fields such as distribution, logistics, transportation, inventory management, access control, finance and etc. To encourage the introduction of RFID systems, several problems (price, size, power consumption, security) should be resolved. In this paper, we proposed an algorithm to significantly alleviate the collision problem caused by simultaneous responses of multiple tags. In the RFID systems, in anti-collision schemes, there are three methods: probabilistic, deterministic, and hybrid. In this paper, we introduce ALOHA-based protocol as a probabilistic method, and Tree-based protocol as a deterministic one. In Aloha-based protocols, time is divided into multiple slots. Tags randomly select their own IDs and transmit it. But Aloha-based protocol cannot guarantee that all tags are identified because they are probabilistic methods. In contrast, Tree-based protocols guarantee that a reader identifies all tags within the transmission range of the reader. In Tree-based protocols, a reader sends a query, and tags respond it with their own IDs. When a reader sends a query and two or more tags respond, a collision occurs. Then the reader makes and sends a new query. Frequent collisions make the identification performance degrade. Therefore, to identify tags quickly, it is necessary to reduce collisions efficiently. Each RFID tag has an ID of 96bit EPC(Electronic Product Code). The tags in a company or manufacturer have similar tag IDs with the same prefix. Unnecessary collisions occur while identifying multiple tags using Query Tree protocol. It results in growth of query-responses and idle time, which the identification time significantly increases. To solve this problem, Collision Tree protocol and M-ary Query Tree protocol have been proposed. However, in Collision Tree protocol and Query Tree protocol, only one bit is identified during one query-response. And, when similar tag IDs exist, M-ary Query Tree Protocol generates unnecessary query-responses. In this paper, we propose Adaptive M-ary Query Tree protocol that improves the identification performance using m-bit recognition, collision information of tag IDs, and prediction technique. We compare our proposed scheme with other Tree-based protocols under the same conditions. We show that our proposed scheme outperforms others in terms of identification time and identification efficiency.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• Journal of radiological science and technology
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• v.33 no.3
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• pp.277-282
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• 2010

The study is to verify non-uniform dose distribution in Field-In-Field (FIF) technique using two-dimensional ionization chamber (MatriXX, Wellhofer Dosimetrie, Germany) for breast tangential irradiation. The MatriXX and an inverse planning system (Eclipse, ver 6.5, Varian, Palo Alto, USA) were used. Hybrid plans were made from the original twenty patients plans. To verify the non-uniform dose distribution in FIF technique, each portal prescribed doses (90 cGy) was delivered to the MatriXX. The measured doses on the MatriXX were compared to the planned doses. The quantitative analyses were done with a commercial analyzing tool (OmniPro IMRT, ver. 1.4, Wellhofer Dosimetrie, Germany). The delivered doses at the normalization points were different to average 1.6% between the calculated and the measured. In analysis of line profiles, there were some differences of 1.3-5.5% (Avg: 2.4%), 0.9-3.9% (Avg: 2.5%) in longitudinal and transverse planes respectively. For the gamma index (criteria: 3 mm, 3%) analyses, there were shown that 90.23-99.69% (avg: 95.11%, std: 2.81) for acceptable range ($\gamma$-index $\geq$ 1) through the twenty patients cases. In conclusion, through our study, we have confirmed the availability of the FIF technique by comparing the calculated with the measured using MatriXX. In the future, various clinical applications of the FIF techniques would be good trials for better treatment results.

• KSBB Journal
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• v.21 no.3
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• pp.204-211
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• 2006

For the production of the recombinant human interferon-gamma(rhIFN-${\gamma}$) in Escherichia coli, human glucagon and ferritin heavy chain were used as fusion partners. Even though rhIFN-${\gamma}$ is expressed as an inclusion body form in E. coli because of strong hydrophobicity of itself, over 50% of fused rhIFN-${\gamma}$ was expressed as soluble form in E. coli $Origami^{TM}$(DE3) harboring pT7FH(HE)-IFN-${\gamma}$ which encodes ferritin heavy chain-fused rhIFN-${\gamma}$. In the case of using glucagon-ferritin heavy chain hybrid mutant as a fusion partner, 6X His-tag was additionally introduced to N-terminus of GFHM(HE)-IFN-${\gamma}$ for enhancing purification yields of rhIFN-${\gamma}$. Fusion protein HGFHM(HE)-IFN-${\gamma}$ with two 6X His-tag was more effectively bound to Ni-NTA agarose bead than GFHM(HE)-IFN-${\gamma}$ with a 6X His-tag. rhIFN-${\gamma}$ was completely purified from enterokinase-treated HGFHM(HE)-IFN-${\gamma}$ by Ni-NTA affinity column. For high-level production of rhIFN-${\gamma}$, glucose was used as the sole carbon source with simple exponential feeding rate($2.4{\sim}7.2g/h$) in fed-batch process. The effective lactose concentration for the expression of the rhIFN-${\gamma}$ was $10{\sim}20mM$. Under the fed-batch culture conditions, rhIFN-${\gamma}$ production yield reached 11 g DCW/L for 6 hours after lactose induction.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.