• Journal of Korean Orthopaedic Sports Medicine
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• v.2 no.1
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• pp.28-36
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• 2003

Purpose: To determine and to compare the effects of cyclic loading on the fixation strength of different femoral fixation methods in ACL reconstruction. Materials and Methods: Biomechanical test using an Instron(R) machine (Model No.5569. Mass, U.S.A) were carried out to compare the pull out strength of six different femoral fixation techniques after a cyclic loading in 72 Yorkshire pig knees. The graft-bone complex was cyclically loaded between 30N and 150N at 50 mm/min rate for 1000 cycles and maximal tensile testing was performed. A preload of 30N was applied to the graft along the axis of the tunnel 15 minutes. ANOVA and the Duncan multiple comparison test was used for the statistical analysis. Results: The mean maximum tensile strength of femoral fixation before and after the cyclic loading test were 1003.4$\pm$145N and 601.1$\pm$154N in hamstring-LA screw(R) group, 595.5$\pm$104N and 360.7$\pm$56N in hamstring-Bioscrew(R) group, 1431.7$\pm$135N and 710.7$\pm$114N in hamstring-Semifix(R) group, 603.6$\pm$54N and 459.1$\pm$46N in hamstring-Endobutton(R) fixation group, 1067.4$\pm$145 and 601.8$\pm$134N in the BPTB-Titanium interference screw group, and 987.1$\pm$168N and 588.7$\pm$124N in the BPTB-Bioscrew(R) group. And these data illustrated that cyclic loading reduces the maximum tensile strength by 40 $\%$, 39 $\%$, 50 $\%$, 24 $\%$, 44 $\%$, 40 $\%$ respectively. Conclusions: With the results of these experiments it should be emphasized that rehabilitation exercises after anterior cruciate ligament reconstruction should be executed with precaution as the repetitive flexion and extension of the knee would compromise the maximum tensile strength of the graft tendon.

• Restorative Dentistry and Endodontics
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• v.33 no.2
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• pp.98-106
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• 2008

The purpose of this study was to evaluate whether intracanal irrigation method could affect the adhesion between intracanal dentin and root canal filling materials (Gutta-percha/AH 26 sealer and Resilon/Epiphany sealer). Thirty extracted human incisor teeth were prepared. Canals were irrigated with three different irrigation methods as a final rinse and obturated with two different canal filling materials (G groups: Gutta-percha/AH 26 sealer, R groups: Resilon/Epiphany sealer) respectively. Group G1, R1-irrigated with 5.25% NaOCl Group G2, R2-irrigated with 5.25% NaOCl, sterile saline Group G3, R3-irrigated with 5.25% NaOCl, 17% EDTA, sterile saline Thirty obturated roots were horizontally sliced and push-out bond strength test was performed in the universal testing machine. After test, the failure patterns of the specimens were observed using Image-analyzing microscope. The results were as follows. 1. Gutta-percha/AH 26 sealer groups had significantly higher push-out bond strength compared with the Resilon/Epiphany sealer groups (p < 0.05). 2. Push-out bond strength was higher when using 17% EDTA followed by sterile saline than using NaOCl as a final irrigation solution in the Resilon/Epiphany sealer groups (p < 0.05). 3. In the failure pattern analysis, there was no cohesive failure in Group G1, G2, and R1. Gutta-percha/AH 26 sealer groups appeared to exhibit predominantly adhesive and mixed failure patterns, whereas Resilon/Epiphany sealer groups exhibited mixed failures with the cohesive failure occurred within the Resilon substrate.

• Restorative Dentistry and Endodontics
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• v.33 no.2
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• pp.148-161
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• 2008

The purpose of this study was to evaluate the effect of chlorhexidine (CHX) on microtensile bond strength (${\mu}TBS$) of dentin bonding systems. Dentin collagenolytic and gelatinolytic activities can be suppressed by protease inhibitors, indicating that MMPs (Matrix metalloproteinases) inhibition could be beneficial in the preservation of hybrid layers. Chlorhexidine (CHX) is known as an inhibitor of MMPs activity in vitro. The experiment was proceeded as follows: At first, flat occlusal surfaces were prepared on mid-coronal dentin of extracted third molars. GI (Glass Ionomer) group was treated with dentin conditioner, and then, applied with 2 % CHX. Both SM (Scotchbond Multipurpose) and SB (Single Bond) group were applied with CHX after acid-etched with 37% phosphoric acid. TS (Clearfil Tri-S) group was applied with CHX, and then, with adhesives. Hybrid composite Z-250 and resin-modified glass ionomer Fuji-II LC was built up on experimental dentin surfaces. Half of them were subjected to 10,000 thermocycle, while the others were tested immediately. With the resulting data, statistically two-way ANOVA was performed to assess the ${\mu}TBS$ before and after thermo cycling and the effect of CHX. All statistical tests were carried out at the 95 % level of confidence. The failure mode of the testing samples was observed under a scanning electron microscopy (SEM). Within limited results, the results of this study were as follows; 1. In all experimental groups applied with 2 % chlorhexidine, the microtensile bond strength increased, and thermo cycling decreased the micro tensile bond strength (P > 0.05). 2. Compared to the thermocycling groups without chlorhexidine, those with both thermocycling and chlorhexidine showed higher microtensile bond strength, and there was significant difference especially in GI and TS groups. 3. SEM analysis of failure mode distribution revealed the adhesive failure at hybrid layer in most of the specimen. and the shift of the failure site from bottom to top of the hybrid layer with chlorhexidine groups. 2 % chlorhexidine application after acid-etching proved to preserve the durability of the hybrid layer and microtensile bond strength of dentin bonding systems.

• 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.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.187-201
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• 2011

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 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
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• v.24 no.3
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• pp.221-241
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• 2018

As we enter the knowledge society, the importance of information as a new form of capital is being emphasized. The importance of information classification is also increasing for efficient management of digital information produced exponentially. In this study, we tried to automatically classify and provide tailored information that can help companies decide to make technology commercialization. Therefore, we propose a method to classify information based on Korea Standard Industry Classification (KSIC), which indicates the business characteristics of enterprises. The classification of information or documents has been largely based on machine learning, but there is not enough training data categorized on the basis of KSIC. Therefore, this study applied the method of calculating similarity between documents. Specifically, a method and a model for presenting the most appropriate KSIC code are proposed by collecting explanatory texts of each code of KSIC and calculating the similarity with the classification object document using the vector space model. The IPC data were collected and classified by KSIC. And then verified the methodology by comparing it with the KSIC-IPC concordance table provided by the Korean Intellectual Property Office. As a result of the verification, the highest agreement was obtained when the LT method, which is a kind of TF-IDF calculation formula, was applied. At this time, the degree of match of the first rank matching KSIC was 53% and the cumulative match of the fifth ranking was 76%. Through this, it can be confirmed that KSIC classification of technology, industry, and market information that SMEs need more quantitatively and objectively is possible. In addition, it is considered that the methods and results provided in this study can be used as a basic data to help the qualitative judgment of experts in creating a linkage table between heterogeneous classification systems.

• Cartoon and Animation Studies
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• s.50
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• pp.275-297
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• 2018

Japan's mechanic animation is widely known throughout the world. 1952년, Japan's first mechanic animation and the first TV animation, , has been popular since it's creation in 1952. Atom, a big hit at the time, has influenced many people. Japanese mechanic animations convey their unique traits and world view to the public In this paper, we are going to discuss the change of the Japanese mechanical design through comparison of the mechanical design, which has been booming since the 1990s in Japan; and the . I expect the results of this analysis to depict Japanese culture and thought reflected in animation, which is a good indication of worldwide cultural view of animation. unexpectedly influenced the Japanese animation industry after it screened in 1995, and there are still people constantly reinterpreting and analyzing it. This is the reaction of the audience to anticipate the mystery and endless conclusions of the work itself. The design elements of Evangelion are distinguished from other mechanical objects. Mechanic design based on human biotechnology can overcome limitations of machine and make you feel more human. The pilot 's boarding structure, which can contain human nature, is reinforced in the form of an enterprising plug, and the attitude of excavation makes humanity more prominent than a straight robot. Thus, pursues a mechanic design that can reflect human identity. can be selected as the mechanic animation of the 80's, and the "Neon Genesis Evangelion" of the 90's shows it with a completely different design. By comparing the mechanical design of two works, therefore, we examine the correlation between the message and the design of the work. presents the close relationship between the identity of the mechanical design and the contents. I would like to point out that mechanical design can be a good example and theoretical basis for the future.

• The Journal of Korean Society for Radiation Therapy
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• v.17 no.2
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• pp.105-111
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• 2005

Purpose : Daily Q.A is the important step which must be preceded in a radiation treatment. Specially, radiation output measurement and laser alignment, SSD indicator related to a patient set-up recurrence must be confirmed for a reasonable radiation treatment. Daily Q.A proceeds correctness and a prompt way, and needs an objective measurement basis. Manufacture of the device which can facilitate confirmation of output measurement and appliances check at one time was requested. Materials and Methods : Produced the phantom formal daily check device which can confirm a lot of appliances check (output measurement and laser alignment. field size, SSD indicator) with one time of set up at a time, and measurement observed a linear accelerator (4 machine) for four months and evaluated efficiency. Results : We were able to confirm an laser alignment, field size, SSD indicator check at the same time, and out put measurement was possible with the same set up, so daily Q.A time was reduced, and we were able to confirm an objective basis about each item measurement. As a result of having measured for four months, output measurement within ${\pm}2%$, and measured laser alignment, field size, SSD indicator in range within ${\pm}1mm$. Conclusion : We can enforce output measurement and appliances check conveniently, and time was reduced and was able to raise efficiency of business. We were able to bring a cost reduction by substitution expensive commercialized equipment. Further It is necessary to makes a product as strong and slight materials, and improve convenience of use.