• Physical Therapy Rehabilitation Science
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• v.7 no.3
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• pp.114-118
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• 2018

Objective: The left and right sides of the brain has different roles. This study investigated the differences in cognitive driving ability between stroke survivors with damage to the left brain and right brain. Therefore, the purpose of this study was to compare the driving cognitive ability of left and right hemispheric drivers following stroke. Design: Cross-sectional study. Methods: The Stroke Drivers' Screening Assessment (SDSA) from the UK was translated to the Korean Stroke Drivers' Screening Assessment (K-SDSA) to meet the specific traffic environments of Korea. The SDSA is composed of 4 tasks :1) a dot cancellation task that measures concentration and visuospatial abilities necessary for driving, 2) a directional matrix task to measure spatio-temporal executive function required for driving, 3) a compass matrix task to measure accurate direction determination ability required for driving, and 4) recognition of traffic signs and reasoning ability to understanding traffic situation. The SDSA assessment time is about 30 minutes. The K-SDSA was used to compare the cognitive driving abilities between 15 stroke survivors with left and 15 stroke survivors with right brain damage. Results: There were significant differences between the persons with stroke patients with left brain lesions (right hemiplegia) compared to the persons with stroke with right brain lesions (left hemiplegia) (p<0.05). It was found that the cognitive driving ability of those with right brain damage was lower than that of the group of left brain damage. Conclusions: This research investigated the driving cognitive ability of persons with stroke. The therapists can use this information as basis for the driving test and training purposes. It could also be used as a basis to understanding if the cognitive ability of not only stroke survivors but also those with brain damage is adequate to actually drive.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.1
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• pp.50-56
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• 2010

This paper presents a new design algorithm for piece-removing dynamical system, based on 6-Sigma DMADOV technique using ARIZ and Brainstorming. Our design target is the piece-removing system installed on a mobile platform of bead-grinding equipment. The 6-Sigma DMADOV technique guides us design process according to 6 steps, i.e., Define - Measure - Analyze - Design - Optimize - Verify. A Design strategy to reduce the weight of piece-removing dynamical system will be explored by using ARIZ, i.e.,(the abbreviation of Algorithm for Inventive Problem Solving in Russian). The ARIZ will result in a final solution that the height and angle control parts for a cutting tool should be replaced by a kinematical approach, rather than complicated mechatronic approach(using motors). The Optimize step is composed of two sub-steps: (i) Generating process for obtaining several ideas of piece-removing system by using Brainstorming technique, satisfying the final solution derived from the Design step using ARIZ, and (ii) Optimizing process for selecting the most optimal idea of piece-removing system by using Pugh's matrix from the viewpoints of weight, cost and accuracy. The laststep of Verify has shown that the final design obtained by the 6-Sigma DMADOV technique with ARIZ & Brainstormingcan improve an initial design with design requirements satisfied. In this paper, we have shown that ARIZ and Brainstorming can be cooperatively merged into 6-Sigma DMADOV to give us both a formulatedproblem-solving approach and diverse candidate solutions(or ideas) without trial-and-error efforts.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.313-322
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• 2005

In this paper, we implement and verify performance monitor for parallel signal processing system, using DSP Starter Kit(DSK) of which the basic Processor is TMS302C6711 chip. The key ideas of this performance monitor is, using Real Time Data Exchange(RTDX) for the Purpose of real-time data transfer and function of DSP/BIOS, the ability to measure the Performance measure like DSP workload, memory usage, and bridge traffic. In the simulation, FFT, 2D FFT, Matrix Multiplication, and Fir Filter, which are widely used DSP algorithms, have been employed. Using performance monitor and Code Composer Studio from Texas Instrument(Tl) , the result has been recorded according to different frequencies, data sizes, and buffer sizes for a single wave file. The accuracy of our performance monitor has been verified by comparing those recorded results.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.1
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• pp.59-66
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• 2007

In this paper, we consider the non-fragile robust guaranteed cost state feedback controllers design method for descriptor systems with parameter uncertainties and static state feedback controller with multiplicative uncertainty. The sufficient condition of controller existence, the design method of non-fragile robust guaranteed cost controller, the measure of non-fragility in controller, the upper bound of guaranteed cost performance measure to minimize the guaranteed cost are presented via LMI(linear matrix inequality) technique. Also, the sufficient condition can be rewritten as LMI form in terms of transformed variables through singular value decomposition, some changes of variables, and Schur complements. Therefore, the obtained non-fragile robust guaranteed cost controller satisfies the asymptotic stability and minimizes the guaranteed cost for the closed loop descriptor systems with parameter uncertainties and controller fragility. Finally, a numerical example is given to illustrate the design method.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.313-318
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• 2016

The recommendation service is changing from client-server based internet service to social networking. Especially in recent years, it is serving recommendations with personalization to users through crowdsourcing and social networking. The social networking based systems can be classified depending on methods of providing recommendation services and purposes by using memory and model based collaborative filtering. In this study, we proposed the social network based sensibility design recommendation using associative user. The proposed method makes {user - associative design} matrix through the social network and recommends sensibility design using the memory based collaborative filtering. For the performance evaluation of the proposed method, recall and precision verification are conducted. F-measure based on recommendation of social networking is used for the verification of accuracy.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.129-142
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• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.67-84
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• 2010

In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.

• Journal of Navigation and Port Research
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• v.28 no.3
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• pp.185-191
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• 2004

The mean and Clustering are important methods of data mining, which is now widely applied to various multi-attributes problem However, feature weighting and feature selection are important in those methods bemuse features may differ in importance and such differences need to be considered in data mining with various multiful-attributes problem. In addition, in the event of arithmetic mean, which is inadequate to figure out the most fitted result for structure of evaluation with attributes that there are weighted and ranked. Moreover, it is hard to catch hold of a specific character for assume the form of user's group. In this paper. we propose a dispersion mean algorithm for evaluation of similarity measure based on the geometrical figure. In addition, it is applied to mean classified by user's group. One of the key issues to be considered in evaluation of the similarity measure is how to achieve objectiveness that it is not change over an item ranking in evaluation process.

• Journal of Fashion Business
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• v.9 no.6
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• pp.39-55
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• 2005

This study examines closely the relationship between beauty art service quality and value. And satisfaction and purchase action that they do perceive to customers who have beauty art service company's service use experience. Moreover, this study was achieved purposely to present service raising plan of good quality to beauty art company managers and business employees. First, to investigate the concept of beauty art service quality and special quality was with doctrines that have been presented through a virtue aspect to achieve this study. Moreover, the wave and beauty art service, human service relativity is a let down unlike manufacture enterprise. Further more, beauty art service by complex composition of existence and nonexistence style is sold, and it could be known by having personality consumed at the same time production. The concept of quality about beauty art service and quality that became perceived through virtue study of concept and measurement about value. Therefor, value was deduced, and could deduce measurement, the linear measure that is applied to measure this. Large majority virtue study found is measuring quality of service to 22 articles on PZB's theory, and this study corrects measurement, the linear measure that is applied in Morritt's study that is based in PZB matrix and supplements and attempted measurement to 22 items. The result measurement dimension is consisted of functional quality, technological quality, physical quality dimension. To measure this through virtue study about value that become perceive, could confirm that all expense and beauty art companies which the customer is paid, connect with offering general quality of service. Therefor, through measurement, 2 dimension was deduced by monetary value and the non-monetary value.

• Economic and Environmental Geology
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• v.53 no.5
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• pp.543-552
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• 2020

In this study, we measured groundwater fluxes with a passive flux meter and a borehole dilution test in the Upper Floridan Aquifer. In addition, the feasibility of the passive flux meter is also evaluated within matrix and non-matrix zones. The results of the PFM (5.96 ± 1.75 cm/day) showed good agreement with those of the BHD (4.68 ± 2.99 cm/day) in matrix zones, whereas the results of the PFM (9.94 ± 0.90 cm/day) showed poor agreement with those of the BHD (1817.37 ± 1795.50 cm/day) in non-matrix zones. We assumed that the groundwater passes through the sorbent material inside the PFM. However, it could not pass through the sorbent when the groundwater flux is faster than 11 cm/day. The flow might bypass between monitoring well and the PFM. The PFM used in this study might be suitable for measuring the groundwater fluxes under 11 cm/day. Therefore, more extensive research is needed in the future to measure fast groundwater fluxes (> 11 cm/day).