• Journal of Korean Society of Disaster and Security
• /
• v.11 no.2
• /
• pp.83-89
• /
• 2018

The significant sedimentation at the estuary in the eastern coast of Korea frequently causes river mouth occlusion where disconnection between the river and sea is observed. River mouth occlusion causing watershed retention raises the environmental risk of the area as it impairs water quality and threatens the area's safety in the event of floods. This study proposes a plan to maintain stability of river channel and flow of flood discharge at the estuary with loss of its function for disaster prevention. To this end, the study tries to change the location and width of stream path, focusing on the center line of stream near the sand bar of river mouth. This allows to identify a shape of stream path that leads the most stable flow. To review the result, this study uses the SRH-2D, a model for two-dimensional hydraulic analysis, and conduct numeric simulation. The simulation result showed that the most effective plan for maintaining the stable flow of running water without having the area sensitive to changes in hydraulic characteristics is to lower the overall river bed height of the sand bar near the center line of stream to a equal level.

• The Journal of Korean Medicine
• /
• v.42 no.2
• /
• pp.1-20
• /
• 2021

Objectives: The purpose of this study was to confirm the relavance between the dependent variables and the treatment effects of nonsurgical spinal decompression(NSD). Methods: 105 patients suffering from disc herniation and treated with NSD were investigated and analyzed. Results: The intention of retreatment showed a tendency to be higher in having occupation, western treatment only before NSD(WTB) and non-western treatment(WTN) group. As the number of NSD increased, satisfaction score and the Numeric Rating Scale(NRS) difference increased and the NRS after NSD decreased. On the other hand, as western treatment after NSD increased, satisfaction score and the NRS difference decreased and the NRS after NSD increased. The odds ratio of having intention of retreatment was lower in western treatment only after NSD(WTA) group than WTN group. The NRS difference showed a high tendency in the age group of 20s, 60s, and 70s and older. The NRS difference of group with NSD more than 10 times was higher than that of the group with less than 10 times. Satisfaction score of WTN and WTB group was higher than that of WTA group. Adjusted NRS after NSD was the lowest in non-western treatment group and the highest in WTA group. Adjusted NRS after NSD was the lowest in the group with NSD over 21 times, and the NRS after NSD increased as the number of NSD decreased. Conclusion: This study included patients with cervical or lumbar disc herniation and showed that occupation, the number of NSD, western treatment and age statistically affected the treatment effect.

• Journal of The Korean Society of Integrative Medicine
• /
• v.11 no.3
• /
• pp.171-183
• /
• 2023

Purpose : This study sought to investigate research trends regarding joint mobilization type among patients with shoulder joint diseases. Methods : A scoping review was conducted according to the five steps outlined by Arskey and O'Malley and PRISMA-ScR. We searched six domestic databases (ScienceOn, DBpia, Riss, Kmbase, Kiss, KCI) and three international databases (CINAHL, Pubmed, Cochrane central) between 2013 and June 2023. The keyword terms used were 'joint mobilization', 'Kaltenborn', 'Maitland', 'Mulligan', and 'shoulder joint'. Results : There were a total of 44 studies that investigated the topic, and these were divided into quantitative analysis and topic analysis. In terms of publication year, the number of studies within the last five years has increased more than compared to the previous five years, with most of them being randomized clinical trials. In shoulder joint diseases, it was found that the majority of joint movement studies focused on adhesive joint cystitis and shoulder collision syndrome. The Mulligan concept was the most commonly studied type of joint motion. The dependent variables used included pain, joint function (disability), and muscle function. The visual analog scale was the most commonly used for the pain variable, followed by the numeric rating scale. For joint function and disability variables, range of motion was the most commonly used, followed by shoulder pain and disability index, and disabilities of the arm, shoulder, and hand. For muscle function, variables such as muscle tone, strength, and activity were used. Conclusion : We believe that findings of this scoping review can serve as valuable mapping data for joint mobilization research on shoulder joint diseases. Further studies including systematic reviews and meta-analyses based on these results are recommended.

• Journal of Korean Medicine Rehabilitation
• /
• v.34 no.1
• /
• pp.83-95
• /
• 2024

Objectives This review was conducted to evaluate the therapeutic effects of manual acupuncture (MA) for shoulder impingement syndrome (SIS). Methods We searched 12 electronic databases (DBpia, Korean studies Information Service System [KISS], Oriental Medicine Advanced Searching Integrated System [OASIS], Research Information Sharing Service [RISS], China National Knowledge Infrastructure [CNKI], CINAHL, Clinical Key, Cochrane Library, Embase, JAMA, PubMed, Web of Science) to find randomized-controlled clinical trials (RCTs) investigating therapeutic effects of MA for treating SIS. Shoulder Pain and Disability Index scores and numeric pain rating scale or visual analogue scale were analyzed as the main evaluation criteria. Results Among 181 studies, 169 were screened and only 12 RCTs were eligible in our review. Finally, 11 RCTs could be statistically analyzed. MA was more effective than sham treatment and physical therapy in terms of reducing pain (p=0.003, p=0.0007 each). Electroacupuncture (EA) showed more significant effect than physical therapy (PT) for improving shoulder pain (p<0.00001) and shoulder functionality (p<0.00001). Conclusions These results suggest that MA and EA could be superior option for treating SIS than sham treatment or PT. However this review has its limitations due to the small sample size and lack of well-designed RCTs that were included in the study. Further well-designed RCTs are necessary to provide high-level evidence.

• Journal of Intelligence and Information Systems
• /
• v.23 no.2
• /
• pp.19-38
• /
• 2017

Collaborative filtering (CF) algorithm has been popularly used for implementing recommender systems. Until now, there have been many prior studies to improve the accuracy of CF. Among them, some recent studies adopt 'hybrid recommendation approach', which enhances the performance of conventional CF by using additional information. In this research, we propose a new hybrid recommender system which fuses CF and the results from the social network analysis on trust and distrust relationship networks among users to enhance prediction accuracy. The proposed algorithm of our study is based on memory-based CF. But, when calculating the similarity between users in CF, our proposed algorithm considers not only the correlation of the users' numeric rating patterns, but also the users' in-degree centrality values derived from trust and distrust relationship networks. In specific, it is designed to amplify the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the trust relationship network. Also, it attenuates the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the distrust relationship network. Our proposed algorithm considers four (4) types of user relationships - direct trust, indirect trust, direct distrust, and indirect distrust - in total. And, it uses four adjusting coefficients, which adjusts the level of amplification / attenuation for in-degree centrality values derived from direct / indirect trust and distrust relationship networks. To determine optimal adjusting coefficients, genetic algorithms (GA) has been adopted. Under this background, we named our proposed algorithm as SNACF-GA (Social Network Analysis - based CF using GA). To validate the performance of the SNACF-GA, we used a real-world data set which is called 'Extended Epinions dataset' provided by 'trustlet.org'. It is the data set contains user responses (rating scores and reviews) after purchasing specific items (e.g. car, movie, music, book) as well as trust / distrust relationship information indicating whom to trust or distrust between users. The experimental system was basically developed using Microsoft Visual Basic for Applications (VBA), but we also used UCINET 6 for calculating the in-degree centrality of trust / distrust relationship networks. In addition, we used Palisade Software's Evolver, which is a commercial software implements genetic algorithm. To examine the effectiveness of our proposed system more precisely, we adopted two comparison models. The first comparison model is conventional CF. It only uses users' explicit numeric ratings when calculating the similarities between users. That is, it does not consider trust / distrust relationship between users at all. The second comparison model is SNACF (Social Network Analysis - based CF). SNACF differs from the proposed algorithm SNACF-GA in that it considers only direct trust / distrust relationships. It also does not use GA optimization. The performances of the proposed algorithm and comparison models were evaluated by using average MAE (mean absolute error). Experimental result showed that the optimal adjusting coefficients for direct trust, indirect trust, direct distrust, indirect distrust were 0, 1.4287, 1.5, 0.4615 each. This implies that distrust relationships between users are more important than trust ones in recommender systems. From the perspective of recommendation accuracy, SNACF-GA (Avg. MAE = 0.111943), the proposed algorithm which reflects both direct and indirect trust / distrust relationships information, was found to greatly outperform a conventional CF (Avg. MAE = 0.112638). Also, the algorithm showed better recommendation accuracy than the SNACF (Avg. MAE = 0.112209). To confirm whether these differences are statistically significant or not, we applied paired samples t-test. The results from the paired samples t-test presented that the difference between SNACF-GA and conventional CF was statistical significant at the 1% significance level, and the difference between SNACF-GA and SNACF was statistical significant at the 5%. Our study found that the trust/distrust relationship can be important information for improving performance of recommendation algorithms. Especially, distrust relationship information was found to have a greater impact on the performance improvement of CF. This implies that we need to have more attention on distrust (negative) relationships rather than trust (positive) ones when tracking and managing social relationships between users.

• Journal of Intelligence and Information Systems
• /
• v.16 no.3
• /
• pp.77-97
• /
• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• International Journal of Highway Engineering
• /
• v.11 no.3
• /
• pp.61-74
• /
• 2009

Traffic accident risk index Computation model's development apply traffic level of significance about area of road user group, road and street network area, population group etc.. through numerical formula or model by countermeasure to reduce the occurrence rate of traffic accidents. Is real condition that is taking advantage of risk by tangent section through estimation model and by method to choose improvement way to intersection from outside the country, and is utilizing being applied in part business in domestic. However, question is brought in the accuracy being utilizing changing some to take external model in domestic real condition than individual development of model. Therefore, selection intersection estimation element through traffic accidents occurrence present condition, geometry structure, control way, traffic volume, turning traffic volume etc. in 96 intersections in this research, and select final variable through correlation analysis of abstracted estimation elements. Developed intersection design model taking advantage of signal type, numeric of lane, intersection type, analysis of variance techniques through ANOVA analysis of three variables of intersection form with selected variable lastly, in signal crossing through three class intersection, distinction variable choice risk in model, no-signal crossing risk distinction analysis model and so on develop.

• Journal of the Korean GEO-environmental Society
• /
• v.23 no.6
• /
• pp.19-23
• /
• 2022

For the substantiation and verification of studies related to the construction of a levee using riverbed soil, real-scale levee construction and experimental studies are essential. One of the most important factors in the experimental study is the reproducibility of the multiple levees with the same initial conditions. Quantitative analysis of the reproducibility should be presented. In this study, a number of physical properties (specific gravity test, sieving test, liquid-plastic limit test, compaction test, on-site Density test) for multiple embankments built with fine-grained bed soil was obtained. The collected data then used to obtain the possibility of reproducing levee through statistical analysis to suggest a process of indicating a numeric initial condition of the real-scale test. As a result of statistical analysis to verify the aforementioned process, it was confirmed that it was possible to quantitatively evaluate the reproducibility of the construction under the same conditions of embankments. This is expected to be a basic data for a full-scale embankment test using riverbed soil including other soil based real-scale tests.

• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 2007.05a
• /
• pp.379-382
• /
• 2007

The MEMS (Micro Electro Mechanical Systems) process is used in a micro/nano pattern manufacturing method. This method is based on the lithography technology. But the MEMS process has some problems such as complicated process, long processing time and high production costs. Many researchers are doing research in substitute manufacturing method to work out a solution to these problems. In this paper, we apply a dieless incremental forming technology to a substitute method of MEMS process. This dieless forming technology is using in the commercial scale sheet forming such as a prototype of automobile sheet parts. 5-axes CNC (Computerized Numeric Control) method are applied in this system to get a micro-scale dieless forming results. These 5-axes system are composed of precision AC servo motor stages (4-axes) and PZT actuator (1-axis). A PZT actuator is used in a precision actuating axis because it can be operated in the nano scale stroke resolution. This micro dieless incremental forming system has the advantage of minimization in manipulating distance and working space. As equipment and tools become smaller in size, minute inertia force and high natural frequency can be obtained. Therefore, high precision forming performance can be obtained. This allows the factory to quickly provide the customer with goods because the manufacturing system and process are reduced. To construct this micro manufacturing system, many technologies are necessary such as high stiffness frame, high precision actuating part, structural analysis, high precision tools and system control. To achieve the optimal forming quality, the micro dieless forming system is designed and made with high stiffness characteristic.

• International Journal of Control, Automation, and Systems
• /
• v.3 no.2
• /
• pp.183-194
• /
• 2005

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the premise part of the rule-based structure of the gHFNN. The consequence part of the gHFNN is designed using PNNs. We distinguish between two types of the linear fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, the models are experimented with a representative numerical example. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when comparing with other neurofuzzy models.