• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.8
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• pp.80-88
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• 2007

High adaptability and scalability are two critical issues in implementing a very complex system in a single chip. To obtain high adaptability and scalability, novel system design methodology known as communication-based system design has gained large attention from SoC designers. NoC (Network-on-Chip) is such an on-chip communication-based design approach for the next generation SoC design. To provide high adaptability and scalability, NoCs employ network interfaces and routers as their main communication structures and transmit and receive packetized data over such structures. However, data packetization, and routing overhead in terms of run time and area may cost too much compared with conventional SoC communication structure. Therefore, in this research, we propose a novel methodology which automatically generates a hybrid communication structure. In this work, we map traditional pin-to-pin wiring structure for frequent and timing critical communication, and map flexible and scalable structure for infrequent, or highly variable communication patterns. Even though, we simplify the communication structure significantly through our algorithm the connectivity or the scalability of the communication modules are almost maintained as the original NoC design. Using this method, we could improve the timing performance by 49.19%, and the area taken by the communication structure has been reduced by 24.03%.

• Journal of the Ergonomics Society of Korea
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• v.34 no.4
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• pp.313-326
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• 2015

Objective: The objective of this research is to quantitatively analyze muscle activities of arm and shoulder, according to direction in various types of one-handed manual material handling, based on surface electromyography. Background: Workers in industrial sites frequently carry out one-handed manual material handling using arm and shoulder muscles. Therefore, chronic load and accumulated fatigue occur to arm and shoulder muscles, which becomes a main cause of upper arm and shoulder musculoskeletal disorders. The shoulder muscles have widely range of motion, and complex interactions take place among various muscles including rotator cuff muscles. In this regard, research on interactions among should muscles, according to such various dynamic motions, is required. Method: Ten male subjects in their 20s participated in this research. This research considered upward, downward, leftward, rightward, forward and backward directions and fourteen muscles around arm and shoulder (biceps brachii and trapezius, etc.) as independent variables. The mean muscle activity was set as the dependent variable. This research extracted $4^{th}{\sim}7^{th}$ repetition signals according to ten times of repetitive muscle contraction, and analyzed the muscle activity concerned using the envelope detection technique. Results: The mean muscle activity of upward direction was analyzed highly statistically significant. The reason is that the effect of gravity works to arm and shoulder muscles. Also, it is conjectured that deformation of coracoacromial ligament was caused, and its contact pressure increased, due mainly to the shoulder flexion, and therefore load was analyzed high. Muscle activity was analyzed significantly low, according to concentric ballistic motion used in the concentric contraction phase by storing elastic energy in the eccentric contraction phase with a motion to bring the weight to the front of subject's body as to downward, leftward and backward directions. Because, elbow joint's flexion-extension motions mainly occurred, biceps brachii was analyzed high muscle activity as the prime mover. Conclusion: The information on the quantitative load of muscles can be applied to ergonomic work design for one-handed manual material handling to minimize muscle load. Application: This research has effectively identified muscle activity according to dynamic contraction by applying an envelope detection technique. The results can be used for ergonomic work design to minimize muscle load during the one-handed manual material handling, according to each direction. The research results are expected to be used for musculoskeletal disorder prevention and physiotherapy in the rehabilitation medical field, based on the muscle load of arm and shoulder in various directions.

• Journal of Korea Soil Environment Society
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• v.1 no.1
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• pp.89-102
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• 1996

An integrated model is presented to describe underground flow and mass transport, using a multicomponent multiphase approach. The comprehensive governing equation is derived considering mass and force balances of chemical species over four phases(water, oil, air, and soil) in a schematic elementary volume. Compact and systemati notations of relevant variables and equations are introduced to facilitate the inclusion of complex migration and transformation processes, and variable spatial dimensions. The resulting nonlinear system is solved by a multidimensional finite element code. The developed code with dynamic array allocation, is sufficiently flexible to work across a wide spectrum of computers, including an IBM ES 9000/900 vector facility, SP2 cluster machine, Unix workstations and PCs, for one-, two and three-dimensional problems. To reduce the computation time and storage requirements, the system equations are decoupled and solved using a banded global matrix solver, with the vector and parallel processing on the IBM 9000. To avoide the numerical oscillations of the nonlinear problems in the case of convective dominant transport, the techniques of upstream weighting, mass lumping, and elementary-wise parameter evaluation are applied. The instability and convergence criteria of the nonlinear problems are studied for the one-dimensional analogue of FEM and FDM. Modeling capacity is presented in the simulation of three dimensional composite multiphase TCE migration. Comprehesive simulation feature of the code is presented in a companion paper of this issue for the specific groundwater or flow and contamination problems.

• Journal of Chest Surgery
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• v.31 no.2
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• pp.216-219
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• 1998

Ebstein's anomaly is a complex malformation that can be treated by various surgical techniques, either repair or replacement of the abnormal tricuspid valve, with variable results. The essence of the malformation is the downward displacement of the septal and posterior leaflets into the ventricle, resulting in the formation of an atrialized portion of the right ventricle. The aim of surgical repair is to correct the tricuspid valve dysfunction and to plicate the atrialized portion of the right ventricle A 12-months old female was admitted with the diagnosis of Carpentier type A of Ebstein's anomaly with severe tricuspid regurgitation. She successfully underwent operation with vertical plication of right ventricle and reimplantation of tricuspid leaflets. Postoperatively cardiac size was significantly reduced and tricuspid regurgitation was trivial in echocardiography. She was diacharged the 14th postoperative day.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.629-635
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• 2019

In Deep Learning models derivative is implemented by error back-propagation which enables the model to learn the error and update parameters. It can find the global (or local) optimal points of parameters even in the complex models taking advantage of a huge improvement in computing power. However, deliberately generated data points can 'fool' models and degrade the performance such as prediction accuracy. Not only these adversarial examples reduce the performance but also these examples are not easily detectable with human's eyes. In this work, we propose the method to detect adversarial datasets with random noise addition. We exploit the fact that when random noise is added, prediction accuracy of non-adversarial dataset remains almost unchanged, but that of adversarial dataset changes. We set attack methods (FGSM, Saliency Map) and noise level (0-19 with max pixel value 255) as independent variables and difference of prediction accuracy when noise was added as dependent variable in a simulation experiment. We have succeeded in extracting the threshold that separates non-adversarial and adversarial dataset. We detected the adversarial dataset using this threshold.

• Proceedings of the Korean Vacuum Society Conference
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• 2011.02a
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• pp.17-18
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• 2011

Plasma in liquid phase has attracted great attention in the last few years by the wide domain of applications in material processing, decomposition of organic and inorganic chemical compounds and sterilization of water. The plasma in liquid is characterized by three main regions which interact each - other during the plasma operation: the liquid phase, which supply the plasma gas phase with various chemical compounds and ions, the plasma in the gas phase at atmospheric pressure and the interface between these two regions. The most complex region, but extremely interesting from the fundamental, chemical and physical processes which occur here, is the boundary between the liquid phase and the plasma gas phase. In our laboratory, plasma in liquid which behaves as a glow discharge type, is generated by using a bipolar pulsed power supply, with variable pulse width, in the range of 0.5~10 ${\mu}s$ and 10 to 30 kHz repetition rate. Plasma in water and other different solutions was characterized by electrical and optical measurements. Strong emissions of OH and H radicals dominate the optical spectra. Generally water with 500 ${\mu}S/cm$ conductivity has a breakdown voltage around 2 kV, depending on the pulse width and the repetition rate of the power supply. The characteristics of the plasma initiated in ultrapure water between pairs of different materials used for electrodes (W and Ta) were investigated by the time-resolved optical emission and the broad-band absorption spectroscopy. The deexcitation processes of the reactive species formed in the water plasma depend on the electrode material, but have been independent on the polarity of the applied voltage pulses. Recently, Coherent anti-Stokes Raman Spectroscopy method was employed to investigate the chemistry in the liquid phase and at the interface between the gas and the liquid phases of the solution plasma system. The use of the solution plasma allows rapid fabrication of the metal nanoparticles without being necessary the addition of different reducing agents, because plasma in the liquid phase provides a reaction field with a highly excited energy radicals. We successfully synthesized gold nanoparticles using a glow discharge in aqueous solution. Nanoparticles with an average size of less than 10 nm were obtained using chlorauric acid solutions as the metal source. Carbon/Pt hybrid nanostructures have been obtained by treating carbon balls, synthesized in a CVD chamber, with hexachloro- platinum acid in a solution plasma system. The solution plasma was successfully used to remove the template remained after the mesoporous silica synthesis. Surface functionalization of the carbon structures and the silica surface with different chemical groups and nanoparticles, was also performed by processing these materials in the liquid plasma.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.7-12
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• 2012

Recently the medical field to efficiently process the vast amounts of information to decision trees, neural networks, Bayesian Networks, including the application method of various data mining techniques are investigated. In addition, the basic personal information or patient history, family history, in addition to information such as MRI, HRCT images and additional information to collect and leverage in the diagnosis of disease, improved diagnostic accuracy is to promote a common status. But in real world situations that affect the results much because of the variable exists for a particular data mining techniques to obtain information through the enemy can be seen fairly limited. Medical images were taken as well as a minor can not give a positive impact on the diagnosis, but the proportion increased subjective judgments by the automated system is to deal with difficult issues. As a result of a complex reality, the situation is more advantageous to deal with the relative probability of the multivariate model based on Bayesian network, or TAN in the K2 search algorithm improves due to expansion model has been proposed. At this point, depending on the type of search algorithm applied significantly influenced the performance characteristics of the extended Bayesian network, the performance and suitability of each technique for evaluation of the facts is required. In this paper, we extend the Bayesian network for diagnosis of diseases using the same data were carried out, K2, TAN and changes in search algorithms such as classification accuracy was measured. In the 10-fold cross-validation experiment was performed to compare the performance evaluation based on the analysis and the onset of high-risk classification for patients with HRCT images could be possible to identify high-risk data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5164-5171
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• 2011

In fuzzy modeling for nonlinear process, typically using the given data, the fuzzy rules are formed by the input variables and the space division by selecting the input variable and dividing the input space for each input variables. The premise part of the fuzzy rule is identified by selection of the input variables, the number of space division and membership functions and the consequent part of the fuzzy rule is identified by polynomial functions in the form of simplified and linear inference. In general, formation of fuzzy rules for nonlinear processes using the given data have the problem that the number of fuzzy rules exponentially increases. To solve this problem complex nonlinear process can be modeled by separately forming the fuzzy rules by means of fuzzy division of each input space. Therefore, this paper utilizes individual input space to generate fuzzy rules. The premise parameters of the fuzzy rules are identified by Min-Max method using the minimum and maximum values of input data set and membership functions are used as a series of triangular, gaussian-like, trapezoid-type membership functions. And lastly, using the data which is widely used in nonlinear process we evaluate the performance and the system characteristics.

• Asia pacific journal of information systems
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• v.23 no.4
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• pp.103-128
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• 2013

When a consumer needs a product or service and multiple sellers are available online, the process of selecting a seller to buy online from is complex since the process involves many behavioral dimensions that have to be taken into account. As a part of this selection process, consumers may set minimum trust expectation that can be used to screen out less trustworthy sellers. In the previous research, the level of consumers' trust expectation has been anchored on two important factors: product involvement and perceived risk. Product involvement refers to the extent to which a consumer perceives a specific product important. Thus, the higher product involvement may result in the higher trust expectation in sellers. On the other hand, other related studies found that when consumers perceived a higher level of risk (e.g., credit card fraud risk), they set higher trust expectation as well. While abundant research exists addressing the relationship between product involvement and perceived risk, little attention has been paid to the integrative view of the link between the two constructs and their impacts on the trust expectation. The present paper is a step toward filling this research gap. The purpose of this paper is to understand the process by which a consumer chooses an online merchant by examining the relationships among product involvement, perceived risk, trust expectation, and intention to buy from an e-tailer. We specifically focus on the mediating role of perceived risk in the relationships between enduring product involvement and the trust expectation. That is, we question whether product involvement affects the trust expectation directly without mediation or indirectly mediated by perceived risk. The research model with four hypotheses was initially tested using data gathered from 635 respondents through an online survey method. The structural equation modeling technique with partial least square was used to validate the instrument and the proposed model. The results showed that three out of the four hypotheses formulated were supported. First, we found that the intention to buy from a digital storefront is positively and significantly influenced by the trust expectation, providing support for H4 (trust expectation ${\rightarrow}$ purchase intention). Second, perceived risk was found to be a strong predictor of trust expectation, supporting H2 as well (perceived risk ${\rightarrow}$ trust expectation). Third, we did not find any evidence of direct influence of product involvement, which caused H3 to be rejected (product involvement ${\rightarrow}$ trust expectation). Finally, we found significant positive relationship between product involvement and perceived risk (H1: product involvement ${\rightarrow}$ perceived risk), which suggests that the possibility of complete mediation of perceived risk in the relationship between enduring product involvement and the trust expectation. As a result, we conducted an additional test for the mediation effect by comparing the original model with the revised model without the mediator variable of perceived risk. Indeed, we found that there exists a strong influence of product involvement on the trust expectation (by intentionally eliminating the variable of perceived risk) that was suppressed (i.e., mediated) by the perceived risk in the original model. The Sobel test statistically confirmed the complete mediation effect. Results of this study offer the following key findings. First, enduring product involvement is positively related to perceived risk, implying that the higher a consumer is enduringly involved with a given product, the greater risk he or she is likely to perceive with regards to the online purchase of the product. Second, perceived risk is positively related to trust expectation. A consumer with great risk perceptions concerning the online purchase is likely to buy from a highly trustworthy online merchant, thereby mitigating potential risks. Finally, product involvement was found to have no direct influence on trust expectation, but the relationship between the two constructs was indirect and mediated by the perceived risk. This is perhaps an important theoretical integration of two separate streams of literature on product involvement and perceived risk. The present research also provides useful implications for practitioners as well as academicians. First, one implication for practicing managers in online retail stores is that they should invest in reducing the perceived risk of consumers in order to lower down the trust expectation and thus increasing the consumer's intention to purchase products or services. Second, an academic implication is that perceived risk mediates the relationship between enduring product involvement and trust expectation. Further research is needed to elaborate the theoretical relationships among the constructs under consideration.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.