• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.11
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• pp.64-74
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• 1999

A new clock skew optimization for clock routing using link-edge insertion is proposed in this paper. It satisfies the given skew bound and prevent the total wire length from increasing. As the clock skew is the major constraint for high speed synchronous ICs, it must be minimized in order to obtain high performance. But clock skew minimization can increase total wire length, therefore clock routing is performed within the given skew bound which can not induce the malfunction. Clock routing under the specified skew bound can decrease total wire length Not only total wire length and delay time minimization algorithm using merging point relocation method but also clock skew reduction algorithm using link-edge insertion technique between two nodes whose delay difference is large is proposed. The proposed algorithm construct a new clock routing topology which is generalized graph model while previous methods uses only tree-structured routing topology. A new cost function is designed in order to select two nodes which constitute link-edge. Using this cost function, delay difference or clock skew is reduced by connecting two nodes whose delay difference is large and distance difference is short. Furthermore, routing topology construction and wire sizing algorithm is developed to reduce clock delay. The proposed algorithm is implemented in C programming language. From the experimental results, we can get the delay reduction under the given skew bound.

• Journal of the Korean Dietetic Association
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• v.4 no.2
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• pp.235-244
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• 1998

Hand washing is an important component of hygiene program for food handlers. Hands can be a source of direct or indirect contamination of foods with pathogenic microorganisms. In this study, the effectiveness of hand washing methods and the use of 70% alcohol solution against transient skin bacteria was tested in an university foodservice facility. 70% alcohol solution is sprayed for 5 seconds automatically when hands are placed in the dispenser. Samples were taken using swab technique in meat cutting area, vegetable trimming area, and vegetable cutting area: before and after washing hands according to planned methods, and after being sprayed with 70% alcohol solution after washing hands. The bacteriological analysis of total plate counts, coliform, fecal coliform of food handlers' hands was done. Statistical data analysis was completed with Mann-Whitney U test and Kruskal-Wallis model using the SPSS program. The levels of initial contamination of workers' hand were significantly different by the work areas($x^2$=9.156, p<0.01). Workers in the vegetable trimming area had more heavily soiled hands than in the other work areas. Mean of TPC counts and coliform was 8.97×$10^5$ CFU/12.4$cm^2$, 2.93×$10^2$ MPN/12.4$cm^2$ respectively, but fecal coliform was not detected. Transient bacteria were removed from hands after washing and using 70% alcohol solution but were not removed completely. Mean reduction percentage in TPC varied among work areas and ranged from 93.19% to 94.99%, and in coliforms from 97.31% to 100%. A significant difference in TPC was found between before and after hand disinfection (Z=-2.714, p<.01) and between standardized hand washing procedures and un-standardized hand washing procedures(z=-2.301, p<.01). Subjects using the hand sanitizer showed a great elimination of TPC(99.45% reduction), but this effect was valid only after following proper washing procedures. Based on the results, the most effective hand washing method was recommended as the combination of the standardized hand washing procedure with warm-water and use of the 70% alcohol solution. The results can be used to develop hand hygiene programs and training strategies for enhancing hand hygiene practices for food handlers in foodservice operations.

• Journal of the Korea Concrete Institute
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• v.17 no.6 s.90
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• pp.975-982
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• 2005

Scientific community agrees about the fact that base Isolation provides interesting solutions to minimize the seismic risk. Reliability of such a technique is nowadays proofed by a large number of applications like public buildings, nuclear plants, bridges, etc. This paper reports the results of performance verification tests of the base isolated RC building with the laminated rubber bearings which is manufactured by enterprise in Korea. The shaking table tests were performed using a three story model scaled to 1/3 of the prototype RC apartment building. Several major earthquake records were scaled to different peak ground accelerations and used as input base excitations. Especially in this study, effect of earthquake characteristics on response reduction and effect of the intensity of excitations are studied. Through the verification tests, the validity of the applied base isolaion device and the response reduction effect against earthquakes are confirmed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.491-501
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• 2019

In side-channel analysis, which exploit physical leakage from a cryptographic device, deep learning based attack has been significantly interested in recent years. However, most of the state-of-the-art methods have been focused on classifying side-channel information in a profiled scenario where attackers can obtain label of training data. In this paper, we propose a new method based on deep learning to improve non-profiling side-channel attack such as Differential Power Analysis and Correlation Power Analysis. The proposed method is a signal preprocessing technique that reduces the noise in a trace by modifying Auto-Encoder framework to the context of side-channel analysis. Previous work on Denoising Auto-Encoder was trained through randomly added noise by an attacker. In this paper, the proposed model trains Auto-Encoder through the noise from real data using the noise-reduced-label. Also, the proposed method permits to perform non-profiled attack by training only a single neural network. We validate the performance of the noise reduction of the proposed method on real traces collected from ChipWhisperer board. We demonstrate that the proposed method outperforms classic preprocessing methods such as Principal Component Analysis and Linear Discriminant Analysis.

• Clean Technology
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• v.27 no.1
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• pp.61-68
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• 2021

Selective catalytic reduction (SCR) is widely used as a method of removing nitrogen oxide in large-capacity thermal power generation systems. Uniform mixing of the injected ammonia and the inlet flue gas is very important to the performance of the denitrification reduction process in the catalyst bed. In the present study, a computational analysis technique was applied to the ammonia injection system design process of a denitrification facility. The applied model is the denitrification facility of an 800 MW class coal-fired power plant currently in operation. The flow field to be solved ranges from the inlet of the ammonia injection system to the end of the catalyst bed. The flow was analyzed in the two-dimensional domain assuming incompressible. The steady-state turbulent flow was solved with the commercial software named ANSYS-Fluent. The nozzle arrangement gap and injection flow rate in the ammonia injection system were chosen as the design parameters. A total of four (4) cases were simulated and compared. The root mean square of the NH3/NO molar ratio at the inlet of the catalyst layer was chosen as the optimization parameter and the design of the experiment was used as the base of the optimization algorithm. The case where the nozzle pitch and flow rate were adjusted at the same time was the best in terms of flow uniformity.

• Journal of the Earthquake Engineering Society of Korea
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• v.3 no.3
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• pp.9-20
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• 1999

A method is proposed to estimate the joint damages of a steel structure from modal data using the neural networks technique. The beam-to-column connection in a steel frame structure is represented by a zero-length rotational spring of the end of the beam element, and the connection fixity factor is defined based on the rotational stiffness so that the factor may be in the range 0~1.0. Then, the severity of joint damage is defined as the reduction ratio of the connection fixity factor. Several advanced techniques are employed to develop the robust damage identification technique using neural networks. The concept of the substructural indentification is used for the localized damage assessment in the large structure. The noise-injection learning algorithm is used to reduce the effects of the noise in the modal data. The data perturbation scheme is also employed to assess the confidence in the estimated damages based on a few sets of actual measurement data. The feasibility of the proposed method is examined through a numerical simulation study on a 2-bay 10-story structure and an experimental study on a 2-story structure. It has been found that the joint damages can be reasonably estimated even for the case where the measured modal vectors are limited to a localized substructure and the data are severely corrupted with noise.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.969-977
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• 2022

In order to respond to the increase in water disasters due to climate change and urbanization, research on low impact development (LID) techniques and application to cities are expanding. The LID technique is a technology that reduces rainwater runoff in the city, controls various water disasters such as flash floods, etc. in an eco-friendly way, and restores the urban water circulation system to a natural water circulation system. However, quantitative analysis of stormwater runoff reduction through the LID technique is insufficient. Therefore, this study analyzed the ratio of the permeable area required to reduce the surface runoff of rainfall (25 mm/hr, 50 mm/hr, 100 mm/hr) with respect to the impervious watershed area of the old city using the permeable pavement. As a result of the analysis, it was found that a permeable area ratio of 7.14 to 12.63% of the total area was required for 25 mm/hr, 15.79 to 26.97% for 50 mm/hr, and 30 to 55.81% for 100 mm/hr.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.131-154
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• 2022

Recently, researches on unstructured data analysis have been actively conducted with the development of information and communication technology. In particular, topic modeling is a representative technique for discovering core topics from massive text data. In the early stages of topic modeling, most studies focused only on topic discovery. As the topic modeling field matured, studies on the change of the topic according to the change of time began to be carried out. Accordingly, interest in dynamic topic modeling that handle changes in keywords constituting the topic is also increasing. Dynamic topic modeling identifies major topics from the data of the initial period and manages the change and flow of topics in a way that utilizes topic information of the previous period to derive further topics in subsequent periods. However, it is very difficult to understand and interpret the results of dynamic topic modeling. The results of traditional dynamic topic modeling simply reveal changes in keywords and their rankings. However, this information is insufficient to represent how the meaning of the topic has changed. Therefore, in this study, we propose a method to visualize topics by period by reflecting the meaning of keywords in each topic. In addition, we propose a method that can intuitively interpret changes in topics and relationships between or among topics. The detailed method of visualizing topics by period is as follows. In the first step, dynamic topic modeling is implemented to derive the top keywords of each period and their weight from text data. In the second step, we derive vectors of top keywords of each topic from the pre-trained word embedding model. Then, we perform dimension reduction for the extracted vectors. Then, we formulate a semantic vector of each topic by calculating weight sum of keywords in each vector using topic weight of each keyword. In the third step, we visualize the semantic vector of each topic using matplotlib, and analyze the relationship between or among the topics based on the visualized result. The change of topic can be interpreted in the following manners. From the result of dynamic topic modeling, we identify rising top 5 keywords and descending top 5 keywords for each period to show the change of the topic. Existing many topic visualization studies usually visualize keywords of each topic, but our approach proposed in this study differs from previous studies in that it attempts to visualize each topic itself. To evaluate the practical applicability of the proposed methodology, we performed an experiment on 1,847 abstracts of artificial intelligence-related papers. The experiment was performed by dividing abstracts of artificial intelligence-related papers into three periods (2016-2017, 2018-2019, 2020-2021). We selected seven topics based on the consistency score, and utilized the pre-trained word embedding model of Word2vec trained with 'Wikipedia', an Internet encyclopedia. Based on the proposed methodology, we generated a semantic vector for each topic. Through this, by reflecting the meaning of keywords, we visualized and interpreted the themes by period. Through these experiments, we confirmed that the rising and descending of the topic weight of a keyword can be usefully used to interpret the semantic change of the corresponding topic and to grasp the relationship among topics. In this study, to overcome the limitations of dynamic topic modeling results, we used word embedding and dimension reduction techniques to visualize topics by era. The results of this study are meaningful in that they broadened the scope of topic understanding through the visualization of dynamic topic modeling results. In addition, the academic contribution can be acknowledged in that it laid the foundation for follow-up studies using various word embeddings and dimensionality reduction techniques to improve the performance of the proposed methodology.

• Journal of Korean Society of Transportation
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• v.19 no.4
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• pp.7-18
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• 2001

This study represents a first attempt in Korea to develop the simultaneous freight supply-demand model which considers the relationship between freight supply and demand. As the existing study was limited in one area, or the supply and the demand was separated and assumed not to affect each other, this study take it into consideration the fact that the demand affects supply and simultaneously vice versa. This approach allows us to diagnose a policy carried on and helps us to make a resonable alternative for the effectiveness of freight transportation system. To find a relationship between them, we use a method of econometrics. a structural equation theory and two stage least-squares(2SLS) estimation technique, to get rid of bias which involves two successive applications of OLS. Based on the domestic freight data, this study consider as explanatory variables a number of population(P), industry(IN), the amount of production of the mining and manufacturing industries(MMI), the rate of the effectiveness of freight capacity(LE) and the distance of an empty carriage operation(VC). This study describes well the simultaneous process of freight supply-demand system in that the increase of VC from the decrease of VC raises the cargo capacity and cargo capacity also augments VC. By the way. it is analyzed that the increment of VC due to the increase of the cargo capacity is larger than the reduction of VC owing to the increase of the quantify of goods. Therefore an alternative policy is needed in a short and long run point of view. That is to say, to promote the effectiveness of the freight transportation system, a short term supply control and a long run logistic infrastructure are urgent based on the restoration of market economy by successive deregulation. So we are able to conclude that gradual deregulation is more desirable to build effective freight market.

• The Transactions of the Korean Institute of Power Electronics
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• v.4 no.5
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• pp.442-448
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• 1999

We introduce the systematization design method using similarity theory which is profitable in the c compatability and standardization of the developed products and the reduction of construction time and price to d develop and design a machine equipment. Systematization design method is to select the standard model for d designing and developing from the large machinery to the super precision one and then to induce the c characteristic of machines step by step in advance in case of miniaturizing and making largelongleftarrowscale. With this m method, we extract the peculiar characteristics through the close analysis on the physical and ttx:hnical part a and predict the characteristic experiment for the magnitude we desire by an머ogical mathematical analysis. At l last, we will get the design sample the users demand with the verification of the data on optimum design p previously. In this paper, we could predict the characteristic of the product the users rC'Quire in advance with the d design method applying similarity theor${\gamma}$ and suggested the design method which could meet the various r requirements the users want. Also, it is shown that the standardization design by the similarity theory is a available as comparing the characteristic values expc'Cted through the experiment of the actual actuator with t the theoretical character data of similarity theoη after selecting the linear actuator as a model.