• Nuclear Engineering and Technology
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• v.56 no.6
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• pp.2352-2366
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• 2024

Reinforced concrete (RC) shear walls with precast steel-concrete composite modular (PSCCM) are strongly recommended in the structural design of nuclear power plants due to the need for a large number of process pipeline crossings and industrial construction. However, the effect of the PSCCM on the mechanical behavior of the whole RC shear wall is still unknown and has received little attention. In this study, three 1:3 scaled specimens, one traditional shear wall specimen (TW) and two shear wall specimens with the PSCCM (PW1, PW2), were designed and investigated under cyclic loadings. The failure mode, hysteretic curve, energy dissipation, stiffness and strength degradations were then comparatively investigated to reveal the effect of the PSCCM. Furthermore, numerical models of the RC shear wall with different PSCCM distributions were analyzed. The results show that the shear wall with the PSCCM has comparable mechanical properties with the traditional shear wall, which can be further improved by adding reinforced concrete constraints on both sides of the shear wall. The accumulated energy dissipation of the PW2 is higher than that of the TW and PW1 by 98.7 % and 60.0 %. The failure of the shear wall with the PSCCM is mainly concentrated in the reinforced concrete wall below the PSCCM, while the PSCCM maintains an elastic working state as a whole. Shear walls with the PSCCM arranged in the high stress zone will have a higher load-bearing capacity and lateral stiffness, but will suffer a higher risk of failure. The PSCCM in the low stress zone is always in an elastic working state.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.35D no.9
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• pp.26-34
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• 1998

This paper presents a characterization method for faults of circuit patterns on MCM(Multichip Module) or PCB(Printed Circuit Board) substrates with electron beams of a SEM(Scanning Electron Microscope) by inducing voltage contrast on the signal line. The experimentation employes dual potential electron beams for the fault characterization of circuit patterns with a commercial SEM without modifying its structure. The testing procedure utilizes only one electron gun for the generation of dual potential electron beams by two different accelerating voltages, one for charging electron beam which introduces the yield of secondary electron $\delta$ < 1 and the other for reading beam which introduces $\delta$ > 1. Reading beam can read open's/short's of a specific net among many test nets, simultaneously discharging during the reading process for the next step, by removing its voltage contrast. The experimental results of testing the copper signal lines on glass-epoxy substrates showed that the state of open's/short's had generated the brightness contrast due to the voltage contrast on the surface of copper conductor line, when the net had charged with charging electron beams of 7KV accelerating voltages and then read with scanning reading electron beams of 2KV accelerating voltages in 10 seconds. The experimental results with Au pads of a IC die and Au plated Cu pads of BGA substrates provided the simple test method of circuit lines with 7KV charging electron beam and 2KV reading beam. Thus the characterization method showed that we can test open and short circuits of the net nondestructively by using dual potential electron beams with one SEM gun.

• Journal of Aerospace System Engineering
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• v.11 no.3
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• pp.1-7
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• 2017

The applications of high-frequency induction heating has recently been studied in various industrial fields. In this study, induction heating is applied to a SCM440 specimen that is widely used in industry. The specimen was made up of a cylinder 20 mm in diameter and 160 mm long. An induction heating power supply module was used to generate heat in the cylinder at a high frequency (approximately 85 kHz) for 50 seconds. The temperature of the specimen was measured at the 150 mm length in 5 second intervals. Results such as joule heat and temperature are compared with the numerical model analysis using an electromagnetic-thermal co-simulation technique. The analytical model of the cylinder was modeled by considering the skin effect. The median measured temperature after induction heating was conducted for 50 seconds was $57.65^{\circ}C$, compared to a predicted analytical value of $57.27^{\circ}C$. Thus, the analytical results are in good agreement with the experimental results, and this model can predict the induction heating phenomenon numerically.

• Journal of Bio-Environment Control
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• v.18 no.4
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• pp.341-347
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• 2009

Experiments and computations were conducted to investigate the heat insulation characteristics of multi layer materials for cultivation greenhouse. In case of the experiments, measurements of temperature were carried out with a K-type thermocouples and data logger to research the heat transfer in the experimental module generated by the heat source. A thermal conductivity meter, QTM-500 based on modified transient hot wire method was used to measure the thermal conductivity of multi layer materials. The numerical analyses were performed by commercial code CFX-11 according to the variation of multi layer materials without air layer. The experimental results showed that the heat insulation of multi layer materials was higher than single layer materials by 50~90%. It was found that the effect of heat insulation was raised by the combination of multi layer materials.

• Journal of the Korean Institute of Gas
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• v.15 no.5
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• pp.50-56
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• 2011

A preliminary experimental study of new concept air hybrid engine, which stores compressed air in the tank during braking and re-use it to propel vehicle during crusing or acceleration, was carried out in this study. A single cylinder engine was modified to realize the concept of air hybrid engine. Independent variable valve lift system was adopted in one of the exhaust valves to store the compressed air into the air tank during compression period. An air injector module was installed in the place of spark plug, and the stored compressed air was supplied during the expansion period to realize air motoring mode. For air compression mode, the tank with volume of 30 liter could be charged up to more than 13 bar. By utilizing this stored compressed air, motoring work of 0.41 bar of IMEP(Indicated mean effective pressure) at maximum can be generated at the 800rpm conditions, which is higher than the case of normal idle condition by 1.1 bar of IMEP.

• Fire Science and Engineering
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• v.30 no.4
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• pp.111-120
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• 2016

This is an experimental study to analyze the explosion and fire hazards of mobile phone batteries. Using the lithium-ion batteries currently used on smart phone as the experiment samples, the experiments were conducted by overcharging, internal and external short circuit, and thermal shock with the potential of explosion and fire caused by careless use or abnormal conditions. The experiment results showed that, in the case of overcharging and external short circuit, there was no explosion and fire hazard in the normal operation of the protection circuit module (PCM), but there were big risks when the PCM faulted conditions were assumed. In the case of the experiments by internal short circuit and thermal shock, such risks varied depending on a battery charge state. In other words, it could be verified that there were low risks of explosion and fire in a full discharge state, but there were high risks in a full charge state. These experiment results suggest that to minimize the explosion and fire hazards of mobile phone batteries, an alarm device is necessary when the PCM fault occurs. In addition, a solid battery case should be made and safety equipment, such as a cooling device to avoid high temperature, is needed.

• Journal of Wetlands Research
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• v.17 no.1
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• pp.53-61
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• 2015

Habitat creation for endangered species Menyanthes trifoliata L. using planting module represents a habitat type such as the rhizome grows horizontally to open water at the margin of the lake. The objectives of this mesocosm experiment are habitat creation with easy construction and low management effort, and to investigate the potential of providing a habitat for aquatic macroinvertebrates. Planting modules had three different substrates of bed soil, perlite and K-SOIL (artificial lightweight soil using bottom ash). These modules were established in two different size of the tub($1170{\times}2250{\times}300mm^3$, $900{\times}1360{\times}190mm^3$). According to the monitoring results, number of leaves and coverage of M. trifoliata showed significant difference with substrate and tub size. The number of leaves showed similar growth responses in bed soil (mean 22.979) and K-SOIL (mean 28.042) substrates but growth was poor in perlite substrate (mean 1.667). The number of leaves in the large tub was more than small tub (p=0.015). Similar responses were obtained with the coverage, the length of rhizome and the number of rhizome in M. trifoliata. A total of 21 taxa of aquatic macroinvertebrates including 1,145 individuals was found in the mesocosm. The Shannon diversity index and colonization index in the mesocosm were similar to the previous studies. These results suggest that the experimental mesocosm could provide sufficient habitats for aquatic macroinvertebrates. If planting modules use bed soil or K-SOIL by planting substrate, establish that taking into account open water surfaces for M. trifoliata growth and manage about 30cm of water depth control, then habitat creation for M. trifoilata will be successful.

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.279-290
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• 2005

Recently, as the serious damage caused by DDoS attacks increases, the rapid detection and the proper response mechanisms are urgent. However, existing security mechanisms do not effectively defend against these attacks, or the defense capability of some mechanisms is only limited to specific DDoS attacks. In this paper, we propose a detection architecture against DDoS attack using data mining technology that can classify the latest types of DDoS attack, and can detect the modification of existing attacks as well as the novel attacks. This architecture consists of a Misuse Detection Module modeling to classify the existing attacks, and an Anomaly Detection Module modeling to detect the novel attacks. And it utilizes the off-line generated models in order to detect the DDoS attack using the real-time traffic. We gathered the NetFlow data generated at an access router of our network in order to model the real network traffic and test it. The NetFlow provides the useful flow-based statistical information without tremendous preprocessing. Also, we mounted the well-known DDoS attack tools to gather the attack traffic. And then, our experimental results show that our approach can provide the outstanding performance against existing attacks, and provide the possibility of detection against the novel attack.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.149-162
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• 2014

Course guidance is a mentoring process which is performed before students register for coming classes. The course guidance plays a very important role to students in checking degree audits of students and mentoring classes which will be taken in coming semester. Also, it is intimately involved with a graduation assessment or a completion of ABEEK certification. Currently, course guidance is manually performed by some advisers at most of universities in Korea because they have no electronic systems for the course guidance. By the lack of the systems, the advisers should analyze each degree audit of students and curriculum information of their own departments. This process often causes the human error during the course guidance process due to the complexity of the process. The electronic system thus is essential to avoid the human error for the course guidance. If the relation data model-based system is applied to the mentoring process, then the problems in manual way can be solved. However, the relational data model-based systems have some limitations. Curriculums of a department and certification systems can be changed depending on a new policy of a university or surrounding environments. If the curriculums and the systems are changed, a scheme of the existing system should be changed in accordance with the variations. It is also not sufficient to provide semantic search due to the difficulty of extracting semantic relationships between subjects. In this paper, we model a course mentoring ontology based on the analysis of a curriculum of computer science department, a structure of degree audit, and ABEEK certification. Ontology-based course guidance system is also proposed to overcome the limitation of the existing methods and to provide the effectiveness of course mentoring process for both of advisors and students. In the proposed system, all data of the system consists of ontology instances. To create ontology instances, ontology population module is developed by using JENA framework which is for building semantic web and linked data applications. In the ontology population module, the mapping rules to connect parts of degree audit to certain parts of course mentoring ontology are designed. All ontology instances are generated based on degree audits of students who participate in course mentoring test. The generated instances are saved to JENA TDB as a triple repository after an inference process using JENA inference engine. A user interface for course guidance is implemented by using Java and JENA framework. Once a advisor or a student input student's information such as student name and student number at an information request form in user interface, the proposed system provides mentoring results based on a degree audit of current student and rules to check scores for each part of a curriculum such as special cultural subject, major subject, and MSC subject containing math and basic science. Recall and precision are used to evaluate the performance of the proposed system. The recall is used to check that the proposed system retrieves all relevant subjects. The precision is used to check whether the retrieved subjects are relevant to the mentoring results. An officer of computer science department attends the verification on the results derived from the proposed system. Experimental results using real data of the participating students show that the proposed course guidance system based on course mentoring ontology provides correct course mentoring results to students at all times. Advisors can also reduce their time cost to analyze a degree audit of corresponding student and to calculate each score for the each part. As a result, the proposed system based on ontology techniques solves the difficulty of mentoring methods in manual way and the proposed system derive correct mentoring results as human conduct.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.