• Journal of KIISE:Databases
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• v.37 no.2
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• pp.71-81
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• 2010

The problem of finding clusters is widely used in numerous applications, such as pattern recognition, image analysis, market analysis. The important factors that decide cluster quality are the similarity measure and the number of attributes. Similarity measures should be defined with respect to the data types. Existing similarity measures are well applicable to numerical attribute values. However, those measures do not work well when the data is described by categorical attributes, that is, when no inherent similarity measure between values. In high dimensional spaces, conventional clustering algorithms tend to break down because of sparsity of data points. To overcome this difficulty, a subspace clustering approach has been proposed. It is based on the observation that different clusters may exist in different subspaces. In this paper, we propose a new similarity measure for clustering of high dimensional categorical data. The measure is defined based on the fact that a good clustering is one where each cluster should have certain information that can distinguish it with other clusters. We also try to capture on the attribute dependencies. This study is meaningful because there has been no method to use both of them. Experimental results on real datasets show clusters obtained by our proposed similarity measure are good enough with respect to clustering accuracy.

• Journal of KIISE
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• v.45 no.2
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• pp.113-125
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• 2018

Recently, with the advent of the Big Data era, we have gained the capability of acquiring vast amounts of knowledge from various fields. The collected knowledge is expressed by well-formed formula and in particular, OWL, a standard language of ontology, is a typical form of well-formed formula. The symbolic reasoning is actively being studied using large amounts of ontology data for extracting intrinsic information. However, most studies of this reasoning support the restricted rule expression based on Description Logic and they have limited applicability to the real world. Moreover, knowledge management for inaccurate information is required, since knowledge inferred from the wrong information will also generate more incorrect information based on the dependencies between the inference rules. Therefore, this paper suggests that the SWAT, knowledge management system should be combined with the SWRL (Semantic Web Rule Language) reasoning based on ATMS (Assumption-based Truth Maintenance System). Moreover, this system was constructed by combining with SWRL reasoning and ATMS for managing large ontology data based on the distributed In-memory framework. Based on this, the ATMS monitoring system allows users to easily detect and correct wrong knowledge. We used the LUBM (Lehigh University Benchmark) dataset for evaluating the suggested method which is managing the knowledge through the retraction of the wrong SWRL inference data on large data.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.9
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• pp.419-428
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• 2017

In an embedded system, modules exchange data by interacting among themselves. Exchanging erroneous resource data among modules may lead to execution errors. The interacting resources produce dependencies between the two modules where any change of the resources by one module affects the functionality of another module. Several investigations of the embedded systems show that interaction faults between the modules are one of the major cause of critical software failure. Therefore, interaction testing is an essential phase for reducing the interaction faults and minimizing the risk. The direct and indirect interactions between the modules generate interaction faults. The direct interaction is the explicit call relation between the modules, and the indirect interaction is the remaining relation that is made underneath the interface that possesses data dependence relationship with resources. In this paper, we investigate the errors that are based on the indirect interaction between modules and introduce a new test criterion for identifying the errors that are undetectable by existing approaches at the integration level. We propose a novel approach for generating the interaction model using the indirect interaction pattern and design test criteria that are based on different interaction errors to generate test cases. Finally, we use the fault injection technique to evaluate the feasibility and effectiveness of our approach.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.47 no.11
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• pp.43-53
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• 2010

Wide deployment of high resolution video services leads to active studies on high speed video processing. Especially, prevalent employment of multi-core systems accelerates researches on high resolution video processing based on parallelization of multimedia software. In this paper, we propose a novel parallel H.264/AVC decoding scheme on a multi-core platform. Parallel H.264/AVC decoding is challenging not only because parallelization may incur significant synchronization overhead but also because software may have complicated dependencies. To overcome such issues, we propose a novel approach called Multi-Threaded Parallelization(MTP). In MTP, to reduce synchronization overhead, a separate thread is allocated to each stage in the pipeline. In addition, an efficient memory reuse technique is used to reduce the memory requirement. To verify the effectiveness of the proposed approach, we parallelized FFmpeg H.264/AVC decoder with the proposed technique using OpenMP, and carried out experiments on an Intel Quad-Core platform. The proposed design performs better than FFmpeg H.264/AVC decoder before the parallelization by 53%. We also reduced the amount of memory usage by 65% and 81% for a high-definition(HD) and a full high-definition(FHD) video, respectively compared with that of popular existing method called 2Dwave.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.10 no.2
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• pp.109-123
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• 2000

Inhaled halogen compound was examined through micronucleus tests and toxicity tests using SD (Sprague-Dawley) rats with repeated dosages for six hours a day, five days a week, during four weeks. In four-week repeated exposure, no specific sign caused by the $CF_3I$ compound was observed on the clinical symptoms, body weight variation, feed consumption, and urinalysis data in the testing groups with reference to the control group. In hematological and biochemical blood tests of the testing groups, the significant, but in their normal ranges, value dependencies of glucose, aspartate aminotransferase, alanine aminotransferase and etc., on the halogen carbohydride concentration was observed. In histo-pathological tests, no specific lesion or concentration dependent change due to the $CF_3I$ compound dosage was observed in both sexes of the female and male in the tested animals. But, micronucleus tests on marrow cells extracted from the tested animals which were repeatedly exposed in the $CF_3I$ compound during four weeks, the frequencies of micronuclei were significantly increased dose-dependently compared to the control groups.

• Korean Journal of Social Welfare
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• v.37
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• pp.33-66
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• 1999

The primary objectives of this research is to identify (1) demographic characteristics and job needs of women with disabilities in Korea and (2) factors that predicts the degrees of their job needs. This study uses the data based on interviews with 805 disabled women selected randomly from the registered disabled women in Seoul, Kuyngki-Do, Pusan, and Kuyngsangnam-Do. In the aspect of disabilities, this study showed that more than half of disabled women in our sample have very severe disabilities (1st degree and 2nd degree of disabilities) and needs others' help in their daily lives. In the demographic aspect, this study found that disabled women's education level is very low and their monthly income is also very low. This study also found that in spite of their disabilities and functional limitations, more than 3/4 of disabled women have needs about jobs. Finally, using logistic regression analysis, this study found that disabled women's type of disabilities, dependencies in daily life, age, job experiences, job training experiences, willingness for independence, and parental attitude about disabled women's future are statistically significant in predicting the degree of their job needs. This study will contribute to developing knowledge about disabled women's characteristics and provide practical implications for intervention strategies and assistance programs related to promoting their welfare.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.38 no.10
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• pp.712-720
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• 2001

Algorithms from the research of the layout migration were proposed in the paper. These are automatic recognition algorithm for the VLSI devices from it, graph based construction algorithm to maintain the constraints, dependencies, and design rule between the devices, and high speed compaction algorithm to reduce size of the VLSI area and reuse the design with compacted size for the new technology. Also, this paper describes that why proposed algorithms are essential for the era of the SoC (System on a Chip), design reuse, and IP DB, which are the big concerns in these days. In addition to introduce our algorithms, the benchmark showed that our performance is superior by 27 times faster than that of the commercial one, and has better efficiency by 3 times in disk usage.

• Journal of KIISE
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• v.43 no.10
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• pp.1115-1123
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• 2016

Assumption-based truth maintenance system (ATMS) is a tool that maintains the reasoning process of inference engine. It also supports non-monotonic reasoning based on dependency-directed backtracking. Bookkeeping all the reasoning processes allows it to quickly check and retract beliefs and efficiently provide solutions for problems with large search space. However, the amount of data has been exponentially grown recently, making it impossible to use a single machine for solving large-scale problems. The maintaining process for solving such problems can lead to high computation cost due to large memory overhead. To overcome this drawback, this paper presents an approach towards incrementally maintaining the reasoning process of inference engine on cluster using Spark. It maintains data dependencies such as assumption, label, environment and justification on a cluster of machines in parallel and efficiently updates changes in a large amount of inferred datasets. We deployed the proposed ATMS on a cluster with 5 machines, conducted OWL/RDFS reasoning over University benchmark data (LUBM) and evaluated our system in terms of its performance and functionalities such as assertion, explanation and retraction. In our experiments, the proposed system performed the operations in a reasonably short period of time for over 80GB inferred LUBM2000 dataset.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.239-240
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• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 1999.04a
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• pp.5-5
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• 1999

The Principal deficiency of the existing notion about the sintering-mixtures consists in the fact that almost no attention is focused on the Phenomenon of alloy formation during sintering, its connection with dimensional changes of powder bodies, and no correct ideas on the driving force for the sintering process in the stage of establishing chemical equilibrium in a system are available as well. Another disadvantage of the classical sintering theory is an erroneous conception on the dissolution mechanism of solid in liquid. The two-particle model widely used in the literature to describe the sintering phenomenon in solid state disregards the nature of the neighbouring surrounding particles, the presence of pores between them, and the rise of so called arch effect. In this presentation, new basic scientific principles of the driving forces for the sintering process of a two-component powder body, of a diffusion mechanism of the interaction between solid and liquid phases, of stresses and deformation arising in the diffusion zone have been developed. The major driving force for sintering the mixture from components capable of forming solid solutions and intermetallic compounds is attributed to the alloy formation rather than the reduction of the free surface area until the chemical equilibrium is achieved in a system. The lecture considers a multiparticle model of the mixed powder-body and the nature of its volume changes during solid-state and liquid-phase sintering. It explains the discovered S-and V-type concentration dependencies of the change in the compact volume during solid-state sintering. It is supposed in the literature that the dissolution of solid in liquid is realised due to the removal of atoms from the surface of the solid phase into the melt and then their diffusicn transfer from the solid-liquid interface into the bulk of liquid. It has been shown in our experimental studies that the mechanism of the interaction between two components, one of them being liquid, consist in diffusion of the solvent atoms from the liquid into the solid phase until the concentration of solid solutions or an intermetallic compound in the surface layer enables them to pass into the liquid by means of melting. The lecture discusses peculimities of liquid phase formation in systems with intermediate compounds and the role of the liquid phase in bringing about the exothermic effect. At the frist stage of liquid phase sintering the diffusion of atoms from the melt into the solid causes the powder body to grow. At the second stage the diminution of particles in size as a result of their dissolution in the liquid draws their centres closer to each other and makes the compact to shrink Analytical equations were derived to describe quantitatively the porosity and volume changes of compacts as a result of alloy formation during liquid phase sinteIing. Selection criteria for an additive, its concentration and the temperature regime of sintering to control the density the structure of sintered alloys are given.