• Proceedings of the Korean Institute of Surface Engineering Conference
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• 한국표면공학회 2016년도 추계학술대회 논문집
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• pp.173.2-173.2
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• 2016

A two-step oxidation method was applied on Al6061 to debate the growth mechanism of plasma electrolytic oxidation (PEO) coating. The specimens were first oxidized in the primary electrolyte solution {$Na_3PO_4$ (8g/l), NaOH (2g/l), consequently, the specimens were transferred into a different electrolyte {$K_2ZrF_6$ (8g/l), NaOH (2g/l), $Na_2SiF_6$ (0.5g/l)} for further oxidation. The processes was conducted for various processing times. It was found the second step electrolyte component were reached to inner layers, in contrast to the primary step components which were thrustle to the outer layer. The presence of the secondary component in the inner layers were significantly varied with processing time which suggest the change in growth properties with processing time. further more the inside growth of the secondary component confirmed the increasing trend in the downward growth of the coating layer. The corrosion and hardness properties of the coatings were found highly improved with change in growth features with increasing the processing time.

• Journal of the Korea Academia-Industrial cooperation Society
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• 제6권6호
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• pp.536-541
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• 2005

Large displacement and rigidity about rubber component are expected by nonlinear and large deformation analysis in this study. Rubber is also used by the model of Mooney-Rivlin and the self contact between rubbers is established. There is the friction between rigid body and rubber, wall and floor. The nonlinear simulation analysis used in this study is expected to be widely applied in design, analysis and development of several rubber components which are used in automotive, railroad, and mechanical elements etc. By utilizing this method, time and cost can also be saved in developing new rubber product. The analysis of rubber components requires special material modeling and non-linear finite element analysis tools that are quite different from those used for metallic parts. The objective of this study is to analyze the rubber component with large deformation and non-linear properties.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• 제27권6호
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• pp.343-349
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• 2014

It is summarized that potential causes of performance degradations and failure mechanisms of crystalline silicon photovoltaic (PV) modules installed in Middle East area. In addition, we also reviewed current PV module qualification test (IEC 61215) and the methods for detection of wear-out fault. The failure of PV modules in the extreme environmental conditions such as deserts is mainly due to high temperature, humidity, and dust storms. In particular, cementation phenomenon caused by combination of sand and moisture leads to rapid degradation in the performance of PV modules. In order to evaluate and guarantee the long term reliability of PV modules, specific qualification tests such as sand dust test, salt mist test and potential induce degradation test considering operating environment of PV module should be carried out.

• Computers and Concrete
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• 제10권6호
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• pp.557-573
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• 2012

This paper discusses the combined application of two different techniques, Neural Networks (NN) and Principal Component Analysis (PCA), for improved prediction of concrete properties. The combination of these approaches allowed the development of six neural networks models for predicting slump and compressive strength of concrete with mineral additives such as blast furnace slag, fly ash and silica fume. The Back-Propagation Multi-Layer Perceptron (BPMLP) with Bayesian regularization was used in all these models. They are produced to implement the complex nonlinear relationship between the inputs and the output of the network. They are also established through the incorporation of a huge experimental database on concrete organized in the form Mix-Property. Thus, the data comprising the concrete mixtures are much correlated to each others. The PCA is proposed for the compression and the elimination of the correlation between these data. After applying the PCA, the uncorrelated data were used to train the six models. The predictive results of these models were compared with the actual experimental trials. The results showed that the elimination of the correlation between the input parameters using PCA improved the predictive generalisation performance models with smaller architectures and dimensionality reduction. This study showed also that using the developed models for numerical investigations on the parameters affecting the properties of concrete is promising.

• Smart Structures and Systems
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• 제6권4호
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• pp.349-362
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• 2010

Ultrasonic Guided Waves (UGWs) are a useful tool in structural health monitoring (SHM) applications that can benefit from built-in transduction, moderately large inspection ranges and high sensitivity to small flaws. This paper describes a SHM method based on UGWs, discrete wavelet transform (DWT), and principal component analysis (PCA) able to detect and quantify the onset and propagation of fatigue cracks in structural waveguides. The method combines the advantages of guided wave signals processed through the DWT with the outcomes of selecting defect-sensitive features to perform a multivariate diagnosis of damage. This diagnosis is based on the PCA. The framework presented in this paper is applied to the detection of fatigue cracks in a steel beam. The probing hardware consists of a PXI platform that controls the generation and measurement of the ultrasonic signals by means of piezoelectric transducers made of Lead Zirconate Titanate. Although the approach is demonstrated in a beam test, it is argued that the proposed method is general and applicable to any structure that can sustain the propagation of UGWs.

• Smart Structures and Systems
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• 제6권4호
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• pp.405-421
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• 2010

This paper proposes a method for early warning of hazard for pipelines. Many pipelines transport dangerous contents so that any damage incurred might lead to catastrophic consequences. However, most of these damages are usually a result of surrounding third-party activities, mainly the constructions. In order to prevent accidents and disasters, detection of potential hazards from third-party activities is indispensable. This paper focuses on recognizing the running of construction machines because they indicate the activity of the constructions. Acoustic information is applied for the recognition and a novel pipeline monitoring approach is proposed. Principal Component Analysis (PCA) is applied. The obtained Eigenvalues are regarded as the special signature and thus used for building feature vectors. One-class Support Vector Machine (SVM) is used for the classifier. The denoising ability of PCA can make it robust to noise interference, while the powerful classifying ability of SVM can provide good recognition results. Some related issues such as standardization are also studied and discussed. On-site experiments are conducted and results prove the effectiveness of the proposed early warning method. Thus the possible hazards can be prevented and the integrity of pipelines can be ensured.

• Asian Pacific Journal of Cancer Prevention
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• 제16권6호
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• pp.2561-2567
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• 2015

Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer death worldwide. Our study aimed to reveal molecular mechanisms. Microarray data of GSE15471 (including 39 matching pairs of pancreatic tumor tissues and patient-matched normal tissues) was downloaded from Gene Expression Omnibus (GEO) database. We identified differentially expressed genes (DEGs) in PDAC tissues compared with normal tissues by limma package in R language. Then GO and KEGG pathway enrichment analyses were conducted with online DAVID. In addition, principal component analysis was performed and a protein-protein interaction network was constructed to study relationships between the DEGs through database STRING. A total of 532 DEGs were identified in the 38 PDAC tissues compared with 33 normal tissues. The results of principal component analysis of the top 20 DEGs could differentiate the PDAC tissues from normal tissues directly. In the PPI network, 8 of the 20 DEGs were all key genes of the collagen family. Additionally, FN1 (fibronectin 1) was also a hub node in the network. The genes of the collagen family as well as FN1 were significantly enriched in complement and coagulation cascades, ECM-receptor interaction and focal adhesion pathways. Our results suggest that genes of collagen family and FN1 may play an important role in PDAC progression. Meanwhile, these DEGs and enriched pathways, such as complement and coagulation cascades, ECM-receptor interaction and focal adhesion may be important molecular mechanisms involved in the development and progression of PDAC.

• Journal of the Korean Society for Advanced Composite Structures
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• 제6권4호
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• pp.51-57
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• 2015

Various hybrid dampers have been developed in Korea to control the vibration due to a wind and earthquake. In order to minimize the installment space, cost and construction process, the new hybrid friction damper is developed. This hybrid damper is composed of several rotary friction components having two frictional joint. Because of these components, the building vibration due to wind and earthquake can be mitigated by hybrid friction damper. In this paper, various dependency tests were carried out to evaluate on the structural performance of two joint rotational friction component of the hybrid damper. Test results show that two joint rotational components do not depend on a displacement and a frequency of forcing but friction coefficients is reducing as a clamping force is increasing.

• Journal of the Korea Institute of Military Science and Technology
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• 제18권6호
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• pp.762-770
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• 2015

Fault tolerance is a crucial design for a mission-critical computer such as engagement control computer that has to maintain its operation for long mission time. In recent years, software fault-tolerant design is becoming important in terms of cost-effectiveness and high-efficiency. In this paper, we propose MPCMCC which is a model-based software component to implement fault tolerance in mission-critical computers. MPCMCC is a fault tolerance design that synchronizes shared data between two computers by using the one-way message-passing scheme which is easy to use and more stable than the shared memory scheme. In addition, MPCMCC can be easily reused for future work by employing the model based development methodology. We verified the functions of the software component and analyzed its performance in the simulation environment by using two mission-critical computers. The results show that MPCMCC is a suitable software component for fault tolerance in mission-critical computers.

• The Transactions of The Korean Institute of Electrical Engineers
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• 제65권6호
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• pp.955-961
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• 2016

Fault location estimation is an important element for rapid recovery of power system when fault occur in transmission line. In order to calculate line impedance, most of fault location algorithm uses by measuring relaying waveform using DFT. So if there is a calculation error due to the influence of phasor by DC offset component, due to large vibration by line impedance computation, abnormal and non-operation of fault locator can be issue. It is very important to implement the robust fault location algorithm that is not affected by DC offset component. This paper describes an enhanced fault location algorithm based on the DC offset elimination filter to minimize the effects of DC offset on a long transmission line. The proposed DC offset elimination filter has not need any erstwhile information. The phase angle delay of the proposed DC offset filter did not occurred and the gain error was not found. The enhanced fault location algorithm uses DFT filter as well as the proposed DC offset filter. The behavior of the proposed fault location algorithm using off-line simulation has been verified by data about several fault conditions generated by the ATP simulation program.