• Journal of Power Electronics
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• v.14 no.3
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• pp.564-571
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• 2014

Rotor initial position is an important factor affecting the control performance of electrically excited synchronous motors. This study presents a novel method for estimating rotor initial position based on sliding discrete Fourier transform (sDFT). By injecting an ac excitation into the rotor winding, an induced voltage is generated in stator windings. Through this voltage, the stator flux can be obtained using a pure integral voltage model. Considering the influence from a dc bias and an integral initial value, we adopt the sDFT to extract the fundamental flux component. A quadrant identification model is designed to realize the accurate estimation of the rotor initial position. The sDFT and high-pass filter, DFT, are compared in detail, and the contrast between dc excitation and ac injection is determined. Simulation and experimental results verify that this type of novel method can eliminate the influence of dc bias and other adverse factors, as well as provide a basis for the control of motor drives.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.5
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• pp.777-782
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• 2002

This study will offer multimodal recognition instead of an existing monomodal bioinfomatics by using facial multi-feature to improve the accuracy of recognition and to consider the convenience of user . Each bioinfomatics vector can be found by the following ways. For a face, the feature is calculated by principal component analysis with wavelet multiresolution. For a lip, a filter is used to find out an equation to calculate the edges of the lips first. Then by using a thinning image and least square method, an equation factor can be drawn. A feature found out the facial parameter distance ratio. We've sorted backpropagation neural network and experimented with the inputs used above. Based on the experimental results we discuss the advantage and efficiency.

• Nuclear Engineering and Technology
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• v.36 no.5
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• pp.388-402
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• 2004

A phenomena identification and ranking table(PIRT) was developed for a main steam line break (MSLB) event for the Advanced Power Reactor-1400 (APR-1400). The selectee event was a double-ended steam line break at full power, with the reactor coolant pump running. The developmental panel selected the fuel performance as the primary safety criterion during the ranking process. The plant design data, the results of the APR-1400 safety analysis, and the results of an additional best-estimate analysis by the MARS computer code were used in the development of the PIRT. The period of the transient was composed of three phases: pre-trip, rapid cool-down, and safety injection. Based on the relative importance to the primary evaluation criterion, the ranking of each system, component, and phenomenon/process was performed for each time phase. Finally, the knowledge-level for each important process for certain components was ranked in terms of existing knowledge. The PIRT can be used as a guide for planning cost-effective experimental programs and for code development efforts, especially for the quantification of those processes and/or phenomena that are highly important, but not well understood.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.76.1-76.1
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• 2019

CTA 102, one of gamma-ray bright active galactic nuclei (AGN) has been observed with Korean very long baseline interferometry (VLBI) network (KVN) during the period of 2012 December-2018 May as part of interferometric Monitoring Of Gamma-ray Bright AGN (iMOGABA). Multi-frequency VLBI observations enable us to compare the milliarcsecond(mas)-scale iMOGABA images of relativistic jets with those from the Monitoring Of Jets in AGN with Very long baseline array (VLBA) Experiments (MOJAVE) and the VLBA-Boston University(BU)-BLAZAR programs which use VLBA with its angular resolutions of 0.2-1.3 mas. In spite of the relative larger beam sizes of KVN (1-10 mas), we are able to identify jet components of CTA 102 using the KVN multi-frequency VLBI observations with those resolved with VLBA. Considering an instrumental beam blending effect on the jet component identification, we were able to obtain a blending shift of the core position based on a convolution analysis using the VLBA data. When we apply the core position shift to the KVN images of CTA 102, we find that the identified jet components of CTA 102 from the KVN observations are well matched with those from the VLBA observations. Based on the results of the analysis, we may be able to study the jet kinematics and its correlation with gamma-ray flare activity.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.20 no.3
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• pp.269-278
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• 2022

The buffer is a critical barrier component in an engineered barrier system, and its purpose is to prevent potential radionuclides from leaking out from a damaged canister by filling the void in the repository. No experimental parameters exist that can describe the buffer expansion phenomenon when Kyeongju bentonite, which is a buffer candidate material available in Korea, is exposed to groundwater. As conventional experiments to determine these parameters are time consuming and complicated, simple swelling pressure tests, numerical modeling, and machine learning are used in this study to obtain the parameters required to establish a numerical model that can simulate swelling. Swelling tests conducted using Kyeongju bentonite are emulated using the COMSOL Multiphysics numerical analysis tool. Relationships between the swelling phenomenon and mechanical parameters are determined via an artificial neural network. Subsequently, by inputting the swelling tests results into the network, the values for the mechanical parameters of Kyeongju bentonite are obtained. Sensitivity analysis is performed to identify the influential parameters. Results of the numerical analysis based on the identified mechanical parameters are consistent with the experimental values.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.3
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• pp.149-157
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• 2022

The primary goal of this study is to investigate the impact of social capital on consumer attitudinal evaluations. We propose a hypothetical receptacle, thereby applying a mediation framework because of social capital. We expect that increasing social capital would improve brand image and consumer-company (C-C) identification, resulting in positive in-role and extra-role customer behavior toward the brand. Data was collected from 425 respondents primarily from Karachi's five zones (East, West, North, South, and Central) and analyzed using confirmatory component analysis and structural equation design. The findings showed that social capital had a positive and significant relationship with customer extra-role behavior, as well as two mediators, brand image and consumer business identity. Furthermore, both mediators have a significant impact on both in-role and extra-role behavior. However, there is no evidence that social capital has a direct impact on in-role behavior. This study will help businesses in gaining a competitive advantage by concentrating on social capital to improve their brand image and customer relationship.

• Journal of Sensor Science and Technology
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• v.32 no.5
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• pp.259-266
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• 2023

The use of various adulterants and harmful chemicals is rapidly increasing in various sectors such as agriculture, food, and pharmaceuticals, and they are also present in our surroundings in the form of pollutants. The regular and repeated intake of harmful chemicals often adversely affects human health. The prolonged exposure of living beings to such adverse components can lead to severe health complications. To avoid the unlimited utilization of these chemical components, a sensing technology that is sensitive and reliable for low-concentration detection is beneficial. Surface-enhanced Raman spectroscopy (SERS) is a powerful method for identifying low-range concentrations of analytes, leading to great applications in molecular identification, including various diagnostic biomarkers. SERS in chemical, gas, and biological sensors can be an excellent approach in the sensing world to achieve rapid and multiple-analyte detection, leading to a new and efficient approach in healthcare monitoring.

• Korean Journal of Materials Research
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• v.34 no.5
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• pp.235-241
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• 2024

Gas identification techniques using pattern recognition methods were developed from four micro-electronic gas sensors for noxious gas mixture analysis. The target gases for the air quality monitoring inside vehicles were two exhaust gases, carbon monoxide (CO) and nitrogen oxides (NO_x), and two odor gases, ammonia (NH₃) and formaldehyde (HCHO). Four MEMS gas sensors with sensing materials of Pd-SnO₂ for CO, In₂O₃ for NO_X, Ru-WO₃ for NH₃, and hybridized SnO₂-ZnO material for HCHO were fabricated. In six binary mixed gas systems with oxidizing and reducing gases, the gas sensing behaviors and the sensor responses of these methods were examined for the discrimination of gas species. The gas sensitivity data was extracted and their patterns were determined using principal component analysis (PCA) techniques. The PCA plot results showed good separation among the mixed gas systems, suggesting that the gas mixture tests for noxious gases and their mixtures could be well classified and discriminated changes.

• Journal of the Korea Computer Graphics Society
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• v.16 no.1
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• pp.1-7
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• 2010

With the widespread use of vision-based surveillance systems, the capability for person identification is now an essential component. However, the CCTV cameras used in surveillance systems tend to produce relatively low-resolution images, making it difficult to use face recognition techniques for person identification. Therefore, an algorithm is proposed for person identification in CCTV camera images based on the clothing. Whenever a person is authenticated at the main entrance of a building, the clothing feature of that person is extracted and added to the database. Using a given image, the clothing area is detected using background subtraction and skin color detection techniques. The clothing feature vector is then composed of textural and color features of the clothing region, where the textural feature is extracted based on a local edge histogram, while the color feature is extracted using octree-based quantization of a color map. When given a query image, the person can then be identified by finding the most similar clothing feature from the database, where the Euclidean distance is used as the similarity measure. Experimental results show an 80% success rate for person identification with the proposed algorithm, and only a 43% success rate when using face recognition.

• Journal of KIISE:Software and Applications
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• v.35 no.2
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• pp.70-80
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• 2008

Today, businesses have to respond with flexibility and speed to ever-changing customer demand and market opportunities. Service-oriented architecture (SOA) is the best methodology for minimizing the complexity and the cost of enterprise-level infrastructure and for maximizing the productivity and the flexibility of an enterprise. Most of the enterprise-level SOA delivery strategies deal with the top-down approach, which organization has to define the business processes, to model business services, and to find the required services or to develop new services. However, a lot of peoples want to maximally reuse legacy component-based systems as well as to deliver SOA into their organizations. In this paper, we propose a bottom-up approach for identifying business services with proper granularity. It can improve the reusability and maintenance of services by considering not data I/O of components of legacy applications but GUI event patterns. Our proposed method is applied to MIS with 129 GUIs and 13 components. As a result, the valiance of the coupling value of components is increased five times and three business services are distinctly exposed. It also provides a 49% improvement in reducing the relationship problems between services over a service identification method using only partitioning information of components.