• Journal of the Semiconductor & Display Technology
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• v.19 no.2
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• pp.11-15
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• 2020

In a typical initial setup of a PCB component inspection system, operators should manually input various information such as category, position, and inspection area for each component to be inspected, thus causing much inconvenience and longer setup time. Although there are many deep learning based object detectors, RetinaNet is regarded as one of best object detectors currently available. In this paper, a method using an extended RetinaNet is proposed that automatically detects its component category and position for each component mounted on PCBs from a high-resolution color input image. We extended the basic RetinaNet feature pyramid network by adding a feature pyramid layer having higher spatial resolution to the basic feature pyramid. It was demonstrated by experiments that the extended RetinaNet can detect successfully very small components that could be missed by the basic RetinaNet. Using the proposed method could enable automatic generation of inspection areas, thus considerably reducing the setup time of PCB component inspection systems.

• The Transactions of the Korea Information Processing Society
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• v.7 no.5
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• pp.1388-1400
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• 2000

An important technical issue in recent software development is to make needed software by the composition of components that are assemblable, and configurable, and independently extracted. The main advantage of component-based software development is reducing development time and cost. It is more cost-effective in development time to use components that are already developed than developing from scratch. There are two ways of component-based software development: one is to compose self-developed components, and the other is to by the components developed by third-parties and compose them. In the second case, existing non component programs must can be used for reuse in the component development. In this paper, we approach two methods for increase of reusability of Java program. First, we suggest the technique of extracting the elements suitable for the Beans component model from Java program, and then we show a process and a guideline of converting the extract elements into the Beans component model. Second, we suggest a technique of automatically extracting component information from the Java Beans component, expressing them in XML, its is possible to reuse the efficient component environment.

• The Journal of Advanced Prosthodontics
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• v.10 no.2
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• pp.155-162
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• 2018

PURPOSE. The aim of this study was to compare the fracture of implant component behavior of external and internal type of implants to suggest directions for successful implant treatment. MATERIALS AND METHODS. Data were collected from the clinical records of all patients who received WARANTEC implants at Seoul National University Dental Hospital from February 2002 to January 2014 for 12 years. Total number of implants was 1,289 and an average of 3.2 implants was installed per patient. Information about abutment connection type, implant locations, platform sizes was collected with presence of implant component fractures and their managements. SPSS statistics software (version 24.0, IBM) was used for the statistical analysis. RESULTS. Overall fracture was significantly more frequent in internal type. The most frequently fractured component was abutment in internal type implants, and screw fracture occurred most frequently in external type. Analyzing by fractured components, screw fracture was the most frequent in the maxillary anterior region and the most abutment fracture occurred in the maxillary posterior region and screw fractures occurred more frequently in NP (narrow platform) and abutment fractures occurred more frequently in RP (regular platform). CONCLUSION. In external type, screw fracture occurred most frequently, especially in the maxillary anterior region, and in internal type, abutment fracture occurred frequently in the posterior region. placement of an external type implant rather than an internal type is recommended for the posterior region where abutment fractures frequently occur.

• Smart Structures and Systems
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• v.33 no.2
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• pp.145-163
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• 2024

The evaluation of dynamic characteristics of bridges under operational traffic loads is a crucial aspect of bridge structural health monitoring. In the vehicle-bridge interaction (VBI) system, the vibration responses of bridge exhibit time-varying characteristics. To address this issue, an accurate time-frequency analysis method that combines the autoregressive power spectrum based empirical wavelet transform (AR-EWT) and local maximum synchrosqueezing transform (LMSST) is proposed to identify the time-varying instantaneous frequencies (IFs) of the bridge in the VBI system. The AR-EWT method decomposes the vibration response of the bridge into mono-component signals. Then, LMSST is employed to identify the IFs of each mono-component signal. The AR-EWT combined with the LMSST method (AR-EWT+LMSST) can resolve the problem that LMSST cannot effectively identify the multi-component signals with weak amplitude components. The proposed AR-EWT+LMSST method is compared with some advanced time-frequency analysis techniques such as synchrosqueezing transform (SST), synchroextracting transform (SET), and LMSST. The results demonstrate that the proposed AR-EWT+LMSST method can improve the accuracy of identified IFs. The effectiveness and applicability of the proposed method are validated through a multi-component signal, a VBI numerical model with a four-degree-of-freedom half-car, and a VBI model experiment. The effect of vehicle characteristics, vehicle speed, and road surface roughness on the identified IFs of bridge are investigated.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.526-529
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• 2015

이 연구에서는 노드 & 컴포넌트 기반 개발(Node & Component Based Development, NCBD)을 지원하는 게임엔진을 직접 설계, 제작하는 내용이다. NCBD를 지원하는 게임엔진은 이미 상용화된 유명게임엔진들(Unity, Unreal, Cocos2d-x)이 공통으로 지원하는 프로그래밍 패러다임이며, 이러한 노드 & 캠포넌트 구조는 깊은 상속구조를 지향하는 구조보다 유연하고 직관적이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2709-2729
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• 2016

Representation based classification, kernel method and sparse representation have received much attention in the field of face recognition. In this paper, we proposed an improved kernel principal component analysis method based on sparse representation to improve the accuracy and robustness for face recognition. First, the distances between the test sample and all training samples in kernel space are estimated based on collaborative representation. Second, S training samples with the smallest distances are selected, and Kernel Principal Component Analysis (KPCA) is used to extract the features that are exploited for classification. The proposed method implements the sparse representation under ℓ₂ regularization and performs feature extraction twice to improve the robustness. Also, we investigate the relationship between the accuracy and the sparseness coefficient, the relationship between the accuracy and the dimensionality respectively. The comparative experiments are conducted on the ORL, the GT and the UMIST face database. The experimental results show that the proposed method is more effective and robust than several state-of-the-art methods including Sparse Representation based Classification (SRC), Collaborative Representation based Classification (CRC), KCRC and Two Phase Test samples Sparse Representation (TPTSR).

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2016.11a
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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.

• Korean Journal of Microbiology
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• v.47 no.2
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• pp.174-178
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• 2011

In Escherichia coli, DcuS/R two-component system controls fumarate import and utilization related gene expression. To understand the dynamic response of the bacterium DcuS/R two-component system with respect to fumarate concentrations, DcuS/R induced dctA promoter was integrated with GFP reporter protein. Expression monitoring study using recombinant strain showed that dctA promoter was upregulated with 1 mM of fumarate in M9 minimal medium.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.741-744
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• 2005

A numerical scattering model for artificial metal structure based on physical optics approximation is developed to identify the height of phase center, and the result is compared with interferometric SAR DEM. The interferometric SAR data were gathered by AIRSAR during PACRIM- II campaign on Jeju Island. Power transmission towers on piedmont pasture along the slopes of Mt. Halla look like elliptic risings in TOPSAR DEM. The heights of risings are quantitatively analyzed using a scattering model in the way of achieving the height of phase centers of power transmission towers. A numerical algorithm is developed on the basis of physical optics approximation. The structure of power transmission tower was decomposed into hundreds of rectangular metal plates, of which the scattering matrix is known in analytic form, and the calculated scattering fields were summed coherently. The effect of direct backscattering component, ground-scatterer component and scatterer-ground component are decomposed and computed individually for each rectangular metal plate. The $\Deltak-radar$ equivalent was used to calculate height of phase center of the scatterer. The heights of a selected power transmission tower and scattering algorithm results give existence and location of the transmission towers but not actual tower heights.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.178-190
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• 2021

Alzheimer's disease (AD) is an insidious and degenerative neurological disease. It is a new topic for AD patients to use magnetic resonance imaging (MRI) and computer technology and is gradually explored at present. Preprocessing and correlation analysis on MRI data are firstly made in this paper. Then kernel principal component analysis (KPCA) is used to extract features of brain gray matter images. Finally supervised classification schemes such as AdaBoost algorithm and support vector machine algorithm are used to classify the above features. Experimental results by means of AD program Alzheimer's Disease Neuroimaging Initiative (ADNI) database which contains brain structural MRI (sMRI) of 116 AD patients, 116 patients with mild cognitive impairment, and 117 normal controls show that the proposed method can effectively assist the diagnosis and analysis of AD. Compared with principal component analysis (PCA) method, all classification results on KPCA are improved by 2%-6% among which the best result can reach 84%. It indicates that KPCA algorithm for feature extraction is more abundant and complete than PCA.