• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2009.10a
• /
• pp.421-426
• /
• 2009

This paper presents vibration analysis of maskless exposure module in Printed Circuit Board (PCB) manufacturing system. In order to complete exposure process in PCB, masking type module has been widely used in electronics industries. However, masking process confronts some limitations of application due to higher production cost for masking as well as lower printing resolution. Therefore, maskless exposure module is started to be in the spotlight for flexible production system to meet the needs of fabrication in variable patterns at low cost. Since maskless exposure process adopts direct patterning to PCB, vibration problems become more critical compared to conventional masking type process. Moreover, movements of exposure engine as well as stage generate vibration sources in the system. Thus, it is imperative to analyze the vibration characteristics for the maskless exposure module to improve the quality and accuracy of PCB. In this study, vibration analysis using the Finite Element Analysis is conducted to identify the critical structural parts deteriorating vibration performance. Also, Experimental investigations are conducted by single/dual encoder measurement process under the operating module speed. Measurement points of vibration are selected by three places, which are base of stage, exposure engine and top of stage, to check the effect of vibration from the exposure engine. Comparisons between analysis results and experimental measurement are conducted to confirm the accuracy of analysis results including the developed FE model. Finally, this studies show feasibility of optimal design using the developed FE analysis model.

• Atmosphere
• /
• v.23 no.2
• /
• pp.143-160
• /
• 2013

The effect of 3DVAR (Three Dimension Variational data Assimilation) was examined by comparing observation and the simulations of CNTL (to which data assimilation was not applied) and ALL (to which data assimilation was applied using ground observation data and radar data) for the case of a heavy snowfall event (case A) of 11-12 February 2011 in the Yeongdong region. In case A, heavy snow intensively came in the Yeongdong coastal region rather than Daegwallyeong, in particular, around the Gangneung and Donghae regions with total precipitation in Bukgangneung at approximately 91 mm according to the AWS observation. It can be seen that compared to CNTL, ALL simulated larger precipitation along the Yeongdong coastline extending from Sokcho to Donghae while simulating smaller precipitation for inland areas including Daegwallyeong. On comparison of the total accumulated precipitations from simulations of CNTL and ALL, and the observed total accumulated precipitation, the positive effect of the assimilation of ground observation data and radar data could be identified in Bukgangneung and Donghae, on the other hand, the negative effect of the assimilation could be identified in the Daegwallyeong and Sokcho regions. In order to examine the average accuracy of precipitation prediction by CNTL and ALL for the entire Gangwon region including the major points mentioned earlier, the three hour accumulated precipitation from simulations of CNTL and ALL were divided into 5, 10, 15, 20, 25 and 30 mm/3hr and threat Scores were calculated by forecasting time. ALL showed relatively higher TSs than CNTL for all threshold values although there were some differences. That is, when considered generally based on the Gangwon region, the accuracy of precipitation prediction from ALL was improved somewhat compared to that from CNTL.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.9
• /
• pp.1816-1821
• /
• 2016

Protein secondary structure is important for the study of protein evolution, structure and function of proteins which play crucial roles in most of biological processes. This paper try to effectively extract protein secondary structure information from the large protein structure database in order to predict the protein secondary structure of a query protein sequence. To find more remote homologous sequences of a query sequence in the protein database, we used PSI-BLAST which can perform gapped iterative searches and use profiles consisting of homologous protein sequences of a query protein. The secondary structures of the homologous sequences are weighed combined to the secondary structure prediction according to their relative degree of similarity to the query sequence. When homologous sequences with a neural network predictor were used, the accuracies were higher than those of current state-of-art techniques, achieving a Q3 accuracy of 92.28% and a Q8 accuracy of 88.79%.

• Journal of Drive and Control
• /
• v.17 no.1
• /
• pp.21-26
• /
• 2020

The load characteristics of a piston pump holder due to the squeeze effect are influenced by the surface shape and gap thickness of the holder (cradle). Therefore, the pressure distribution and the load capacity of the piston pump holder due to the squeeze effect are studied by using the CFD software and the surface shape and gap thickness of the piston pump holder are considered. In order to verify the accuracy of numerical results, the load capacities of a circular plate holder are numerically studied, and the accuracy of numerical results is verified by comparing with the theoretical results. Also, the pressure distribution and load capacity of the rectangular plate holder of a piston pump are obtained by using the CFD software. The results show that the load capacity of the square plate holder with grooves is slightly higher at a low gap thickness, but the effects of the number and arrangement of grooves on the load capacity of the holder are weak. We conclude that the load capacity and the maximum pressure are slightly affected due to the existence of grooves on the holder surface, and the fluid storing effect of the holder surface grooves during the operation is likely to prevent the dry-sticking phenomenon.

• Tunnel and Underground Space
• /
• v.22 no.3
• /
• pp.214-220
• /
• 2012

Deformation behaviour of rock mass around an opening measured during tunnel excavation is very important in order to assess the stability of the tunnel. Unfortunately displacement measured only after the installation of displacement measuring device can be acquired, which results in inevitably excluding the pre-displacement occurred and accumulated before the displacement measuring devices are installed. So it is very important to consider the pre-displacement based on the elapsed time before zero reading after deformation behaviour started. In this study, the accuracy of total estimated displacement depending on the distance between face and measurement position is calculated by statistical non-linear fitting on measurable displacement data. Besides, the influence of the unavoidable measurement error is considered by using Monte-Carlo simulation. As a result, the faster the initial reading started and the smaller the measurement error is, the higher the accuracy of estimating total displacement is obtained.

• Journal of the Korea Convergence Society
• /
• v.11 no.10
• /
• pp.89-96
• /
• 2020

Linked Data allows structured data to be published in a standard way that datasets from various domains can be interlinked. With the rapid evolution of Linked Open Data(LOD), researchers are exploiting it to solve particular problems such as semantic similarity assessment. In this paper, we propose a method, on top of the basic concept of Linked Data Semantic Distance (LDSD), for calculating the Linked Data semantic distance between resources that can be used in the LOD-based recommender system. The semantic distance measurement model proposed in this paper is based on a similarity measurement that combines the LOD-based semantic distance and a new link weight using TF-IDF, which is well known in the field of information retrieval. In order to verify the effectiveness of this paper's approach, performance was evaluated in the context of an LOD-based recommendation system using mixed data of DBpedia and MovieLens. Experimental results show that the proposed method shows higher accuracy compared to other similar methods. In addition, it contributed to the improvement of the accuracy of the recommender system by expanding the range of semantic distance calculation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.11
• /
• pp.2784-2799
• /
• 2012

Biological data have been increased exponentially in recent years, and analyzing these data using data mining tools has become one of the major issues in the bioinformatics research community. This paper focuses on the protein construction process in higher organisms where the deoxyribonucleic acid, or DNA, sequence is filtered. In the process, "unmeaningful" DNA sub-sequences (called introns) are removed, and their meaningful counterparts (called exons) are retained. Accurate recognition of the boundaries between these two classes of sub-sequences, however, is known to be a difficult problem. Conventional approaches for recognizing these boundaries have sought for solely enhancing machine learning techniques, while inherent nature of the data themselves has been overlooked. In this paper we present an approach which makes use of the data attributes inherent to species in order to increase the accuracy of the boundary recognition. For experimentation, we have taken the data sets for four different species from the University of California Santa Cruz (UCSC) data repository, divided the data sets based on the species types, then trained a preprocessed version of the data sets on neural network(NN)-based and support vector machine(SVM)-based classifiers. As a result, we have observed that each species has its own specific features related to the splice sites, and that it implies there are related distances among species. To conclude, dividing the training data set based on species would increase the accuracy of predicting splicing junction and propose new insight to the biological research.

• Journal of KIISE:Software and Applications
• /
• v.37 no.2
• /
• pp.120-128
• /
• 2010

A hypernetwork is a generalized hypo-graph and a probabilistic graphical model based on evolutionary learning. Hypernetwork models have been applied to various domains including pattern recognition and bioinformatics. Nevertheless, conventional hypernetwork models have the limitation that they can manage data with categorical or discrete attibutes only since the learning method of hypernetworks is based on equality comparison of hyperedges with learned data. Therefore, real-valued data need to be discretized by preprocessing before learning with hypernetworks. However, discretization causes inevitable information loss and possible decrease of accuracy in pattern classification. To overcome this weakness, we propose a novel feature-wise L1-distance based method for real-valued attributes in learning hypernetwork models in this study. We show that the proposed model improves the classification accuracy compared with conventional hypernetworks and it shows competitive performance over other machine learning methods.

• Proceedings of KOSOMES biannual meeting
• /
• 2005.11a
• /
• pp.197-202
• /
• 2005

Generally, a semiconductor chip measured with a few micro units is captured by line scan camera for higher inspection accuracy. However, the faulty inspection requires an exact boundary detection algorithm because it is very sensitive to scan speed and lighting conditions. In this paper we propose boundary detection using subpixel edge detection method in order to increase the accuracy of bump faulty detection on chips. The bump edge is detected by first derivative to four directions from bump center point and the exact edge positions are searched by the subpixel method. Also, the exact bump boundary to calculate the actual bump size is computed by LSM(Least Squares Method) to minimize errors since the bump size is varied such as bump protrusion, bump bridge, and bump discoloration. Experimental results exhibit that the proposed algorithm shows large improvement comparable to the other conventional boundary detection algorithms.

• The Journal of the Acoustical Society of Korea
• /
• v.19 no.6
• /
• pp.62-70
• /
• 2000

In usual language models(LMs), the probability has been estimated by selecting highly frequent words from a large text side database. However, in case of adopting LMs in a specific task, it is unnecessary to using the general method; constructing it from a large size tent, considering the various kinds of cost. In this paper, we propose a construction method of LMs using a small size text database in order to be used in specific tasks. The proposed method is efficient in increasing the low frequent words by applying same sentences iteratively, for it will robust the occurrence probability of words as well. We carried out continuous speech recognition(CSR) experiments on 200 sentences uttered by 3 speakers using LMs by iterative teaming(IL) in a air flight reservation task. The results indicated that the performance of CSR, using an IL applied LMs, shows an 20.4% increased recognition accuracy compared to those without it. This system, using the IL method, also shows an average of 13.4% higher recognition accuracy than the previous one, which uses context-free grammar(CFG), implying the effectiveness of it.