• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.281-285
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• 2000

This paper proposes context-based coding methods for variable length coding of inter-frame DCT coefficients. The proposed methods classify run-level symbols depending on the preceding coefficients. No extra overhead needs to be transmitted, since the information of the previously transmitted coefficients is used for classification. Two entropy coding methods, arithmetic coding and Huffman coding, are used for the proposed context-based coding. For Huffman coding, there is no complexity increase from the current standards by using the existing inter/intra VLC tables. Experimental results show that the proposed methods give ～ 19% bits gain and ～ 0.8 dB PSNR improvement for adaptive inter/intra VLC table selection, and ～ 37% bits gain and ～ 2.7dB PSNR improvement for arithmetic coding over the current standards, MPEG-4 and H.263. Also, the proposed methods obtain larger gain for small quantizaton parameters and the sequences with fast and complex motion. Therefore, for high quality video coding, the proposed methods have more advantage.

• Journal of information and communication convergence engineering
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• v.16 no.2
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• pp.106-113
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• 2018

Although deep learning methods of convolutional neural networks (CNNs) and long-/short-term memory (LSTM) are widely used for text categorization, they still have certain shortcomings. CNNs require that the text retain some order, that the pooling lengths be identical, and that collateral analysis is impossible; In case of LSTM, it requires the unidirectional operation and the inputs/outputs are very complex. Against these problems, we thus improved these traditional deep learning methods in the following ways: We created collateral CNNs accepting disorder and variable-length pooling, and we removed the input/output gates when creating bidirectional LSTMs. We have used four benchmark datasets for topic and sentiment classification using the new methods that we propose. The best results were obtained by combining LTSM regional embeddings with data convolution. Our method is better than all previous methods (including deep learning methods) in terms of topic and sentiment classification.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.8
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• pp.66-71
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• 2022

Metal additive manufacturing (AM) has revolutionized several manufacturing industries. AM can generate large-scale metal components and produce complex geometries close to net-shapes. WAAM is an AM technology that has garnered considerable interest among industries owing to its economics and relatively high deposition rates. However, the heat accumulation in the weld bead during deposition triggers distortion and residual stress. To address these problems, various methods of interpass pressure rolling systems have been suggested in recent research. In addition, combining the rolling and WAAM processes can mitigate residual stresses. The constant-pressure rolling of the interlayer also affect the microstructure. The coarse microstructure of the as-deposited sample was altered to finer equiaxed grains via these methods. However, the bead-shape accuracy of the interlayer constant-pressure method does not consider the heat accumulation in each layer. Therefore, this study develops an interpass variable pressure rolling system that considers the heat accumulation of each layer. The interpass variable pressure rolling system comprises deposition, detection, pressure, and transport units. Finally, verification tests are performed on the interpass variable-pressure rolling system (at 500 kg) with the WAAM process, and the obtained results are discussed.

• International Journal of Precision Engineering and Manufacturing
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• v.3 no.4
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• pp.72-77
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• 2002

The load interaction effect can be best illustrated by the phenomenon of overload retardation. Some prediction methods for retardation are reviewed and the problems discussed in the present paper. The so-called under-load effect much of the retardation disappears if a very low level minimum stress follows the overload, is also of importance for a prediction model to work properly under random load spectrum. The concept of Interactive Zone (IZ) fully considering reversed plasticity during unloading was discussed. This IZ concept can be combined with existing models to derive some improved models that can naturally take account of the under-load effect. Some simulations by IZ improved models for test under complex load sequences including multiple overloads and both over/under loads are compared with test results. It is seen that the improvement by IZ concept greatly enhanced the ability of existing models to accommodate complex load interaction effects.

• Bulletin of the Korean Chemical Society
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• v.18 no.9
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• pp.1022-1028
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• 1997

The reaction between Ni(Ⅱ) and sodium salt of 5,6-dihydro-1,4-dithiin-2,3-dithiolate (DDDT2-) in the presence of oxygen results in the formation of Ni(Ⅲ) species, Ni(DDDT)2-, which is isolated as tetraalkylammonium salt. The same reaction performed in the absence of oxygen yields dianionic Ni(Ⅱ) species, Ni(DDDT)22-, which is also isolated as the tetraethylammonium salt. The bis(5,6-dihydro-1,4-dithiin-2,3-dithiolato) nickelate (Ⅱ) dianion, Ni(DDDT)22-, reacts with methyl iodide to yield unusually stable bis(methylthio)dithiolene complex, Ni(CH3)2C8H8S8. All the isolated dithiolato-nickel(Ⅱ) and nickel(Ⅲ) complexes are characterized by 1H NMR, UV/Vis, IR and mass spectroscopic methods. The internal redox reaction of the nickel(Ⅱ)-dithiolate has been studied by spectro-electrochemical method and the results were compared with those of other metal-dithiolenes. The alkylated nickel(dithiolene) complex presumably undergoes cis-trans isomerization reaction in solution, judging from the experimental results of variable-temperature 1H NMR measurements.

• The Korean Journal of Applied Statistics
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• v.35 no.6
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• pp.703-724
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• 2022

Although longitudinal studies mainly produce multivariate longitudinal data, most of existing statistical models analyze univariate longitudinal data and there is a limitation to explain complex correlations properly. Therefore, this paper describes various methods of modeling the covariance matrix to explain the complex correlations. Among them, modified Cholesky decomposition, modified Cholesky block decomposition, and hypersphere decomposition are reviewed. In this paper, we review these methods and analyze Korean children and youth panel (KCYP) data are analyzed using the Bayesian method. The KCYP data are multivariate longitudinal data that have response variables: School adaptation, academic achievement, and dependence on mobile phones. Assuming that the correlation structure and the innovation standard deviation structure are different, several models are compared. For the most suitable model, all explanatory variables are significant for school adaptation, and academic achievement and only household income appears as insignificant variables when cell phone dependence is a response variable.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.22 no.3
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• pp.504-518
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• 1998

Degree-Of-Freedom(DOF) for most power transmission system varies according to the operation status which consists of friction elements to change the power flow or to adjust the speed ratio such as clutches, brakes or one-way clutches. To simulate the dynamic characteristics of automatic power transmission system which is a typical example of such a variable DOF systems, many sets of governing equations and complicated phase decision routines are necessary. In this paper dynamic constraint theorem is derived explaining the torque transmission characteristics during the clutch engagement process and a robust stable algorithm is developed describing this phase transition phenomenon effectively by introducing the concept of direct torque and virtual damping. Finally, applying this algorithm to a passenger car automatic transmission gear consisting of several friction elements, an efficient simulation methods for such a complex system will be suggested that is very simple and systematic.

• Genomics & Informatics
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• v.14 no.4
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• pp.149-159
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• 2016

With the success of the genome-wide association studies (GWASs), many candidate loci for complex human diseases have been reported in the GWAS catalog. Recently, many disease prediction models based on penalized regression or statistical learning methods were proposed using candidate causal variants from significant single-nucleotide polymorphisms of GWASs. However, there have been only a few systematic studies comparing existing methods. In this study, we first constructed risk prediction models, such as stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN), using a GWAS chip and GWAS catalog. We then compared the prediction accuracy by calculating the mean square error (MSE) value on data from the Korea Association Resource (KARE) with body mass index. Our results show that SLR provides a smaller MSE value than the other methods, while the numbers of selected variables in each model were similar.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2022.10a
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• pp.169-170
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• 2022

As the size of construction projects has become larger in recent years, the nature of the disaster safety factors along with the progress of construction is also becoming more complex and diversified. Accordingly, the introduction of computerized construction, new technologies, and new construction methods has resulted in a variety of risk factors in the process and operations, and many new safety construction methods are being tried. Since the construction industry has variable working environments and constantly changes working methods and components depending on the rate of progress, workers at construction sites are always exposed to risk depending on the specific circumstances of the construction industry. This study attempts to collect and analyze major accident cases generated by major accident construction projects as a result of progress in construction work, and to derive the major causes and causes for each safety management activity that affect safety accidents.

• Journal of Advanced Navigation Technology
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• v.8 no.2
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• pp.155-162
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• 2004

Recently, significant interest is shown to creation rather inexpensive global systems communications on base of Low-Earth-Orbit Satellite Networks (LEOSN). One of problems of design and creation LEOSN is development of the stream control methods and estimation it's efficiency in such networks. The given problem is complicated, that the topology of the satellite networks varies in time. It essentially hinders the analytical decision of the given problem. An effective way of overcoming of these difficulties is simulation modeling. For realization of research experiments on learning the information streams routing algorithms in LEOSN a special program complex SANET was developed. In the given paper principles of development of LEOSN simulation models and architecture of the manager by the process of a simulation modeling of the unit are considered. Methods of promotion of modeling time and architecture of a simulator complex offered in the article allow to boost essentially efficiency of simulation analysis and to ensure simulation modeling of the satellite networks consisting of several hundreds space vehicles.