• Journal of KIISE:Software and Applications
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• v.29 no.7
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• pp.510-520
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• 2002

This paper proposes a transformation-based approach to design constraint-based analyses for Java at a coarser granularity. In this approach, we design a less or equally precise but more efficient version of an original analysis by transforming the original construction rules into new ones. As applications of this rule transformation, we provide two instances of analysis design by rule-transformation. The first one designs a sparse version of class analysis for Java and the second one deals with a sparse exception analysis for Java. Both are designed based on method-level, and the sparse exception analysis is shown to give the same information for every method as the original analysis.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.43-54
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• 2021

Human capital and innovation capacities are essential elements and one of the sustainable approaches to driving economic growth. However, there is debate among scholars concerning these two factors in fostering economic growth. This study investigates the relationships between human capital and innovation capacity and economic growth in selected ASEAN countries, namely, Malaysia, Thailand, and Indonesia. Economists widely discussed the interrelation of human capital and innovation. A large body of literature stated that human capital is an essential factor and engine of economic growth. Innovation has become key in transforming the economic development of developing countries. We analyze human capital (HC) and innovation capacity (INC) using static panel data analysis. The data analysis shows that the fixed-effect model is the best model in this study. Further, human capital (HC) has a significant positive relationship with economic growth. Meanwhile, innovation capacity has no significant relationship with economic growth. We also found that Malaysia's coefficient of human capital and innovation capacity is higher and more efficient than in Thailand and Indonesia. In conclusion, human capital and innovation capacity are crucial elements for measuring economic growth. Skilled human capital contributes significantly to the economic growth and economic development of a nation.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.171-176
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• 2022

A dramatic rise in the number of people dying from heart disease has prompted efforts to find a way to identify it sooner using efficient approaches. A variety of variables contribute to the condition and even hereditary factors. The current estimate approaches use an automated diagnostic system that fails to attain a high level of accuracy because it includes irrelevant dataset information. This paper presents an effective neural network with convolutional layers for classifying clinical data that is highly class-imbalanced. Traditional approaches rely on massive amounts of data rather than precise predictions. Data must be picked carefully in order to achieve an earlier prediction process. It's a setback for analysis if the data obtained is just partially complete. However, feature extraction is a major challenge in classification and prediction since increased data increases the training time of traditional machine learning classifiers. The work integrates the CNN-MDRP classifier (convolutional neural network (CNN)-based efficient multimodal disease risk prediction with TANFIS (tuned adaptive neuro-fuzzy inference system) for earlier accurate prediction. Perform data cleaning by transforming partial data to informative data from the dataset in this project. The recommended TANFIS tuning parameters are then improved using a Laplace Gaussian mutation-based grasshopper and moth flame optimization approach (LGM²G). The proposed approach yields a prediction accuracy of 98.40 percent when compared to current algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3121-3144
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• 2023

As interest in the metaverse has grown, there has been a demand for avatars that can represent individual users. Consequently, research has been conducted to reduce the time and cost required for the current 3D human modeling process. However, the recent automatic generation of 3D humans has been focused on creating avatars with a realistic human form. Furthermore, the existing methods have limitations in generating avatars with imbalanced or unrealistic body shapes, and their utilization is limited due to the absence of datasets. Therefore, this paper proposes a new framework for automatically transforming and creating stylized 3D avatars. Our research presents a definitional approach and methodology for creating non-realistic character avatars, in contrast to previous studies that focused on creating realistic humans. We define a new shape representation parameter and use a deep learning-based method to extract character body information and perform automatic template mesh transformation, thereby obtaining non-realistic or unbalanced human meshes. We present the resulting outputs visually, conducting user evaluations to demonstrate the effectiveness of our proposed method. Our approach provides an automatic mesh transformation method tailored to the growing demand for avatars of various body types and extends the existing method to the 3D cartoon stylized avatar domain.

• Proceedings of the Korean Society of Applied Pharmacology
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• 1995.04a
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• pp.79-79
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• 1995

Although synthetic antisense oligodeoxynucleotides (ODNs) have been used to dissect gene function in vitro, technical difficulties of targeted delivery prevented the use of this approach for investigating the effect of gene products in vivo. Here we report the use of local delivery of antisense transforming growth factor-${\beta}$l (TGF-${\beta}$1) oligonucleotides to decrease the fibrosis in the skin wound. Adult wounds heal with scar-tissue formation, whereas fetal wounds heal without scarring and with a lesser inflammatory and cytokine response. We reasoned that strategy emptying antisense TGF-${\beta}$1 ODNs complementary to TGF-${\beta}$1 mRNA might decrease the scarring in dermal wound of mouse. To evaluate this concept, we tested the effects of antisense ODNs targeted to TGF-${\beta}$1 mRNA by topical application of the chemical on the skin wound. Phosphorothioate antisense ODNs was employed to retard their degradation. When antisense TGF-${\beta}$1 ODNs were applied into the wound site, there was a maked reduction of scar compared with control wound site, These effects of antisense TGF-${\beta}$1 ODNs on the scar formation were associated with decreased expression of TGF-${\beta}$1 gene. However sense TGF-${\beta}$l ODNs had no effect on expression of TGF-${\beta}$1 gene. Also, control wounds healed with excessive fibrosis, whereas the antisense treated wounds healed with less fibrosis. In conclusion, our results indicate that antisense TGF-${\beta}$1 ODNs could be used for amelioating scar formation during wound healing.

• Journal of Korean Neurosurgical Society
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• v.65 no.2
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• pp.186-195
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• 2022

Objective : To explore the histological feature of the cervical disc degeneration in patients with degenerative ossification (DO) and its potential mechanisms. Methods : A total of 96 surgical segments, from cervical disc degenerative disease patients with surgical treatment, were divided into ossification group (group O, n=46) and non-ossification group (group NO, n=50) based on preoperative radiological exams. Samples of disc tissues and osteophytes were harvested during the decompression operation. The hematoxylin-eosin staining, Masson trichrome staining and Safranin O-fast green staining were used to compare the histological differences between the two groups. And the distribution and content of transforming growth factor (TGF)-β1, p-Smad2 and p-Smad3 between the two groups were compared by a semi-quantitative immunohistochemistry (IHC) method. Results : For all the disc tissues, the content of disc cells and collagen fibers decreased gradually from the outer annulus fibrosus (OAF) to the central nucleus pulposus (NP). Compared with group NO, the number of disc cells in group O increased significantly. But for proteoglycan in the inner annulus fibrosus (IAF) and NP, the content in group O decreased significantly. IHC analysis showed that TGF-β1, p-Smad2, and p-Smad3 were detected in all tissues. For group O, the content of TGF-β1 in the OAF and NP was significantly higher than that in group NO. For p-Smad2 in IAF and p-Smad3 in OAF, the content in group O were significantly higher than group NO. Conclusion : Histologically, cervical disc degeneration in patients with DO is more severe than that without DO. Local higher content of TGF-β1, p-Smad2, and p-Smad3 are involved in the disc degeneration with DO. Further studies with multi-approach analyses are needed to better understand the role of TGF-β/Smads signaling pathway in the disc degeneration with DO.

• KIEE International Transactions on Power Engineering
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• v.3A no.3
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• pp.139-147
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• 2003

The paper presents a novel approach of constructing the state matrix of the multi-machine power system for SSR (subsynchronous resonance) analysis using the linearized equations of individual devices including electrical transmission network dynamics. The machine models in the local d-q reference frame are integrated with the network models in the common R-I reference frame by simply transforming their output equations into the R-I frame where the transformed output is used as the input to the network dynamics or vice versa. The salient feature of the formulation is that it allows for modular construction of various component models without rearranging the overall state space formulation. This universal SSR small signal stability program provides a flexible tool for systematic analyses of SSR small-signal stability impacts of both conventional devices such as generation systems and novel devices such as power electronic apparatus and their controllers. The paper also presents its application results to IEEE benchmark models.

• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.127-134
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• 2005

A suitable representation for the conceptual simplicity of the data in statistics and signal processing is essential for a subsequent analysis such as prediction, pattern recognition, and spatial analysis. Independent component analysis (ICA) is a statistical method for transforming an observed high-dimensional multivariate data into statistically independent components. ICA has been applied increasingly in wide fields of spectrum application since ICA is able to extract unknown components of a mixture from spectra. We focus on application of ICA for separating independent sources and predicting each composition using extracted components. The theory of ICA is introduced and an application to a metal surface spectra data will be described, where subsequent analysis using non-negative least square method is performed to predict composition ratio of each sample. Furthermore, some simulation experiments are performed to demonstrate the performance of the proposed approach.

• Korean Institute of Interior Design Journal
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• v.13 no.3
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• pp.187-196
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• 2004

The purpose of this study is to develope'a digital space design process using user's motion data' through a theoretical and experimental study. In the progress of developing a developing of design process, this study was concentrated on searching a digital method applying user's interactive reflections. As introducing a concept of space form being generated by user's experiences, we proposed'a digital design process using user's motion data'. In the experimental stage, user's motion data were extracted and transferred as digital information by user behavior analysis, optical motion capture system, immersive VR system, 3D softwares com computer programming. As the result of this study, another useful digital design process was embodied by building up a digital form-transforming method using 3D softwares providing internal algorithm. This study would be meaningful in terms of attempting a creative and interactive digital space design method, avoiding dehumanization of existing ones through the theoretical study and the experimental approach.

• MALSORI
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• no.44
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• pp.83-92
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• 2002

Speech recognition performance is severly degraded under noisy envrionments. One approach to cope with this problem is audio-visual speech recognition. In this paper, we discuss the experiment results of bimodal speech recongition based on enhanced speech feature vectors using lip information. We try various kinds of speech features as like linear predicion coefficient, cepstrum, log area ratio and etc for transforming lip information into speech parameters. The experimental results show that the cepstrum parameter is the best feature in the point of reconition rate. Also, we present the desirable weighting values of audio and visual informations depending on signal-to-noiso ratio.