• Journal of the Korean Institute of Telematics and Electronics
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• v.5 no.4
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• pp.1-8
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• 1968

A TANT network is a three-level network composed solely of NAND gates having only true(i.e. uncomplemented) inputs. The paper presents a technique for finding for any given Boolean function a least-cost TANT network. The first step of the technique is to determine essential prime implicants(EPI) by Quine-McCluskey procedure or other methods and generate prime implicants(PI) hving the same head as any one of EPI by consensus operation. The second step is to select common factors among the usable tail factors. The selcetion phase is analogous to the use of C-C table. The last step is to minimize inputs by deleting the redundant PI. the technique permits hand solution of typical five-and six-variable problems.

• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.83-91
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• 2004

The purpose of this paper is to obtain an accurate nonlinear system model to test various control schemes for a motion control system that requires high speed, robustness and accuracy. An industrial sewing machine equipped with a Brushless DC motor is considered. It is modeled by a neural network that is configured as an output-error dynamical system. The identified model is essentially a one step ahead prediction structure in which past inputs and outputs are used to calculate the current output. Using the model, a 2 degree-of-freedom PID controller to compensate the effects of disturbance without degrading tracking performance has been de-signed. In this experiment, it is not preferable for safety reasons to tune the controller online on the actual machinery. Experimental results confirm that the model is a good approximation of sewing machine dynamics and that the proposed control methodology is effective.

• Journal of Radiation Protection and Research
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• v.32 no.4
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• pp.168-177
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• 2007

A neural network model to evaluate the neutron exposure on the reactor pressure vessel inner diameter was developed. By using the three dimensional synthesis method described in Regulatory Guide 1.190, a simple linear equation to calculate the neutron spectrum on the reactor pressure vessel was constructed. This model can be used in a quick estimation of fast neutron flux which is the most important parameter in the assessment of embrittlement of reactor pressure vessel. This model also used in the selection of an optimum core loading pattern without the neutron transport calculation. The maximum relative error of this model was less than 3.4% compared to the transport calculation for the calculations from cycle 1 to cycle 23 of Kori unit 1.

• JSTS:Journal of Semiconductor Technology and Science
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• v.17 no.4
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• pp.505-513
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• 2017

As mobile systems are performing various functionality in the IoT (Internet of Things) era, network-on-chip (NoC) plays a pivotal role to support communication between the tens and in the future potentially hundreds of interacting modules in system-on-chips (SoCs). Owing to intensive research efforts more than a decade, NoCs are now widely adopted in various SoC designs. Especially, studies on application-specific NoCs (ASNoCs) that consider the heterogeneous nature of modern SoCs contribute a significant share to use of NoCs in actual SoCs, i.e., ASNoC connects non-uniform processing units, memory, and other intellectual properties (IPs) using flexible router positions and communication paths. Although it is not difficult to find the prior works on ASNoC synthesis and optimization, little research has addressed the issues how to convert different protocols and data widths to make a NoC compatible with various IPs. Thus, in this paper, we address important issues on ASNoC implementation to support and convert multiple interfaces. Based on the in-depth discussions, we finally introduce our FPGA-proven full-custom ASNoC.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.87-94
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• 2001

Silicon nitride films grown by plasma-enhanced chemical vapor deposition (PECVD) are useful for a variety of applications, including anti-reflecting coatings in solar cells, passivation layers, dielectric layers in metal/insulator structures, and diffusion masks. PECVD systems are controlled by many operating variables, including RF power, pressure, gas flow rate, reactant composition, and substrate temperature. The wide variety of processing conditions, as well as the complex nature of particle dynamics within a plasma, makes tailoring SiN film properties very challenging, since it is difficult to determine the exact relationship between desired film properties and controllable deposition conditions. In this study, SiN PECVD modeling using optimized neural networks has been investigated. The deposition of SiN was characterized via a central composite experimental design, and data from this experiment was used to train and optimize feed-forward neural networks using the back-propagation algorithm. From these neural process models, the effect of deposition conditions on film properties has been studied. A recipe synthesis (optimization) procedure was then performed using the optimized neural network models to generate the necessary deposition conditions to obtain several novel film qualities including high charge density and long lifetime. This optimization procedure utilized genetic algorithms, hybrid combinations of genetic algorithm and Powells algorithm, and hybrid combinations of genetic algorithm and simplex algorithm. Recipes predicted by these techniques were verified by experiment, and the performance of each optimization method are compared. It was found that the hybrid combinations of genetic algorithm and simplex algorithm generated recipes produced films of superior quality.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.7
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• pp.3313-3317
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• 2012

Prostate cancer is a highly prevalent disease in older men of the western world. MicroRNAs (miRNAs) are small RNA molecules that regulate gene expression via posttranscriptional inhibition of protein synthesis. To identify the diagnostic potential of miRNAs in prostate cancer, we downloaded the miRNA expression profile of prostate cancer from the GEO database and analysed the differentially expressed miRNAs (DE-miRNAs) in prostate cancerous tissue compared to non-cancerous tissue. Then, the targets of these DE-miRNAs were extracted from the database and mapped to the STRING and KEGG databases for network construction and pathway enrichment analysis. We identified a total of 16 miRNAs that showed a significant differential expression in cancer samples. A total of 9 target genes corresponding to 3 DE-miRNAs were obtained. After network and pathway enrichment analysis, we finally demonstrated that miR-20 appears to play an important role in the regulation of prostate cancer onset. MiR-20 as single biomarker or in combination could be useful in the diagnosis of prostate cancer. We anticipate our study could provide the groundwork for further experiments.

• Bulletin of the Korean Chemical Society
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• v.30 no.5
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• pp.1021-1025
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• 2009

A novel supramolecular network containing binuclear copper unit $[Cu(phen)_{2}({\mu}-ID\;A)Cu(phen){\cdot}(NO_{3})](NO_{3}){\cdot}4(H_{2}O)$ (1) was synthesized through the self-assembly of iminodiacetic acid ($H_2IDA$) and 1,10-phenanthroline (phen) in the condition of pH = 6. It has been characterized by the infrared (IR) spectroscopy, elemental analysis, single crystal X-ray diffraction, and thermogravimetric analysis (TGA). 1 shows a 2-D supramolecular structure assembled through strong and unique $\pi-\pi$ packing interactions. Density functional theory (DFT) calculations show that theoretical optimized structures can well reproduce the experimental structure. The TGA and powder X-ray diffraction (PXRD) curves indicate that the complex 1 can maintain the structural integrity even at the loss of free water molecules. The magnetic property is also reported in this paper.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.465-467
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• 2019

본 논문에서는 영상생성이 가능한 딥러닝 네트워크를 이용하여 조영증강 CT 영상을 획득하는 연구를 수행하였다. CT는 고해상도 영상을 바탕으로 환자의 질병 및 암 세포 진단에 사용되는 의료영상 기법 중 하나이다. 특히, 조영제를 투여한 다음 CT 영상을 획득되는 영상을 조영증강 CT 영상이라 한다. 조영증강된 CT 영상은 물질의 구성 성분의 영상대비를 강조하여 임상의로 하여금 진단 및 치료반응 평가의 정확성을 향상시켜준다. 하지많은 수의 환자들이 조영제 부작용을 갖기 때문에 이에 해당되는 환자의 경우 조영증강 CT 영상 획득이 불가능해진다. 따라서 본 연구에서는 조영증강 영상을 얻지 못하는 환자 및 일반 환자의 불필요한 방사선의 노출을 최소화 하기 위하여 영상생성 딥러닝 기법을 이용하여 CT 영상에서 조영증강 CT 영상을 생성하는 연구를 진행하였다. 영상생성 딥러닝 네트워크는 generative adversarial network (GAN) 모델을 사용하였다. 연구결과 아무런 전처리도 거치지 않은 CT 영상을 이용하여 영상을 생성하는 것 보다 히스토그램 균일화 과정을 거친 영상이 더 좋은 결과를 나타냈으며 생성영상이 기존의 실제 영상과 영상의 구조적 유사도가 높음을 확인할 수 있다. 본 연구결과 딥러닝 영상생성 모델을 이용하여 조영증강 CT 영상을 생성할 수 있었으며, 이를 통하여 환자의 불필요한 방사선 피폭을 최소하며, 생성된 조영증강 CT 영상을 바탕으로 정확한 진단 및 치료반응 평가에 기여할 수 있을거라 기대된다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.11-14
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• 2020

The importance of data in the development of deep learning is very high. Data with high morphological features are usually utilized in the domains where careful lens calibrations are needed by a human to capture those data. Synthesis of high morphological data for that domain can be a great asset to improve the classification accuracy of systems in the field. Unsupervised learning can be employed for this task. Generating photo-realistic objects of interest has been massively studied after Generative Adversarial Network (GAN) was introduced. In this paper, we propose Morpho-GAN, a method that unifies several GAN techniques to generate quality data of high morphology. Our method introduces a new suitable training objective in the discriminator of GAN to synthesize images that follow the distribution of the original dataset. The results demonstrate that the proposed method can generate plausible data as good as other modern baseline models while taking a less complex during training.

• IMMUNE NETWORK
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• v.22 no.2
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• pp.18.1-18.15
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• 2022

Dysfunction of mitochondrial metabolism is implicated in cellular injury and cell death. While mitochondrial dysfunction is associated with lung injury by lung inflammation, the mechanism by which the impairment of mitochondrial ATP synthesis regulates necroptosis during acute lung injury (ALI) by lung inflammation is unclear. Here, we showed that the impairment of mitochondrial ATP synthesis induces receptor interacting serine/threonine kinase 3 (RIPK3)-dependent necroptosis during lung injury by lung inflammation. We found that the impairment of mitochondrial ATP synthesis by oligomycin, an inhibitor of ATP synthase, resulted in increased lung injury and RIPK3 levels in lung tissues during lung inflammation by LPS in mice. The elevated RIPK3 and RIPK3 phosphorylation levels by oligomycin resulted in high mixed lineage kinase domain-like (MLKL) phosphorylation, the terminal molecule in necroptotic cell death pathway, in lung epithelial cells during lung inflammation. Moreover, the levels of protein in bronchoalveolar lavage fluid (BALF) were increased by the activation of necroptosis via oligomycin during lung inflammation. Furthermore, the levels of ATP5A, a catalytic subunit of the mitochondrial ATP synthase complex for ATP synthesis, were reduced in lung epithelial cells of lung tissues from patients with acute respiratory distress syndrome (ARDS), the most severe form of ALI. The levels of RIPK3, RIPK3 phosphorylation and MLKL phosphorylation were elevated in lung epithelial cells in patients with ARDS. Our results suggest that the impairment of mitochondrial ATP synthesis induces RIPK3-dependent necroptosis in lung epithelial cells during lung injury by lung inflammation.