• Journal of information and communication convergence engineering
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• v.14 no.4
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• pp.258-267
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• 2016

Three-dimensional integrated circuits (3D ICs) experience die-to-die variations in addition to the already challenging within-die variations. This adds an additional design complexity and makes variation estimation and full-chip optimization even more challenging. In this paper, we show that the industry standard on-chip variation (AOCV) tables cannot be applied directly to 3D paths that are spanning multiple dies. We develop a new machine learning-based model and methodology for an accurate variation estimation of logic paths in 3D designs. Our model makes use of key parameters extracted from existing GDSII 3D IC design and sign-off simulation database. Thus, it requires no runtime overhead when compared to AOCV analysis while achieving an average accuracy of 90% in variation evaluation. By using our model in a full-chip variation-aware 3D IC physical design flow, we obtain up to 16% improvement in critical path delay under variations, which is verified with detailed Monte Carlo simulations.

• Korean Journal of Construction Engineering and Management
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• v.6 no.6 s.28
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• pp.115-123
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• 2005

Design of steel $bridge^{\circ}AEs$ temporary works has conducted relying on the experiences of engineers based on the previous similar projects. Consequently, there have been arguments against over-design of temporary bents to be required at the actual construction sites, and unnecessary design changes have been issued at the construction stage. In this study, we have developed an optimum design support system for temporary works of the steel bridge construction through establishing the database for the materials to be needed for implementation of temporary works. We've also improved the accuracy and efficiency of the works through the design optimization for temporary works, and contributed to reduce design changes as well as to utilize the design informations at the construction stage.

• Bulletin of the Korean Chemical Society
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• v.34 no.4
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• pp.1043-1046
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• 2013

Prediction of physicochemical properties of organic molecules is an important process in chemistry and chemical engineering. The MSEP approach developed in our lab calculates the molecular surface electrostatic potential (ESP) on van der Waals (vdW) surfaces of molecules. This approach includes geometry optimization and frequency calculation using hybrid density functional theory, B3LYP, at the 6-31G(d) basis set to find minima on the potential energy surface, and is known to give satisfactory QSPR results for various properties of organic molecules. However, this MSEP method is not applicable to screen large database because geometry optimization and frequency calculation require considerable computing time. To develop a fast but yet reliable approach, we have re-examined our previous work on organic molecules using two semi-empirical methods, AM1 and PM3. This new approach can be an efficient protocol in designing new molecules with improved properties.

• ETRI Journal
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• v.32 no.3
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• pp.395-405
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• 2010

A novel multiclassifier system (MCS) strategy is proposed and applied to a text-dependent speaker verification task. The presented scheme optimizes the linear combination of classifiers on an on-line basis. In contrast to ordinary MCS approaches, neither a priori distributions nor pre-tuned parameters are required. The idea is to improve the most accurate classifier by making use of the incremental information provided by the second classifier. The on-line multiclassifier optimization approach is applicable to any pattern recognition problem. The proposed method needs neither a priori distributions nor pre-estimated weights, and does not make use of any consideration about training/testing matching conditions. Results with Yoho database show that the presented approach can lead to reductions in equal error rate as high as 28%, when compared with the most accurate classifier, and 11% against a standard method for the optimization of linear combination of classifiers.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.107-110
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• 2017

According to researches, while the speed of processor and network in embedded devices is fast enough to meet user requirement, the IO speed is recognized as the main performance bottleneck. Meanwhile it is known that more than 70 percent of IOs are issued from SQLite database. Many researches related SQLite performance optimization is based on WAL mode because WAL mode optimized for write IO performance. In this paper, I propose to optimize SQLite with Atomic Write in the Rollback Journal Mode, which is mainly used in Android and Tizen. I have observed that Atomic Write have a significant write performance improvement(300%) by reducing write, file sync operation and memory usage improvement(80%). Additionally it can block JOJ(Journaling of Journal) and extend the life of the flash memory.

• Korean Journal of Materials Research
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• v.27 no.6
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• pp.301-306
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• 2017

The discovery of new luminescent materials for use in light-emitting diodes(LEDs) has been of great interest, since LED-based solid state lighting applications are attracting a lot of attention in the energy saving and environmental fields. Recent research trends have centered on the discovery of new luminescent materials rather than on fine changes in well-known luminescent materials. In a sense, the novelty of our study beyond simple modification or improvement of existing phosphors. A good strategy for the discovery of new fluorescent materials is to introduce activators that are appropriate for conventional inorganic compounds, that have well-defined structures in the crystal structure database, but have not been considered as phosphor hosts. Another strategy is to discover new host compounds with structures that cannot be found in any existing databases. We have pursued these two strategies at the same time using composite search technology with particle swarm optimization(PSO). In this study, using PSO, we have tracked down a search space composed of Sr-Al-Si-O-N and have discovered a new phosphor structure with yellow luminescence; this material is a potential candidate for UV-LED applications.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2852-2859
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• 2020

This paper deals with the simulation-based design optimization and experimental validation of the characteristics of an in-core silver Self-Powered Neutron Detector (SPND). Optimized dimensions of the SPND are determined by combining Monte Carlo simulations and analytical methods. As a first step, the Monte Carlo transport code MCNPX is used to follow the trajectory and fate of the neutrons emitted from an external source. This simulation is able to seamlessly integrate various phenomena, including neutron slowing-down and shielding effects. Then, the expected number of beta particles and their energy spectrum following a neutron capture reaction in the silver emitter are fetched from the TENDEL database using the JANIS software interface and integrated with the data from the first step to yield the origin and spectrum of the source electrons. Eventually, the MCNPX transport code is used for the Monte Carlo calculation of the ballistic current of beta particles in the various regions of the SPND. Then, the output current and the maximum insulator thickness to avoid breakdown are determined. The optimum design of the SPND is then manufactured and experimental tests are conducted. The calculated design parameters of this detector have been found in good agreement with the obtained experimental results.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1363-1373
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• 2022

The recent seismic events that occurred in South Korea have increased the interest in the re-evaluation of the seismic capacity of nuclear power plant (NPP) equipment, which is often conservatively estimated. To date, various approaches-including the Bayesian method proposed by the United States (US) Electric Power Research Institute -have been developed to quantify the seismic capacity of NPP equipment. Among these, the Bayesian approach has advantages in accounting for both prior knowledge and new information to update the probabilistic distribution of seismic capacity. However, data availability and region-specific issues exist in applying this Bayesian approach to Korean NPP equipment. Therefore, this paper proposes to construct an earthquake experience database by combining available earthquake records at Korean NPP sites and the general location of equipment within NPPs. Also, for the better representation of the seismic demand of Korean earthquake datasets, which have distinct seismic characteristics from those of the US at a high-frequency range, a broadband frequency range optimization is suggested. The proposed data construction and seismic demand optimization method for seismic capacity re-evaluation are demonstrated and tested on a 480 V motor control center of a South Korea NPP.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.53-64
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• 2023

Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.382-388
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• 2011

Being aware of the risk in advance necessitates intricate processes but is feasible. Although previous studies have demonstrated high accuracy, their performance still leaves room for improvement. A self-organizing feature map (SOM) based neurofuzzy model is developed in this study to provide another alternative for forecasting corporate financial distress. The model is designed to yield high prediction accuracy, as well as reference rules for evaluating corporate financial status. As a database, the study collects all financial reports from listed construction companies during the latest decade, resulting in over 1000 effective samples. The proportion of "failed" and "non-failed" companies is approximately 1:2. Each financial report is comprised of 25 ratios which are set as the input variable s. The proposed model integrates the concepts of pattern classification, fuzzy modeling and SOM-based optimization to predict corporate financial distress. The results exhibit a high accuracy rate at 85.1%. This model outperforms previous tools. A total of 97 rules are extracted from the proposed model which can be also used as reference for construction practitioners. Users may easily identify their corporate financial status by using these rules.