• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.15 no.7
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• pp.576-582
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• 2002

Nonvolatile semiconductor memory devices with reoxidized nitrided oxide(RONO) gate dielectrics were fabricated, and nitrogen distribution and bonding species which contribute to memory characteristics were analyzed. Also, memory characteristics of devices depending on the anneal temperatures were investigated. The devices were fabricated by retrograde twin well CMOS processes with $0.35\mu m$ design rule. The processes could be simple by in-situ process in growing dielectric. The nitrogen distribution and bonding states of gate dielectrics were investigated by Dynamic Secondary Ion Mass Spectrometry(D-SIMS), Time-of-Flight Secondary Ion Mass Spectrometry(ToF-SIMS), and X-ray Photoelectron Spectroscopy(XPS). As the nitridation temperature increased, nitrogen concentration increased linearly, and more time was required to form the same reoxidized layer thickness. ToF-SIMS results showed that SiON species were detected at the initial oxide interface which had formed after NO annealing and $Si_2NO$ species within the reoxidized layer formed after reoxidation. As the anneal temperatures increased, the device showed worse retention and degradation properties. It could be said that nitrogen concentration near initial interface is limited to a certain quantity, so the excess nitrogen is redistributed within reoxidized layer and contribute to electron trap generation.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.8 s.197
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• pp.58-66
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• 2007

In order to improve the recyclability and to reduce the recycling cost and time, the disassembly technology should be systemized because the worn out products can be reused or recycled after disassembly processes. This paper attempts to propose the integrated CATIA-based DfD (Design for Disassembly) support system to promote the disassemblability of products. The system is composed of two modules; evaluation of disassemblability, generation of DfD alternatives. The disassemblability of current vehicle is evaluated to identify the weak point in terms of disassembly using the DELMIA and developed evaluation system. Furthermore a new expert system is developed to propose the optimal redesign rule and principle for generating the DfD alternatives. In order to generate the DfD alternatives, a CATIA-based design support system is implemented. The system can provide quick results and ensure consistency and completeness of the redesign alternatives.

• Journal of Industrial Technology
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• v.3
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• pp.69-74
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• 1983

For the determination of a reservoir capacity Rippl's mass-curve method has long been used with the past river flow data assuming the same flow records will be repeated in the future. This study aims to find out a better method for determining the reservoir capacity by employing the analytical theory based on the stochastic process. For the present study the synthetic generation methods of Thomas-Fiering type was used to synthetically generate 50 years of monthly river inflows to three single-purpose reservoirs and three multi-purpose reservoirs. The generated sequences of monthly flows were analyzed based on the range concept. With the optimum operation rule of the reservoirs as the one which maximizes the water-use downstream the waterrelease from the reservoir was determined and with due consideration to the mean inflows and the range of monthly flows the required reservoirs capacity was stochastically determined. It was possible to repersent the so-determined reservoir capacity in terms of the mean monthly inflows and the number of subseries in the determination of ranges. It is suggested that the result obtained in this study would be applied to approximately estimate, in the stage of preliminary design, the required capacity of a reservoir in question with the limited information such as the mean monthly inflow and the period of reservoir operation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.29-37
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• 2020

Conventional firewalls cannot cope with attacks immediately. It is because security professionals or administrators need to analyze them and enter relevant policies to the firewalls. In addition, those policies may often block even normal accesses. Even though the packet themselves are normal, there exist many attacks that cause denial of service due to the inflow of a large amount of those packets. In this paper, we propose a method to block attacks such as Flooding, Spoofing and Scanning while allowing normal accesses based on whitelist policies which are automatedly generated by learning normal access patterns.

• Wind and Structures
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• v.18 no.6
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• pp.599-618
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• 2014

In this study, a square rectangular tall building is considered to investigate the effects of turbulence integral length scale and turbulence intensity on the along-wind responses, across-wind responses and torsional responses of the tall building by Large Eddy Simulation (LES). A recently proposed inflow turbulence generator called the discretizing and synthesizing random flow generation (DSRFG) approach is applied to simulate turbulent flow fields. It has been proved that the approach is able to generate a fluctuating turbulent flow field satisfying any given spectrum, desired turbulence intensity and wind speed profiles. Five profiles of turbulence integral length scale and turbulence intensity are respectively generated for the inflow fields by the DSRFG approach for investigating the effects of turbulence integral length scale and turbulence intensity on the wind-induced responses of the tall building. The computational results indicate that turbulence integral length scale does not have significant effect on the along-wind (displacement, velocity and acceleration) responses, across-wind displacement and velocity responses, while the across-wind acceleration and torsional responses vary without a clear rule with the parameter. On the other hand, the along-wind, across-wind and torsional responses increase with the growth of turbulence intensity.

• Journal of Electrical Engineering and Technology
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• v.5 no.4
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• pp.552-560
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• 2010

This paper presents an application of layered Artificial Neural Network controller to study load frequency control problem in power system. The objective of control scheme guarantees that steady state error of frequencies and inadvertent interchange of tie-lines are maintained in a given tolerance limitation. The proposed controller has been designed for a two-area interconnected power system. Only one artificial neural network controller (ANN), which controls the inputs of each area in the power system together, is considered. In this study, back propagation-through time algorithm is used as neural network learning rule. The performance of the power system is simulated by using conventional integral controller and ANN controller, separately. For the first time comparative study has been carried out between SMES and CES unit, all of the areas are included with SMES and CES unit separately. By comparing the results for both cases, the performance of ANN controller with CES unit is found to be better than conventional controllers with SMES, CES and ANN with SMES.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.14-18
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• 1997

In this paper, we suggested and made a classifier or qualitative and quantitative analysis in urine analysis system. Input variable number and fuzzy membership function was made from determination of standard sample, and the fuzzy rules were determined by the analysis of spectroscopic properties of pads in strip. Fuzzy classifier used in urine analysis system was evaluated or the standard samples in each items and degrees. Negative and positive response of urine test was classified in good property, but detail classification or quantitative analysis had 8% maximum error in each items. If fuzzy membership unction and generation of rule are supplemented, suggested fuzzy classifier can be applied to the clinical test.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1083-1094
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• 2020

Recognizing plant species based on leaf images is challenging because of the large inter-class variation and inter-class similarities among different plant species. The effective extraction of leaf descriptors constitutes the most important problem in plant leaf recognition. In this paper, a multi-scale angular description method is proposed for fast and accurate leaf recognition and retrieval tasks. The proposed method uses a novel scale-generation rule to develop an angular description of leaf contours. It is parameter-free and can capture leaf features from coarse to fine at multiple scales. A fast Fourier transform is used to make the descriptor compact and is effective in matching samples. Both support vector machine and k-nearest neighbors are used to classify leaves. Leaf recognition and retrieval experiments were conducted on three challenging datasets, namely Swedish leaf, Flavia leaf, and ImageCLEF2012 leaf. The results are evaluated with the widely used standard metrics and compared with several state-of-the-art methods. The results and comparisons show that the proposed method not only requires a low computational time, but also achieves good recognition and retrieval accuracies on challenging datasets.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.569-574
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• 2011

In this paper, we introduce the subspace contrasting group identification problem and propose an algorithm to solve the problem. In order to identify contrasting groups, the algorithm first determines two groups of which attribute values are in one of the contrasting ranges specified by the analyst, and searches for the contrasting groups while increasing the dimension of subspaces with an association rule mining strategy. Because the dimension of microarray data is likely to be tens of thousands, it is burdensome to find all contrasting groups over all possible subspaces by query generation. It is very useful in the sense that the proposed method allows to find those contrasting groups without analyst's involvement.

• The Transactions of the Korean Institute of Power Electronics
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• v.21 no.2
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• pp.182-189
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• 2016

A modeling method of electric power network inside a fishing boat less than 5 tons is proposed for its high-energy efficiency with renewable energy sources. The power network inside the fishing boat consists of a diesel engine, a starter motor, an alternator, battery packs, and electric loads, which are connected in parallel. To obtain proper power network model, the voltage -current characteristics of the electric components are considered to develop elaborate electrical models under several load conditions. Measured data of the battery and alternator current include noise. By using an average method, the AC components from the power network of the fishing boat can be reduced, which is verified by KCL rule. Using the proposed power network model, the power generation of the alternator and the reduction of diesel consumption in the boat's engine are predictable under various operating conditions. The validity of the proposed methodology is verified by comparing simulation results with experimental measurements using statistical inferences.