• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.3104-3104
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• 2001

The research described here was undertaken with the aim of monitoring, optimizing and ultimately controlling the production of heterofermentative microbes used as starters in the salami industry. The use of starter cultures in the fermented meats industry is a well-established technique used to shorten and standardize the ripening process, and to improve and control the organoleptic quality of the final product. Starter cultures are obtained by the submerged cultivation of suitable microorganisms in stirred, and sometimes aerated, fermenters where monitoring of key physiological parameters such as the concentration of biomass, substrates and metabolites suffers from the general lack of real-time measurement techniques applicable to aseptic processes. In this respect, the results of the present work are relevant to all submerged fermentation processes. Previous work on the application of on-line NIR spectroscopy to the lactic acid fermentation (Dosi et al. - Monreal NIR1995) had successfully used a system based on a measuring cell included in a circulation loop external to the fermenter. The fluid handling and sterility problems inherent in an external circulation system prompted us to explore the use of an in-line system where the NIR probe is immersed in the culture and is thus exposed to the hydrodynamic conditions of the stirred and aerated fluid. Aeration was expected to be a potential source of problems in view of the possible interference of air bubbles with the measurement device. The experimental set-up was based on an in-situ sterilizable NIR probe connected to the instrument by means of an optical fiber bundle. Preliminary work was carried out to identify and control potential interferences with the measurement, in particular the varying hydrodynamic conditions prevailing at the probe tip. We were successful in defining the operating conditions of the fermenter and the geometrical parameters of the probe (flow path, positioning, etc.) were the NIR readings were reliable and reproducible. The system thus defined was then used to construct and validate calibration curves for tile concentration of biomass, carbon source and major metabolites of two different microorganisms used as salami starters. Real-time measurement of such parameters coupled with the direct interfacing of the NIR instrument with the PC-based measurement and control system of the fermenter enabled the development of automated strategies for the interactive optimization of the starter production process.

• Journal of Korean Society of Environmental Engineers
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• v.22 no.3
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• pp.495-504
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• 2000

This study discussed the optimal use of bentonite and cement for the compacted soil liner of landfill. Techniques employed in this optimization included permeability(by KSF 2322) and compressive strength(by KSF 2314). The optimal amount of these materials to the compacted soil liner was determined in accordance with a regulatory guideline of the government: that is, $k=1{\times}10^{-7}cm/sec$. The testing sods were CL(Clayey Soil) and SM(Sandy Soil), which were classified according to LSCS(Unifed Soil Classify System), The results showed that the optimal amounts of bentonite and cement to mix with the compacted CL soil liner were 5% of bentonite and 5% of cement : namely, $k=9.98{\times}10^{-8}cm/sec$ and ${\sigma}_{28}=1275kg/cm^2$. For the compacted SM soil liner. the optimal amount of bentonite was 15%, in conjunction with 5% of cement : namely, $k=9.86{\times}10^{-8}cm/sec$ and ${\sigma}_{28}=18.72kg/cm^2$. It was concluded that the compacted CL or SM soil liner, with containing the optimal amounts of bentonite and cement showed the acceptable permeability and the compressive strength, referring to a regulatory guideline of the government for construction of the landfill.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.9_10
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• pp.485-496
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• 2008

As the complexity of SoC (System-on-Chip) design increases dramatically. traditional system performance analysis and verification methods based on RTL (Register Transfer Level) are no more valid for increasing time-to-market pressure. Therefore a new design methodology is desperately required for system verification in early design stages. and hardware software (HW-SW) cosimulation at TLM (Transaction Level Modeling) level has been researched widely for solving this problem. However, most of HW-SW cosimulators support few restricted ion levels only, which makes it difficult to integrate HW-SW cosimulators with different ion levels. To overcome this difficulty, this paper proposes a multipurpose framework for HW SW cosimulation to provide systematic SoC design flow starting from software application design. It supports various design techniques flexibly for each design step, and various HW-SW cosimulators. Since a platform design is possible independently of ion levels and description languages, it allows us to generate simulation models with various ion levels. We verified the proposed framework to model a commercial SoC platform based on an ARM9 processor. It was also proved that this framework could be used for the performance optimization of an MJPEG example up to 44% successfully.

• Journal of KIISE:Databases
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• v.30 no.3
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• pp.336-349
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• 2003

Histograms have been getting a lot of attention recently. Histograms are commonly utilized in commercial database systems to capture attribute value distributions for query optimization Recently, in the advent of researches on approximate query answering and stream data, the interests in histograms are widely being spread. The simplest approach assumes that the attributes in relational tables are independent by AVI(Attribute Value Independence) assumption. However, this assumption is not generally valid for real-life datasets. To alleviate the problem of approximation on multi-dimensional data with multiple one-dimensional histograms, several techniques such as wavelet, random sampling and multi-dimensional histograms are proposed. Among them, GENHIST is a multi-dimensional histogram that is designed to approximate the data distribution with real attributes. It uses overlapping buckets that allow more efficient approximation on the data distribution. In this paper, we propose a scheme, OPT that can determine the optimal frequencies of overlapped buckets that minimize the SSE(Sum Squared Error). A histogram with overlapping buckets is first generated by GENHIST and OPT can improve the histogram by calculating the optimal frequency for each bucket. Our experimental result confirms that our technique can improve the accuracy of histograms generated by GENHIST significantly.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.483-490
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• 2016

Automobile door latch is a fine design and assembly techniques are required in order to produce them in a small component assembly shape such as a spring, injection products, a small-sized motor. The door latch is fixed to not open the door of the car plays an important role it has a direct impact on the driver's safety. In this study, during trimming of the terminals of the connector main components of the car door latch, reduce rollover and conducted a research to find a suitable effective shear surface. Using the Taguchi method with orthogonal array of Finite Element Analysis and optimal Design of Experiments were set up parameters for the shear surface quality of the car door latch connector terminals. The design parameters used in the analysis is the clearance, the radius, and the blank holding force, the material of the connector terminal is a C2600. Trimming process optimum conditions suggested by the analysis has been verified by experiments, the shear surface shape and dimensions of a final product in good agreement with forming analysis results.Taguchi method from the above results in the optimization for the final rollover and effective shear surface improved for a vehicle door latch to the connector terminal can be seen that the applicable and useful for a variety of metal forming processes other than the trimming process is determined to be applicable.

• Journal of the Korean Society for Railway
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• v.13 no.1
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• pp.71-77
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• 2010

It is increasing annually that the cost for bridge Maintenance Repair & Rehabilitation (MR&R) in developed countries. Based on Intelligent Technology, Bridge Management System (BMS) is developed for optimization of Life Cycle Cost (LCC) and reliability to predict long-term bridge deteriorations. However, such data are very limited amongst all the known bridge agencies, making it difficult to reliably predict future structural performances. To alleviate this problem, an Artificial Neural Network (ANN) based Backward Prediction Model (BPM) for generating missing historical condition ratings has been developed. Its reliability has been verified using existing condition ratings from the Maryland Department of Transportation, USA. The function of the BPM is to establish the correlations between the known condition ratings and such non-bridge factors as climate and traffic volumes, which can then be used to obtain the bridge condition ratings of the missing years. Since the non-bridge factors used in the BPM can influence the variation of the bridge condition ratings, well-selected non-bridge factors are critical for the BPM to function effectively based on the minimized discrepancy rate between the BPM prediction result and existing data (deck; 6.68%, superstructure; 6.61%, substructure; 7.52%). This research is on the generation of usable historical data using Artificial Intelligence techniques to reliably predict future bridge deterioration. The outcomes (Long-term Bridge deterioration Prediction) will help bridge authorities to effectively plan maintenance strategies for obtaining the maximum benefit with limited funds.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.4
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• pp.1105-1116
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• 2014

A stochastic rainfall model, NSRPM (Neyman-Scott Rectangular Pulse Model), is able to reflect the cluster characteristics of rainfall events which is unable in the RPM (Rectangular Pulse Model). Therefore NSRPM has advantage in the hydrological applications. The NSRPM consists of five model parameters and the parameters are estimated using optimization techniques such as DFP (Davidon-Fletcher-Powell) method and genetic algorithm. However the DFP method is very sensitive in initial values and is easily converge to local minimum. Also genetic algorithm has disadvantage of long computation time. Nelder-Mead method has several advantages of short computation time and no need of a proper initial value. In this study, the applicability of parameter estimation methods was evaluated using rainfall data of 59 national rainfall networks from 1973-2011. Overall results demonstrated that accuracy in parameter estimation is in the order of Nelder-Mead method, genetic algorithm, and DFP method.

• Journal of the Korea Society of Computer and Information
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• v.15 no.10
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• pp.229-237
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• 2010

Recently, forest management objectives become more complex and complicated, and spatial constraints were necessarily considered for ecological stability. Now forest planning is required to provide an optimized solution that is able to achieve a number of management objectives and constraints. In this study, we developed a decision supporting system based on the one of dynamic planning techniques, Tabu Search (TS) heuristic algorithm, which enable one to generate an optimized solution for given objectives and constraints. For this purpose, we analyzed the logical flow of the algorithm and designed the subsequence of processes. To develop a high-performance computing system, we examined a number of strategy to minimize execution time and workloads in each process and to maximize efficiency of using system resources. We examined two model based on the original TS algorithm and revised version of TS algorithm and compared their performance in optimization process. The results showed high performance of the developed system in providing feasible solutions for several management objectives and constraints. Moreover, the revised version of TS algorithm was appeared to be more stable for providing results with minimum variation. The developed system is expected to use for developing forest management plans in Korea.

• Journal of IKEEE
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• v.23 no.4
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• pp.1440-1447
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• 2019

Currently, the prevalence of autism spectrum disorders in children is reported to be higher and shows various types of disorders. In particular, they are having difficulty in communication due to communication impairment in the area of social communication and need to be improved through training. Thus, this study proposes a method of acquiring voice information through a microphone mounted on a robot designed through preliminary research and using this information to make intelligent motions. An ANN(Artificial Neural Network) was used to classify the speech data into robot motions, and we tried to improve the accuracy by combining the Recurrent Neural Network based on Convolutional Neural Network. The preprocessing of input speech data was analyzed using MFCC(Mel-Frequency Cepstral Coefficient), and the motion of the robot was estimated using various data normalization and neural network optimization techniques. In addition, the designed ANN showed a high accuracy by conducting an experiment comparing the accuracy with the existing architecture and the method of human intervention. In order to design robot motions with higher accuracy in the future and to apply them in the treatment and education environment of children with autism.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4678-4702
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• 2018

Traffic Matrix estimation has always caught attention from researchers for better network management and future planning. With the advent of high traffic loads due to Cloud Computing platforms and Software Defined Networking based tunable routing and traffic management algorithms on the Internet, it is more necessary as ever to be able to predict current and future traffic volumes on the network. For large networks such origin-destination traffic prediction problem takes the form of a large under- constrained and under-determined system of equations with a dynamic measurement matrix. Previously, the researchers had relied on the assumption that the measurement (routing) matrix is stationary due to which the schemes are not suitable for modern software defined networks. In this work, we present our Compressed Sensing with Dynamic Model Estimation (CS-DME) architecture suitable for modern software defined networks. Our main contributions are: (1) we formulate an approach in which measurement matrix in the compressed sensing scheme can be accurately and dynamically estimated through a reformulation of the problem based on traffic demands. (2) We show that the problem formulation using a dynamic measurement matrix based on instantaneous traffic demands may be used instead of a stationary binary routing matrix which is more suitable to modern Software Defined Networks that are constantly evolving in terms of routing by inspection of its Eigen Spectrum using two real world datasets. (3) We also show that linking this compressed measurement matrix dynamically with the measured parameters can lead to acceptable estimation of Origin Destination (OD) Traffic flows with marginally poor results with other state-of-art schemes relying on fixed measurement matrices. (4) Furthermore, using this compressed reformulated problem, a new strategy for selection of vantage points for most efficient traffic matrix estimation is also presented through a secondary compression technique based on subset of link measurements. Experimental evaluation of proposed technique using real world datasets Abilene and GEANT shows that the technique is practical to be used in modern software defined networks. Further, the performance of the scheme is compared with recent state of the art techniques proposed in research literature.