• Nuclear Engineering and Technology
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• v.42 no.2
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• pp.219-230
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• 2010

In nuclear power plants (NPPs), periodic sensor calibrations are required to assure that sensors are operating correctly. By checking the sensor's operating status at every fuel outage, faulty sensors may remain undetected for periods of up to 24 months. Moreover, typically, only a few faulty sensors are found to be calibrated. For the safe operation of NPP and the reduction of unnecessary calibration, on-line instrument calibration monitoring is needed. In this study, principal component-based auto-associative support vector regression (PCSVR) using response surface methodology (RSM) is proposed for the sensor signal validation of NPPs. This paper describes the design of a PCSVR-based sensor validation system for a power generation system. RSM is employed to determine the optimal values of SVR hyperparameters and is compared to the genetic algorithm (GA). The proposed PCSVR model is confirmed with the actual plant data of Kori Nuclear Power Plant Unit 3 and is compared with the Auto-Associative support vector regression (AASVR) and the auto-associative neural network (AANN) model. The auto-sensitivity of AASVR is improved by around six times by using a PCA, resulting in good detection of sensor drift. Compared to AANN, accuracy and cross-sensitivity are better while the auto-sensitivity is almost the same. Meanwhile, the proposed RSM for the optimization of the PCSVR algorithm performs even better in terms of accuracy, auto-sensitivity, and averaged maximum error, except in averaged RMS error, and this method is much more time efficient compared to the conventional GA method.

• KSBB Journal
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• v.32 no.1
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• pp.16-21
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• 2017

The multiple sclerosis caused by multiple inflammatory disease or immune system disorder, is usually treated by interferon ${\beta}$ through adjusting the abnormal immune reactions. For high production of human interferon ${\beta}$ using recombinant E. coli, codon optimized and wild type genes were synthesized. When pET-15b or pET-21a vector was used as an expression vector with each gene, there was no target protein expression. When pQE30 vector was used as an expression vector, human interferon ${\beta}$ was expressed by recombinant E. coli XL1-blue and E. coli JM109. Using the codon optimized gene, the expression of human interferon ${\beta}$ was slightly increased as compared to that from wild type gene. However, most of expressed human interferon ${\beta}$ was insoluble form.

• Journal of Microbiology and Biotechnology
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• v.19 no.6
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• pp.596-603
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• 2009

This study aimed to examine the influence of adding an oxygen vector, n-dodecane, on hyaluronic acid (HA) production by batch culture of Streptococcus zooepidemicus. Owing to the high viscosity of culture broth, microbial HA production during 8-16 h was limited by the oxygen transfer coefficient $K_La$, which could be enhanced by adding n-dodecane. With the addition of n-dodecane to the culture medium to a final concentration of 5% (v/v), the average value of $K_La$ during 8-16 h was increased to $36{\pm}2h^{-1}$, which was 3.6 times that of the control without n-dodecane addition. With the increased $K_La$ and dissolved oxygen (DO) by adding 5% (v/v) of n-dodecane, a 30% increase of HA production was observed compared with the control. Furthermore, the comparison of the oxygen mass transfer in the absence and presence of n-dodecane was conducted with two proposed mathematical models. The use of n-dodecane as an oxygen vector, as described in this paper, provides an efficient alternative for the optimization of other aerobic biopolymer productions, where $K_La$ is usually a limiting factor.

• The Journal of Korean Institute of Information Technology
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• v.17 no.10
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• pp.49-55
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• 2019

In this paper, we propose a simple precoding vector design scheme for multi-user multiple-input single-output (MISO) interference channel when there are multiple eavesdroppers. We aim to obtain a mathematical closed-form solution of the secrecy rate optimization problem. For this goal, we design the precoding vector based on the signal-to-leakage plus noise ratio (SLNR). More specifically, the proposed precoding vector is designed to completely eliminate a wiretap channel capacity for refraining the eavesdroppers from detecting the transmitted information, and to maximize the transmitter-receiver link achievable rate. We performed simulation for the performance investigation. Simulation results show that the proposed scheme has better secrecy rate than the conventional scheme over all signal-to-noise ratio (SNR) range even though the special condition among the numbers of transmit antennas, transmitter-receiver links, and eavesdroppers is not satisfied.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.4
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• pp.526-533
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• 2014

In this study, we introduce design methodology to develop a guidance for issuing heavy rainfall warning by using both RBFNNs(Radial basis function neural networks) and SVR(Support vector regression) model, and then carry out the comparative studies between two pattern classifiers. Individual classifiers are designed as architecture realized with the aid of optimization and pre-processing algorithm. Because the predictive performance of the existing heavy rainfall forecast system is commonly affected from diverse processing techniques of meteorological data, under-sampling method as the pre-processing method of input data is used, and also data discretization and feature extraction method for SVR and FCM clustering and PSO method for RBFNNs are exploited respectively. The observed data, AWS(Automatic weather wtation), supplied from KMA(korea meteorological administration), is used for training and testing of the proposed classifiers. The proposed classifiers offer the related information to issue a heavy rain warning in advance before 1 to 3 hours by using the selected meteorological data and the cumulated precipitation amount accumulated for 1 to 12 hours from AWS data. For performance evaluation of each classifier, ETS(Equitable Threat Score) method is used as standard verification method for predictive ability. Through the comparative studies of two classifiers, neuro-fuzzy method is effectively used for improved performance and to show stable predictive result of guidance to issue heavy rainfall warning.

• Journal of Broadcast Engineering
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• v.25 no.2
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• pp.157-165
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• 2020

In this paper. we propose an effective compensation scheme to the quantization error arisen from quantized learning in a machine learning on an embedded system. In the machine learning based on a gradient descent or nonlinear signal processing, the quantization error generates early vanishing of a gradient and occurs the degradation of learning performance. To compensate such quantization error, we derive an orthogonal compensation vector with respect to a maximum component of the gradient vector. Moreover, instead of the conventional constant learning rate, we propose the adaptive learning rate algorithm without any inner loop to select the step size, based on a nonlinear optimization technique. The simulation results show that the optimization solver based on the proposed quantized method represents sufficient learning performance.

• Journal of Broadcast Engineering
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• v.21 no.4
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• pp.578-591
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• 2016

In this paper, the fast encoding algorithm in High Efficiency Video Coding (HEVC) encoder was studied. For the encoding efficiency, the current HEVC reference software is divided the input image into Coding Tree Unit (CTU). then, it should be re-divided into CU up to maximum depth in form of quad-tree for RDO (Rate-Distortion Optimization) in encoding precess. But, it is one of the reason why complexity is high in the encoding precess. In this paper, to reduce the high complexity in the encoding process, it proposed the method by determining the maximum depth of the CU using a hierarchical clustering at the pre-processing. The hierarchical clustering results represented an average combination of motion vectors (MV) on neighboring blocks. Experimental results showed that the proposed method could achieve an average of 16% time saving with minimal BD-rate loss at 1080p video resolution. When combined the previous fast algorithm, the proposed method could achieve an average 45.13% time saving with 1.84% BD-rate loss.

• Korean Chemical Engineering Research
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• v.56 no.2
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• pp.176-185
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• 2018

As global warming accelerates, greenhouse gas reduction becomes more important. Carbon dioxide dry reforming is a promising green-house gas reduction technology that can obtain CO and $H_2$ which are high value-added materials by utilizing $CO_2$ and $CH_4$ which are greenhouse gases. However, there is a significant coking problem during operation of the dry reforming reactor. Because the carbon dioxide dry reforming is a strong endothermic reaction, the temperature of the reactor drops near the reactor inlet and causes coke formation. To solve this problem, it is important to ensure that the reaction takes place in a temperature range where coke production is minimized. In this study, we proposed a design method that can maintain reaction temperature in the region where the coke is rarely generated by using the new catalyst configuration method. The design method also optimizes the reactor by solving the optimization problem which minimizes the reactor length for a given reaction conversion by using the fuel flow rate, catalyst density, and output temperature by section as optimization variables.

• Tunnel and Underground Space
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• v.31 no.6
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• pp.508-519
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• 2021

In this study, a model was developed to predict the peak particle velocity (PPV) that affects people and the surrounding environment during blasting. Four machine learning models using the k-nearest neighbors (kNN), classification and regression tree (CART), support vector regression (SVR), and particle swarm optimization (PSO)-SVR algorithms were developed and compared with each other to predict the PPV. Mt. Yogmang located in Changwon-si, Gyeongsangnam-do was selected as a study area, and 1048 blasting data were acquired to train the machine learning models. The blasting data consisted of hole length, burden, spacing, maximum charge per delay, powder factor, number of holes, ratio of emulsion, monitoring distance and PPV. To evaluate the performance of the trained models, the mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were used. The PSO-SVR model showed superior performance with MAE, MSE and RMSE of 0.0348, 0.0021 and 0.0458, respectively. Finally, a method was proposed to predict the degree of influence on the surrounding environment using the developed machine learning models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2422-2443
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• 2020

This paper studies a single-user Mobile Edge Computing (MEC) system where mobile device (MD) includes an application consisting of multiple computation components or tasks with dependencies. MD can offload part of each computation-intensive latency-sensitive task to the AP integrated with MEC server. In order to accomplish the application faultlessly, we calculate out the optimal task offloading strategy in a time-division manner for a predetermined execution order under the constraints of limited computation and communication resources. The problem is formulated as an optimization problem that can minimize the energy consumption of mobile device while satisfying the constraints of computation tasks and mobile device resources. The optimization problem is equivalently transformed into solving a nonlinear equation with a linear inequality constraint by leveraging the Lagrange Multiplier method. And the proposed dual Bi-Section Search algorithm Bi-JOTD can efficiently solve the nonlinear equation. In the outer Bi-Section Search, the proposed algorithm searches for the optimal Lagrangian multiplier variable between the lower and upper boundaries. The inner Bi-Section Search achieves the Lagrangian multiplier vector corresponding to a given variable receiving from the outer layer. Numerical results demonstrate that the proposed algorithm has significant performance improvement than other baselines. The novel scheme not only reduces the difficulty of problem solving, but also obtains less energy consumption and better performance.