• Journal of the Korean Applied Science and Technology
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• v.39 no.5
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• pp.632-643
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• 2022

Although probiotics have been shown to improve health when consumed, recent studies have reported that they can cause unwanted side effects due to bacterial-human interactions. Therefore, the importance of prebiotics that can form beneficial microbiome in the gut has been emphasized. This study isolated and identified bacteria capable of producing biopoymer as a candidate prebiotic from traditional fermented foods. The isolated and identified strain was named WCYSK01 (Wissella sp. strain YSK01). The composition of the medium for culturing this strain was prepared by dissolving 3 g K₂HPO₄, 0.2 g MgSO₄, 0.05 g CaCl₂, 0.1 g NaCl in 1 L of distilled water. The LMBP(low molecular weight biopoymers) produced when fermentation was performed with sucrose and maltose as substrates were mainly consisted of DP3 (degree of polymer; isomaltotriose), DP4 (isomaltotetraose), DP5 (isomaltopentaose), and DP6 (isomaltoheptaose). The optimization of LMBP (low molecular weight of biopolymer) production was performed using the response surface methodology. The fermentation process temperature range of 18 to 32℃, the fermentation medium pH in the range of 5.1 to 7.9. The yield of LMBP production by the strain was found to be significantly affected by q fermentation temperature and pH. The optimal fermentation conditions were found at the normal point, and the production yield was more than 75% at pH 7.5 and temperature of 23℃.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.873-882
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• 2022

In this paper, we studied the voice recognition system kiosk for convenience, thinking that the kiosk widely used in modern society should compensate for the inconvenience of using by the visually impaired. Using ultrasonic sensor and PIR(Passive Infrared), it recognizes the visually impaired within the range of 80cm-40cm, introduces the kiosk through the MP3 module and induces them to come closer. Also, when the visually impaired within 40cm is recognized, the product description and order are guided through the MP3 module. A recording-based data voice recognition system and a kiosk that outputs desired items through servo motors were studied. A kiosk for the convenience of the visually impaired was manufactured through operation and optimization experiments of PIR, ultrasonic, voice recognition, and shock sensor for the manufactured voice recognition kiosk. Finally, it was confirmed that security can be strengthened by using shock sensors and emergency bells to enhance security.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2334-2340
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• 2021

Radioactive particle tracking (RPT) is a minimally invasive nuclear technique that tracks a radioactive particle inside a volume of interest by means of a mathematical location algorithm. During the past decades, many algorithms have been developed including ones based on artificial intelligence techniques. In this study, RPT technique is applied in a simulated test section that employs a simplified mixer filled with concrete, six scintillator detectors and a¹³⁷Cs radioactive particle emitting gamma rays of 662 keV. The test section was developed using MCNPX code, which is a mathematical code based on Monte Carlo simulation, and 3516 different radioactive particle positions (x,y,z) were simulated. Novelty of this paper is the use of a location algorithm based on a deep learning model, more specifically a 6-layers deep rectifier neural network (DRNN), in which hyperparameters were defined using a Bayesian optimization method. DRNN is a type of deep feedforward neural network that substitutes the usual sigmoid based activation functions, traditionally used in vanilla Multilayer Perceptron Networks, for rectified activation functions. Results show the great accuracy of the DRNN in a RPT tracking system. Root mean squared error for x, y and coordinates of the radioactive particle is, respectively, 0.03064, 0.02523 and 0.07653.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.356-367
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• 2021

With the recent development of IoT and artificial intelligence technology, research and applications for optimization of real-world problems by collecting and analyzing data in real-time have increased in various fields such as manufacturing and smart city. Representatively, the digital twin platform that supports real-time synchronization in both directions with the virtual world digitized from the real world has been drawing attention. In this paper, we define a digital twin concept and propose a digital twin platform prototype that links real objects and predicted results from the virtual world in real-time by utilizing the oneM2M-based IoT platform. In addition, we implement an application that can predict accidents from object collisions in advance with the prototype. By performing predefined test cases, we present that the proposed digital twin platform could predict the crane's motion in advance, detect the collision risk, perform optimal controls, and that it can be applied in the real environment.

• The Journal of the Acoustical Society of Korea
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• v.40 no.2
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• pp.176-182
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• 2021

This paper evaluates and compares the performance of the Deep Nerual Network (DNN)-based speech enhancement models according to various loss functions. We used a complex network that can consider the phase information of speech as a baseline model. As the loss function, we consider two types of basic loss functions; the Mean Squared Error (MSE) and the Scale-Invariant Source-to-Noise Ratio (SI-SNR), and two types of perceptual-based loss functions, including the Perceptual Metric for Speech Quality Evaluation (PMSQE) and the Log Mel Spectra (LMS). The performance comparison was performed through objective evaluation and listening tests with outputs obtained using various combinations of the loss functions. Test results show that when a perceptual-based loss function was combined with MSE or SI-SNR, the overall performance is improved, and the perceptual-based loss functions, even exhibiting lower objective scores showed better performance in the listening test.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.625-632
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• 2021

This paper studies the effect of the number of filters in the CNN (Convolutional Neural Network) layer on the performance of a noise attenuator. Speech is estimated from a noised speech signal using a 64-neuron, 16-kernel CNN filter and an error back-propagation algorithm. In this study, in order to verify the performance of the noise attenuator with respect to the number of filters, a program using Keras library was written and simulation was performed. As a result of simulation, it can be seen that this system has the smallest MSE (Mean Squared Error) and MAE (Mean Absolute Error) values when the number of filters is 16, and the performance is the lowest when there are 4 filters. And when there are more than 8 filters, it was shown that the MSE and MAE values do not differ significantly depending on the number of filters. From these results, it can be seen that about 8 or more filters must be used to express the characteristics of the speech signal.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2789-2802
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• 2020

Small Modular Reactors (SMRs) are attracting wide attention due to their outstanding performance, extensive studies have been carried out for lead-based fast reactors (LFRs) that cooled with Lead or Lead-bismuth (LBE), and small modular natural circulation LFR is one of the promising candidates for SMRs and LFRs development. One of the challenges for the design small modular natural circulation LFR is to master the natural circulation thermal-hydraulic performance in the reactor primary circuit, while the natural circulation characteristics is a coupled thermal-hydraulic problem of the core thermal power, the primary loop layout and the operating state of secondary cooling system etc. Thus, accurate predicting the natural circulation LFRs thermal-hydraulic features are highly required for conducting reactor operating condition evaluate and Thermal hydraulic design optimization. In this study, a thermal-hydraulic analysis code is developed for small modular natural circulation LFRs, which is based on several mathematical models for natural circulation originally. A small modular natural circulation LBE cooled fast reactor named URANUS developed by Korea is chosen to assess the code's capability. Comparisons are performed to demonstrate the accuracy of the code by the calculation results of MARS, and the key thermal-hydraulic parameters agree fairly well with the MARS ones. As a typical application case, steady-state analyses were conducted to have an assessment of thermal-hydraulic behavior under nominal condition, and several parameters affecting natural circulation were evaluated. What's more, two characteristics parameters that used to analyze natural circulation LFRs natural circulation capacity were established. The analyses show that the core thermal power, thermal center difference and flow resistance is the main factors affecting the reactor natural circulation. Improving the core thermal power, increasing the thermal center difference and decreasing the flow resistance can significantly increase the reactor mass flow rate. Characteristics parameters can be used to quickly evaluate the natural circulation capacity of natural circulation LFR under normal operating conditions.

• Korean Journal of Optics and Photonics
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• v.32 no.6
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• pp.259-265
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• 2021

Recently, lens manufacturing and assembly technology has greatly improved. However, tight requirements of manufacturing and assembly lead to an increase in cost and manufacturing time, and in some cases the performance of an optical system may deteriorate depending on the operating environment's conditions, such as temperature or vibration. In addition, the use of a compensator is an effective method to reduce sensitivity in an ultra-precision optical system, but in the case of a small lens, such as that in an endoscope, it is difficult to use a compensator due to the size limitation of the lens barrel. Therefore, minimizing lens sensitivity is the most important technology in lens design. For this reason, there have been various attempts to reduce the lens sensitivity, and there is a trend to add functions to reduce the sensitivity in the lens design S/W. In this paper, we introduce a design technology that minimizes lens sensitivity. We first design a lens with quite good performance, then analyze the sensitivity of this lens, make a multi-configuration with high-sensitivity element error, and then reoptimize it. We prove with an example that this design technique is very effective.

• Journal of Animal Science and Technology
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• v.63 no.6
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• pp.1453-1463
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• 2021

Feeding is the most important behavior that represents the health and welfare of weanling pigs. The early detection of feed refusal is crucial for the control of disease in the initial stages and the detection of empty feeders for adding feed in a timely manner. This paper proposes a real-time technique for the detection and recognition of small pigs using a deep-leaning-based method. The proposed model focuses on detecting pigs on a feeder in a feeding position. Conventional methods detect pigs and then classify them into different behavior gestures. In contrast, in the proposed method, these two tasks are combined into a single process to detect only feeding behavior to increase the speed of detection. Considering the significant differences between pig behaviors at different sizes, adaptive adjustments are introduced into a you-only-look-once (YOLO) model, including an angle optimization strategy between the head and body for detecting a head in a feeder. According to experimental results, this method can detect the feeding behavior of pigs and screen non-feeding positions with 95.66%, 94.22%, and 96.56% average precision (AP) at an intersection over union (IoU) threshold of 0.5 for YOLOv3, YOLOv4, and an additional layer and with the proposed activation function, respectively. Drinking behavior was detected with 86.86%, 89.16%, and 86.41% AP at a 0.5 IoU threshold for YOLOv3, YOLOv4, and the proposed activation function, respectively. In terms of detection and classification, the results of our study demonstrate that the proposed method yields higher precision and recall compared to conventional methods.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.6
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• pp.440-448
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• 2022

Recently, due to the introduction of distributed energy resources, the optimal resource allocation problem of the power network is more and more important, and the distributed resource allocation method is required to process huge amount of data in large-scale power networks. In the optimal resource allocation problem, many studies have been conducted on the case when the supply-demand balance is satisfied due to the limitation of the generation capacity of each generator, but the studies considering the supply-demand imbalance, that total demand exceeds the maximum generation capacity, have rarely been considered. In this paper, we propose the consensus-based distributed algorithm for the optimal resource allocation of power network considering the supply-demand imbalance condition as well as the supply-demand balance condition. The proposed distributed algorithm is designed to allocate the optimal resources when the supply-demand balance condition is satisfied, and to measure the amount of required resources when the supply-demand is imbalanced. Finally, we conduct the simulations to verify the performance of the proposed algorithm.