• Journal of Power System Engineering
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• v.21 no.3
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• pp.60-65
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• 2017

A GA is one of the best method to find optimal value in searching area. A GA is driven by probabilistic selection that based on the survival of the fittest. So this algorithm need a huge solving time even if it can be used lots of optimizing problem such as structural design, machine learning, system's identification and so on. This GA's characteristic constrain the program to drive offline. Some studies try to use this algorithm on online or reduce the GA's running time with parallel GA or micro GA. Unfortunately these studies still didn't reduce amount of fitness solving. If the chromosome was imported to the system, it affected system's stability. And when the control system uses online GA, it also doesn't have enough learning time. In this study, try to find stability criterion to reduce the chromosome's affection and find the characteristic of the number of population and generation when GA was driven into the wide searching area.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.457-467
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• 2004

In this paper, a robot is considered as an agent, a structure of robot is presented which consisted by multi-subagents and they have diverse capacity such as perception, intelligence, action etc., required for robot. Also, subagents are consisted by micro-agent($\mu$agent) charged for elementary action required. The structure of robot control have two sub-agents, the one is behavior based reactive controller and action selection sub agent, and action selection sub-agent select a action based on the high label action and high performance, and which have a learning mechanism based on the reinforcement learning. For presented robot structure, it is easy to give intelligence to each element of action and a new approach of multi robot control. Presented robot is simulated for two goals: chaotic exploration and obstacle avoidance, and fabricated by using 8bit microcontroller, and experimented.

• International Journal of Contents
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• v.17 no.4
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• pp.62-78
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• 2021

This ethnographic multiple case study, based on Zappa-Hollman and Duff's construct of individual networks of practice (INoPs), explored English as a second language (L2) competence development and socialization process of a group of English-major undergraduates through their social connections and interactions at a public university located in an underdeveloped city in Northwest China. The study lasted for one academic semester and three students were selected as primary participants. Semi-structured interviews, student observations in English-related micro-settings, and associated texts were used to collect data. These data were coded to identify the thematic categories, and then data triangulation and member checking were conducted to select the most representative evidence to provide an in-depth description of students' perspective about mediating their English L2 socialization by their INoPs. Findings showed that factors in the formation of students' INoPs, including intensity, density, and nature, played significant roles in their academic or affective returns from their English learning, both of which had a substantial influence on the students' English L2 socialization. Considering that the macro-setting was a non-English, underdeveloped monolingual society, both educational institutions and individual students need to seek and create more English-mediated interactional opportunities to develop their English proficiency and adapt to local English learning communities.

• Computers and Concrete
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• v.32 no.5
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• pp.475-486
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• 2023

Nondestructive evaluation (NDE) is an important task of civil engineering structure monitoring and inspection, but minor damage such as small cracks in local structure is difficult to observe. If cracks continued expansion may cause partial or even overall damage to the structure. Therefore, monitoring and detecting the structure in the early stage of crack propagation is important. The crack detection technology based on machine vision has been widely studied, but there are still some problems such as bad recognition effect for small cracks. In this paper, we proposed a deep learning method based on sweep signals to evaluate concrete surface crack with a width less than 1 mm. Two convolutional neural networks (CNNs) are used to analyze the one-dimensional (1D) frequency sweep signal and the two-dimensional (2D) time-frequency image, respectively, and the probability value of average damage (ADPV) is proposed to evaluate the minor damage of structural. Finally, we use the standard deviation of energy ratio change (ERVSD) and infrared thermography (IRT) to compare with ADPV to verify the effectiveness of the method proposed in this paper. The experiment results show that the method proposed in this paper can effectively predict whether the concrete surface is damaged and the severity of damage.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.6
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• pp.555-567
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• 2023

The cracks in the tunnel are currently determined through visual inspections conducted by inspectors based on images acquired using tunnel imaging acquisition systems. This labor-intensive approach, relying on inspectors, has inherent limitations as it is subject to their subjective judgments. Recently research efforts have actively explored the use of deep learning to automatically detect tunnel cracks. However, most studies utilize public datasets or lack sufficient objectivity in the analysis process, making it challenging to apply them effectively in practical operations. In this study, we selected test datasets consisting of images in the same format as those obtained from the actual inspection system to perform an objective evaluation of deep learning models. Additionally, we introduced ensemble techniques to complement the strengths and weaknesses of the deep learning models, thereby improving the accuracy of crack detection. As a result, we achieved high recall rates of 80%, 88%, and 89% for cracks with sizes of 0.2 mm, 0.3 mm, and 0.5 mm, respectively, in the test images. In addition, the crack detection result of deep learning included numerous cracks that the inspector could not find. if cracks are detected with sufficient accuracy in a more objective evaluation by selecting images from other tunnels that were not used in this study, it is judged that deep learning will be able to be introduced to facility safety inspection.

• Journal of Korean Home Economics Education Association
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• v.28 no.4
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• pp.21-43
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• 2016

The purpose of this study is to examine the text and learning activities of Korean and American home economics textbooks from the perspective of building adolescent empowerment and to suggest an alternative framework for the textbook. An in-depth content analysis was conducted for the Korean and American home economics textbooks. We analyzed the text and learning activities in the textbook on three levels of empowerment: Micro, Meso, and Macro. Major findings are as follows. First, in the case of Korean textbooks, the results showed that the three levels of empowerment were off-balance (Individual Empowerment: 55%, Group E: 37%, Organizational E: 8%). The educational contents in Korean textbooks were described at the Meso-level. In the case of the American textbooks, the result showed that the educational contents of IE(43%), GE(40%), and OE(17%) were relatively balanced. Therefore, the educational contents of the American textbooks were described at the Macro-level. Second, the learning activities in the Korean textbooks put a greater weight on IE at 66%, followed by GE at 25%, but OE at 9% only. The results showed that learning activities in Korean textbooks were presented at the Meso-level, but that the three levels of empowerment were significantly off-balance. In the case of the American textbooks, the results showed that the learning activities were comparatively well balanced at IE(36%), GE(40%) and OE(23%). Therefore, learning activities in the American textbooks were presented at the Macro-level. Based on the results, we suggested an alternative framework for 'understanding of self as an adolescent' unit, to build adolescent empowerment at the Macro-level.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.177-188
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• 2022

Recently, the application of distributed acoustic sensors (DAS), which can replace geophones and seismometers, has significantly increased along with interest in micro-seismic monitoring technique, which is one of the CO₂ storage monitoring techniques. A significant amount of temporally and spatially continuous data is recorded in a DAS monitoring system, thereby necessitating fast and accurate data processing techniques. Because event detection and seismic phase picking are the most basic data processing techniques, they should be performed on all data. In this study, a machine learning-based P, S wave phase picking algorithm was developed to compensate for the limitations of conventional phase picking algorithms, and it was modified using a transfer learning technique for the application of DAS data consisting of a single component with a low signal-to-noise ratio. Our model was constructed by modifying the convolution-based EQTransformer, which performs well in phase picking, to the ResUNet structure. Not only the global earthquake dataset, STEAD but also the augmented dataset was used as training datasets to enhance the prediction performance on the unseen characteristics of the target dataset. The performance of the developed algorithm was verified using K-net and KiK-net data with characteristics different from the training data. Additionally, after modifying the trained model to suit DAS data using the transfer learning technique, the performance was verified by applying it to the DAS field data measured in the Pohang Janggi basin.

• Preventive Nutrition and Food Science
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• v.19 no.4
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• pp.343-347
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• 2014

The present study investigated the effects of Vaccinium uliginosum L. (bilberry) on the learning and memory impairments induced by amyloid-${\beta}$ protein ($A{\beta}P$) 1-42. ICR Swiss mice were divided into 4 groups: the control ($A{\beta}40$-1A), control with 5% bilberry group ($A{\beta}40$-1B), amyloid ${\beta}$ protein 1-42 treated group ($A{\beta}1$-42A), and $A{\beta}1$-42 with 5% bilberry group ($A{\beta}1$-42B). The control was treated with amyloid ${\beta}$-protein 40-1 for placebo effect, and Alzheimer's disease (AD) group was treated with amyloid ${\beta}$-protein 1-42. Amyloid ${\beta}$-protein 1-42 was intracerebroventricular (ICV) micro injected into the hippocampus in 35% acetonitrile and 0.1% trifluoroacetic acid. Although bilberry added groups tended to decrease the finding time of hidden platform, no statistical significance was found. On the other hand, escape latencies of $A{\beta}P$ injected mice were extended compared to that of $A{\beta}40$-1. In the Probe test, bilberry added $A{\beta}1$-42B group showed a significant (P<0.05) increase of probe crossing frequency compared to $A{\beta}1$-42A. Administration of amyloid protein ($A{\beta}1$-42) decreased working memory compared to $A{\beta}40$-1 control group. In passive avoidance test, bilberry significantly (P<0.05) increased the time of staying in the lighted area compared to AD control. The results suggest that bilberry may help to improve memory and learning capability in chemically induced Alzheimer's disease in experimental animal models.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.83-99
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• 2021

With the advent of the fourth industrial revolution, studies on driving management and driving strategies of autonomous vehicles are emerging. While obtaining microscopic traffic data on vehicles is essential for such research, we also see that conventional traffic data collection methods cannot collect the driving behavior of individual vehicles. In this study, UAV videos were used to collect traffic data from the viewpoint of the aerial base that is microscopic. To overcome the limitations of the related research in the literature, the micro-traffic data were estimated using the multiple object tracking of deep learning and an image registration technique. As a result, the speed obtained error rates of MAE 3.49 km/h, RMSE 4.43 km/h, and MAPE 5.18 km/h, and the traffic obtained a precision of 98.07% and a recall of 97.86%.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.127-134
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• 2021

This study aims to determine the role of innovative capabilities as a mediator in analyzing entrepreneurial marketing's effect on sustainable competitive advantage in food and beverage micro-, small-, and medium- enterprises (MSMEs). Data was obtained from a food and beverage store manager in Tangerang City, comprising 119 samples. Furthermore, the G⁎Power, a tool used to calculate statistical power analysis for various t-tests, F tests, χ2 tests, z tests, and several exact tests, was used to determine the number of research samples, the α error probability of 5%, and 3 variables. The data collection method used questionnaires with Likert Scale 1-5 to indicate strongly disagree to strongly agree. To analyze data, we used Path Analysis supported by SmartPLS statistics software. Path analysis is a form of multiple regression statistical analysis that is used to evaluate causal models by examining the relationships between a dependent variable and two or more independent variables. It aims to provide estimates of the magnitude and significance of hypothesized causal connections between sets of variables. The data processing process took place in two stages, namely the estimation model testing with validity and reliability, and the structural model testing to decide the impact or correlation between variables utilizing the t-test. The result showed a positive and significant effect of entrepreneurial marketing to innovative capability and competitive advantage through the innovative capability of MSMEs.