• Journal of the Korean Society for Railway
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• 제20권3호
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• pp.374-381
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• 2017

Concrete sleepers, installed on majority of railroad track in this nation can, if not maintained properly, threaten the safety of running trains. In this paper, an algorithm for automatically identifying cracks in a sleeper image, taken by high-resolution camera, is developed based on Adaboost, known as the strongest adaptive algorithm and most actively utilized algorithm of current days. The developed algorithm is trained using crack characteristics drawn from the analysis results of crack and non-crack images of field-installed sleepers. The applicability of the developed algorithm is verified using 48 images utilized in the training process and 11 images not used in the process. The verification results show that cracks in all the sleeper images can be successfully identified with an identification rate greater than 90%, and that the developed automatic crack identification algorithm therefore has sufficient applicability.

• Journal of Practical Engineering Education
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• 제10권1호
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• pp.25-33
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• 2018

As the IT industry develops, The field of e-learning based on existing theoretical subjects has expanded to fields requiring actual education. Courses that require practical training in e-learning must be evaluated to have learning outcomes through on-line practice. In this research, a student at Cyber University mechatronics engineering studies 'PLC Control' and 'Servo Motor Control', which are subjects of the undergraduate major, through 'Prime College's CyberLAB' for learning by e-learning investigated whether there was a learning result. For this reason, CyberLAB was conducted. And for the students who took the course for two years, We confirmed through the 5 - point scale questionnaire and grades that the practical subjects had a significant effect on e - learning.

• Journal of the Korean institute of surface engineering
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• 제51권6호
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• pp.415-420
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• 2018

The possibility of an improvement in heat dissipation performance of aluminum alloy heat sink for shipboard LED luminaries through plasma electrolytic oxidation (PEO) was investigated. Four different PEO coatings were produced on aluminum alloy 5052 in silicate based alkaline solution by varying current density ($50{\sim}200mA/cm^2$). On voltage-time response curves, three stages were clearly distinguished at all current densities, namely an initial linear increase, slowdown of increase rate, and steady state(constant voltage). It was found that the increase in current density caused the breakdown voltage to increase. Two different surface morphologies - coralline porous structure and pancake structure - were confirmed by SEM examination. The coralline porous structure was predominant in the coatings produced at lower current densities (50 and $100mA/cm^2$) while under high current densities(150 and $200mA/cm^2$) the pancake structure became dominant. The coating thickness was measured and found to be in a range between about $13{\mu}m$ and $44{\mu}m$, showing increasing thickness with increasing current density. As a result, $100mA/cm^2$ was proposed as an effective process parameter to improve the heat dissipation performance of aluminum alloy heat sink, which could lower the LED operating temperature by about 30%.

• The Journal of Engineering Geology
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• 제28권4호
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• pp.583-592
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• 2018

Recently, concern has arisen regarding the lowering of groundwater levels in the hinterland caused by the development of high-capacity radial collector wells in riverbank filtration areas. In this study, groundwater levels are estimated using Modflow software in relation to the water volume pumped by the radial collector well in Anseongcheon Stream. Using the water volume data, an artificial neural network (ANN) model is developed to determine the amount of water that can be withdrawn while minimizing the reduction of groundwater level. We estimate that increasing the pumping rate of the horizontal well HW-6, which is drilled parallel to the stream direction, is necessary to minimize the reduction of groundwater levels in wells OW-7 and OB-11. We also note that the number of input data and the classification of training and test data affect the results of the ANN model. This type of approach, which supplements ANN modeling with observed data, should contribute to the future groundwater management of hinterland areas.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권7호
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• pp.2496-2512
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• 2021

The requirements for powerful computing capability, high capacity, low latency and low energy consumption of emerging services, pose severe challenges to the fifth-generation (5G) network. As a promising paradigm, mobile edge networks can provide services in proximity to users by deploying computing components and cache at the edge, which can effectively decrease service delay. However, the coexistence of heterogeneous services and the sharing of limited resources lead to the competition between various services for multiple resources. This paper considers two typical heterogeneous services: computing services and content delivery services, in order to properly configure resources, it is crucial to develop an effective offloading and caching strategies. Considering the high energy consumption of 5G base stations, this paper considers the hybrid energy supply model of traditional power grid and green energy. Therefore, it is necessary to design a reasonable association mechanism which can allocate more service load to base stations rich in green energy to improve the utilization of green energy. This paper formed the joint optimization problem of computing offloading, caching and resource allocation for heterogeneous services with the objective of minimizing the on-grid power consumption under the constraints of limited resources and QoS guarantee. Since the joint optimization problem is a mixed integer nonlinear programming problem that is impossible to solve, this paper uses deep reinforcement learning method to learn the optimal strategy through a lot of training. Extensive simulation experiments show that compared with other schemes, the proposed scheme can allocate resources to heterogeneous service according to the green energy distribution which can effectively reduce the traditional energy consumption.

• Journal of the Korea Institute of Information and Communication Engineering
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• 제25권1호
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• pp.36-43
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• 2021

In this research, the accuracy of YOLO v3 algorithm in object detection during AGV (Automated Guided Vehicle) operation was investigated. First of all, AGV with 2D LiDAR and stereo camera was prepared. AGV was driven along the route scanned with SLAM (Simultaneous Localization and Mapping) using 2D LiDAR while front objects were detected through stereo camera. In order to evaluate the accuracy of YOLO v3 algorithm, recall, AP (Average Precision), and mAP (mean Average Precision) of the algorithm were measured with a degree of machine learning. Experimental results show that mAP, precision, and recall are improved by 10%, 6.8%, and 16.4%, respectively, when YOLO v3 is fitted with 4000 training dataset and 500 testing dataset which were collected through online search and is trained additionally with 1200 dataset collected from the stereo camera on AGV.

• Journal of the Institute of Convergence Signal Processing
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• 제22권3호
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• pp.128-133
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• 2021

Recently Recently, the incidence of spinal diseases due to poor posture among students and office workers is increasing, and various studies have been conducted to help maintain correct posture. In previous studies, a membrane sensor or a pressure sensor was placed on the seat cushion to see the weight bias, or a sensor that restrained the user was attached to measure the position change. In our previous study, we developed a sensor device which can be easily attached to the body with an adhesive gel sheet and that measures and outputs the user's posture and body position in real time, but it has a limitation in the accuracy of the sensor value. In this study, a study was conducted to improve the performance of the position conversion sensor device and quantitatively evaluate the accuracy of the angle conversion measurement value, and a high accuracy with 2.53% of error rate was confirmed. In future research, it is considered that additional research targeting actual users is needed by diversifying posture correction training contents with multimedia elements added.

• Journal of the Korea Institute of Information Security & Cryptology
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• 제32권5호
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• pp.1009-1017
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• 2022

Log data has been used as a basis in understanding and deciding the main functions and state of information systems. It has also been used as an important input for the various applications in cybersecurity. It is an essential part to get necessary information from log data, to make a decision with the information, and to take a suitable countermeasure according to the information for protecting and operating systems in stability and reliability, but due to the explosive increase of various types and amounts of log, it is quite challenging to effectively and efficiently deal with the problem using existing tools. Therefore, this study has suggested a multiclass classification of the security severity level of multi-source event log using machine learning based on natural language processing. The experimental results with the training and test samples of 472,972 show that our approach has archived the accuracy of 99.59%.

• Advances in nano research
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• 제12권2호
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• pp.197-212
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• 2022

The purpose of this study is to develop an automatic software system for bone age evaluation and to evaluate its accuracy in testing and feasibility in clinical practice. 20394 left-hand radiographs of healthy children (2-18 years old) were collected from China Skeletal Development Survey data of 1998 and China Skeletal Development Survey data of 2005. Three experienced radiologists and China-05 standard maker jointly evaluate the stages of bone development and the reference bone age was determined by consensus. 1020 from 20394 radiographs were picked randomly as test set and the remaining 19374 radiographs as training set and validation set. Accuracy of the automatic software system for bone age assessment is evaluated in test set and two clinical test sets. Compared with the reference standard, the automatic software system based on RUS-CHN for bone age assessment has a 0.04 years old mean difference, ±0.40 years old in 95% confidence interval by single reading, a 85.6% percentage agreement of ratings, a 93.7% bone age accuracy rate, 0.17 years old of MAD, 0.29 years old of RMS; Compared with the reference standard, the automatic software system based on TW3-C RUS has a 0.04 years old mean difference, a ±0.38 years old in 95% confidence interval by single reading, a 90.9% percentage agreement of ratings, a 93.2% bone age accuracy rate, a 0.16 years of MAD, and a 0.28 years of RMS. Automatic software system, AI-China-05 showed reliably accuracy in bone age estimation and steady determination in different clinical test sets.

• Journal of the Korea Society of Computer and Information
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• 제27권11호
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• pp.13-18
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• 2022

In this paper, we proposed VW-FedAVG(Validation based Weighted FedAVG) which updates the global model by weighting according to performance verification from the models of each device participating in the training. The first method is designed to validate each local client model through validation dataset before updating the global model with a server side validation structure. The second is a client-side validation structure, which is designed in such a way that the validation data set is evenly distributed to each client and the global model is after validation. MNIST, CIFAR-10 is used, and the IID, Non-IID distribution for image classification obtained higher accuracy than previous studies.