• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.157-165
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• 2020

Aiming at the development of neural network and self-driving data set, it is also an idea to improve the performance of network model to detect moving objects by dividing the data set. In Darknet network framework, the YOLOv4 (You Only Look Once v4) network model was used to train and test Udacity data set. According to 7 proportions of the Udacity data set, it was divided into three subsets including training set, validation set and test set. K-means++ algorithm was used to conduct dimensional clustering of object boxes in 7 groups. By adjusting the super parameters of YOLOv4 network for training, Optimal model parameters for 7 groups were obtained respectively. These model parameters were used to detect and compare 7 test sets respectively. The experimental results showed that YOLOv4 can effectively detect the large, medium and small moving objects represented by Truck, Car and Pedestrian in the Udacity data set. When the ratio of training set, validation set and test set is 7:1.5:1.5, the optimal model parameters of the YOLOv4 have highest detection performance. The values show mAP50 reaching 80.89%, mAP75 reaching 47.08%, and the detection speed reaching 10.56 FPS.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1172-1179
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• 2020

With the establishment of the railway integrated radio network (LTE-R) environment, radio-based train control transmission and reception and various forms of service are provided. The smooth delivery of these services requires improved performance in a highly reliable and available wireless environment. This paper measured the LTE-R radio communication environment to improve radio communication performance of railway integrated wireless network reliability and availability, analyzed the results, and established the wireless environment model. Based on the built-up model, we also proposed an improved radio-access algorithm to control trains for improved reliability, suggesting a way to improve stability for handover that occur during open-air operation, and proposed an algorithm for frequency auto-heating to improve availability. For simulation, data were collected from the Korea Rail Network Authority (Daejeon), Manjong-Gangneung KTX route, which can measure the actual data of LTE-R wireless environment, and the results of the simulation show performance improvement through algorithm.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.1
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• pp.75-83
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• 2021

Over-urbanization has contributed to the increase in traffic problems. This makes the need for effective road planning and design more important than ever. I have been able to learn how to build a new road, and how to use it. However, in spite of the importance of good road planning, there are no systematic standards or methods for calculating traffic volume on railroad routes. Therefore, in this study, to strengthen the competitiveness of railroads, the concept of line capacity is introduced to railroads, and a clear standard and method for calculating railroad line capacity are presented. Based on the results, the line capacity of main railway lines for domestic railways was calculated. By applying the method of calculating the line capacity presented in this study, the capacity of existing railway lines and newly expanded routes can be calculated. It is expected that our findings will be able to provide systematic standards that can be applied to yield a more effective investment and design planning stage; the findings will also help improve the efficiency of railroad operation.

• Journal of Advanced Navigation Technology
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• v.24 no.6
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• pp.620-626
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• 2020

This research propose and simulate a solar power generation system monitoring system based on Modbus TCP communication using RaspberryPi, an IOT equipment, as a master and an inverter as a slave. In this model, various sensors are added to the RaspberryPi to add necessary information for monitoring solar power plants, and power generation prediction and monitoring information are transmitted to the smart phone through real-time power generation prediction. In addition, information that is continuously generated by the solar power plant is built on the server as big data, and a deep learning model for predicting power generation is trained and updated. As a result of the study, stable communication was possible based on Modbus TCP with the Raspberry Pi in the inverter, and real-time prediction was possible with the deep learning model learned in the Raspberry Pi. The server was able to train various deep learning models with big data, and it was confirmed that LSTM showed the best error with a learning error of 0.0069, a test error of 0.0075, and an RMSE of 0.0866. This model suggested that it is possible to implement a real-time monitoring system that is simpler, more convenient, and can predict the amount of power generation for inverters of various manufacturers.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.2
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• pp.131-140
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• 2022

Installing Continuous Welded Rail (CWR) is one of the economical ways to resolve the challenges of noise, vibration, and the open-deck steel railway bridge impact, and the SSF method using the interlocking sleeper fastener has recently been developed. In this study, the method employed for determining the optimum vertical stiffness of the sleeper pad installed under the bridge sleeper, which is utilized to adjust the rail height and absorb shock when the train passes when the interlocking sleeper fastener is applied, is presented. To determine the optimal vertical stiffness of the sleeper pad, related existing design codes are reviewed, and, running safety, ride comfort, track safety, and bridge vibration according to the change in the vertical stiffness of the sleeper pad are estimated via flexible multi-body dynamic analysis,. The flexible multi-body dynamic analysis is performed using commercial programs ABAQUS and VI-Rail. The numerical analysis is conducted using the bridge model for a 30m-long plate girder bridge, and the response is calculated when passing ITX Saemaeul and KTX vehicles and freight wagon when the vertical stiffness of the sleeper pad is altered from 7.5 kN/mm to 240 kN/mm. The optimum stiffness of the sleeper pad is calculated as 200 kN/mm under the conditions of the track components applied to the numerical analysis.

• The Korean Journal of Physiology and Pharmacology
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• v.26 no.4
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• pp.277-285
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• 2022

To investigate the adverse effects of clozapine on cardiovascular ion channels, we examined the inhibitory effect of clozapine on voltage-dependent K⁺ (Kv) channels in rabbit coronary arterial smooth muscle cells. Clozapine-induced inhibition of Kv channels occurred in a concentration-dependent manner with an half-inhibitory concentration value of 7.84 ± 4.86 µM and a Hill coefficient of 0.47 ± 0.06. Clozapine did not shift the steady-state activation or inactivation curves, suggesting that it inhibited Kv channels regardless of gating properties. Application of train pulses (1 and 2 Hz) progressively augmented the clozapine-induced inhibition of Kv channels in the presence of the drug. Furthermore, the recovery time constant from inactivation was increased in the presence of clozapine, suggesting that clozapine-induced inhibition of Kv channels is use (state)-dependent. Pretreatment of a Kv1.5 subtype inhibitor decreased the Kv current amplitudes, but additional application of clozapine did not further inhibit the Kv current. Pretreatment with Kv2.1 or Kv7 subtype inhibitors partially blocked the inhibitory effect of clozapine. Based on these results, we conclude that clozapine inhibits arterial Kv channels in a concentration-and use (state)-dependent manner. Kv1.5 is the major subtype involved in clozapine-induced inhibition of Kv channels, and Kv2.1 and Kv7 subtypes are partially involved.

• Journal of Korean Society of Archives and Records Management
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• v.21 no.4
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• pp.1-18
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• 2021

Today, record management has become more important in management as records generated from administrative work and data production have increased significantly, and the development of information and communication technology, the working environment, and the size and various functions of the government have expanded. It is explained as an example in connection with the concept of public records with the characteristics of big data and big data characteristics. Social, Technological, Economical, Environmental and Political (STEEP) analysis was conducted to examine such areas according to the big data generation environment. The appropriateness and necessity of applying big data technology in the field of public record management were identified, and the top priority applicable framework for public record management work was schematized, and business implications were presented. First, a new organization, additional research, and attempts are needed to apply big data analysis technology to public record management procedures and standards and to record management experts. Second, it is necessary to train record management specialists with "big data analysis qualifications" related to integrated thinking so that unstructured and hidden patterns can be found in a large amount of data. Third, after self-learning by combining big data technology and artificial intelligence in the field of public records, the context should be analyzed, and the social phenomena and environment of public institutions should be analyzed and predicted.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.830-860
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• 2022

[Introduction] Nowadays, many companies are shifting their businesses online due to the growing trend among customers to buy and shop online, as people prefer online purchasing products. [Problem] Users share a vast amount of information about products, making it difficult and challenging for the end-users to make certain decisions. [Motivation] Therefore, we need a mechanism to automatically analyze end-user opinions, thoughts, or feelings in the social media platform about the products that might be useful for the customers to make or change their decisions about buying or purchasing specific products. [Proposed Solution] For this purpose, we proposed an automated SentiDecpective approach, which classifies end-user reviews into negative, positive, and neutral sentiments and identifies deceptive crowd-users rating information in the social media platform to help the user in decision-making. [Methodology] For this purpose, we first collected 11781 end-users comments from the Amazon store and Flipkart web application covering distant products, such as watches, mobile, shoes, clothes, and perfumes. Next, we develop a coding guideline used as a base for the comments annotation process. We then applied the content analysis approach and existing VADER library to annotate the end-user comments in the data set with the identified codes, which results in a labelled data set used as an input to the machine learning classifiers. Finally, we applied the sentiment analysis approach to identify the end-users opinions and overcome the deceptive rating information in the social media platforms by first preprocessing the input data to remove the irrelevant (stop words, special characters, etc.) data from the dataset, employing two standard resampling approaches to balance the data set, i-e, oversampling, and under-sampling, extract different features (TF-IDF and BOW) from the textual data in the data set and then train & test the machine learning algorithms by applying a standard cross-validation approach (KFold and Shuffle Split). [Results/Outcomes] Furthermore, to support our research study, we developed an automated tool that automatically analyzes each customer feedback and displays the collective sentiments of customers about a specific product with the help of a graph, which helps customers to make certain decisions. In a nutshell, our proposed sentiments approach produces good results when identifying the customer sentiments from the online user feedbacks, i-e, obtained an average 94.01% precision, 93.69% recall, and 93.81% F-measure value for classifying positive sentiments.

• Journal of the Korea Society for Simulation
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• v.31 no.1
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• pp.11-18
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• 2022

Recently, research to classify human activity using imagery has been actively conducted for the purpose of crime prevention and facility safety in public places and facilities. In order to improve the performance of human activity classification, most studies have applied deep learning based-transfer learning. However, despite the increase in the number of backbone network models that are the basis of deep learning as well as the diversification of architectures, research on finding a backbone network model suitable for the purpose of operation is insufficient due to the atmosphere of using a certain model. Thus, this study applies the transfer learning into recently developed deep learning backborn network models to build an intelligent system that classifies human activity using imagery. For this, 12 types of active and high-contact human activities based on sports, not basic human behaviors, were determined and 7,200 images were collected. After 20 epochs of transfer learning were equally applied to five backbone network models, we quantitatively analyzed them to find the best backbone network model for human activity classification in terms of learning process and resultant performance. As a result, XceptionNet model demonstrated 0.99 and 0.91 in training and validation accuracy, 0.96 and 0.91 in Top 2 accuracy and average precision, 1,566 sec in train process time and 260.4MB in model memory size. It was confirmed that the performance of XceptionNet was higher than that of other models.

• Korean Journal of Construction Engineering and Management
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• v.23 no.1
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• pp.46-53
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• 2022

The railway platform is an important facility closely related to the safety of passengers, trains, and images of railway facilities, and requires thorough facility management. However, the problem that the finishing material (plastering mortar) for the joint finishing of dissimilar materials (concrete+granite) falls off in the direction of the track at the platform podium is occurring multiple times across the country. Since these problems threaten the safety of train operation and the safety of passengers, immediate and continuous management is required. This study tried to derive improvement plans through the analysis of the drop-off problem of finishing materials occurring at the platform podium. The status of missing finishing materials for the platform podiums of about 200 railway stations and the related design and construction standards of the Korea National Railway were investigated. After that, the cause of the drop-off of the finishing material was analyzed, and as a result, it was found that the main cause was the boundary between the roadbed and the architectural process that occurred during construction. Subsequently, in connection with the derived causes and design, construction standards, (1) improvement of finishing materials or construction methods, (2) design of finishing materials that are easy to adjust height, (3) design of separate finishing methods, (4) improvement methods and durability were suggested.