• 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.

• Journal of Practical Agriculture & Fisheries Research
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• v.23 no.2
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• pp.87-97
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• 2021

To investigate the current pest management status in Korea, a survey was conducted from 151 students and graduates in the Korea National College of Agriculture and Fisheries (KNCAF) by on-line. The questionnaire consists of two divisions, basic questions and pest control questions. The basic questions were including the respondent's age, academic status, cultivating crops and cultivating area. The pest control questions were including pest control methods, pesticide selection rationale, and pest forecasting methods. As a summary of basic questions, the respondents in their 20s accounted for 91.2%. Moreover, 34.5% of the respondents had over 3 hectares of cultivating area. The cultivating methods were differed by cultivating crops. As a summary of pest control questions, major control methods were using the conventional chemicals (>66%). To understand the pesticide selection rationale, farmers/respondents made their own decisions based on existing control techniques (30%) or depended on the decisions of pesticide vendors (29%). As for the pest forecasting method, it was mainly conducted by the Rural Development Administration affiliated organization (29%) and the National Crop Pest Management System (27%). Regarding the reliability of the pest diagnosis and pesticide prescription of pesticide vendors, 97% of the respondents marked above average. However, there was no choice on strong reliability. Interestingly, 79% of the respondents agreed to train experts for pest diagnosis and pesticide prescription with high necessity and, in particular, 47% of respondents were very strongly supported. These results suggest that the farmers might be need more qualified experts in pest diagnosis and pesticide prescriptions. Taken together, these survey results would provide important information to understand the current status of pest management by farmers' point of view and useful to set the direction of pest control.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.2
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• pp.28-36
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• 2022

As port structures are exposed to various extreme external loads such as wind (typhoons), sea waves, or collision with ships; it is important to evaluate the structural safety periodically. To monitor the port structure, especially the rubber fender, a fender segmentation system using a vision sensor and deep learning method has been proposed in this study. For fender segmentation, a new deep learning network that improves the encoder-decoder framework with the receptive field block convolution module inspired by the eccentric function of the human visual system into the DenseNet format has been proposed. In order to train the network, various fender images such as BP, V, cell, cylindrical, and tire-types have been collected, and the images are augmented by applying four augmentation methods such as elastic distortion, horizontal flip, color jitter, and affine transforms. The proposed algorithm has been trained and verified with the collected various types of fender images, and the performance results showed that the system precisely segmented in real time with high IoU rate (84%) and F1 score (90%) in comparison with the conventional segmentation model, VGG16 with U-net. The trained network has been applied to the real images taken at one port in Republic of Korea, and found that the fenders are segmented with high accuracy even with a small dataset.

• Journal of Korea Entertainment Industry Association
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• v.15 no.3
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• pp.203-210
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• 2021

Yuri Vasilyev - actor, director and drama teacher. Russian meritorious artist, winner of the stage "Medal of Friendship" awarded by Russian President Vladimir Putin; academician of the Petrovsky Academy of Sciences and Arts in Russia, professor of the Russian National Academy of Performing Arts, and professor of the Bavarian Academy of Drama in Munich, Germany. The physiological sense stimulation method based on the improvement of voice, language and motor function of drama actors. On the basis of a systematic understanding of performing arts, Yuri Vasiliev created a unique training method of speech expression and skills. From the complicated art training, we find out the most critical skills for focused training, which we call basic skills training. Throughout the whole training process, Professor Yuri made a clear request for the actor's lines: "action! This is the basis of actors' creation. So action is the key! Action and voice are closely linked. Actor's voice is human voice, human life, human feeling, human experience and disaster. It is also the foundation of creation that actors acquire their own voice. What we are engaged in is pronunciation, breathing, tone and intonation, speed and rhythm, expressiveness, sincerity, stage voice and movement, gesture, all of which are used to train the voice of actors according to the standard of drama. In short, Professor Yuri's training course is not only the training of stage performance and skills, but also contains a rich view of drama and performance. I think, in addition to learning from the means and methods of training, it is more important for us to understand the starting point and training objectives of Professor Yuri's use of these exercises.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.991-1005
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• 2022

Satellite imaging is an effective supplementary data source for detecting and verifying nuclear activity. It is also highly beneficial in regions with limited access and information, such as nuclear installations. Time series analysis, in particular, can identify the process of preparing for the conduction of a nuclear experiment, such as relocating equipment or changing facilities. Differences in the semantic segmentation findings of time series photos were employed in this work to detect changes in meaningful items connected to nuclear activity. Building, road, and small object datasets made of KOMPSAT 3/3A photos given by AIHub were used to train deep learning models such as U-Net, PSPNet, and Attention U-Net. To pick relevant models for targets, many model parameters were adjusted. The final change detection was carried out by including object information into the first change detection, which was obtained as the difference in semantic segmentation findings. The experiment findings demonstrated that the suggested approach could effectively identify altered pixels. Although the suggested approach is dependent on the accuracy of semantic segmentation findings, it is envisaged that as the dataset for the region of interest grows in the future, so will the relevant scope of the proposed method.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1709-1722
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• 2022

The rise in temperature induced by global warming caused in El Nino and La Nina, and abnormally changed the temperature of seawater. Rainfall concentrates in some locations due to abnormal variations in seawater temperature, causing frequent abnormal floods. It is important to rapidly detect flooded regions to recover and prevent human and property damage caused by floods. This is possible with synthetic aperture radar. This study aims to generate a model that directly derives flood-damaged areas by using modified U-NET and TerraSAR-X images based on Multi Kernel to reduce the effect of speckle noise through various characteristic map extraction and using two images before and after flooding as input data. To that purpose, two synthetic aperture radar (SAR) images were preprocessed to generate the model's input data, which was then applied to the modified U-NET structure to train the flood detection deep learning model. Through this method, the flood area could be detected at a high level with an average F1 score value of 0.966. This result is expected to contribute to the rapid recovery of flood-stricken areas and the derivation of flood-prevention measures.