• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.153-160
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• 2022

This study presents the bogie arrangement of the load-out system for on-ground shipbuilding. The load-out system is one of the most important systems to perform the bogie arrangement of the on-ground shipbuilding technique without dry dock facilities, and this system is composed of four pieces of equipment: bogies, driving bogie with motors, trestles, and power packs. Also, the bogie arrangement analysis (BAA) is employed to simply calculate the reaction forces at the trestle for structural safety. In this context, the purpose of this study is to propose an optimal design method to perform the bogie arrangement satisfying structural safety requirements with minimal cost. It is expected that the proposed methodology will contribute to the effective practice as well as to the improvement of competitive capability for shipbuilding companies at the on-ground shipbuilding stage. Furthermore, we describe some problems and their solutions of the deformation that may occur in the bottom of the hull during the load-out process. As a result, it is shown that we applied it to the 114K crude oil tanker (Minimum bogie 54EA) and the 174K CBM LNG carrier (Minimum bogie 88EA), it can minimize the number of bogie and critical risks (Safety rate 1.61) during the load-out of on-ground shipbuilding. Through this study, the reader will be able to learn successful load-out operation and economic shipbuilding in the future.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.15-28
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• 2022

Due to recent expansion of online market such as clothing, utilizing customer review has become a major marketing measure. User review has been used as a tool of analyzing sentiment of customers. Sentiment analysis can be largely classified with machine learning-based and lexicon-based method. Machine learning-based method is a learning classification model referring review and labels. As research of sentiment analysis has been developed, multi-modal models learned by images and video data in reviews has been studied. Characteristics of words in reviews are differentiated depending on products' and customers' categories. In this paper, sentiment is analyzed via considering review data and metadata of products and users. Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Self Attention-based Multi-head Attention models and Bidirectional Encoder Representation from Transformer (BERT) are used in this study. Same Multi-Layer Perceptron (MLP) model is used upon every products information. This paper suggests a multi-modal sentiment analysis model that simultaneously considers user reviews and product meta-information.

• Journal of the Korea Organic Resources Recycling Association
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• v.29 no.4
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• pp.15-24
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• 2021

This study was conducted to evaluate effects of microbial fertilizer (MF) containing Bacillus amyloliquefaciens on the growth in the lettuce by treating MF without and with organic fertilizer (OF), or by its formulation types, and to investigate its application in the eco-friendly agriculture. B. amyloliquefaciens, active microbe of MF, had activities of amylase and protease. Applied only MF without OF, MF treatments were not significantly different with non-fertilizer (NF). As compared to control, dry weight of MOF2 treatment (2,500 kg OF/ha + 50 kg MF/ha) was increased by about 30%. As applied with wettable powder type (WP) and soluble powder type (SP) of MF, the dry weight of WP was increased by 43% than that of control, but SP not significantly different. In the comparison with two MF formulation, dry weight of WP was increased by about 37% than that of SP. These results indicated that an application of MF improved the growth of lettuce by prompting a mineralization of OF, and that the formulation type of MF was better WP than SP.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.33-43
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• 2022

In this paper, we propose an ensemble model facilitated by multi-channel palm images with attention U-Net models and pretrained convolutional neural networks (CNNs) for establishing a contactless palm-based user identification system using conventional inexpensive camera sensors. Attention U-Net models are used to extract the areas of interest including hands (i.e., with fingers), palms (i.e., without fingers) and palm lines, which are combined to generate three channels being ped into the ensemble classifier. Then, the proposed palm information-based user identification system predicts the class using the classifier ensemble with three outperforming pre-trained CNN models. The proposed model demonstrates that the proposed model could achieve the classification accuracy, precision, recall, F1-score of 98.60%, 98.61%, 98.61%, 98.61% respectively, which indicate that the proposed model is effective even though we are using very cheap and inexpensive image sensors. We believe that in this COVID-19 pandemic circumstances, the proposed palm-based contactless user identification system can be an alternative, with high safety and reliability, compared with currently overwhelming contact-based systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2169-2190
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• 2022

The COVID-19 pandemic has affected many aspects of human life. The pandemic not only caused millions of fatalities and problems but also changed public sentiment and behavior. Owing to the magnitude of this pandemic, governments worldwide adopted full lockdown measures that attracted much discussion on social media platforms. To investigate the effects of these lockdown measures, this study performed sentiment analysis and latent Dirichlet allocation topic modeling on textual data from Twitter published during the three lockdown waves in Malaysia between 2020 and 2021. Three lockdown measures were identified, the related data for the first two weeks of each lockdown were collected and analysed to understand the public sentiment. The changes between these lockdowns were identified, and the latent topics were highlighted. Most of the public sentiment focused on the first lockdown as reflected in the large number of latent topics generated during this period. The overall sentiment for each lockdown was mostly positive, followed by neutral and then negative. Topic modelling results identified staying at home, quarantine and lockdown as the main aspects of discussion for the first lockdown, whilst importance of health measures and government efforts were the main aspects for the second and third lockdowns. Governments may utilise these findings to understand public sentiment and to formulate precautionary measures that can assure the safety of their citizens and tend to their most pressing problems. These results also highlight the importance of positive messaging during difficult times, establishing digital interventions and formulating new policies to improve the reaction of the public to emergency situations.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.4
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• pp.533-541
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• 2022

An analysis of marine accidents that occurred in the last five years, revealed that 77.0 % of all grounding accidents and 66.1% of all marine casualties involved small vessels, which was a very high level relatively. The Mokpo Regional Maritime Safety Tribunal (Mokpo-KMST) inquired on 72 cases of marine accidents in 2021, of which 10 cases were grounding accidents. Furthermore, eight cases of grounding accidents occurred in small fishing vessels. This study analyzed eight cases of grounding accidents on small fishing vessels that inquired in the jurisdictional area of Mokpo-KMST in 2021. I found out that this grounding occurred in clear weather with good visibility (2-4 miles) and good sea conditions with a wave height of less than 1 meter. Furthermore, I found that the main causes of grounding were drowsy navigation due to fatigue, neglect of vigilance, neglect of checking ship's position, overconfidence in GPS plotter, and lack of understanding of chart symbols and tidal differences. To reduce grounding accidents of small fishing vessels, I suggested the following measures. First, crew members who have completed the able seafarer training course on bridge watchkeeping should assist to the master. Second, alarm systems to prevent drowsiness should be installed in the bridge. Third, the regulation should be prepared for the performance standards and updating GPS plotter. Finally, the skipper of small vessels should be trained periodically to be familiar with chart symbols and basic terrestrial navigation.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.48 no.2
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• pp.135-146
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• 2022

In order to prevent microbial contamination and safely use cosmetics, it is essential to possess preservative power. In this study, the antifungal effect was confirmed by improving the preservative system of the aqueous dispersion formulation, which has a weak preservative power against fungi, and various preservative systems were established to strengthen the preservative power against fungi. Five kinds of raw materials (sodium anisate, p-anisic acid, caprylhydroxamic acid, o-cymen-5-ol, hydroxyacetophenone) that have a benzene ring structure having a hydroxyl group and exist as protonated form in cosmetic formulations expected to improve antifungal activity in cosmetics were selected, and the minimum growth inhibitory concentration of the raw materials was determined through MIC assay. It was confirmed that the preservative power against mold was improved through the preservative efficacy test of 4 types of water dispersion formulations (cream, lotion, toner, and sun cream) in which 4 types of raw materials showing antimicrobial activity against mold were added to the preservative system. When p-anisic acid was used, it was confirmed that the preservative activity against mold was strengthened without the effect of inhibiting the preservative power against bacteria and yeast in all four formulations.

• Journal of the Korea Society for Simulation
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• v.31 no.3
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• pp.1-10
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• 2022

Defense fields widely operate unmanned systems to lower vulnerability and enhance combat effectiveness. In the navy, swarm unmanned surface vehicles(USVs) form a cluster within communication range, share situational awareness information among the USVs, and cooperate with them to conduct military missions. This paper proposes an interface system, i.e., Interface Adapter System(IAS), to achieve inter-USV and intra-USV interoperability. We focus on the mission planning subsystem(MPS) for interoperability, which is the core subsystem of the USV to decide courses of action such as automatic path generation and weapon assignments. The central role of the proposed system is to exchange interface data between MPSs and other subsystems in real-time. To this end, we analyzed the operational requirements of the MPS and identified interface messages. Then we developed the IAS using the distributed real-time middleware. As experiments, we conducted several integration tests at swarm USVs simulation environment and measured delay time and loss ratio of interface messages. We expect that the proposed IAS successfully provides bridge roles between the mission planning system and other subsystems.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.51-58
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• 2022

Drivable area detection, one of the main functions of advanced driver assistance systems, means detecting an area where a vehicle can safely drive. The drivable area detection is closely related to the safety of the driver and it requires high accuracy with real-time operation. To satisfy these conditions, V-disparity-based method is widely used to detect a drivable area by calculating the road disparity value in each row of an image. However, the V-disparity-based method can falsely detect a non-road area as a road when the disparity value is not accurate or the disparity value of the object is equal to the disparity value of the road. In a road environment including vegetation, such as a highway and a country road, the vegetation area may be falsely detected as the drivable area because the disparity characteristics of the vegetation are similar to those of the road. Therefore, this paper proposes a drivable area detection method and hardware architecture with a high accuracy in road environments including vegetation areas by reducing the number of false detections caused by V-disparity characteristic. When 289 images provided by KITTI road dataset are used to evaluate the road detection performance of the proposed method, it shows an accuracy of 90.12% and a recall of 97.96%. In addition, when the proposed hardware architecture is implemented on the FPGA platform, it uses 8925 slice registers and 7066 slice LUTs.