• Safety and Health at Work
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• v.13 no.2
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• pp.163-169
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• 2022

Background: It is estimated that about 13 million artisanal and small-scale miners carry out their activities under harsh, precarious, unfriendly, and risky conditions. Yet, our understanding of the extent to which these workers use personal protective equipment (PPE) and navigate through the various risks and hazards they face is still limited. This article has two main objectives. First, it explores the extent of usage of PPE among artisanal and small-scale miners for the prevention of hazards and risks. Second, it examines the coping strategies used by these miners as a response to experiences of occupational injuries and risks Methods: A cross-sectional survey of small-scale miners was conducted in six communities across three districts in Ghana, West Africa. The mixed methods approach was adopted. A total of 148 small-scale miners participated in the study. Six focus group discussions (FGDs) were held across the six communities. The data were analysed using descriptive statistics. Chi-square tests were used to analyse the relationship between some socio-demographic characteristics (sex, age, and educational background) and the usage of PPE. Open-ended questions and responses from FGDs were analysed based on the content and verbatim quotations from miners. Results: Findings suggest that 78% of the miners interviewed do not use the appropriate PPE citing reasons such as cost, and their personal discomfort associated with use of PPE. There was no significant relationship between socio-demographic characteristics (i.e., sex, age, education and major mining activity) and the usage of PPE. The study further revealed four main coping strategies used by miners to handle the risks. These are rest, taking unprescribed medication and hard drugs, registration with health insurance scheme and savings and investments. Conclusion: This study shows that very few artisanal miners use PPE despite the significant hazards and risks to which they are exposed. The study recommends to the government to put in place measures to ensure that miners adhere to health and safety regulations before undertaking mining activities. This means that health and safety plans and use of PPE should be linked to the license acquisition process for miners.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.32 no.4
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• pp.121-127
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• 2022

β-Gallium oxide (Ga₂O₃), an ultra-wide bandgap semiconductor, has attracted great attention due to its promising applications for high voltage power devices. The most stable phase among five different polytypes, β-Ga₂O₃ has the wider bandgap of 4.9 eV and higher breakdown electric field of 8 MV/cm. Furthermore, it can be grown from melt source, implying higher growth rate and lower fabrication cost than other wide bandgap semiconductors such as SiC, GaN and diamond for the power device applications. In this study, β-Ga₂O₃ bulk crystals were grown by the edge-defined film-fed growth (EFG) process. The growth direction and the principal surface were set to be the [010] direction and the (100) plane of the β-Ga₂O₃ crystal, respectively. The spectra measured by Raman an alysis could exhibit the crystal phase an d impurity dopin g in the β-Ga₂O₃ ingot, and the crystallinity quality and crystal direction were analyzed using high-resolution X-ray diffraction (HRXRD). The crystal quality and various properties of as-grown β-Ga₂O₃ ribbon was systematically analyzed in order to investigate the spatial variation in entire crystal grown by EFG method.

• Journal of the Korean Electrochemical Society
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• v.24 no.4
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• pp.77-92
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• 2021

The energy density of lithium-ion batteries (LIBs) determines the mileage of electric vehicles. For increasing the energy density of LIBs, it is necessary to develop high-capacity active materials that can store more lithium ions within constrained weight. The rapid progress made in cathode technology has realized the utilization of the near-theoretical capacity of cathode materials. In contrast, commercial LIBs have still exploited graphite as active material in anodes since the 1990s. The most promising way to increase anodes' capacity is to mix high-capacity and long-cycle-life silicon oxides (SiOx) with graphite. However, the low initial Coulombic efficiency (ICE) of SiOx limits its content below 15 wt%, impeding the capacity increase in anodes. To address this issue, various prelithiation techniques have been proposed, which can improve the ICE of high-capacity anode materials. In this review paper, we introduce the principles and expected effects of prelithiation techniques reported so far. According to the reaction mechanisms, the strategies are categorized. Mainly, we focus on the recent progress of solution-based chemical prelithiation methods with commercial viability, of which lithiation reaction occurs homogeneously at liquid-solid interfaces. We believe that developing a cost-effective and mass-scalable prelithiation process holds the key to dominating the anode market for next-generation LIBs.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.32 no.2
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• pp.77-82
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• 2022

Recently, research on the development of low-cost/high-efficiency water electrolysis catalysts to replace noble metal catalysts is being actively conducted. Since overvoltage reduces the overall efficiency of the water splitting device, lowering the overvoltage of the oxygen evolution reaction (OER) is the most important task in order to generate hydrogen more efficiently. Currently, noble metal catalysts show excellent characteristics in OER performance, but they are experiencing great difficulties in commercialization due to their high price and efficiency limitations due to low reactivity. In this study, a water electrolysis catalyst Ni-MWCNT was prepared by successfully doping Ni into the MWCNTs structure through the pulsed laser ablation in liquid (PLAL) process. High resolution-transmission electron microscopy (HR-TEM) and X-ray photoelectron spectroscopy (XPS) were performed for the structure and chemical composition of the synthesized Ni-MWCNT. Catalytic oxygen evolution reaction evaluation was performed by linear sweep voltammetry (LSV) overvoltage characteristics, Tafel slope, electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV) and Chronoamperometry (CA) was used for measurement.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.569-575
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• 2022

It is most desirable to build a crane training program in the same evvironment as the actual port, but it has problem such as time constraint and cost. To overcome these limitations, next-generation training programs based on AR/VR are receiving a lot of attention. In this paper, a prototype of a harbor crane training system based on virtual reality was designed and implemented. The system implemented in this paper consists of two elements: an Arduino-based IoT terminal and an HMD equipped with a Unity application program. The IoT terminal consists of 2 controllers, 2 toggle switches, and 8 button switches to process data generated according to the user's operation. The HMD uses Oculus Quest2 and is connected to the IoT terminal through wireless communication to provide user convenience. The training system implemented in this paper is expected to provide trainees with a training environment independent of time and place through virtual reality and to save time and money.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.1
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• pp.41-49
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• 2023

A convolutional neural network (CNN) is a representative algorithm for implementing artificial neural networks. CNNs have improved on the issues of rapid increase in calculation amount and low object classification rates, which are associated with a conventional multi-layered fully-connected neural network (FNN). However, because of the rapid development of IT devices, the maximum resolution of images captured by current smartphone and tablet cameras has reached 108 million pixels (MP). Specifically, a traditional CNN algorithm requires a significant cost and time to learn and process simple, high-resolution images. Therefore, this study proposes an improved CNN algorithm for implementing an object classification learning model for simple, high-resolution images. The proposed method alters the adjacency matrix value of the pooling layer's max pooling operation for the CNN algorithm to reduce the high-resolution image learning model's creation time. This study implemented a learning model capable of processing 4, 8, and 12 MP high-resolution images for each altered matrix value. The performance evaluation result showed that the creation time of the learning model implemented with the proposed algorithm decreased by 36.26% for 12 MP images. Compared to the conventional model, the proposed learning model's object recognition accuracy and loss rate were less than 1%, which is within the acceptable error range. Practical verification is necessary through future studies by implementing a learning model with more varied image types and a larger amount of image data than those used in this study.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.107-119
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• 2023

The technology that recognizes a soldier's motion and movement status has recently attracted large attention as a combination of wearable technology and artificial intelligence, which is expected to upend the paradigm of troop management. The accuracy of state determination should be maintained at a high-end level to make sure of the expected vital functions both in a training situation; an evaluation and solution provision for each individual's motion, and in a combat situation; overall enhancement in managing troops. However, when input data is given as a timer series or sequence, existing feedforward networks would show overt limitations in maximizing classification performance. Since human behavior data (3-axis accelerations and 3-axis angular velocities) handled for military motion recognition requires the process of analyzing its time-dependent characteristics, this study proposes a high-performance data-driven classifier which utilizes the long-short term memory to identify the order dependence of acquired data, learning to classify eight representative military operations (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). Since the accuracy is highly dependent on a network's learning conditions and variables, manual adjustment may neither be cost-effective nor guarantee optimal results during learning. Therefore, in this study, we optimized hyperparameters using Bayesian optimization for maximized generalization performance. As a result, the final architecture could reduce the error rate by 62.56% compared to the existing network with a similar number of learnable parameters, with the final accuracy of 98.39% for various military operations.

• Journal of Platform Technology
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• v.11 no.3
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• pp.24-31
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• 2023

Recently, interest in UAM (Urban Air Mobility, UAM), which can take off and land vertically in the operation of urban air transportation systems, has been increasing. Therefore, various start-up companies are developing related technologies as eco-friendly future transportation with advanced technology. However, studies on ways to increase safety in the operation of UAM are still insignificant. In particular, efforts are more urgent to improve the safety of risks generated in the process of attempting to land in the city center by UAM equipped with autonomous driving. Accordingly, this study proposes a plan to safely land by avoiding dangerous region that interfere when autonomous UAM attempts to land in the city center. To this end, first, the latitude and longitude coordinate values of dangerous objects observed by the sense of the UAM are calculated. Based on this, we proposed to convert the coordinates of the distorted planar image from the 3D image to latitude and longitude and then use the calculated latitude and longitude to compare the pre-learned feature descriptor with the HOG (Histogram of Oriented Gradients, HOG) feature descriptor to extract the dangerous Region. Although the dangerous region could not be completely extracted, generally satisfactory results were obtained. Accordingly, the proposed research method reduces the enormous cost of selecting a take-off and landing site for UAM equipped with autonomous driving technology and contribute to basic measures to reduce risk increase safety when attempting to land in complex environments such as urban areas.

• Smart Media Journal
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• v.12 no.7
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• pp.43-51
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• 2023

As online transactions increase, the image of clothing has a great influence on consumer purchasing decisions. The importance of image information for clothing materials has been emphasized, and it is important for the fashion industry to analyze clothing images and grasp the materials used. Textile materials used for clothing are difficult to identify with the naked eye, and much time and cost are consumed in sorting. This study aims to classify the materials of textiles from clothing images based on deep learning algorithms. Classifying materials can help reduce clothing production costs, increase the efficiency of the manufacturing process, and contribute to the service of recommending products of specific materials to consumers. We used machine vision-based deep learning algorithms ResNet and Vision Transformer to classify clothing images. A total of 760,949 images were collected and preprocessed to detect abnormal images. Finally, a total of 167,299 clothing images, 19 textile labels and 20 fabric labels were used. We used ResNet and Vision Transformer to classify clothing materials and compared the performance of the algorithms with the Top-k Accuracy Score metric. As a result of comparing the performance, the Vision Transformer algorithm outperforms ResNet.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.55-66
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• 2023

In recent years, naval vessels have been developed to fulfill a variety of missions by being equipped with various cutting-edge equipment and ICT technologies. One of the main missions of Korean naval vessels is anti-ballistic missile warfare to defend key units and areas against the growing threat of ballistic missiles. Because the process of detection and interception is too complex and the cost of failure is much high, a lot of preparation is required to effectively conduct anti-ballistic missile warfare. This paper describes the development of a simulation model of anti-ballistic missile warfare with combat systems and equipment to be installed on future naval vessels. In particular, the DEVS formalism providing a modular and hierarchical modeling manner was applied to the simulation model, which can be utilized to efficiently represent various anti-ballistic missile warfare situations. In the simulation results presented, experiments were conducted to analyze the effectiveness of the model for effective detection resource management in anti-ballistic missile warfare. This study is expected to be utilized as a variety of analysis tools necessary to determine the optimal deployment and configuration of combat resources and operational tactics required for effective anti-ballistic missile warfare of ships in the future.