• Journal of ILASS-Korea
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• v.28 no.4
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• pp.176-183
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• 2023

Many countries are striving to reduce carbon emissions with the goal of net zero by 2050. Accordingly, vehicles are rapidly being electrified to reduce greenhouse gases in the transportation sector. However, many organizations predict that internal combustion engines of LDV (light-duty vehicle) will exist even in 2050, and it is difficult to electrify aircraft and large ships in a short time. Therefore, synthetic fuel (i.e., e-Fuel) that can reduce carbon emissions and replace existing fossil fuels is in the spotlight. The e-Fuel refers to a fuel synthesized by using carbon obtained through various carbon capture technologies and green hydrogen produced by eco-friendly renewable energy. The purpose of this study is to compare and analyze the injection and spray characteristics of the simulated e-Gasoline. We mixed the hydrocarbon fuel components according to the composition ratio of the synthetic fuel produced based on the FT(Fischer-Tropsch) process. As a result of injection rate measurement, simulated e-Gasoline showed no significant difference in injection delay and injection period compared to standard gasoline. However, due to the low vapor pressure of the simulated e-Gasoline, the spray tip penetration (STP) was lower, and the size of spray droplets was larger than that of traditional gasoline.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2862-2882
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• 2023

With the development of networking technology, it has become common to use various types of network services to replace physical ones. Among all such services, electronic voting is one example that tends to be popularized in many countries. However, due to certain concerns regarding information security, traditional paper voting mechanisms are still widely adopted in large-scale elections. This study utilizes blockchain technology to design a novel electronic voting mechanism. Relying on the transparency, decentralization, and verifiability of the blockchain, it becomes possible to remove the reliance on trusted third parties and also to enhance the level of trust of voters in the mechanism. Besides, the mechanism of blind signature with its complexity as difficult as solving an elliptic curve discrete logarithmic problem is adopted to strengthen the features related to the security of electronic voting. Last but not least, the mechanism of self-certification is incorporated to substitute the centralized certificate authority. Therefore, the voters can generate the public/private keys by themselves to mitigate the possible risks of impersonation by the certificate authority (i.e., a trusted third party). The BAN logic analysis and the investigation for several key security features are conducted to verify that such a design is sufficiently secure. Since it is expected to raise the level of trust of voters in electronic voting, extra costs for re-verifying the results due to distrust will therefore be reduced.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.791-802
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• 2023

The manual inspection of photovoltaic (PV) panels to meet the requirements of inspection work for large-scale PV power plants is challenging. We present a hot spot detection and positioning method to detect hot spots in batches and locate their latitudes and longitudes. First, a network based on the YOLOv3 architecture was utilized to identify hot spots. The innovation is to modify the RU_1 unit in the YOLOv3 model for hot spot detection in the far field of view and add a neural network residual unit for fusion. In addition, because of the misidentification problem in the infrared images of the solar PV panels, the DeepLab v3+ model was adopted to segment the PV panels to filter out the misidentification caused by bright spots on the ground. Finally, the latitude and longitude of the hot spot are calculated according to the geometric positioning method utilizing known information such as the drone's yaw angle, shooting height, and lens field-of-view. The experimental results indicate that the hot spot recognition rate accuracy is above 98%. When keeping the drone 25 m off the ground, the hot spot positioning error is at the decimeter level.

• Journal of Korean Society for Quality Management
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• v.51 no.3
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• pp.461-479
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• 2023

Purpose: It emphasizes the importance of cost analysis for weapons systems that require enormous develop- ment costs, analyzes the problems of cost analysis steps from a practical point of view, and presents the direction of business development in terms of cost analysis reliability, timeliness, and efficiency. Methods: It analyzes the R&D cost of Micro satellites with a complex cost structure and large scale according to engineering estimation procedures, derives major analysis step-by-step problems, and presents business development directions. Results: Problems with standards and assumptions, data collection, cost division structure, and cost estimation methods were derived through the micro satellite cost analysis process, and business development directions such as expanding common standards, standardizing basic data, standardizing cost division structures and cost items, and data asset were presented. Conclusion: In order to develop work in terms of cost analysis reliability, timeliness, and efficiency, it is important to prepare and standardize standards and rules for detailed tasks at each analysis stage, and through this, it is expected that high utilization value and systematic cost data will be assetized in the future.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.117-126
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• 2023

Traditional markets, formed naturally, often feature aged buildings and facilities that are susceptible to fire. However, the lack of adequate fire detection systems in these markets can easily lead to large-scale fires upon ignition. Therefore, this study was conducted with the aim of detecting fires in traditional markets, utilizing thermal imaging cameras for data collection and the YOLOv8 model for object detection experiments. Data were collected in the night markets within traditional markets of xx city and by simulating fire scenarios. A comparative analysis of the Nano and XL models of YOLOv8 revealed that the XL model is more effective in detecting fires. The XL model not only demonstrated higher accuracy in correctly identifying flames but also tended to miss fewer fires compared to the Nano model. In the case of objects other than flames, the XL model showed superior performance over the Nano model. Taking all these factors into account, it is anticipated that with further data collection and improvement in model performance, a suitable fire detection system for traditional markets can be developed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.1
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• pp.9-19
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• 2024

Deep learning-based computer vision anomaly detection algorithms are widely utilized in various fields. Especially in the manufacturing industry, the difficulty in collecting abnormal data compared to normal data, and the challenge of defining all potential abnormalities in advance, have led to an increasing demand for unsupervised learning methods that rely on normal data. In this study, we conducted a comparative analysis of deep learning-based unsupervised learning algorithms that define and detect abnormalities that can occur when transparent contact lenses are immersed in liquid solution. We validated and applied the unsupervised learning algorithms used in this study to the existing anomaly detection benchmark dataset, MvTecAD. The existing anomaly detection benchmark dataset primarily consists of solid objects, whereas in our study, we compared unsupervised learning-based algorithms in experiments judging the shape and presence of lenses submerged in liquid. Among the algorithms analyzed, EfficientAD showed an AUROC and F1-score of 0.97 in image-level tests. However, the F1-score decreased to 0.18 in pixel-level tests, making it challenging to determine the locations where abnormalities occurred. Despite this, EfficientAD demonstrated excellent performance in image-level tests classifying normal and abnormal instances, suggesting that with the collection and training of large-scale data in real industrial settings, it is expected to exhibit even better performance.

• Advances in nano research
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• v.16 no.1
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• pp.91-109
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• 2024

Carbon nanotubes are drawing wide attention of research communities and several industries due to their versatile capabilities covering mechanical and other multi-physical properties. However, owing to extreme operating conditions of the synthesis process of these nanostructures, they are often imposed with certain inevitable structural deformities such as single vacancy and nanopore defects. These random irregularities limit the intended functionalities of carbon nanotubes severely. In this article, we investigate the mechanical behaviour of double-wall carbon nanotubes (DWCNT) under the influence of arbitrarily distributed single vacancy and nanopore defects in the outer wall, inner wall, and both the walls. Large-scale molecular simulations reveal that the nanopore defects have more detrimental effects on the mechanical behaviour of DWCNTs, while the defects in the inner wall of DWCNTs make the nanostructures more vulnerable to withstand high longitudinal deformation. From a different perspective, to exploit the mechanics of damage for achieving defect-induced shape modulation and region-wise deformation control, we have further explored the localized longitudinal and transverse spatial effects of DWCNT by designing the defects for their regional distribution. The comprehensive numerical results of the present study would lead to the characterization of the critical mechanical properties of DWCNTs under the presence of inevitable intrinsic defects along with the aspect of defect-induced spatial modulation of shapes for prospective applications in a range of nanoelectromechanical systems and devices.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1369-1389
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• 2024

In the era of 6G, the rapid increase in communication data volume poses higher demands on traditional channel estimation techniques and those based on deep learning, especially when processing large-scale data as their computational load and real-time performance often fail to meet practical requirements. To overcome this bottleneck, this paper introduces quantum computing techniques, exploring for the first time the application of Quantum Generative Adversarial Networks (QGAN) to broadband channel estimation challenges. Although generative adversarial technology has been applied to channel estimation, obtaining instantaneous channel information remains a significant challenge. To address the issue of instantaneous channel estimation, this paper proposes an innovative QGAN with a dual-module design in the generator. The adversarial loss function and the Mean Squared Error (MSE) loss function are separately applied for the parameter updates of these two modules, facilitating the learning of statistical channel information and the generation of instantaneous channel details. Experimental results demonstrate the efficiency and accuracy of the proposed dual-module QGAN technique in channel estimation on the Pennylane quantum computing simulation platform. This research opens a new direction for physical layer techniques in wireless communication and offers expanded possibilities for the future development of wireless communication technologies.

• Electronics and Telecommunications Trends
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• v.39 no.1
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• pp.83-94
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• 2024

There have been continuous requirements for developing more reliable energy storage systems that could address unsolved problems in conventional lithium-ion batteries (LIBs) and thus be a proper option for large-scale applications like energy storage system (ESS). As a promising solution, aqueous metal-ion batteries (AMIBs) where water is used as a primary electrolyte solvent, have been emerging owing to excellent safety, cost-effectiveness, and eco-friendly feature. Particularly, AMIBs adopting mutivalence metal ions (Ca²⁺, Mg²⁺, Zn²⁺, and Al³⁺) as mobile charge carriers has been paid much attention because of their abundance on globe and high volumetric capacity. In this research trend review, one of the most popular AMIBs, zinc-ion batteries (ZIBs), will be discussed. Since it is well-known that ZIBs suffer from various (electro) chemical/physical side reactions, we introduce the challenges and recent advances in the study of ZIBs mainly focusing on widening the electrochemical window of aqueous electrolytes as well as improving electrochemical properties of cathode, and anode materials.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.4
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• pp.168-178
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• 2008

The hybrid simulation test method is a versatile technique for evaluating the seismic performance of structures by seamlessly integrating both physical and numerical simulations of substructures into a single test mode. In this paper, a software framework that integrates computational and experimental simulation has been developed to simulate and test a bridge structural system under earthquake loading. Using hybrid simulation, the seismic response of complex bridge structural systems partitioned into multiple large-scale experimental and computational substructures at networked distributed experimental and computational facilities can be evaluated. In this paper, the examples of application are presented in terms of a bridge model with soil-foundation-structure interaction.