• Current Optics and Photonics
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• v.7 no.6
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• pp.755-760
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• 2023

In this paper, an illumination optical system that can mitigate the saturation phenomenon in the center of an image (caused by the typical flexible-endoscope illumination system using LEDs with Lambertian light distribution) is designed. When an LED with Lambertian light distribution is used as a light source, the amount of light in the center of the endoscopic illumination system is relatively high, compared to the periphery, causing saturation in the image. Since this phenomenon causes difficulty in detecting the patient's lesion, it is necessary to find a lighting-system design that can alleviate the saturation phenomenon. Therefore, in this paper a lighting system with bat-wing light distribution, which can lower the intensity at the center and secure the maximum amount of light at the maximum light distribution angle, is designed. In addition, to check the performance of the designed lighting system, a simulation of illumination and luminance is conducted for a system using a common aspherical lens with otherwise the same components. As a result, it is confirmed that the lighting system designed in this paper effectively reduces the luminance value at the center and secures more luminance values at the periphery than the familiar lighting system.

• The Journal of Economics, Marketing and Management
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• v.11 no.6
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• pp.9-22
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• 2023

Purpose: This paper aims to suggest ways to apply the leading technologies of Industry 5.0 to the housing welfare field, tasks for this, and policy implications. Research design, data, and methodology: The analysis method of this study is a literature study. The analysis steps are as follows. Technology trends and characteristics of Industry 5.0 were investigated and analyzed. The following is a method of applying technology 5.0 in the industrial field. Finally, the application areas of each technology and the challenges to be solved in the process were presented. Results: The results of the analysis are 1) the accessibility and diffusion of technology. This means that all citizens have equal access to and use of the latest technology. To this end, the appropriate use of technology and the development of a user-centered interface are needed. 2) Data protection and privacy. Residential welfare-related technologies may face risks such as personal information leakage and hacking in the process of collecting and analyzing residents' data. 3) Stability, economic feasibility, and sustainability of the technology. Conclusions: The policy implications include: 1) Enhancing technology education and promotion to improve tech accessibility for groups like the low-income, rural areas, and the elderly, 2) Strengthening security policies and regulations to safeguard resident data and mitigate hacking risks, 3) Standardization of technology, 4) Investment and support in R&D.

• 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 Environmental Science International
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• v.32 no.11
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• pp.811-820
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• 2023

This study investigated the protective effects of shade nets on Abies koreana seedlings subjected to high temperature and luminosity stress, which are pertinent for plant survival in climate change scenarios. This study, conducted at Konkuk University, compared the growth, survival, and soil conditions of 3-year-old specimens across natural, greenhouse, and shaded settingsfrom July to September 2022. Our findings demonstrated that shade nets significantly enhanced seedling survival by moderating soil temperature and moisture. This is particularly evident in high-temperature conditions, where shade nets mitigate stress on seedlings and safeguard them from excessive sunlight exposure. Proper net installation height and location are crucial for optimal temperature and humidity control, suggesting broader applicability for various species and offering strategies to combat the ecological impacts of climate change.

• Computers and Concrete
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• v.32 no.6
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• pp.625-637
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• 2023

The application of ML approaches in determining the resisting capacity of fire damaged RC columns is introduced in this paper, on the basis of analysis data driven ML modeling. Considering the characteristics of the structural behavior of fire damaged RC columns, the representative five approaches of Kernel SVM, ANN, RF, XGB and LGBM are adopted and applied. Additional partial monotonic constraints are adopted in modelling, to ensure the monotone decrease of resisting capacity in RC column with fire exposure time. Furthermore, additional suggestions are also added to mitigate the heterogeneous composition of the training data. Since the use of ML approaches will significantly reduce the computation time in determining the resisting capacity of fire damaged RC columns, which requires many complex solution procedures from the heat transfer analysis to the rigorous nonlinear analyses and their repetition with time, the introduced ML approach can more effectively be used in large complex structures with many RC members. Because of the very small amount of experimental data, the training data are analytically determined from a heat transfer analysis and a subsequent nonlinear finite element (FE) analysis, and their accuracy was previously verified through a correlation study between the numerical results and experimental data. The results obtained from the application of ML approaches show that the resisting capacity of fire damaged RC columns can effectively be predicted by ML approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2590-2608
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• 2023

The high-altitude and mobility characteristics of unmanned aerial vehicles (UAVs) have made them a key element of new radio systems, particularly because they can exceed the limits of terrestrial networks. However, at high altitudes, UAVs can be significantly affected by intercell interference at a high line-of-sight probability. To mitigate this drawback, we propose an algorithm that selects the optimal beam to reduce interference and maximize transmission efficiency. The proposed algorithm comprises two steps: constructing a user-location-based fingerprint database according to the user types presented herein and cooperative beam selection. Simulations were conducted using cellular cooperative downlink systems for analyzing the performance of the proposed method, and the signal-to-interference-plus-noise cumulative distribution function and spectral efficiency cumulative distribution function were used as performance analysis indicators. Simulation results showed that the proposed algorithm could reduce the effect of interference and increase the performance of the desired signal. Moreover, the algorithm could efficiently reduce overheads and system cost by reducing the amount of resources required for information exchange.

• Explosives and Blasting
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• v.41 no.3
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• pp.1-12
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• 2023

A basic assessment of techniques to mitigate the risk of blast shock waves from proximity explosions was conducted. Common existing techniques include using mitigant materials to form barriers around the explosive or in the direction of propagation of the shock wave. Various explosive energy dissipation mechanisms have been proposed, and research on blast shock wave mitigation utilizing impedance differences has drawn considerable interest. In this study, shear thickening fluid (STF) was applied as a blast mitigation material to evaluate the effectiveness of STF mitigation material on explosion shock wave mitigation through explosion experiments and numerical analysis. As a result, the effectiveness of the STF mitigant material in reducing the explosion shock pressure was verified.

• Journal of Forest and Environmental Science
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• v.39 no.4
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• pp.213-224
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• 2023

As abnormal weather events due to climate change continue to rise, landslide damage is also increasing. Given the substantial time and financial resources required for post-landslide recovery, it becomes imperative to formulate a proactive response plan. In this regard, landslide susceptibility analysis has emerged as a valuable tool for establishing preemptive measures against landslides. Accordingly, this study conducted an annual landslide susceptibility analysis using the history of landslides that occurred over many years in the Jeolla region, and analyzed areas with a high potential for landslides in the Jeolla region. The analysis employed an ensemble model that amalgamated 10 data-based models, aiming to mitigate uncertainties associated with a single-model approach. Furthermore, based on the cumulative data regarding landslide susceptible areas, this research identified regions vulnerable to recurring landslide damage in Jeolla region and proposed specific strategies for utilizing this information at various levels, including local government initiatives, adaptation plan development, and development approval processes. In particular, this study outlined approaches for local government utilization, the determination of adaptation plan types, and considerations for development permits. It is anticipated that this research will serve as a valuable opportunity to underscore the significance of information concerning regions vulnerable to recurring landslide damage.

• Korean Journal of Environmental Agriculture
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• v.42 no.4
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• pp.418-426
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• 2023

This study focused on nitrous oxide, a major greenhouse gas produced in agricultural settings through bacterial nitrogen oxidation in aerobic soil. Nitrogen fertilizer in farmland is identified as a primary source of nitrous oxide. The importance of reducing excess nitrogen in soil to mitigate nitrous oxide production is well-known. The study investigated the use of liquefied pig manure as an alternative to urea fertilizer in conventional agriculture. Results showed a more than two-fold reduction in nitrous oxide emissions in pepper cultivation areas with liquefied pig manure compared to that with urea fertilizer. The population of Nitrosospira, a nitrous oxide-producing bacterium, decreased by over 10% with liquefied pig manure. Additionally, nirK and nosZ, which are related to the denitrification process, significantly increased in the urea fertilizer group, whereas levels in the liquefied pig manure group resembled those with no nitrogen treatment. In conclusion, the experiment confirmed that liquefied pig manure can serve as an eco-friendly nitrogen fertilizer, significantly reducing nitrous oxide production, a major contributor to the atmospheric greenhouse effect.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.842-857
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• 2023

Currently, most sentiment classification models on microblogging platforms analyze sentence parts of speech and emoticons without comprehending users' emotional inclinations and grasping moral nuances. This study proposes a hybrid sentiment analysis model. Given the distinct nature of microblog comments, the model employs a combined stop-word list and word2vec for word vectorization. To mitigate local information loss, the TextCNN model, devoid of pooling layers, is employed for local feature extraction, while BiLSTM is utilized for contextual feature extraction in deep learning. Subsequently, microblog comment sentiments are categorized using a classification layer. Given the binary classification task at the output layer and the numerous hidden layers within BiLSTM, the Tanh activation function is adopted in this model. Experimental findings demonstrate that the enhanced TextCNN-BiLSTM model attains a precision of 94.75%. This represents a 1.21%, 1.25%, and 1.25% enhancement in precision, recall, and F1 values, respectively, in comparison to the individual deep learning models TextCNN. Furthermore, it outperforms BiLSTM by 0.78%, 0.9%, and 0.9% in precision, recall, and F1 values.