• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.1-6
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• 2023

In the realm of dental prosthesis fabrication, obtaining accurate impressions has historically been a challenging and inefficient process, often hindered by hygiene concerns and patient discomfort. Addressing these limitations, Company D recently introduced a cutting-edge solution by harnessing the potential of intraoral scan images to create 3D dental models. However, the complexity of these scan images, encompassing not only teeth and gums but also the palate, tongue, and other structures, posed a new set of challenges. In response, we propose a sophisticated real-time image segmentation algorithm that selectively extracts pertinent data, specifically focusing on teeth and gums, from oral scan images obtained through Company D's oral scanner for 3D model generation. A key challenge we tackled was the detection of the intricate molar regions, common in dental imaging, which we effectively addressed through intelligent data augmentation for enhanced training. By placing significant emphasis on both accuracy and speed, critical factors for real-time intraoral scanning, our proposed algorithm demonstrated exceptional performance, boasting an impressive accuracy rate of 0.91 and an unrivaled FPS of 92.4. Compared to existing algorithms, our solution exhibited superior outcomes when integrated into Company D's oral scanner. This algorithm is scheduled for deployment and commercialization within Company D's intraoral scanner.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.4
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• pp.9-14
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• 2024

This study developed a voice recognition chatbot system to address depression and loneliness among the elderly in an aging society. The system utilizes the Whisper model, GPT 2.5, and XTTS2 to provide high-performance voice recognition, natural language processing, and text-to-speech conversion. Users can express their emotions and states and receive appropriate responses, with voice recognition functionality using familiar voices for comfort and reassurance. The UX/UI design considers the cognitive responses, visual impairments, and physical limitations of the smart senior generation, using high contrast colors and readable fonts for enhanced usability. This research is expected to improve the quality of life for the elderly through voice-based interfaces.

• Computers and Concrete
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• v.34 no.2
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• pp.151-168
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• 2024

This research aimed to appraise the effectiveness of four optimization approaches - cuckoo optimization algorithm (COA), multi-verse optimization (MVO), particle swarm optimization (PSO), and teaching-learning-based optimization (TLBO) - that were enhanced with an artificial neural network (ANN) in predicting the bearing capacity of shallow foundations located on cohesionless soils. The study utilized a database of 97 laboratory experiments, with 68 experiments for training data sets and 29 for testing data sets. The ANN algorithms were optimized by adjusting various variables, such as population size and number of neurons in each hidden layer, through trial-and-error techniques. Input parameters used for analysis included width, depth, geometry, unit weight, and angle of shearing resistance. After performing sensitivity analysis, it was determined that the optimized architecture for the ANN structure was 5×5×1. The study found that all four models demonstrated exceptional prediction performance: COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP. It is worth noting that the MVO-MLP model exhibited superior accuracy in generating network outputs for predicting measured values compared to the other models. The training data sets showed R² and RMSE values of (0.07184 and 0.9819), (0.04536 and 0.9928), (0.09194 and 0.9702), and (0.04714 and 0.9923) for COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP methods respectively. Similarly, the testing data sets produced R² and RMSE values of (0.08126 and 0.07218), (0.07218 and 0.9814), (0.10827 and 0.95764), and (0.09886 and 0.96481) for COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP methods respectively.

• Smart Structures and Systems
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• v.33 no.4
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• pp.291-300
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• 2024

Uncertainty of the model, system delay and drive dynamics can be considered as normal uncertainties, and the main source of uncertainty in the seismic control system is related to the nature of the simulated seismic error. In this case, optimizing the management strategy for one particular seismic record will not yield the best results for another. In this article, we propose a framework for online management of active structural management systems with seismic uncertainty. For this purpose, the concept of reinforcement learning is used for online optimization of active crowd management software. The controller consists of a differential controller, an unplanned gain ratio, the gain of which is enhanced using an online reinforcement learning algorithm. In addition, the proposed controller includes a dynamic status forecaster to solve the delay problem. To evaluate the performance of the proposed controllers, thousands of ground motion data sets were processed and grouped according to their spectrum using fuzzy clustering techniques with spatial hazard estimation. Finally, the controller is implemented in a laboratory scale configuration and its operation is simulated on a vibration table using cluster location and some actual seismic data. The test results show that the proposed controller effectively withstands strong seismic interference with delay. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results is believed to achieved in the near future by the ongoing development of AI and control theory.

• Asian Journal of Innovation and Policy
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• v.13 no.1
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• pp.1-28
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• 2024

Enhancing the efficiency of research and development (R&D) is crucial for organizations to remain competitive and generate innovative solutions. Data Envelopment Analysis (DEA) has emerged as a powerful tool for evaluating R&D efficiency. However, traditional DEA models heavily rely on the selection of input and output variables, which can limit their effectiveness. To overcome this dependency and improve the robustness of DEA, this study proposes a novel methodology that integrates machine learning techniques with DEA for determining the most suitable input and output variables. The proposed approach is particularly relevant for specialized R&D fields, such as Radiation Emergency Medicine (REM). REM is a critical domain that deals with the medical and public health consequences of nuclear emergencies. The selection of REM as the focus of this study is motivated by several factors, including the unique challenges posed by the field, the potential for significant societal impact, and the need for efficient resource allocation in emergency situations. By leveraging machine learning algorithms, such as Support Vector Machines (SVM), the proposed methodology aims to identify the most relevant input and output variables for DEA in the context of REM. The integration of machine learning enables the DEA model to capture complex relationships and non-linearities in the data, leading to more accurate and reliable efficiency assessments. The effectiveness of the proposed methodology is demonstrated through a comprehensive evaluation using real-world REM data. The results highlight the superior performance of the machine learning-integrated DEA approach compared to traditional DEA models. This study contributes to the advancement of R&D efficiency assessment in specialized fields and provides valuable insights for decision-makers in REM and other critical domains.

• Advances in materials Research
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• v.13 no.6
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• pp.527-546
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• 2024

The adhesive bonding technique has become widely prevalent in recent years, especially in fields such as engineering, aerospace, and sports. During operational service, adhesives are subjected to severe environmental conditions, including temperature variations, humidity, and UV radiation, which can impact their performance. In this study, we utilized the mechanical properties of the aged epoxy adhesive Adekit A140 in a finite element model to assess the impact of temperature and water absorption on the degradation of mechanical properties in metal-metal adhesive joints used in aeronautical structures. Our primary objective was to analyze, using the finite element method, the influence of these environmental factors on the joint's strength by evaluating the distribution of Von Mises stresses. The adhesive's mechanical properties, such as Young's modulus (E), were measured at different immersion periods and then integrated into the numerical modeling. The results revealed that water absorption leads to a significant degradation of the adhesive's mechanical properties, primarily manifested as a reduction in Young's modulus. Despite this degradation, an increase in plasticity was observed, which surprisingly improved the overall strength of the bonded assembly under certain conditions. Notably, after 90 days of immersion, the joint's strength demonstrated a 15% reduction in stiffness but exhibited enhanced durability due to plastic deformation, indicating a potential tradeoff between stiffness and durability in long-term service. This provides valuable insight into the design of adhesive joints under varying environmental conditions.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.4
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• pp.331-341
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• 2013

In a solar domestic hot water (SDHW) system, solar energy is collected using collector panels, transferred to a circulating heat transfer fluid (brine), and eventually stored in a thermal storage tank (TST) as hot water. In this study, a computational fluid dynamics (CFD) model was developed to predict the solar thermal energy storage in a hybrid-type TST equipped with a helical jacket heater (mantle heat exchanger) and an immersed spiral coil heater. The helical jacket heater, which is the brine flow path attached to the side wall of a TST, has advantages including simple system design, low brine flow rate, and enhanced thermal stratification. In addition, the spiral coil heater further enhances the thermal performance and thermal stratification of the TST. The developed model was validated by the good agreement between the CFD results and the experimental results performed with the hybrid-type TST in SDHW settings.

• Journal of Korean Society of Transportation
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• v.34 no.2
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• pp.107-122
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• 2016

This study aims to develop a framework to estimate mobile source emissions with the macroscopic travel demand model including enhanced estimates of intra-zonal travel emissions using Space Syntax analysis. It is acknowledged that "the land-use and transportation interaction model explains the influence of urban structure on accessibility and mobility pattern". Based upon this theory, the estimation model of intra-zonal travel emissions is presented with the models of total travel distance, total travel demand, and average travel speed of intra-zonal trips. Thess statistical models include several spatial indices derived from the Space Syntax analysis. It explains that urban spatial structure is a critical factor for intra-zonal travel emissions, which is lower in compact zone with smaller portion of land area, lower sprawl indicator, and more grid-type of road network. Also the suggested framework is applied in the evaluation of the effectiveness of bicycle lane project in Suwon, Korea. The estimated emissions including intra-zonal travel is as double as the results only with inter-zonal demands, which shows better performance of the suggested framework for more realistic outcomes. This framework is applicable to the estimation of mobile source emissions in nation-wide and the assessment of transportation-environment policies in regional level.

• International Union of Geodesy and Geophysics Korean Journal of Geophysical Research
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• v.21 no.1
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• pp.31-46
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• 1993

A radiative transfer model with two-stream/delta-Eddington approximation has been developed to calculate the vertical distributions of atmospheric heating rates and radiative fluxes. The performance of the model has been evaluated by comparison with the results of ICRCCM (Intercomparison of radiative codes in climate models). It has been demonstrated that the presented model has a capability to calculate the solar radiation not only accurately but also economically. The characteristics of ultraviolet-B radiation (UV-B; 280-320nm) are examined by comparison of relation between the flux at the top of atmosphere and that at the surface. The relation of UV-B is quadratic due to the strong ozone absorption in this band. Also, the dependence of the UV-B radiation on the stratospheric ozone depletion and stratospheric aerosol haze due to volcanic eruption on the stratospheric ozone depletion and stratospheric aerosol haze due to volcanic eruption has been tested with various solar zenith angles. The surface UV-B increases as the solar zenith angle increases. The existence of stratospheric aerosols causes an increase in the planetary albedo due to the aerosols' backscattering. The planetary albedo with aerosol's effect has been increases as the solar zenith angle is not sensitive. It may be caused by the fact that the aerosols' scattering effect becomes saturated with the relatively long path length in a large solar zenith angle. Finally, the regional impact of stratospheric aerosols due to volcanic eruption on the intensity of UV-B radiation at the surface has been estimated. A direct effect is that the flux is diminished at the low latitudes, while it is enhanced in the high latitudes by the aerosols' photon trap or twilight effect. In the high latitudes, both aerosols' scattering and ozone absorption have strong and opposite impacts to the surface UV-B radiation is located at the mid-latitudes during spring season in both hemispheres.

• Journal of the Earthquake Engineering Society of Korea
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• v.12 no.3
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• pp.1-10
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• 2008

The seismic performance of a structure designed without consideration of seismic loading can be effectively enhanced through seismic rehabilitation. The appropriate level of rehabilitation should be determined based on the decision criteria that minimize the anticipated earthquake-related losses. To estimate the anticipated losses, seismic risk analysis should be performed considering the probabilistic characteristics of the hazard and the structural damage. This study presents the decision procedure in which the probabilistic seismic demand model is utilized for the effective estimation and minimization of the total seismic losses through seismic rehabilitation. The probability density function and the cumulative distribution function of the structural damage for a specified time period are established in a closed form, and are combined with the loss functions to derive the expected seismic loss. The procedure presented in this study could be effectively used for making decisions on the seismic rehabilitation of structural systems.