• Journal of Adhesion and Interface
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• v.23 no.2
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• pp.53-58
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• 2022

Here, the surface characteristics of the dielectric were controlled by introducing the self-assembled monolayers (SAMs) as the intermediate layers on the surface of the AlOx dielectric, and the electrical performances of the organic charge modulated transistor (OCMFET) were significantly improved. The organic intermediate layer was applied to control the surface energy of the AlOx gate dielectric acting as a capacitor plate between the control gate (CG) and the floating gate (FG). By applying the intermediate layers on the gate dielectric surface, and the field-effect mobility (μ_OCMFET) of the OCMFET devices could be efficiently controlled. We used the four kinds of SAM materials, octadecylphosphonic acid (ODPA), butylphosphonic acid (BPA), (3-bromopropyl)phosphonic acid (BPPA), and (3-aminopropyl)phosphonic acid (APPA), and each μ_OCMFET was measured at 0.73, 0.41, 0.34, and 0.15 cm2V-1s-1, respectively. The results could be suggested that the characteristics of each organic SAM intermediate layer, such as the length of the alkyl chain and the type of functionalized end-group, can control the electrical performances of OCMFET devices and be supported to find the optimized fabrication conditions, as an efficient sensing platform device.

• 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 Intelligence and Information Systems
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• v.29 no.1
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• pp.377-396
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• 2023

As the cryptocurrency market continues to grow, it has developed into a new financial market. The need for investment strategy research on the cryptocurrency market is also emerging. This study aims to conduct an empirical analysis on an investment methodology of cryptocurrency that combines short-term trading strategy and deep learning. Daily price data of the Ethereum was collected through the API of Upbit, the Korean cryptocurrency exchange. The investment performance of the experimental model was analyzed by finding the optimal parameters based on past data. The experimental model is a volatility breakout strategy(VBS), a Long Short Term Memory(LSTM) model, moving average cross strategy and a combined model. VBS is a short-term trading strategy that buys when volatility rises significantly on a daily basis and sells at the closing price of the day. LSTM is suitable for time series data among deep learning models, and the predicted closing price obtained through the prediction model was applied to the simple trading rule. The moving average cross strategy determines whether to buy or sell when the moving average crosses. The combined model is a trading rule made by using derived variables of the VBS and LSTM model using AND/OR for the buy conditions. The result shows that combined model is better investment performance than the single model. This study has academic significance in that it goes beyond simple deep learning-based cryptocurrency price prediction and improves investment performance by combining deep learning and short-term trading strategies, and has practical significance in that it shows the applicability in actual investment.

• Journal of the Korean Geosynthetics Society
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• v.22 no.3
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• pp.11-25
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• 2023

In this study, the design and numerical analysis method of the inclined self-supported wall using cement treated soil were studied. In the case of the inclined self-supported wall, the active earth pressure decreased due to the decrease in the coefficient, Ka according to the slope (angle) and the weight decreasing effect, thereby increasing the overall stability. The wall with the slope caused a change in failure mode from overturning to sliding on the excavation side, and the optimal slope was evaluated to be about 10°. Compared to the strength reduction method, the overall stability in numerical analysis results in conservative results in limit equilibrium analysis, so it was found that this method should be attended when designing. As a result of the parameteric study, the stability on bearing capacity and compression failure did not significantly increase above the slope of 10° when the surcharge was small (about 20kPa or less). In the case of cohesion of the backfill, The results similar to numerical analysis were found to consider cohesion. It was evaluated that stability on sliding, oveturning, shear, and tension failure increases in proportion to the thickness of the wall, but there is no significant change in the stability on the bearing capacity and compressive failure regardless of the thickness of the wall above a certain angle (about 10°).

• Journal of Service Research and Studies
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• v.12 no.4
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• pp.82-93
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• 2022

The food service industry is a major driver of global sustainable food consumption. By understanding food consumption behavior, restaurant managers can forecast demands and reduce pre-consumer food waste. This study investigates the relationship between influencing factors and the number of customers at restaurants and cafés. These factors are weather-related factors, including rain and temperature, and school-related factors, including exams and the day of the week. Based on these four factors, 24 possible combinations were created. Three representtive days were chosen for each weekday combination. Besides, one representative day was chosen for each weekend combination. In total, 48 days were sampled throughout the year. Customer data were collected from six restaurants and cafes on a Korean university campus. Conjoint analysis was used to determine the relative importance of each variable to customer numbers. Following that, utility scores were standardized and mapped to determine the best condition when the number of customers was at its peak. In addition, each store's sales were compared using Pearson's Correlation Coefficient. The findings support that temperature and rain influences are correlated with the number of customers. Furthermore, we discovered that temperature was far more significant than rain in determining the number of customers. The paper discusses the implications of weather to forecast food and beverage demand and predict meal choices.

• Korean Journal of Environmental Biology
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• v.40 no.2
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• pp.214-223
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• 2022

Among migratory insect pests, Mythimna seperata and Cnaphalocrocis medinalis are invasive pests introduced into South Korea through westerlies from southern China. M. seperata and C. medinalis are insect pests that use rice as a host. They injure rice leaves and inhibit rice growth. To understand the distribution of M. seperata and C. medinalis, it is important to understand environmental factors such as temperature and humidity of their habitat. This study predicted current and future habitat suitability models for understanding the distribution of M. seperata and C. medinalis. Occurrence data, SSPs (Shared Socio-economic Pathways) scenario, and RCP (Representative Concentration Pathway) were applied to MaxEnt (Maximum Entropy), a machine learning model among SDM (Species Distribution Model). As a result, M. seperata and C. medinalis are aggregated on the west and south coasts where they have a host after migration from China. As a result of MaxEnt analysis, the contribution was high in the order of Land-cover data and DEM (Digital Elevation Model). In bioclimatic variables, BIO_4 (Temperature seasonality) was high in M. seperata and BIO_2 (Mean Diurnal Range) was found in C. medinalis. The habitat suitability model predicted that M. seperata and C. medinalis could inhabit most rice paddies.

• Korean Journal of Construction Engineering and Management
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• v.24 no.3
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• pp.12-19
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• 2023

In the case of the construction industry, the relationship between process and cost should be appropriately distributed so that the finished product can be delivered at the minimum fee within the construction period. At that time, it should be considered the size of the bridge, the construction method, the environment and production capacity of the factory, and the transport distance. However, due to various reasons that occur during the construction period, problems such as construction delay, construction cost increase, and quality and reliability degradation occur. Therefore, a systematic and scientific construction technique and process management technology are needed to break away from the conventional method. The prefab(Pre-Fabrication) is a representative OSC (Off-Site Construction) method manufactured in a factory and constructed onsite. This study develops a resource and process plan optimization system for the process management of the Nodular girder, a prefab bridge girder. A simulation algorithm develops to automatically test various variables in the personnel equipment mobilization plan to derive the optimal value. And, the algorithm was applied to the Paju-Pocheon Expressway Construction (Section 3) Dohwa 4 Bridge under construction, and the results compare. Based on construction work standard product calculation, actual input manpower, equipment type, and quantity were applied to the Activity Card, and the amount of work by quantity counting, resource planning, and resource requirements was reflected. In the future, we plan to improve the accuracy of the program by applying forecasting techniques including various field data.

• Applied Chemistry for Engineering
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• v.34 no.3
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• pp.299-306
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• 2023

In this study, the O/W emulsification processes with the natural surfactants that were extracted from Medicago sativa L. and Sapindus saponaria L. as emulsifiers were optimized using the central composite design-response surface methodology (CCD-RSM). Herein, independent parameters were the amounts of mixed emulsifiers, the mixing ratio of natural emulsifiers (soapberry saponin/alfalfa saponin), and the emulsification time, whereas the reaction parameters were the emulsion stability index (ESI), mean droplet size (MDS), and antioxidant activity (DPPH radical scanvenging activity). Through basic experiments, the ranges of operation variables for the amount of mixed emulsifiers, the mixing ratio of natural emulsifiers, and the emulsification time were 12~14 wt%, 30~70%, and 20~30 min, respectively. The optimum operation variables deduced from CCD-RSM for the amount of mixed emulsifiers, the mixing ratio of natural emulsifiers, and the emulsification time were 13.2 wt%, 44.2%, and 25.8 min, respectively. Under these optimal conditions, the expected values of the ESI, MDS, and antioxidant activity were 88.7%, 815.5 nm, and 38.7%, respectively. And, the measured values of the ESI, MDS, and antioxidant activity were 90.6%, 830.2 nm, and 39.6%, respectively, and the average experimental error for validating the accuracy was about 2.1%. Therefore, it was possible to design an optimization process for evaluating the O/W emulsion process using CCD-RSM.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.145-151
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• 2023

3D-CNN is one of the deep learning techniques for learning time series data. Such three-dimensional learning can generate many parameters, so that high-performance machine learning is required or can have a large impact on the learning rate. When learning dynamic hand-gestures in spatiotemporal domain, it is necessary for the improvement of the efficiency of dynamic hand-gesture learning with 3D-CNN to find the optimal conditions of input video data by analyzing the learning accuracy according to the spatiotemporal change of input video data without structural change of the 3D-CNN model. First, the time ratio between dynamic hand-gesture actions is adjusted by setting the learning interval of image frames in the dynamic hand-gesture video data. Second, through 2D cross-correlation analysis between classes, similarity between image frames of input video data is measured and normalized to obtain an average value between frames and analyze learning accuracy. Based on this analysis, this work proposed two methods to effectively select input video data for 3D-CNN deep learning of dynamic hand-gestures. Experimental results showed that the learning interval of image data frames and the similarity of image frames between classes can affect the accuracy of the learning model.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.33 no.3
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• pp.116-123
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• 2023

A high intensity focuses ultrasound (HIFU) is one of the emerging technologies in the biomedical field. The piezoelectric HIFU transducer is a device that utilizes the thermal energy generated by high ultrasound energy. Recently an operating frequency of the HIFU transducer is to expand above a 7 MHz. In this study, the acoustic pressures and temperature distributions in the tissue that generated by the HIFU transducer at 10 MHz were calculated with the finite element method. In addition, the pressure focusing characteristics of the device were analyzed. The geometrical variables are the piezomaterial thickness, lens shape, water height, and film thickness. The results shown that the acoustic pressure increased and saturated gradually when the height/radius (H_L/R_L) ratio of the lens increased. Moreover, the focal area was gradually decreases with H_L/R_L ratio of the lens. In case of the optimized HIFU transducer, the maximum pressure and temperature were analyzed about 19 MPa and 65℃ respectively. And the -3 dB focused distances in the axial and lateral direction are around 2.3 mm and 0.23 mm respectively.