• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.936-939
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• 2022

As air combat system technologies developed in recent years, the development of air defense systems is required. In the operating concept of the anti-aircraft defense system, selecting an appropriate armament for the target is one of the system's capabilities in efficiently responding to threats using limited anti-aircraft power. Much of the flying threat identification relies on the operator's visual identification. However, there are many limitations in visually discriminating a flying object maneuvering high speed from a distance. In addition, as the demand for unmanned and intelligent weapon systems on the modern battlefield increases, it is essential to develop a technology that automatically identifies and classifies the aircraft instead of the operator's visual identification. Although some examples of weapon system identification with deep learning-based models by collecting video data for tanks and warships have been presented, aerial vehicle identification is still lacking. Therefore, in this paper, we present a model for classifying fighters, helicopters, and drones using a convolutional neural network model and analyze the performance of the presented model.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.147-155
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• 2022

In this study, we developed a system to dynamically balance a daily stock portfolio and performed trading simulations using gradient boosting and genetic algorithms. We collected various stock market data from stocks listed on the KOSPI and KOSDAQ markets, including investor-specific transaction data. Subsequently, we indexed the data as a preprocessing step, and used feature engineering to modify and generate variables for training. First, we experimentally compared the performance of three popular gradient boosting algorithms in terms of accuracy, precision, recall, and F1-score, including XGBoost, LightGBM, and CatBoost. Based on the results, in a second experiment, we used a LightGBM model trained on the collected data along with genetic algorithms to predict and select stocks with a high daily probability of profit. We also conducted simulations of trading during the period of the testing data to analyze the performance of the proposed approach compared with the KOSPI and KOSDAQ indices in terms of the CAGR (Compound Annual Growth Rate), MDD (Maximum Draw Down), Sharpe ratio, and volatility. The results showed that the proposed strategies outperformed those employed by the Korean stock market in terms of all performance metrics. Moreover, our proposed LightGBM model with a genetic algorithm exhibited competitive performance in predicting stock price movements.

• Journal of Practical Engineering Education
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• v.14 no.1
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• pp.119-125
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• 2022

Materials used for education are materials such as SM20C, Al6061, and acrylic. SM20C materials are carbon steel and are often used in certification tests and functional competitions, but are also widely used in industrial sites. The Al6061 material is said to be a material that has lower hardness and stronger flexibility than carbon steel, so it is a material that generates a lot of compositional selection of tools. If students are taught practical training using acrylic materials, vibration occurs due to excessive cutting in some parts and damage to the tool occurs. In this process, we examine to what extent the impact on the 2NC head, which is a five-axis equipment, can affect precision control. The weakest part of the five-axis equipment can be said to be the weakest part of the head that controls the AC axis. When the accuracy and cumulative tolerance of this part occur, the accuracy of all products decreases. Therefore, the core part of the 2NC head, the spindle housing, was carried out using an Al7075 T6 (Alcoa, USA) material. In the process of vibration and cutting applied to this material, the analysis was conducted to find out the value applied to the finite element analysis under extreme conditions. It is hoped that this analysis data will help students see and understand the structure of 5-axis machining rather than 5-axis cutting.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.4
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• pp.1-9
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• 2023

In this paper, we propose a method to recognize the braille blocks for embedded devices in real time through deep learning. First, a deep learning model for braille block recognition is trained on a high-performance computer, and the learning model is applied to a lightweight tool to apply to an embedded device. To recognize the walking information of the braille block, an algorithm is used to determine the path using the distance from the braille block in the image. After detecting braille blocks, bollards, and crosswalks through the YOLOv8 model in the video captured by the embedded device, the walking information is recognized through the braille block path discrimination algorithm. We apply the model lightweight tool to YOLOv8 to detect braille blocks in real time. The precision of YOLOv8 model weights is lowered from the existing 32 bits to 8 bits, and the model is optimized by applying the TensorRT optimization engine. As the result of comparing the lightweight model through the proposed method with the existing model, the path recognition accuracy is 99.05%, which is almost the same as the existing model, but the recognition speed is reduced by 59% compared to the existing model, processing about 15 frames per second.

• The Journal of the Acoustical Society of Korea
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• v.42 no.5
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• pp.412-421
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• 2023

Ultrasound brain stimulation is spot-lighted by its capability of inducing brain cell activation in a localized deep brain region and ultimately treating impaired brain function while the efficiency and directivity of neural modulation are highly dependent on types of stimulus waveforms. Therefore, to optimize the types of stimulation parameters, we propose a cell-cultivable ultrasonic transducer having a series stack of a spin-coated polymer piezoelectric element (Poly-vinylidene fluoride-trifluorethylene, PVDF-TrFE) and a parylene insulating layer enhancing output acoustic pressure on a glass-coverslip which is commonly used in culturing cells. Due to the uniformity and high accuracy of stimulus waveform, tens of neuronal cell responses located on the transducer surface can be recorded simultaneously with fluorescence microscopy. By averaging the cell response traces from tens of cells, small changes to the low intensity ultrasound stimulations can be identified. In addition, the reduction of stimulus distortions made by standing wave generated from reflections between the transducers and other strong reflectors can be achieved by placing acoustic absorbers. Through the proposed ultrasound transducer, we could successfully observe the calcium responses induced by low-intensity ultrasound stimulation of 6 MHz, 0.2 MPa in astrocytes cultured on the transducer surface.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.77-87
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• 2010

Currently, our society has been changed from the industrial society to the information society. As the war progresses to Information Warfare, Network-Centric Warfare, Long-Range Precision Engagement and Robot Warfare, the military should advance to High-tech Scientific force. For this creation of the war potential, it is regarded as the warfighting experiment is a critical method. Surely it is rational that LVC(Live Virtual Constructive simulation) is desirable to make the warfighting experiment. But because it is limited by the cost, the time, the place and the resource, the constructive simulation(M&S : Modeling&Simulation) is a good tool to solve those problems. There are some studies about the evaluation process for developing the model, but it is unsatisfying in the process of the constructive simulations' operation. This study focuses on the way of constructive simulation operation, which is supplied with the evaluation process(VV&A : Verification Validation & Accreditation). We introduce the example of the rear area operation simulation for "appropriateness evaluation to the organization of logistic corps" by the AWAM(Army Weapon Analysis Model). This study presents the effective methods of the constructive simulations, which is based on the reliable evaluation process, so it will contribute to the warfighting experiments.

• Clean Technology
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• v.15 no.2
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• pp.81-90
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• 2009

In this study various cleaning evaluation methods were tested and comparatively evaluated to help cleaning industry. In order to select alternative cleaning agents objectively and systematically, various cleaning evaluation methods such as gravimetric, optically simulated electron emission (OSEE), contact angle, and analytical instrument methods were employed for cleaning contaminants such as flux, solder and grease. The analytical instruments used in this work were Fourier transform infrared spectroscopy (FTIR), ultraviolet visible spectroscopy (UV-VIS) and high performance liquid chromatography (HPLC). The gravimetric method was able to measure cleaning efficiencies easily and simply, but it was not easy to analyze them precisely because of its limitation in the gravimetric measurement. However, the OSEE technique was able to measure quickly and precisely the clean ability of cleaning agents in comparison with the gravimetric method. The contact angle method was found to be necessary for taking special precaution in its application to the cleaning evaluation due to possible formation of tiny organic film on the substrate surface which might be generated from contaminants and cleaning agents. In case of precision analysis that cannot be done by gravimetric method, fine analytical instruments such as UV-VIS, FTIR and HPLC could be used in analyzing trace amount of flux, solder and grease quantitatively, which were extracted from the surface by special solvents.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.691-695
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• 2023

Currently, the utilization of measurement results of the automated measurement system is very low and is at the level of providing only fragmentary measurement results. In this study, we are going to study a structure behavior analysis 3D display system with high precision and reliability for automated measurement data obtained by constructing big data by transmitting massive data values measured in real time to the cloud and using a Python-based algorithm. As a result of the study, as a system that can evaluate the behavior of a structure to a manager in real time, it provides analysis data in real time without significant restrictions regardless of the type of measurement data and sensor, and derived it as a 3D display. In addition, it was analyzed that the manager could grasp the behavior graph of the structure in real time and more easily judge the derivation of the weak part of the structure through data analysis. In the future, by analyzing the behavior of structures in three dimensions using past and present data, it is expected that more effective measurement results can be obtained in terms of repair, reinforcement, and maintenance of realistic structures.

• Journal of the Korean Geosynthetics Society
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• v.22 no.2
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• pp.1-12
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• 2023

Research on the causes of landslides and prediction of vulnerable areas is being conducted globally. This study aims to predict the effective soil depth, a critical element in analyzing and forecasting landslide disasters, using topographic information. Topographic data from various institutions were collected and assigned as attribute information to a 100 m × 100 m grid, which was then reduced through data grading. The study predicted effective soil depth for two cases: three depths (shallow, normal, deep) and five depths (very shallow, shallow, normal, deep, very deep). Three classification models, including K-Nearest Neighbor, Random Forest, and Deep Artificial Neural Network, were used, and their performance was evaluated by calculating accuracy, precision, recall, and F1-score. Results showed that the performance was in the high 50% to early 70% range, with the accuracy of the three classification criteria being about 5% higher than the five criteria. Although the grading criteria and classification model's performance presented in this study are still insufficient, the application of the classification model is possible in predicting the effective soil depth. This study suggests the possibility of predicting more reliable values than the current effective soil depth, which assumes a large area uniformly.

• Journal of the Korean Geotechnical Society
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• v.25 no.2
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• pp.45-54
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• 2009

The electrical characteristics of soils have been used for investigating soil properties. The purpose of this study is the development and application of the electrical resistivity cone probe (ERCP) for the evelation of the porosity in the field with high precision. The shape of the probe tip is a cone shape to minimize the disturbance during penetration. In addition, the four terminal pair configuration is adopted to minimize the electrical interference. The electrical resistances are continuously measured during penetration of the ERCP using penetration rigs with 0.33 mm/sec penetration rate at Incheon and Busan sites. With the measured resistance profile and electrical resisivity of electrolyte of undisturbed samples, soil porosity profiles are obtained by using Archie's law. The empirical coefficients for the Archie's law are obtained based on the electrolyte extracted from the undisturbed samples. The estimated porosity profiles show similar trends to those of in-situ penetration tests such as SPT, CPT, and DMT. This study suggests that the ERCP may be an effective tool for the porosity estimation in the field with minimum disturbance.