• International Journal of Internet, Broadcasting and Communication
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• v.10 no.1
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• pp.71-76
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• 2018

The technology convergence of television, communication, and computing devices enables the rich social and entertaining experience through Smart TV in personal living space. The powerful smart TV computing platform allows to provide various user interaction interfaces like IR remote control, web based control, body gesture based control, etc. The presently used smart TV interaction user control methods are not efficient and user-friendly to access different type of media content and services and strongly required advanced way to control and access to the smart TV with easy user interface. This paper propose the optical wireless communication (OWC) based remote controller design for Smart TV using smart device Flashlights. In this approach, the user smart device act as a remote controller with touch based interactive smart device application and transfer the user control interface data to smart TV trough Flashlight using visible light communication method. The smart TV built-in camera follows the optical camera communication (OCC) principle to decode data and control smart TV user access functions according. This proposed method is not harmful as radio frequency (RF) radiation does it on human health and very simple to use as well user does need to any gesture moves to control the smart TV.

• Journal of Biosystems Engineering
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• v.39 no.2
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• pp.134-141
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• 2014

Purposes: This study was to validate the Robotic Smart Work System that can provides better working conditions and high productivity in unstructured environments like bio-industry, based on a tele-operation system for fruit harvesting with low cost 3-D positioning system on the laboratory level. Methods: For the Robotic Smart Work System for fruit harvesting and cultivation management in agriculture, a vision based tele-operating system and 3-D position information are key elements. This study proposed Robotic Smart Farming, an agricultural version of Robotic Smart Work System, and validated a 3-D position information system with a low cost omni camera and a laser marker system in the lab environment in order to get a vision based tele-operating system and 3-D position information. Results: The tasks like harvesting of the fixed target and cultivation management were accomplished even if there was a short time delay (30 ms ~ 100 ms). Although automatic conveyor works requiring accurate timing and positioning yield high productivity, the tele-operation with user's intuition will be more efficient in unstructured environments which require target selection and judgment. Conclusions: This system increased work efficiency and stability by considering ancillary intelligence as well as user's experience and knowhow. In addition, senior and female workers will operate the system easily because it can reduce labor and minimized user fatigue.

• International journal of advanced smart convergence
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• v.7 no.1
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• pp.42-47
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• 2018

Recently, as the demand for limited resources continues to rise and problems of resource depletion rise worldwide, the importance of renewable energy is gradually increasing. In order to solve these problems, various methods such as energy conservation and alternative energy development have been suggested, and biogas, which can utilize the gas produced from biomass as fuel, is also receiving attention as the next generation of innovative renewable energy. New and renewable energy using biogas is an energy production method that is expected to be possible in large scale because it can supply energy with high efficiency in compliance with energy supply method of recycling conventional resources. In order to more efficiently produce and manage these biogas, a biogas plant has emerged. In recent years, a large number of biogas plants have been installed and operated in various locations. Organic wastes corresponding to biogas production resources in a biogas plant exist in a wide variety of types, and each of the incoming raw materials is processed in different processes. Because such a process is required, the case where the biogas plant process is inefficiently operated is continuously occurring, and the economic cost consumed for the operation of the biogas production relative to the generated biogas production is further increased. In order to solve such problems, various attempts such as process analysis and feedback based on the feedstock have been continued but it is a passive method and very limited to operate a medium/large scale biogas plant. In this paper, we propose "CNN-based production yield prediction algorithm for increasing process efficiency of biogas plant" for efficient operation of biogas plant process. Based on CNN-based production yield forecasting, which is one of the deep-leaning technologies, it enables mechanical analysis of the process operation process and provides a solution for optimal process operation due to process-related accumulated data analyzed by the automated process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1530-1544
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• 2023

Data monitoring is an important foundation of modern science. In most cases, the monitoring data is time-series data, which has high application value. The deep learning algorithm has a strong nonlinear fitting capability, which enables the recognition of time series by capturing anomalous information in time series. At present, the research of time series recognition based on deep learning is especially important for data monitoring. Deep learning algorithms require a large amount of data for training. However, abnormal sample is a small sample in time series, which means the number of abnormal time series can seriously affect the accuracy of recognition algorithm because of class imbalance. In order to increase the number of abnormal sample, a data augmentation method called GANBATS (GAN-based Bi-LSTM and Attention for Time Series) is proposed. In GANBATS, Bi-LSTM is introduced to extract the timing features and then transfer features to the generator network of GANBATS.GANBATS also modifies the discriminator network by adding an attention mechanism to achieve global attention for time series. At the end of discriminator, GANBATS is adding averagepooling layer, which merges temporal features to boost the operational efficiency. In this paper, four time series datasets and five data augmentation algorithms are used for comparison experiments. The generated data are measured by PRD(Percent Root Mean Square Difference) and DTW(Dynamic Time Warping). The experimental results show that GANBATS reduces up to 26.22 in PRD metric and 9.45 in DTW metric. In addition, this paper uses different algorithms to reconstruct the datasets and compare them by classification accuracy. The classification accuracy is improved by 6.44%-12.96% on four time series datasets.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.551-562
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• 2024

This study relates to a method and device for extracting and tracking moving objects. In particular, objects are extracted using different images between adjacent images, and the location information of the extracted object is continuously transmitted to provide accurate location information of at least one moving object. It relates to a method and device for extracting and tracking moving objects based on tracking moving objects. People tracking, which started as an expression of the interaction between people and computers, is used in many application fields such as robot learning, object counting, and surveillance systems. In particular, in the field of security systems, cameras are used to recognize and track people to automatically detect illegal activities. The importance of developing a surveillance system, that can detect, is increasing day by day.

• Journal of Chest Surgery
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• v.41 no.5
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• pp.598-604
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• 2008

Background: The improvements in endoscopic equipment and surgical robots has encouraged the performance of minimally invasive cardiac operations. Yet only a few Korean studies have compared this procedure with the sternotomy approach. Material and Method: Between December 2005 and July 2007, 48 patients (group A) underwent minimally invasive cardiac surgery with AESOP through a small right thoracotomy. During the same period, 50 patients (group B) underwent conventional surgery. We compared the operative time, the operative results, the post-operative pain and the recovery of both groups. Result: There was no hospital mortality and there were no significant differences in the incidence of operative complications between the two groups. The operative $(292.7{\pm}61.7\;and\;264.0{\pm}47.9min$, respectively; p=0.01) and CPB times ($128.4{\pm}37.6\;and\;101.7{\pm}32.5min$, respectively; <0.01) were longer for group A, whereas there was no difference between the aortic cross clamp times ($82.1{\pm}35.0\;and\;87.8{\pm}113.5min$, respectively; p=0.74) and ventilator times ($18.0{\pm}18.4\;and\;19.7{\pm}9.7$ hr, respectively; p=0.57) between the groups. The stay on the ICU $(53.2{\pm}40.2\;and\;72.8{\pm}42.1hr$, respectively; p=0.02) and the hospitalization time ($9.7{\pm}7.2\;and\;14.8{\pm}11.9days$, respectively; p=0.01) were shorter for group A. The Patients in group B had more transfusions, but the difference was not significant. For the overall operative intervals, which ranged from one to four weeks, the pair score was significantly lower for the patients of group A than for the patients of group B. In terms of the postoperative activities, which were measured by the Duke Activity Scale questionnaire, the functional status score was clearly higher for group A compared to group B. The analysis showed no difference in the severity of either post-repair of mitral ($0.7{\pm}1.0\;and\;0.9{\pm}0.9$, respectively; p=0.60) and tricuspid regurgitation ($1.0{\pm}0.9\;and\;1.1{\pm}1.0$, respectively; p=0.89). In both groups, there were no valve related complications, except for one patient with paravalvular leakage in each group. Conclusion: These results show that compared with the median sternotomy patients, the patients who underwent minimally invasive surgery enjoyed significant postoperative advantages such as less pain, a more rapid return to full activity, improved cosmetics and a reduced hospital stay. The minimally invasive surgery can be done with similar clinical safety compared to the conventional surgery that's done through a median sternotomy.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.889-898
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• 2021

Currently, due to the demographic cliff phenomenon in Republic of Korea, A serious defense vacuum could occur due to the lack of South Korean military's personal strength. As a result, The South Korean military has a possibility to implement the polices the prepare for military provocations and preemptive strikes by the North Korean military while resolving the South Korean defense vacuum caused by the shrinking population. It seems like that the only way for the South Korean military to solve the shortage of personal strength due to the population decline is to reduce the number of Mechanized Units(MU) other than, infantry and automate, and autonomous driving the weapons system of the Mechanized Units(MU). In this paper, we propose the use of the virtual autonomous driving of the self propelled artillery K-9's in self selection of the position and occupation of position and self positioning in the position. At the same time in this paper, the self propelled artillery K-9 model robot is used to simulate and the explain about the operation method, necessity and feasibility in the self propelled artillery K-9. In addition, this paper predicted the problems that would arise if the South Korean military deployed autonomous driving self propelled K-9, in real combat.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.398-407
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• 2021

Firefighters (who are responsible for people's safety) have one of the jobs that are expected to have problems due to aging in the workforce. An increase in the average age of firefighters can lead to serious social problems. The aim of this study is to survey firefighters in Jeollanam-do about their awareness of aging in firefighters, and to propose a plan to prepare them for aging through investigation and analysis of work problems that may occur due to an aging workforce. The survey shows that the higher the age group, the higher the awareness of aging firefighters, and the higher the total work experience and internal/external work experience, the higher the awareness of aging. As a plan to solve various problems that may arise from aging in firefighters, regular operation of physical fitness promotion programs, field work, job rotation, and managerial measures (such as a change of position to an administrative department) are prepared, and drone or robot technology is used. These solutions include the introduction of applied high-tech technologies to firefighting activities, establishment of retirement management policies, and preparation of plans to revitalize the connection to private employment. In order to maximize the applicability of the field, government institutional plans and preparations are essential.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.230-238
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• 2023

Small and medium-sized food manufacturing enterprises are largely reliant on manual labor, from inputting raw materials to palletizing the final product. Recently, there has been a trend toward smartness and digitization through the implementation of robotics and sensor data technology. In this study, we examined the effectiveness of improvement through 3D simulation on two repetitive work processes within a food manufacturing company. These processes involve workers whose speed cannot match the capacity of the applied equipment. Two manual processes were selected: the weighing and packing process performed by workers after skewer assembly, and the manual batch process of counting randomly delivered frozen foods, packing (both internal and external), and palletizing. The production volume, utilization rate, and number of workers were chosen as verification indicators. As a result of the simulation for improving the 3D process, production increased by 13.5% and 56.8% compared to the existing process, respectively. This was particularly evident in the process of applying palletizing robots. In both processes, as the utilization rate and number of input workers decreased, robots could replace tasks with high worker fatigue, thereby reducing work overload. This study demonstrates the potential to visually compare the process flow improvement using 3D simulations and confirms the possibility of pre-validation for improvement.