• Journal of Yeungnam Medical Science
• /
• v.41 no.1
• /
• pp.48-52
• /
• 2024

While simultaneous bilateral partial nephrectomy with a conventional multiport robot has been consistently reported since the 2010s, the introduction of the DaVinci SP system (Intuitive Surgical, Sunnyvale, CA, USA) could provide a novel way to perform surgery on bilateral kidneys while innovatively reducing the number of incisions. In our first report worldwide, the patient with bilateral small renal mass (2.0 cm for the left and 1.5 cm for the right side) and preoperative normal renal function was placed in the lateral decubitus position on an inverted bed. After tilting the bed to be as horizontal as possible, a 4-cm incision was made in the lower part of the umbilicus for the floating trocar technique. The partial nephrectomy was performed reliably as with the conventional transperitoneal approach, and then the patient could be repositioned to the contralateral side for the same procedure, maintaining all trocars. Total operation time (skin to skin), total console time, and the left- and right-side warm ischemic times were 260, 164, 27, and 23 minutes, respectively, without applying the early declamping technique. The estimated blood loss was 200 mL. The serum creatinine right after the operation, on the first day, 3 days, and 90 days after surgery were 0.92, 0.77, 0.79, and 0.81 mg/dL, respectively. For 90 days after the procedure, no complications or radiologic recurrence were observed. Further clinical studies will reveal the advantages of using the DaVinci SP device for this procedure over traditional multiport surgery, maximizing the benefit of a single port-based approach.

• Journal of Distribution Science
• /
• v.11 no.2
• /
• pp.45-55
• /
• 2013

Purpose - The development and implementation of OSS (Open Source Software) led to a dramatic change in corporate IT infrastructure, from system server to smart phone, because the performance, reliability, and security functions of OSS are comparable to those of commercial software. Today, OSS has become an indispensable tool to cope with the competitive business environment and the constantly-evolving IT environment. However, the use of OSS is insufficient in small and medium-sized companies and software houses. This study examines the need for OSS Intermediaries in the Software Distribution Channel. It is expected that the role of the OSS Intermediary will be reduced with the improvement of the distribution process. The purpose of this research is to prove that OSS Intermediaries increase the efficiency of the software distribution market. Research design, Data, and Methodology - This study presents the analysis of data gathered online to determine the extent of the impact of the intermediaries on the OSS market. Data was collected using an online survey, conducted by building a personal search robot (web crawler). The survey period lasted 9 days during which a total of 233,021 data points were gathered from sourceforge.net and Apple's App store, the two most popular software intermediaries in the world. The data collected was analyzed using Google's Motion Chart. Results - The study found that, beginning 2006, the production of OSS in the Sourceforge.net increased rapidly across the board, but in the second half of 2009, it dropped sharply. There are many events that can explain this causality; however, we found an appropriate event to explain the effect. It was seen that during the same period of time, the monthly production of OSS in the App store was increasing quickly. The App store showed a contrasting trend to software production. Our follow-up analysis suggests that appropriate intermediaries like App store can enlarge the OSS market. The increase was caused by the appearance of B2C software intermediaries like App store. The results imply that OSS intermediaries can accelerate OSS software distribution, while development of a better online market is critical for corporate users. Conclusion - In this study, we analyzed 233,021 data points on the online software marketplace at Sourceforge.net. It indicates that OSS Intermediaries are needed in the software distribution market for its vitality. It is also critical that OSS intermediaries should satisfy certain qualifications to play a key role as market makers. This study has several interesting implications. One implication of this research is that the OSS intermediary should make an effort to create a complementary relationship between OSS and Proprietary Software. The second implication is that the OSS intermediary must possess a business model that shares the benefits with all the participants (developer, intermediary, and users).The third implication is that the intermediary provides an OSS of high quality like proprietary software with a high level of complexity. Thus, it is worthwhile to examine this study, which proves that the open source software intermediaries are essential in the software distribution channel.

• Journal of Advanced Navigation Technology
• /
• v.21 no.6
• /
• pp.537-543
• /
• 2017

Underwater glider is the long-term operating underwater robot that was developed with a purpose of continuous oceanographic observations and explorations. Torpedo-type underwater glider is not efficient from an aspect of maneuverability, because it uses a single buoyancy engine and motion controller for obtaining propulsive forces and moments. This paper introduces a ray-type underwater glider(RUG) with dual buoyancy engine, which improves the control performance of buoyancy and motion compared with torpedo-type underwater glider. Carrying out Computational Fluid Dynamics (CFD) analysis as static pitch drift test, the performance of fluid resistance for gliding motion was identified. Based on the calculated hydrodynamic coefficients, the dynamic simulation compared and analyzed the motion performance of torpedo-type and ray-type while controlling same volume of buoyancy engine. Small-sized model of RUG was developed to perform fundamental performance tests.

• Journal of the Economic Geographical Society of Korea
• /
• v.24 no.3
• /
• pp.283-299
• /
• 2021

As the intelligent revolution triggered by digital technology, unmanned vehicles such as self-driving cars, robots, and drones appeared, which brought about innovative changes in the industry. Gyeonggi Local government has established both an ordinance and a basic plan regarding unmanned vehicles. It is time to prepare a data-based policy by understanding the current state of the unmanned vehicle industry in the province. As a result of the survey, the unmanned vehicle industry in Gyeonggi Province is 25% of the nationwide, and more than 88% is concentrated in the southern part of Gyeonggi Province. The land sector such as the robot and autonomous vehicles are focused on 71.4% and the aviation sector such as drones are 26.7%. However, unmanned vehicle companies in Gyeonggi-do are mostly small-sized businesses with less than 10 years of experience and are in the stage of introduction and growth level. They have a plan to improve technology through continuous R&D by hiring human resources. Therefore, Gyeonggi-do needs to consider policy support for sustainable growth of start-up and small enterprises and for fostering professional manpower and technical skills as well as for establishing an unmanned vehicle industry network to create, share, and spread knowledge.

• Journal of Practical Engineering Education
• /
• v.14 no.1
• /
• pp.127-136
• /
• 2022

As manufacturing items have changed in various ways, changes in the mass production of small-scale small-scale production of multiple varieties have become commonplace. As a result, the method of the manufacturing site has also changed, and the "smart factory," which emphasizes the production efficiency aspect using automation lines and big data of factories, is in the spotlight according to the global market economy. The introduction performance of smart factories has a positive effect in terms of production efficiency and is drawing a steep upward curve. In addition to the positive aspects, the aspect that needs to be supplemented in the future is the support and cooperation of specialized smart equipment suppliers, but education on standardized smart factories and the relocation of existing manpower, education, evaluation, and creative production that robots cannot replace Various support measures are also needed for activities. In addition, continuous management and systematic education are required to enter the upper stage. Through the case of companies that have built smart factories, it is intended to emphasize the need for proper use of manpower and support management for settlement and maintenance after introduction and continuous on-the-job training through the comparison of productivity before and after introduction to ensure the effect continues.

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

• Journal of Welding and Joining
• /
• v.30 no.2
• /
• pp.59-64
• /
• 2012

Welding is an essential process in the automotive industry. Most welding processes that are used for auto body are spot welding and $CO_2$ welding are used in a small part. In production field, $CO_2$ welding process is decreased and spot welding process is increased due to welding quality is poor and defects are occurred in $CO_2$ welding process frequently. But $CO_2$ welding process should be used at robot interference parts and closed parts where spot welding couldn't. Because of the 0.65mm ~ 2.0mm thickness steel sheet were used in the automotive industry, poor quality of welding area such as burn through and under fill were happened frequently in $CO_2$ process. In this paper, we will study about the penetration depth which gives a huge impact on burn through changing a degree of base metal, welding position and torch angle. Voltage, current and welding speed were fixed but degree of base metal, welding position and torch angle were changed. And Cold- Rolled(CR) steel sheet was used. Penetration depth was analysed by multiple regression analysis to derive approximate calculations. And reliability of approximate calculations were confirmed through additional experiments. As the results of this research, we confirmed the effect of torch and plate angle to bead shape. And we present a possibility that can simulate more accurate to weld geometry, as deduced the verification equations that has tolerance of less than 21.69%.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37C no.11
• /
• pp.1094-1103
• /
• 2012

Recently, software industry regarding national defense increases system development of distributed simulation system of M&S based to overcome limit of resource and expense. It is one of key technologies for offering of mutual validation among objects and reuse of objects which are discussed for developing these systems. RTI, implementation of HLA interface specification as software providing these technologies uses Federation Object Model for exchanging information with joined federates in the federation and each federate has a characteristic that is supposed to have identical FOM in the federation. This technology is a software which is to provide the core technology which was suggested by the United state's military M&S standard framework. Simulator, virtual simulation, and inter-connection between military weapons system S/W which executes on network which is M&S's core base technology, and it is a technology which also can be used for various inter-connection between S/W such as game and on-line phone. These days although RTI is used in military war game or tactical training unit field, there is none in Korea. Also, it is used in mobile-game, distribution game, net management, robot field, and other civilian field, but the number of examples are so small and informalized. Through this developing project, we developed the core technique and RTI software and provided performance of COTS level to improve communication algorithms.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.42 no.1
• /
• pp.1-11
• /
• 2005

Several Solid Freeform Fabrication Systems(SFFS) are commercialized in a few companies for rapid prototyping. However, they have many technical problems including the limitation of applicable materials. A new method of agile prototyping is required for the recent manufacturing environments of multi-item and small quantity production. The objectives of this paper include the development of a novel method of SFFS, the CAFL/sup VM/(Computer Aided Fabrication of Lamination for Various Material), and the manufacture of the various material samples for the certification of the proposed system and the creation of new application areas. For these objectives, the technologies for a highly accurate robot path control, the optimization of support structure, CAD modeling, adaptive slicing was implemented. However, there is an important problem with the conventional 2D lamination method. That is the inaccuracy of 3D model surface, which is caused by the stair-type surface generated in virtue of vertical 2D cutting. In this paper, We design the new control algorithm that guarantees the constant speed, precise positioning and tangential cutting on the 5DOF SFFS. We develop the tangential cutting algorithm to be controlled with constant speed and successfully implemented in the 5DOF CAFL/sup VM/ system developed in this paper. Finally, this paper confirms its high-performance through the experimental results from the application into CAFL/sup VM/ system.

• Journal of Advanced Navigation Technology
• /
• v.25 no.1
• /
• pp.115-123
• /
• 2021

Pine nuts are Korea's representative nut forest products and profitable crops. However, pine nuts are harvested by climbing the trees themselves, thus the risk is high. In order to solve this problem, it is necessary to harvest pine nuts using a robot or an unmanned aerial vehicle(UAV). In this paper, we propose a deep learning based detection method for harvesting pine nut in UAV aerial images. For this, a video was recorded in a real pine forest using UAV, and a data augmentation technique was used to supplement a small number of data. As the data for 3D detection, Unity3D was used to model the virtual pine nut and the virtual environment, and the labeling was acquired using the 3D transformation method of the coordinate system. Deep learning algorithms for detection of pine nuts distribution area and 2D and 3D detection of pine nuts objects were used DeepLabV3+, YOLOv4, and CenterNet, respectively. As a result of the experiment, the detection rate of pine nuts distribution area was 82.15%, the 2D detection rate was 86.93%, and the 3D detection rate was 59.45%.