• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.854-856
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• 2014

Has been released of make it possible to control the using for smart devices of a wide variety home appliances and electronics in smart appliances in accordance with the one person multi devices. In addition, is increasing rapidly for the number of the product on cleaning robot and refrigerator, air conditioning, TV, etc. these devices are using the implement up DLNA system. And at home and abroad for development and has provided with Iot and Alljoyn such systems. But currently using home appliances or electronic devices of there are a lot of the operating system non installed than the installed products. In addition, smart appliances do not use for user than buying existing electronic products a lot more. In addition, more occur for smart appliances of that do not use for the user on smart appliances rather than buying existing electronics. In this paper, Suggested and implemented for system of control such as smart devices to existed home appliance on not have an operating system, Using mobile device for want users to quantify the data to transfer from arduino board.

• Journal of Gastric Cancer
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• v.19 no.2
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• pp.165-172
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• 2019

Purpose: The robotic system for surgery was introduced to gastric cancer surgery in the early 2000s to overcome the shortcomings of laparoscopic surgery. The more recently introduced da Vinci $Xi^{(R)}$ system offers benefits allowing four-quadrant access, greater range of motion, and easier docking through an overhead boom rotation with laser targeting. We aimed to identify whether the $Xi^{(R)}$ system provides actual advantages over the $Si^{(R)}$ system in gastrectomy for gastric cancer by comparing the operative outcomes. Materials and Methods: We retrospectively reviewed all patients who underwent robotic gastrectomy as treatment for gastric cancer from March 2016 to March 2017. Patients' demographic data, perioperative information, and operative and pathological outcomes were collected and analyzed. Results: A total of 109 patients were included in the $Xi^{(R)}$ group and 179 in the $Si^{(R)}$ group. Demographic characteristics were similar in both groups. The mean operative time was 229.9 minutes in the $Xi^{(R)}$ group and 223.7 minutes in the $Si^{(R)}$ group. The mean estimated blood loss was 72.7 mL in the $Xi^{(R)}$ group and 62.1 mL in the $Si^{(R)}$ group. No patient in the $Xi^{(R)}$ group was converted to open or laparoscopy, while 3 patients in the $Si^{(R)}$ group were converted, 2 to open surgery and 1 to laparoscopy, this difference was not statistically significant. Bowel function was resumed 3 days after surgery, while soft diet was initiated 4 days after surgery. Conclusions: We found no difference in surgical outcomes after robotic gastrectomy for gastric cancer between the da Vinci $Xi^{(R)}$ and da Vinci $Si^{(R)}$ procedures.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1039-1045
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• 2021

In modern society, due to the influence of the 4th industrial revolution, camera sensors and image-based automation systems are being used in various fields, and interest in image and signal processing is increasing. In this paper, we propose a digital filter algorithm for image reconstruction in an impulse noise environment. The proposed algorithm divides the image into eight masks in vertical, horizontal, and diagonal directions based on the local mask set in the image, and compares the standard deviation of each segmentation mask to obtain a reference value. The final output is calculated by applying the weight according to the spatial distance and the weight using the reference value to the local mask. To evaluate the performance of the proposed algorithm, it was simulated with the existing algorithm, and the performance was compared using enlarged images and PSNR.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.2_2
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• pp.247-255
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• 2022

Robot Process Automation (RPA) is a computer technology called Robotic Process Automation, a form of business process automation based on the concept of software robots or artificial intelligence (AI) walkers. In general, in traditional workflow automation tools, software developers design software that creates a set of actions to automate tasks and interfaces for the back-end systems using internal APIs or dedicated script languages. However, in RPA software, automation can be implemented by configuring an operating processor as if the general user is directly performing the task of the application. In other words, it can be said that it is a suitable development method for automating simply repetitive tasks rather than developing specific programs in which all necessary functions are implemented, as in general software development. Thus, this is more appropriate for configuring and automating RPA software in traditional manufacturing companies that are not easy to develop and apply smart factories or high-end AI software. Therefore, this research aims to analyze the requirements required at the actual manufacturing companies, focusing on the manufacturer's case in Changwon, Gyeongsangnam-do, called SinceWin Co., Ltd., and to examine the possibility of RPA software in the manufacturing companies by implementing actual RPA software that supports office automation. Through the research, it was confirmed that the actually implemented RPA software met the requirements of the company and helped manufacturer practice significantly by automating the parts that were worked error-prone and manually periodically.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4084-4104
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• 2021

Visual place recognition (VPR) is a fundamental yet challenging task of mobile robot navigation and localization. The existing VPR methods are usually based on some pairwise similarity of image descriptors, so they are sensitive to visual appearance change and also computationally expensive. This paper proposes a simple yet effective four-step method that achieves adaptive convolutional neural network (CNN) landmark matching for VPR. First, based on the features extracted from existing CNN models, the regions with higher significance scores are selected as landmarks. Then, according to the coordinate positions of potential landmarks, landmark matching is improved by removing mismatched landmark pairs. Finally, considering the significance scores obtained in the first step, robust image retrieval is performed based on adaptive landmark matching, and it gives more weight to the landmark matching pairs with higher significance scores. To verify the efficiency and robustness of the proposed method, evaluations are conducted on standard benchmark datasets. The experimental results indicate that the proposed method reduces the feature representation space of place images by more than 75% with negligible loss in recognition precision. Also, it achieves a fast matching speed in similarity calculation, satisfying the real-time requirement.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.160-167
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• 2023

This study focuses on the development of a Last-Mile delivery service using unmanned vehicles to deliver goods directly to the end consumer utilizing drones to perform autonomous delivery missions and an image-based precision landing algorithm for handoff to a robot in an intermediate facility. As the logistics market continues to grow rapidly, parcel volumes increase exponentially each year. However, due to low delivery fees, the workload of delivery personnel is increasing, resulting in a decrease in the quality of delivery services. To address this issue, the research team conducted a study on a Last-Mile delivery service using unmanned vehicles and conducted research on the necessary technologies for drone-based goods transportation in this paper. The flight scenario begins with the drone carrying the goods from a pickup location to the rooftop of a building where the final delivery destination is located. There is a handoff facility on the rooftop of the building, and a marker on the roof must be accurately landed upon. The mission is complete once the goods are delivered and the drone returns to its original location. The research team developed a mission planning algorithm to perform the above scenario automatically and constructed an algorithm to recognize the marker through a camera sensor and achieve a precision landing. The performance of the developed system has been verified through multiple trial operations within ETRI.

• 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 Convergence on Culture Technology
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• v.9 no.3
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• pp.677-682
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• 2023

A large number of motors and sensors are used to drive a humanoid robot. In order to solve the wiring problem that occurs when connecting multiple actuators, a controller based on a communication network has been used, and CAN, which is advantageous in terms of cost and a highly reliable communication protocol, was mainly used. In terms of the structure of the controller, a torque control type structure that is easy to implement an advanced algorithm into the upper controller is preferred. In this case, the low communication bandwidth of CAN becomes a problem, and in order to obtain sufficient communication bandwidth, a communication network is configured by separating into a plurality of CAN networks. In this study, a stability analysis on transmission time delay is performed for a multi-motor control system in which high-speed FlexRay and low-speed CAN communication networks are hierarchically connected in order to obtain a high communication bandwidth, and sensor information and driving signals are delivered within the allowed transmission time. The proposed hierarchical network-based control system is expected to improve control performance because it can implement multiple motor control systems with a single network.

• Transactions of Materials Processing
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• v.31 no.3
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• pp.124-128
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• 2022

NdFeB magnets have been positioned as the core materials in advanced technologies such as MRI (magnetic resonance imaging), FA (factory automation system), robot, motors, and so on based on the highest magnetic properties. To effectively improve the refined microstructure, the plastic deformation has been known as the good alternatives by the recrystallization. However, it has been regarded as being impossible because of the few slip systems in the RE-Fe-B magnets at room temperature. The purpose of this study was to investigate the possibility of control of grain refinement and magnetic anisotropy of NdFeB alloy powder by the severe plastic deformation. The NdFeB magnet powder was fabricated by gas atomization process, and the powder was pre-compacted at high temperature. The pre-compacted billets were deformed by HPT (high pressure torsion), and then the deformed billets were observed microstructure and magnetic properties. After the HPT process at room temperature, the grain size decreased with increasing because of the melted Nd-rich phase, and the anisotropy of Nd₂Fe₁₄B phase was formed after the HPT process.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.583-588
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• 2024

With the emergence of Brain-Computer Interface (BCI) technology, analyzing mirror neurons has become more feasible. However, evaluating the accuracy of BCI systems that rely on human thoughts poses challenges due to their qualitative nature. To harness the potential of BCI, we propose a new approach to measure accuracy based on the characteristics of mirror neurons in the human brain that are influenced by speech speed, depending on the ultimate goal of movement. In Chapter 2 of this paper, we introduce mirror neurons and provide an explanation of human posture estimation for mirror neurons. In Chapter 3, we present a powerful pose estimation method suitable for real-time dynamic environments using the technique of human posture estimation. Furthermore, we propose a method to analyze the accuracy of BCI using this robotic environment.