• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.421-427
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• 2012

In this work, 4 DOF ER haptic master is proposed and integrated with a slave robot for minimally invasive surgery (MIS). Using a controllable ER fluid, the haptic master can generate a repulsive force/torque with the 4-DOF motion. For realization of master-slave robot system, the motion command of the haptic master is realized by slave surgery robot. In order to follow the 4 DOF motion of the haptic master, novel mechanism of slave surgery robot with gimbal joint is devised. Accordingly, the haptic master-slave robot system is established by incorporating the slave robot with the haptic master device in which the desired repulsive force/torque and position are transferred to each other via wireless communications. In order to obtain the desired force/torque and position trajectories, tracking controllers for haptic master and slave robot are designed and implemented, respectively. It has been demonstrated that the desired effective torque tracking control performance is well achieved using the proposed haptic master-slave robot system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.331-336
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• 2012

This paper proposes a new type of high-frequency directional valve controlled by the piezostack actuator associated with displacement amplifier. As a first step, a dynamic model of directional valve which can operate at 200 Hz with a flow rate of 12 l/min is derived by considering pressure drop and flow force. As a second step, an appropriate piezostack is selected by considering actuation force as well as field-dependent displacement. Subsequently, in order to control spool displacement and flow rate a proportional-derivative (PD) controller is designed based on the $3^{rd}$-order valve system. Control performances such as sinusoidal trajectory tracking of the spool displacement in time domain are evaluated. In addition, the field-dependent flow rate is also presented to verify the required performance of the valve system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.3
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• pp.166-171
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• 2009

In recent years, video detection systems have been implemented in various infrastructures such as airport, public transportation, power generation system, water dam and so on. Recognizing moving objects in video sequence is an important problem in computer vision, with applications in several fields, such as video surveillance and target tracking. Segmentation and tracking of multiple vehicles in crowded situations is made difficult by inter-object occlusion. In the system described in this paper, the mean shift algorithm is firstly used to filter and segment a color vehicle image in order to get candidate regions. These candidate regions are then analyzed and classified in order to decide whether a candidate region contains a license plate or not. And then some characters in the license plate is recognized by using the fuzzy ARTMAP neural network, which is a relatively new architecture of the neural network family and has the capability to learn incrementally unlike the conventional BP network. We finally design a license plate recognition system using the mean shift algorithm and fuzzy ARTMAP neural network and show its performance via some computer simulations.

• KSCI Review
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• v.15 no.1
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• pp.53-64
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• 2007

Avoid at damage systems in order to avoid own IP address exposure, and an invader does not attack directly a system in recent hacking accidents at these papers, and use Stepping stone and carry out a roundabout attack. Use network audit Policy and use a CIS, AIAA technique and algorithm, the Sleep Watermark Tracking technique that used Thumbprints Algorithm, Timing based Algorithm, TCP Sequence number at network bases, and Presented a traceback system at TCP bases at log bases, and be at these papers Use the existing algorithm that is not one module in a system one harm for responding to invasion technology develop day by day in order to supplement the disadvantage where is physical logical complexity of configuration of present Internet network is large, and to have a fast technology development speed, and presentation will do an effective traceback system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.11
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• pp.2064-2075
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• 2011

In this paper, robust multi-level perturbation and observation (MLPO) maximum power point tracking (MPPT) control are presented of the environmental change including the solar radiation and temperature. Because the maximum power point of the Photovoltaic (PV) is changing according to the solar radiation and temperature, the technology which traces the maximum power point in order to increase the power efficiency is recognized as the very important part. The general requirement for the MPPT is that system is simple, the cost is inexpensive, the PV tracking function and output change are small. Conventional perturbation and observation (PO) method is a simple system but there is the disadvantage that an efficiency of system becomes low. In addation, the incremental conductance (IC) control is required expensive CPU because of a large of calculations. In order to solve this problem, in this paper, the MLPO MPPT control using the method diversifying the step size according to the environment condition is presented. The validity of the MLPO method presenting from this paper is proved through analyzing the solar power generation output error at the steady state.

• Journal of Information Processing Systems
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• v.6 no.4
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• pp.609-620
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• 2010

In order to provide a tailored education for learners within the ubiquitous environment, it is critical to undertake an analysis of the learning activities of learners. For this purpose, SCORM (Sharable Contents Object Reference Model), IMS LD (Instructional Management System Learning Design) and other standards provide learning design support functions, such as, progress checks. However, in order to apply these types of standards, contents packaging is required, and due to the complicated standard dimensions, the facilitation level is lower than the work volume when developing the contents and this requires additional work when revision becomes necessary. In addition, since the learning results are managed by the server there is the problem of the OS being unable to save data when the network is cut off. In this study, a system is realized to manage the actions of learners through the event interception of a web-browser by using event hooking. Through this technique, all HTMLbased contents can be facilitated again without additional work and saving and analysis of learning results are available to improve the problems following the application of standards. Furthermore, the ubiquitous learning environment can be supported by tracking down learning results when the network is cut off.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.10
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• pp.1020-1026
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• 2012

This paper proposes a new type of high-frequency directional valve controlled by the piezostack actuator associated with displacement amplifier. As a first step, a dynamic model of directional valve which can operate at 200 Hz with a flow rate of 12 litter/min is derived by considering pressure drop and flow force. As a second step, an appropriate piezostack is selected by considering actuation force as well as field-dependent displacement. Subsequently, in order to control spool displacement and flow rate a proportional-derivative(PD) controller is designed based on the 3rd-order valve system. Control performances such as sinusoidal trajectory tracking of the spool displacement in time domain are evaluated. In addition, the field-dependent flow rate is also presented to verify the required performance of the valve system.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.1
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• pp.39-45
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• 2019

We propose an active color model based method for tracking motions of multiple human using a networked multiple-camera system in IoT space as a human-robot coexistent system. An IoT space is a space where many intelligent devices, such as computers and sensors(color CCD cameras for example), are distributed. Human beings can be a part of IoT space as well. One of the main goals of IoT space is to assist humans and to do different services for them. In order to be capable of doing that, IoT space must be able to do different human related tasks. One of them is to identify and track multiple objects seamlessly. In the environment where many camera modules are distributed on network, it is important to identify object in order to track it, because different cameras may be needed as object moves throughout the space and IoT space should determine the appropriate one. This paper describes appearance based unknown object tracking with the distributed vision system in IoT space. First, we discuss how object color information is obtained and how the color appearance based model is constructed from this data. Then, we discuss the global color model based on the local color information. The process of learning within global model and the experimental results are also presented.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.3
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• pp.511-518
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• 2021

In order to obtain a good tracking performance of the motor, it is necessary to design a controller that can respond to a disturbance by including a disturbance observer. The disturbance observer of the motor is designed to estimate the load torque and the back electromotive voltage based on the first-order low-pass filter. A PI controller and an IP controller were designed to compare the correlation between the disturbance observer and the controller and to obtain improved control performance. To check the performance of the designed observer and the controller, it was applied to a 120 [W] class BLDC motor. As a result, overshoot is reduced, and it can be seen that the steady-state error converges to zero.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.89-106
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• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.