• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.1
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• pp.1-11
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• 2014

In this paper we propose a system for vehicle detection and tracking which has the ability to learn on-line appearance changes of vehicles being tracked. The proposed system uses feature-based tracking method to estimate rapidly and robustly the motion of the newly detected vehicles between consecutive frames. Simultaneously, the system trains an online vehicle detector for the tracked vehicles. If the tracker fails, it is re-initialized by the detection of the online vehicle detector. An improved vehicle appearance model update rule is presented to increase a tracking performance and a speed of the proposed system. Performance of the proposed system is evaluated on the dataset acquired on various driving environment. In particular, the experimental results proved that the performance of the vehicle tracking is significantly improved under bad conditions such as entering a tunnel and passing rain.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.6
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• pp.47-56
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• 2015

In the multi-sensor system, sensor registration errors such as a sensor bias must be corrected so that the individual sensor data are expressed in a common reference frame. If registration process is not properly executed, large tracking errors or formation of multiple track on the same target can be occured. Especially for launch vehicle tracking system, each multiple observation lies on the same reference frame and then fused trajectory can be the best track for slaving data. Hence, this paper describes an on-line bias estimation/correction and asynchronous sensor fusion for launch vehicle tracking. The bias estimation architecture is designed based on pseudo bias measurement which derived from error observation between GPS and radar measurements. Then, asynchronous sensor fusion is adapted to enhance tracking performance.

• Journal of Biosystems Engineering
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• v.41 no.4
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• pp.319-327
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• 2016

Purpose: Automated guidance systems (AGSs) for mobile farm machinery have several advantages over manual operation in the crop production industry. Many researchers and companies have tried to develop such a system. However, it is not easy to evaluate the performance of an AGS because there is no established device used to evaluate it that complies with the ISO 12188 standard. The objective of this study was to develop a tracking sensor system using five laser distance measurement sensors. Methods: One sensor-for long-range distance measurement-was used to measure travel distance and velocity. The other four sensors-for mid-range distance measurement-were used to measure lateral deviation. Stationary, manual driving, and A-B line tests were conducted, and the results were compared with the real-time kinematic differential global positioning system (RTK-DGPS) signal used by the AGS. Results: For the stationary test, the average error of the tracking sensor system was 1.99 mm, and the average error of the RTK-DGPS was 15.19 mm. For the two types of driving tests, the data trends were similar. A comparison of the changes in lateral deviation showed that the data stability of the developed tracking system was better. Conclusions: Although the tracking system was not capable of measuring long travel distances under strong sunlight illumination because of the long-range sensor's limitations, this dilemma could be overcome using a higher-performance sensor.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.250-256
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• 2009

This paper presents a robotic tower-crane system using encoder and gyroscope sensors as path tracking devices. Tower crane work is often associated with falling accidents and industrial disasters. Such problems often incur a loss of time and money for the contractor. For this reason, many studies have been done on an automatic tower crane. As a part of 5-year 23-million-dollar research project in Korea, we are developing a robotic tower crane which aims to improve the safety level and productivity. We selected a luffing tower crane, which is commonly used in urban construction projects today, as a platform for the robotic tower crane system. This system comprises two modules: the automated path planning module and the path tracking module. The automated path planning system uses the 3D Cartesian coordinates. When the robotic tower crane lifts construction material, the algorithm creates a line, which represents a lifting path, in virtual space. This algorithm seeks and generates the best route to lift construction material while avoiding known obstacles from real construction site. The path tracking system detects the location of a lifted material in terms of the 3D coordinate values using various types of sensors including adopts encoder and gyroscope sensors. We are testing various sensors as a candidate for the path tracking device. This specific study focuses on how to employ encoder and gyroscope sensors in the robotic crane These sensors measure a movement and rotary motion of the robotic tower crane. Finally, the movement of the robotic tower crane is displayed in a virtual space that synthesizes the data from two modules: the automatically planned path and the tracked paths. We are currently field-testing the feasibility of the proposed system using an actual tower crane. In the next step, the robotic tower crane will be applied to actual construction sites with a following analysis of the crane's productivity in order to ascertain its economic efficiency.

• Journal of Auto-vehicle Safety Association
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• v.12 no.2
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• pp.33-39
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• 2020

This paper presents an Around View Monitoring (AVM) stop-line detection based longitudinal position correction algorithm for automated driving on urban roads. Poor positioning accuracy of low-cost GPS has many problems for precise path tracking. Therefore, this study aims to improve the longitudinal positioning accuracy of low-cost GPS. The algorithm has three main processes. The first process is a stop-line detection. In this process, the stop-line is detected using Hough Transform from the AVM camera. The second process is a map matching. In the map matching process, to find the corrected vehicle position, the detected line is matched to the stop-line of the HD map using the Iterative Closest Point (ICP) method. Third, longitudinal position of low-cost GPS is updated using a corrected vehicle position with Kalman Filter. The proposed algorithm is implemented in the Robot Operating System (ROS) environment and verified on the actual urban road driving data. Compared to low-cost GPS only, Test results show the longitudinal localization performance was improved.

• Journal of Appropriate Technology
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• v.5 no.2
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• pp.70-81
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• 2019

The garment industry is one of the most labor-intensive manufacturing industries, with its sewing process relying almost entirely on manual labor. Its costs highly depend on the efficiency of this production line and thus is crucial to determine the production rate in real-time for line balancing. However, current production tracking methods are costly and make it difficult for many Small and Medium-sized Enterprises (SMEs) to implement them. As a result, their reliance on manual counting of finished products is both time consuming and prone to error, leading to high manufacturing costs and inefficiencies. In this paper, a production tracking system that uses the sewing machines' energy consumption data to track and count the total number of sewing tasks completed through Convolutional Neural Network (CNN) classifiers is proposed. This system was tested on two target sewing tasks, with a resulting maximum classification accuracy of 98.6%; all sewing tasks were detected. In the developing countries, the garment sewing industry is a very important industry, but the use of a lot of capital is very limited, such as applying expensive high technology to solve the above problem. Applied with the appropriate technology, this system is expected to be of great help to the garment industry in developing countries.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1824-1827
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• 1997

Majority of industrial robots are controlled by a simple joint servo control of joint actuators. In this type of control, the performance of control is influenced greatly by the joint interaction torques including Coriolis and centrifugal forces, which act as disturbance torques to the control system. As the speed of the robot increases, the effect of this disturbance torque increases, and makes the high speed-high precision control more difficult to achieve. In this paper, the joint disturbance torque of robots is analyzed. The joint disturbance torque is defined using the coefficients of dynamic equation of motion, and for the case of a 2DOF planar robot, the conditions for the maximum joint disturbance torques are identified, and the effect of link parameters and joint variables on the joint disturbance torque are examined. Then, a solutioin to the optimal path placement problem is proposed that minimizes the joint disturbance torque are examined. then, a solution to the optimal path placement problem is proposed that minimizes the joint disturbance torque during a straight line motion. the proposed method is illustrated using computer simulation. the proposed solution method cna be applied to the class of robots that are controlled by independent joint sevo control, which includes the vast majority of industrial robots. By minimizing the joint disturbacne torque during the motion, the simple joint servo controlled robot can move with improved path tracking accuracy at high speed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.550-555
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• 1993

The aim of this paper is to present the development of visual seam tracking system equipped with visual range finder. The visual range finder, which consists of a CCD camera and a diode laser system with line generating optics, developed to recognize the types of weld joints and detect the location of weld joints. In practical applications, however, images of the weld joints are often degraded due to spatters, are flares, surface specularity, and welding smoke. To overcome the problem, this paper proposes a syntactic approach which is a class of artificial intelligence techniques. In the approach, the type of weld joint is inferred based upon the production rules which are linguiques grammars consisting of a set of line and junction primitives of laser strip image projected on weld joint. The production rules eliminate several noisy primitives to create new primitives through the merging process of primitives. After the recognition of weld joint, arc welding is started and the location of weld joints is repeatedly detected using a spring model-based template matching in which the template model is a by-product of the recognition process of weld joint. To show the effectiveness of the proposed approach a series of experiments-identification and robotic tracking-are conducted for four different types of weld joints.

• The Transactions of the Korean Institute of Power Electronics
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• v.13 no.2
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• pp.119-127
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• 2008

In this paper, the modular line-connected photovoltaic PCS (photovoltaic power conditioning system) is proposed. A step-up DC-DC converter using a active-clamp circuit and a dual series-resonant rectifier is proposed to achieve a high efficiency and a high input-output voltage ratio efficiently. An IncCond (incremental conductance) MPPT (maximum power point tracking) algorithm that improves MPPT characteristic is used. The PV module current is estimated without using a DC current sensor. By control a inverter using a linearized output current controller, a unity power factor is achieved. All algorithms and controllers are implemented on a single-chip microcontroller and the superiority of the proposed DC-DC converter and controllers is proved by experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.497-497
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• 2000

This paper is concerned with the design of LMI based H$_{\infty}$ controller for a line of sight(LOS) stabilization system. This system which is even linearized to analyse nonlinear characteristic has also a lot of uncertainties. In addition, the angular velocity disturbance from the vehicle's driving deteriorates the stabilized LOS, main purpose of this system. In case of fast driving, particularly, all components which are ignored and skipped to make mathematical modelling act as the uncertainties against this system. The robustness against these uncertainties has been also continuously demanded including the well tracking performance for the target. Therefore, this paper employed H$_{\infty}$ control theory to satisfy these problems and LMI method to make suitable controller with few constraints for this system. Although this system matrix doesn't have full rank, this method make it possible to design H$_{\infty}$ controller and deal with R and S matrices for reducing its order. Consequently, this paper shows that the re-analyses on the real disturbances are achieved and the proposed robust controller for them has better disturbance attenuation and tracking performance. This paper contributes the applicability of reduced order H$_{\infty}$ controller to real system by handling LMI..