• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.10
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• pp.1977-1985
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• 2007

The character recognition technique is one of the artificial intelligence and has been widely applied in the automated system robot HCI(Human Computer Interaction), etc. This paper introduces the character set and the representative character that can be used in the recognition of the mage ROI. The character codes in this ROI include the digit, symbol, English and Hereat etc. We proposed the efficient multi-classifier structure by combining the small-size classifiers hierarchically. Moreover, we generated each small-size classifiers by delta-bar-delta learning algorithm. We tested the performance with various kinds of images and achieved the accuracy of 99%. The proposed multi-classifier showed the efficiency and the reliability for the mixed character code set.

• Proceedings of the Korean Society Of Semiconductor Equipment Technology
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• 2005.09a
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• pp.110-123
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• 2005

This paper proposes an intelligent method for the optimal load distribution of two cooperating robots(TCRs) using fuzzy logic. The proposed scheme requires the knowledge of the robots' dynamics, which in turn depend upon the characteristics of large flat panel displays(LFPDs) carried by the TCRs. However, the dynamic properties of the LFPD are not known exactly, so that the dynamics of the robots, and hence the required joint torque, must be calculated for nominal set of the LFPD characteristics. The force of the TCRs is an important factor in carrying the LFPD. It is divided into external force and internal force. In general , the effects of the internal force of the TCRs are not considered in performing the load distribution in terms of optimal time, but they are essential in optimal trajectory planning: if they are not taken into consideration, the optimal scheme is no longer fitting. To alleviate this deficiency, we present an algorithm for finding the internal-force factors for the TCRs in terms of optimal time. The effectiveness of the proposed system is demonstrated by computer simulations using two three-joint planner robot manipulators.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.2
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• pp.45-53
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• 2019

RGB-D SLAM (Simultaneous Localization and Mapping) refers to the technology of using deep camera as a visual sensor for SLAM. In view of the disadvantages of high cost and indefinite scale in the construction of maps for laser sensors and traditional single and binocular cameras, a method for creating three-dimensional map of indoor environment with deep environment data combined with RGB-D SLAM scheme is studied. The method uses a mobile robot system equipped with a consumer-grade RGB-D sensor (Kinect) to acquire depth data, and then creates indoor three-dimensional point cloud maps in real time through key technologies such as positioning point generation, closed-loop detection, and map construction. The actual field experiment results show that the average error of the point cloud map created by the algorithm is 0.0045m, which ensures the stability of the construction using deep data and can accurately create real-time three-dimensional maps of indoor unknown environment.

• The Journal of Korea Robotics Society
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• v.16 no.3
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• pp.179-188
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• 2021

Construction monitoring is one of the key modules in smart construction. Unlike structured urban environment, construction site mapping is challenging due to the characteristics of an unstructured environment. For example, irregular feature points and matching prohibit creating a map for management. To tackle this issue, we propose a system for data acquisition in unstructured environment and a framework for Intensity and Ambient Enhanced Lidar Inertial Odometry via Smoothing and Mapping, IA-LIO-SAM, that achieves highly accurate robot trajectories and mapping. IA-LIO-SAM utilizes a factor graph same as Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping (LIO-SAM). Enhancing the existing LIO-SAM, IA-LIO-SAM leverages point's intensity and ambient value to remove unnecessary feature points. These additional values also perform as a new factor of the K-Nearest Neighbor algorithm (KNN), allowing accurate comparisons between stored points and scanned points. The performance was verified in three different environments and compared with LIO-SAM.

• The Journal of Korea Robotics Society
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• v.17 no.4
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• pp.417-424
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• 2022

In this paper, the developed trunk cargo unloading automation system is introduced, and the RGB-D sensor-based box loading situation recognition method and unloading plan applied to this system are suggested. First of all, it is necessary to recognize the position of the box in a truck. To do this, we first apply CNN-based YOLO, which can recognize objects in RGB images in real-time. Then, the normal vector of the center of the box is obtained using the depth image to reduce misrecognition in parts other than the box, and the inner wall of the truck in an image is removed. And a method of classifying the layers of the boxes according to the distance using the recognized depth information of the boxes is suggested. Given the coordinates of the boxes on the nearest layer, a method of generating the optimal path to take out the boxes the fastest using this information is introduced. In addition, kinematic analysis is performed to move the conveyor to the position of the box to be taken out of the truck, and kinematic analysis is also performed to control the robot arm that takes out the boxes. Finally, the effectiveness of the developed system and algorithm through a test bed is proved.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.1
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• pp.80-88
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• 1994

In the automation of rigid parts mating process with the intelligent robots, Peg-In-Hole is the most available task since inserting is some analytic and needs suitable range of forces that can be controlled by induatrial manipulators. In this Peg-In-Hole process, it is very important to identify the contact state between tow parts, peg and hole, to build the strategies for robot motion that leads to avoid the jamming condition occurs during insertion process. In this paper, we adpopted 3 parameters for identification, lFzl, lFxy/Fzl, and lMxy/Fxyl, derived from axes value of Whitney's jamming diagram. Also, we defined the fuzzy membership functions for these parameters and developed the identification algorithm based on fuzzy inference method of max-product. As an experimental result, we obtained about 96% of identification ratio that could be raised up to industrial requirements by further research.

• The Journal of Information Systems
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• v.32 no.3
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• pp.133-150
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• 2023

Purpose This study aims to develop classification models using a decision tree algorithm to identify core keywords and rules influencing online consumer review evaluations for the robot vacuum cleaner on Amazon.com. The difference from previous studies is that we analyze core keywords that affect the evaluation results by dividing the subjects that evaluate online consumer reviews into self-evaluation (star ratings) and peer evaluation (helpfulness votes). We investigate whether the core keywords influencing star ratings and helpfulness votes vary across different products and whether there is a similarity in the core keywords related to star ratings or helpfulness votes across all products. Design/methodology/approach We used random under-sampling to balance the dataset. We progressively removed independent variables based on decreasing importance through backwards elimination to evaluate the classification model's performance. As a result, we identified classification models that best predict star ratings and helpfulness votes for each product's online consumer reviews. Findings We have identified that the core keywords influencing self-evaluation and peer evaluation vary across different products, and even for the same model or features, the core keywords are not consistent. Therefore, companies' producers and marketing managers need to analyze the core keywords of each product to highlight the advantages and prepare customized strategies that compensate for the shortcomings.

• Journal of Drive and Control
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• v.21 no.1
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• pp.46-52
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• 2024

The advancement of autonomous driving technology has heightened the importance of Autonomous Mobile Robotics (AMR) within smart factories. Notably, in tasks involving the transportation of heavy objects, the consideration of weight in route optimization and path planning has become crucial. There is ongoing research on local path planning, such as Dijkstra, A^*, and RRT^*, focusing on minimizing travel time and distance within smart factory warehouses. Additionally, there are ongoing simultaneous studies on route optimization, including TSP algorithms for various path explorations and on minimizing energy consumption in mobile robotics operations. However, previous studies have often overlooked the weight of the objects being transported, emphasizing only minimal travel time or distance. Therefore, this research proposes route planning that accounts for the maximum payload capacity of mobile robotics and offers load-optimized path planning for multi-destination transportation. Considering the load, a genetic algorithm with the objectives of minimizing both travel time and distance, as well as energy consumption is employed. This approach is expected to enhance the efficiency of mobility within smart factories.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.498-505
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• 2024

In this paper, a cable-driven endoscopic manipulator (CEM) is designed for the Chinese latest compact fusion reactor. The whole CEM arm is more than 3000 mm long and includes end vision tools, an endoscopic manipulator/control system, a feeding system, a drag chain system, support systems, a neutron shield door, etc. It can cover a range of ±45° of the vacuum chamber by working in a wrap-around mode, etc., to meet the need for observation at any position and angle. By placing all drive motors in the end drive box via a cable drive, cooling, and radiation protection of the entire robot can be facilitated. To address the CEM motion control problem, a discrete trajectory tracking method is proposed. By restricting each joint of the CEM to the target curve through segmental fitting, the trajectory tracking control is completed. To avoid the joint rotation angle overrun, a joint limit rotation angle optimization method is proposed based on the equivalent rod length principle. Finally, the CEM simulation system is established. The rationality of the structure design and the effectiveness of the motion control algorithm are verified by the simulation.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.8
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• pp.87-96
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• 2014

In this paper, we propose a directionally adaptive aliasing and noise removal using dictionary learning based on space-frequency analysis. The proposed aliasing and noise removal algorithm consists of two modules; i) aliasing and noise detection using dictionary learning and analysis of frequency characteristics from the combined wavelet-Fourier transform and ii) aliasing removal with suppressing noise based on the directional shrinkage in the detected regions. The proposed method can preserve the high-frequency details because aliasing and noise region is detected. Experimental results show that the proposed algorithm can efficiently reduce aliasing and noise while minimizing losses of high-frequency details and generation of artifacts comparing with the conventional methods. The proposed algorithm is suitable for various applications such as image resampling, super-resolution image, and robot vision.