• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.10
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• pp.3038-3047
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• 1996

In telerobot systems, visual servoing of a task object for a slave arm with an eye-in-hand has drawn an interesting attention. As such a task ingenerally conducted in an unstructured environment, it is very difficult to define the inverse feature Jacobian matrix. To overcome this difficulty, this paper proposes an auto-tuning fuzzy rule-based visual servo algorithm. In this algorithm, a visual servo controller composed of fuzzy rules, receives feature errors as inputs and generates the change of have position as outputs. The fuzzy rules are tuned by using steepest gradient method of the cost function, which is defined as a quadratic function of feature errors. Since the fuzzy rules are tuned automatically, this method can be applied to the visual servoing of a slave arm in real time. The effctiveness of the proposed algorithm is verified through a series of simulations and experiments. The results show that through the learning procedure, the slave arm and track object in real time with reasonable accuracy.

• The Journal of Korea Robotics Society
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• v.9 no.4
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• pp.258-263
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• 2014

An adaptive robot hand (AR-Hand) has a stable grasp of different objects in unstructured environments. In this study, we propose an AR-Hand based on a tendon-driven mechanism which consists of 4 fingers and 12 DOFs. It weighs 0.5 kg and can grasp an object up to 1 kg. This hand based on the adaptive grasp mechanism is able to provide a stable grasp without a complex control algorithm or sensor system. The fingers are driven by simple tendon structures with each finger capable of adaptively grasping the objects. This paper presents a method to decide the joint stiffness. The adaptive grasping is verified by various grasping experiments involving objects with different shapes and sizes.

• International Journal of Control, Automation, and Systems
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• v.4 no.4
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• pp.524-528
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• 2006

A controller built with the gyro effect for a balance-beam can freely control the attitude of an unstructured object by changing the position of an inner gimbal. In this paper, we propose a new balance-beam controller that can detect the inertia of the load to limit the velocity of the load commanded by a user. We found that when there was smaller load inertia, a larger restoration displacement occurred. Therefore, the load can be identified by issuing a predefined command to measure the restoration displacement, which enables us to construct a controller that can limit the angular velocity of the load by planning the motion. Experimental results show the performance of the controller with different loads.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.22-28
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• 1992

The need for intelligent systems that can operate in an unstructured, dynamic environment has created a growing demand for the use of multiple, distributed sensors. While most research in multisensor fusion has revolved around applications in object recognition-including military applications for automatic target recognition-developments in microsensor technology are encouraging more research in affordable, highly-redundant sensor networks. Three trends that are described at length are the increasing use of microsensors, the techniques that are used in the handling of partial or uncertain data, and the application of neural network techniques for sensor fusion.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.16-22
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• 2024

Robotic grasping in unstructured environments poses a significant challenge, demanding precise estimation of gripping positions for diverse and unknown objects. Generative Grasping Convolution Neural Network (GG-CNN) can estimate the position and direction that can be gripped by a robot gripper for an unknown object based on a three-dimensional depth map. Since GG-CNN uses only a depth map as an input, the precision of the depth map is the most critical factor affecting the result. To address the challenge of depth map precision, we integrate the Segment Anything Model renowned for its robust zero-shot performance across various segmentation tasks. We adjust the components corresponding to the segmented areas in the depth map aligned through external calibration. The proposed method was validated on the Cornell dataset and SurgicalKit dataset. Quantitative analysis compared to existing methods showed a 49.8% improvement with the dataset including surgical instruments. The results highlight the practical importance of our approach, especially in scenarios involving thin and metallic objects.

• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1724-1737
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• 1999

Multimedia data service applications have to store and manipulate LOBs(unstructured large objects) composing multimedia data. As such, the tertiary storage devices such as an optical disk jukebox and a tape library that consist of a number of platters (the disks in case of an optical disk jukebox and the cartridge tapes in case of a tape library) have been considered essential for the storage system of a DBMS in order to efficiently support storage and management of vary large volume of data. Since the latency with tertiary storage is too long, the schemes for efficient retrieval of LOBs out of tertiary storage need to be investigated. In this paper, we investigated the tertiary I/O Considering the performance characteristics of the LOBs, we proposed various I/O scheduling heuristic algorithms that reduce latency in query processing with LOB retrieval from tertiary storage, and evaluated their performance through a detailed simulation.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.5 s.311
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• pp.1-9
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• 2006

Because most applications of vision-based object tracking demonstrate satisfactory operations only under very constrained environments that have simplifying assumptions or specific visual attributes, these approaches can't track target objects for the highly variable, unstructured, and dynamic environments like a traffic scene. An adaptive fusion framework is essential that takes advantage of the richness of visual information such as color, appearance shape and so on, especially at cluttered and dynamically changing scenes with partial occlusion[1]. This paper develops a particle filter based adaptive fusion framework and improves the robustness and adaptation of this framework by adding a new distinctive visual attribute, an image feature descriptor using SIFT (Scale Invariant Feature Transform)[2] and adding an automatic teaming scheme of the SIFT feature library according to viewpoint, illumination, and background change. The proposed algorithm is applied to track various traffic objects like vehicles, pedestrians, and bikes in a driver assistance system as an important component of the Intelligent Transportation System.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.101-111
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• 2024

In this paper, we propose a concpetual architecture and its implementation approach for contextualizing unstructured CCTV-video frame data into structured XML-video textual data by using the deep-learning neural network models and frameworks. Conclusively, through the conceptual architecture and the implementation approach proposed in this paper, we can eventually realize and implement the so-called sharable working and experiencing knowledge management platforms to be adopted to smart factories in various industries.

• Journal of Digital Convergence
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• v.14 no.10
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• pp.137-145
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• 2016

The explosive growth of social networking services based on smart devices popularize these relationships and activities online in accordance with the far larger impact of this on the real life offline, the interest and importance for the online activity is increasing. In this study, factors affecting the SNS activity are defined by object, user, influence direction, influence distance and proposed a method to measure organic terms in effect between the SNS users. Influence Direction and Influence Strength (or Distance) are elaborated by using the existing influence measurement element such as structured data - the number of friends, the difference between the number of contacts - and the new influence measurement element such as unstructured data - gap between the former time and the latter time, preference and type of response behavior - that occur in social network service. In addition, the system for collecting and analysing data for measuring influence from social network service and the process model on the method for measuring influence is tested by using sample data on Facebook and explained the implementation probability.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1448-1451
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• 2004

Position estimation is one of the most important functions for the mobile robot navigating in the unstructured environment. Most of previous localization schemes estimate current position and pose of mobile robot by applying various localization algorithms with the information obtained from sensors which are set on the mobile robot, or by recognizing an artificial landmark attached on the wall, or objects of the environment as natural landmark in the indoor environment. Several drawbacks about them have been brought up. To compensate the drawbacks, a new localization method that estimates the absolute position of the mobile robot by using a fixed camera on the ceiling in the corridor is proposed. And also, it can improve the success rate for position estimation using the proposed method, which calculates the real size of an object. This scheme is not a relative localization, which decreases the position error through algorithms with noisy sensor data, but a kind of absolute localization. The effectiveness of the proposed localization scheme is demonstrated through the experiments.