• Journal of KIISE
• /
• v.43 no.12
• /
• pp.1365-1375
• /
• 2016

One of the most important capabilities for autonomous service robots working in living environments is to recognize and understand the correct context in dynamically changing environment. To generate high-level context knowledge for decision-making from multiple sensory data streams, many technical problems such as multi-modal sensory data fusion, uncertainty handling, symbolic knowledge grounding, time dependency, dynamics, and time-constrained spatio-temporal reasoning should be solved. Considering these problems, this paper proposes an effective dynamic context management and spatio-temporal reasoning method for intelligent service robots. In order to guarantee efficient context management and reasoning, our algorithm was designed to generate low-level context knowledge reactively for every input sensory or perception data, while postponing high-level context knowledge generation until it was demanded by the decision-making module. When high-level context knowledge is demanded, it is derived through backward spatio-temporal reasoning. In experiments with Turtlebot using Kinect visual sensor, the dynamic context management and spatio-temporal reasoning system based on the proposed method showed high performance.

• Proceedings of the Korea Contents Association Conference
• /
• 2003.05a
• /
• pp.74-87
• /
• 2003

A model of STS (Science-Technology-Society) creativity education program for the gifted and talented children has been developed, based on GENEPLORE thinking process and Knowledge development theory. The GENEPLORE creative thinking process, developed by Finke et al. (1990, 1992), has two phases such as generative phase and exploratory phase. And The knowledge development theories of Piaget (1977) and Gallagher(1981) assume that knowledge-bases are developed on the basis of empirical as well as reflective abstraction, which could imply that knowledge-bases are crucial in creative thinking process. The creativity education model for the gifted and talented of the present study attempted to integrate 'the individual, creative thinking process, and social/scientific technology' by employing topics of the science-technology-society such as computer, network, biotech, robot, e-business, e-education, e-health, nanotech and entertainment and the structure and contents of the program are proposed

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.12 no.3
• /
• pp.242-250
• /
• 2019

In this paper, we propose a method to predict the failure of industrial robot using Seq2Seq (Sequence to Sequence) model, which is a model for transforming time series data among Artificial Neural Network models. The proposed method uses the data of the joint current and angular value, which can be measured by the robot itself, without additional sensor for fault diagnosis. After preprocessing the measured data for the model to learn, the Seq2Seq model was trained to convert the current to angle. Abnormal degree for fault diagnosis uses RMSE (Root Mean Squared Error) during unit time between predicted angle and actual angle. The performance evaluation of the proposed method was performed using the test data measured under different conditions of normal and defective condition of the robot. When the Abnormal degree exceed the threshold, it was classified as a fault, and the accuracy of the fault diagnosis was 96.67% from the experiment. The proposed method has the merit that it can perform fault prediction without additional sensor, and it has been confirmed from the experiment that high diagnostic performance and efficiency are available without requiring deep expert knowledge of the robot.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.2356-2361
• /
• 2003

Proteome research in the medical field is expected to accelerate the understanding of disease mechanism, and to create new diagnostic concept. For protein profiling, this paper proposes a new methodology named CPDIB (Crude Protein Direct Blotting). In the CPDIB procedure, crude protein sample is directly immobilized on a membrane and the expression of protein molecules in the sample are analyzed quantitatively by using a special device called ImmobiChip, where the membrane is used as a field of the immune reaction. The over-all structure of the ImmobiChip is based on the conventional Slot blot device. Mechanical improvement in the air-tightness of the case holding the membrane realizes the direct blotting and results in high performance of stability in the immune reaction. In the measurement of multiple proteins, a dispensing robot is used for increasing the efficiency of handling of liquid. Cooperation of the dispensing robot with the ImmobiChip for immobilizing proteins realizes automated and stable performance of the CPDIB procedure. This paper shows the evaluation of the air-tightness of the ImmobiChip, the ability of analyzing proteins using the CPDIB procedure and the performance of the automated equipment.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2017.04a
• /
• pp.877-880
• /
• 2017

가정이나 사무실 등과 같은 다양한 현실 세계에서 서비스 로봇이 자율적으로 동작하기 위해서는 복잡한 작업을 수행할 수 있어야만 한다. 다양한 센서 데이터가 있는 서비스 환경에서 고수준의 의미 정보를 이해하는 것은 지식 기반 로봇에게 필수적인 능력 중 하나이다. 본 논문에서는 서비스 로봇에게 다양한 환경에서 주어진 작업을 효과적으로 해결할 수 있도록 저레벨의 센서 데이터와 고레벨의 의미 정보를 통합하는 인간-로봇 상호작용 온톨로지를 소개한다. 지능형 로봇 지식에는 다양한 서비스의 확장성을 위해 사용자, 로봇, 인지, 환경, 행위 5가지 온톨로지로 분류한다. 지능형 로봇 지식은 일반 지식 뿐만 아니라 로봇의 수행 능력, 구성요소 등의 전문 지식까지 정의하고 서비스 에이전트 간 상호작용을 위한 인터페이스를 표준화함으로써 지능형 로봇에 적합한 지능을 제공한다. Turtlebot2을 이용한 실험을 통해 온톨로지 기반의 통합 로봇 지식의 높은 효율성을 확인 할 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.3
• /
• pp.1477-1491
• /
• 2017

Inspired by the multi-scale nature of hippocampal place cells, a biologically inspired model based on a multi-scale spatial representation for goal-directed navigation is proposed in order to achieve robotic spatial cognition and autonomous navigation. First, a map of the place cells is constructed in different scales, which is used for encoding the spatial environment. Then, the firing rate of the place cells in each layer is calculated by the Gaussian function as the input of the Q-learning process. The robot decides on its next direction for movement through several candidate actions according to the rules of action selection. After several training trials, the robot can accumulate experiential knowledge and thus learn an appropriate navigation policy to find its goal. The results in simulation show that, in contrast to the other two methods(G-Q, S-Q), the multi-scale model presented in this paper is not only in line with the multi-scale nature of place cells, but also has a faster learning potential to find the optimized path to the goal. Additionally, this method also has a good ability to complete the goal-directed navigation task in large space and in the environments with obstacles.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.8 no.6
• /
• pp.26-34
• /
• 1999

This study shows an alternative method for the determination of object's position, based on a computer vision method. This approach develops the vision system model to define the reciprocal relationship between the 3-D real space and 2-D image plane. The developed model involves the bilinear six-view parameters, which is estimated using the relationship between the camera space location and real coordinates of known position. Based on estimated parameters in independent cameras, the position of unknown object is accomplished using a sequential estimation scheme that permits data of unknown points in each of the 2-D image plane of cameras. This vision control methods the robust and reliable, which overcomes the difficulties of the conventional research such as precise calibration of the vision sensor, exact kinematic modeling of the robot, and correct knowledge of the relative positions and orientation of the robot and CCD camera. Finally, the developed vision control method is tested experimentally by performing determination of object position in the space using computer vision system. These results show the presented method is precise and compatible.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.16 no.4
• /
• pp.113-118
• /
• 2017

As the demand for industrial robots and Automated Guided Vehicles (AGVs) increases, higher performance is also required from them. Fuzzy controllers, as part of an intelligent control system, are a direct control method that leverages human knowledge and experience to easily control highly nonlinear, uncertain, and complex systems. This paper uses a $LabVIEW^{(R)}-based$ fuzzy controller with gain scheduling to demonstrate better performance than one could obtain with a fuzzy controller alone. First, the work area was set based on forward kinematics and inverse kinematics programs. Next, $LabVIEW^{(R)}$ was used to configure the fuzzy controller and perform the gain scheduling. Finally, the proposed fuzzy gain scheduling controller was compared with to controllers without gain scheduling.

• Journal of Institute of Control, Robotics and Systems
• /
• v.20 no.6
• /
• pp.606-611
• /
• 2014

The understanding of another's behavior is a fundamental cognitive ability for primates including humans. Recent neuro-physiological studies suggested that there is a direct matching algorithm from visual observation onto an individual's own motor repertories for interpreting cognitive ability. The mirror neurons are known as core regions and are handled as a functionality of intent recognition on the basis of imitative learning of an observed action which is acquired from visual-information of a goal-directed action. In this paper, we addressed previous works used to model the function and mechanisms of mirror neurons and proposed a computational model of a mirror neuron system which can be used in human-robot interaction environments. The major focus of the computation model is the reproduction of an individual's motor repertory with different embodiments. The model's aim is the design of a continuous process which combines sensory evidence, prior task knowledge and a goal-directed matching of action observation and execution. We also propose a biologically inspired plausible equation model.

• Journal of Internet Computing and Services
• /
• v.15 no.1
• /
• pp.55-61
• /
• 2014

Various human-friendly robot services have been developed and mobile cloud computing is a real time computing service that allows users to rent IT resources what they want over the internet and has become the new-generation computing paradigm of information society. The enterprises and nations are actively underway of the business process using mobile cloud computing and they are aware of need for implementing mobile cloud computing to their business practice, but it has some week points such as authentication services and distributed processing technologies of big data. Sometimes it is difficult to clarify the objective of cloud computing service. In this study, the vulnerability of authentication services on mobile cloud computing is analyzed and mobile cloud computing model is constructed for efficient and safe business process. We will also be able to study how to process and analyze unstructured data in parallel to this model, so that in the future, providing customized information for individuals may be possible using unstructured data.