• Journal of KIISE:Databases
• /
• v.37 no.6
• /
• pp.343-350
• /
• 2010

We can get answers we want to know via questioning in Knowledge Search Service (KSS) based on Q&A Community. However, it is getting more difficult to find credible documents in enormous documents, since many anonymous users regardless of credibility are participate in answering on the question. In previous works in KSS, researchers evaluated the quality of documents based on textual information, e.g. recommendation count, click count and non-textual information, e.g. answer length, attached data, conjunction count. Then, the evaluation results are used for enhancing search performance. However, the non-textual information has a problem that it is difficult to get enough information by users in the early stage of Q&A. The textual information also has a limitation for evaluating quality because of judgement by partial factors such as answer length, conjunction counts. In this paper, we propose the QualityRank algorithm to improve the problem by textual and non-textual information. This algorithm ranks the relevant and credible answers by considering textual/non-textual information and user centrality based on Social Network Analysis(SNA). Based on experimental validation we can confirm that the results by our algorithm is improved than those of textual/non-textual in terms of ranking performance.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 1998.04a
• /
• pp.173-178
• /
• 1998

An automatic Q. C. tester for a production line of electric grinder is developed using vibration signals. The methods of measuring and analyzing the vibration signals are found through several experiments at laboratory and production line. The experiments show that checking a vibration signal at running condition only, without any sound signal, is enough to judge whether the product is good or not. The Q. C. tester is made of accelerometer and PC. Measured vibration signal using accelerometer is transmitted to PC through A/D board. Vibration level are calculated using FFT algorithm in PC for already selected five frequency bands, which can specify the cause of fault. The Judging criteria of vibration levels of each bands are decided through a lot of experiment with the comparison of manual judgement.

• Proceedings of the IEEK Conference
• /
• 2009.05a
• /
• pp.118-120
• /
• 2009

In this paper, we present the Q-learning method for adaptive traffic signal control on the basis of multi-agent technology. The structure is composed of sixphase agents and one intersection agent. Wireless communication network provides the possibility of the cooperation of agents. As one kind of reinforcement learning, Q-learning is adopted as the algorithm of the control mechanism, which can acquire optical control strategies from delayed reward; furthermore, we adopt dynamic learning method instead of static method, which is more practical. Simulation result indicates that it is more effective than traditional signal system.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.173.5-173
• /
• 2001

Q-Learning is a most widely used reinforcement learning, which addresses the question of how an autonomous agent can learn to choose optimal actions to achieve its goal about any one problem. Q-Learning can acquire optimal control strategies from delayed rewards, even when the agent has no prior knowledge of the effects of its action in the environment. If agent has an ability using previous knowledge, then it is expected that the agent can speed up learning by interacting with environment. We present a novel reinforcement learning method using domain knowledge, which is represented by problem-independent features and their classifiers. Here neural network are implied as knowledge classifiers. To show that an agent using domain knowledge can have better performance than the agent with standard Q-Learner. Computer simulations are ...

• Korean Journal of Computational Design and Engineering
• /
• v.3 no.2
• /
• pp.127-134
• /
• 1998

This paper presents d new algorithm to compute the offlet curve of a given planar parametric curve. We reduce the problem of computing an offset curve to that of intersecting a surface to a paraboloid. Given an input curve C(t)=(x(t), y(t))∈R², the corresponding surface D/sub c(t)/ is constructed symbolically as the envelope surface of a one-parameter family of tangent planes of the paraboloid Q:z=x²+y²along a lifted curve C(t)=(x(t), y(t), x(t)²+y(t)²∈Q. Given an offset distance d∈R, the offset curve C/sub d/(t) is obtained by the projection of the intersection curve of D/sub c(t)/ and a paraboloid Q:z=x²+y²-d² into the xy-plane.

• Journal of Institute of Control, Robotics and Systems
• /
• v.12 no.7
• /
• pp.637-643
• /
• 2006

Generally, the tracking performance of optical disk drive(ODD) system can be improved using a disturbance observer(DOB). However, a DOB is not easily applied in an ODD system because an additional microprocessor, such as a digital signal processor(DSP), is needed. This paper shows how a DOB system can be replaced by the error-based modified disturbance observer(EM-DOB) when two mathematical conditions are satisfied. Due to the simplified structure of EM-DOB, the algorithm is easily implemented as an analog circuit, which is suitable for the ODD servo system. Additionally, in these algorithms, disturbances rejection performances can be tuned as Q filter parameters. Similar to a DOB system, three design guidelines of a Q filter can be applied. Experimental results of DOB and EM-DOB are evaluated under forced disturbances.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.15 no.4
• /
• pp.395-399
• /
• 2005

In this paper, we presents HIGH-Q learning algorithm with reinforcement learning for more fast and exact topic-directed web spidering. The purpose of reinforcement learning is to maximize rewards from environment, an reinforcement learning agents learn by interacting with external environment through trial and error. We performed experiments that compared the proposed method using reinforcement learning with breath first search method for searching the web pages. In result, reinforcement learning method using future discounted rewards searched a small number of pages to find result pages.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2003.05a
• /
• pp.1170-1175
• /
• 2003

Recently, the disturbance caused by an optical disk vibration and the external vibration/shock are more serious problem in an optical disk drives (ODD) as an ODD become small size and rotation speed increases. The conventional controller cannot cope with the mentioned problems properly when the disturbance and vibration are larger than some range. Therefore, we propose a new control scheme using a disturbance observer (DOB) and it can control the aforementioned problems. The designed the controller is applied to a commercial ODD in focusing direction, then its validity is proved by experimental method. By rising the disturbance observer theory, the focusing performance is conspicuously improved in the presence of sinusoidal vibrations or a shock disturbance. This algorithm also applies to a tracking structure also, because focusing structure is very similar to it.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.54 no.6
• /
• pp.372-375
• /
• 2005

The tracking performance of optical disk drive(ODD) system can be improved using disturbance observer(DOB). But, DOB was not easily applied in the ODD system because the additional microprocessor was needed. In this paper, we propose an error-based modified disturbance observer(EM-DOB) for ODD system. Due to the simplified structure of EM-DOB, the system is easily implemented to the digital control algorithm or the analog circuit. In these algorithms, disturbances rejection performance of system can be tuned as Q filter parameters are selected. Based on analysis of sensitivity function, three guidelines of Q filter design are suggested. Experimental results of DOB and EM-DOB are evaluated under the forced disturbances.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2000.04a
• /
• pp.577-581
• /
• 2000

폭우선생성트리 등의 문제를 해결하는데 필요한 정보가 네트워크상의 프로세서에 분산되어 있는 상황에서, 그들 정보를 교환하면서 그 문제를 해결하는 알고리즘을 분산알고리즘(Distributed Algorithm)이라고 한다. 폭우선생성트리가 이미 구성되어 있는 비동기식 네트워크상에서 네트워크 형상이 변할 경우, 이로인해 구성되어 있던 폭우선생성트리를 갱신해야 하는 경우가 발생한다. 본 논문에서는 이러한 경우 폭우선생성트리를 효율적으로 갱신하는 메시지 복잡도와 이상시간복잡도 모두 O($p{\surd}q$ + q + a + n')인 분산알고리즘을 제안한다. 여기서, a는 추가 링크의 수, n'는 네트워크의 토폴로지가 변한후의 네트워크상에 존재하는 노드수를 각각 나타낸다. 그리고, p는 삭제 또는 추가 링크를 가진 이중연결성분에 속하는 전체 노드 수를 나타내며, q는 삭제 또는 추가 링크를 가진 이중연결성분에 속하는 전체 링크수를 나타낸다.