• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.15.1-21
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• 1999

Radiosity method is a global illumination model for image synthesis. It computes all energy interactions among diffuse elements in a virtual environment. One of the major drawbacks if its time consuming computation. Existing radiosity algorithms for static scene is difficult to be applicable to dynamic environments. In this paper we proposed an hierarchical scene partition scheme to speedup the link update computations in the dynamic environments. Since the proposed spatial data structure is global, it not only can be used to speedup the culling of non-affected links after geometry change, but also can be used to accelerate the subsequent visibility computation. Several empirical tests are given to show the efficiency of our improved algorithm.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.429-432
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• 2003

In this paper, a novel neural network approach is proposed for cleaning robot to complete coverage path planning with obstacle avoidance in stationary and dynamic environments. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley's membrane equation. There are only local lateral connections among neurons. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location without any prior knowledge of the dynamic environment.

• International Journal of Contents
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• v.8 no.2
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• pp.1-6
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• 2012

Recently, content-based music information retrieval (MIR) systems for mobile devices have attracted great interest. However, music retrieval systems are greatly affected by background noise when music is recorded in noisy environments. Therefore, we evaluated various pre-processing methods using the Philips method to determine the one that performs most robust music retrieval in such environments. We found that dynamic noise reduction (DNR) is the best pre-processing method for a music retrieval system in noisy environments.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.116-123
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• 2024

In the manufacturing industry, dispatching systems play a crucial role in enhancing production efficiency and optimizing production volume. However, in dynamic production environments, conventional static dispatching methods struggle to adapt to various environmental conditions and constraints, leading to problems such as reduced production volume, delays, and resource wastage. Therefore, there is a need for dynamic dispatching methods that can quickly adapt to changes in the environment. In this study, we aim to develop an agent-based model that considers dynamic situations through interaction between agents. Additionally, we intend to utilize the Q-learning algorithm, which possesses the characteristics of temporal difference (TD) learning, to automatically update and adapt to dynamic situations. This means that Q-learning can effectively consider dynamic environments by sensitively responding to changes in the state space and selecting optimal dispatching rules accordingly. The state space includes information such as inventory and work-in-process levels, order fulfilment status, and machine status, which are used to select the optimal dispatching rules. Furthermore, we aim to minimize total tardiness and the number of setup changes using reinforcement learning. Finally, we will develop a dynamic dispatching system using Q-learning and compare its performance with conventional static dispatching methods.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1184-1189
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• 2003

Map building and position estimation capabilities are practically indispensable for a mobile robot to execute its given tasks in its working environments. An autonomous map building method and a smart localization method is proposed in our previous works. The experimental verifications are carried out in this paper. We applied the proposed algorithms to mobile service robots in large-scale indoor buildings. Experimental results show that our strategy is reliable and feasible in tough conditions like non-polygonal and dynamic environments. The advantages of the algorithms are well-illustrated through real experiments.

• International Journal of Automotive Technology
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• v.3 no.4
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• pp.129-135
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• 2002

Durability estimation of a prototype vehicle has traditionally relied heavily on accelerated durability tests using predefined proving grounds or rig tests using a road simulator. By use of those tests, it is very difficult to predict durability failures in actual service environments. This motivated the development of an integrated CAE (Computer Aided Engineering) methodology for the durability estimation of a prototype vehicle in actual service environments. Since expensive computational costs such as computation time and hardware resources are required for a full vehicle simulation in those environments with a very long span of event time, the conventional CAE methodologies have little feasibility. An efficient computational methodology for durability estimations is applied with theoretical developments. The effectiveness of the proposed methodology is shown by the comparison of results of the typical actual service environment such as the city mode with those of the typical accelerated durability test over the Belgian road.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.3
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• pp.297-305
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• 1999

Autonomous mobile robots are required to achieve multiple goals while responding quickly to the dynamic environments. An appropriate robot control architecture, which clearly and systematically defines the relationship among the inputs, the processing functions and the outputs, thus needs to be embedded in the robot controller. This paper proposes a kind of hybrid control architecture which combines the key features of the two well-known robot control architectures; hierarchical and behavioral- based. The overall control architecture consists of three layers, i.e. the highest planner, the middle plan executor, and the lowest monitor and behavior-based controller. In the planned situation, only one behavior module is chosen by the logical coordinator in the plan executor according to the way point bin. In the exceptional situation, the central controller in the plan executor issues an additional control command to reach the planned way point. Several simulations and experiments with autonomous mobile robot show that the proposed architecture enables the robot controller to achieve the multiple sequential goals even in dynamic and uncertain environments.

• Journal of the Korean Society of Industry Convergence
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• v.21 no.3
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• pp.125-131
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• 2018

In this paper, we propose a homogeneous multisensor-based navigation algorithm for a mobile robot, which is intelligently searching the goal location in unknown dynamic environments with moving obstacles using multi-ultrasonic sensor. Instead of using "sensor fusion" method which generates the trajectory of a robot based upon the environment model and sensory data, "command fusion" method by fuzzy inference is used to govern the robot motions. The major factors for robot navigation are represented as a cost function. Using the data of the robot states and the environment, the weight value of each factor using fuzzy inference is determined for an optimal trajectory in dynamic environments. For the evaluation of the proposed algorithm, we performed simulations in PC as well as real experiments with mobile robot, AmigoBot. The results show that the proposed algorithm is apt to identify obstacles in unknown environments to guide the robot to the goal location safely.

• Speech Sciences
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• v.13 no.1
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• pp.129-139
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• 2006

In this paper, we propose two effective energy feature normalization methods for robust speech recognition in noisy environments. In the first method, we estimate the noise energy and remove it from the noisy speech energy. In the second method, we propose a modified algorithm for the Log-energy Dynamic Range Normalization (ERN) method. In the ERN method, the log energy of the training data in a clean environment is transformed into the log energy in noisy environments. If the minimum log energy of the test data is outside of a pre-defined range, the log energy of the test data is also transformed. Since the ERN method has several weaknesses, we propose a modified transform scheme designed to reduce the residual mismatch that it produces. In the evaluation conducted on the Aurora2.0 database, we obtained a significant performance improvement.

• The Journal of Information Systems
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• v.14 no.3
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• pp.48-52
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• 2005

The Mobile agent-based distributed systems become obtaining significant popularity as a potential vehicle to allow software components to be executed on heterogeneous environments despite mobility of users and computations. However, as these systems generally force mobile agents to use only common functionalities provided in every execution environment, the agents may not access environment-specific resources. In this paper, we propose a new framework using Aspect Oriented Programming technique to accommodate a variety of static resources as well as dynamic ones whose amount is continually changed at runtime even in the same execution environment. Unlike previous works, this framework divides roles of software developers into three groups to relieve application programmers from the complex and error prone parts of implementing dynamic adaptation and allowing each developer to only concentrate on his own part. Also, the framework enables policy decision makers to apply various adaptation policies to dynamically changing environments for adjusting mobile agents to the change of their resources.