• Journal of Institute of Control, Robotics and Systems
• /
• v.14 no.8
• /
• pp.779-784
• /
• 2008

This paper presents Kalman filter based system modeling algorithm for autonomous robot systems. State of the robot system is measured using embedded sensor systems and then carried to a host computer via ubiquitous sensor network (USN). We settle a linear state-space motion equation for unknown robot system dynamics and modify a popular Kalman filter algorithm in deriving suitable parameter estimation mechanism. To represent time-delay nature due to network media in system modeling, we construct an augmented state-space model which is mainly composed of original state and estimated parameter vectors. We conduct real-time experiment to test our proposed estimation algorithm where speed state of the constructed robot is used as system observation.

• Journal of the Korean Society for Precision Engineering
• /
• v.14 no.7
• /
• pp.29-38
• /
• 1997

A closed-loop system modeling of an active/semiactive suspension system has been accomplished through an artificial neural network. A 7DOF full model as a system's equation of motion has been derived and an output feedback linear quadratic regulator has been designed for control purpose. A training set of a sample data has been obtained through a computer simulation. A 7DOF full model with LQR controller simulated under several road conditions such as sinusoidal bumps and rectangular bumps. A general multilayer perceptron neural network is used for dynamic modeling and target outputs are fedback to the a layer. A backpropagation method is used as a training algorithm. Model validation of new dataset have been shown through computer simulations.

• Journal of Institute of Control, Robotics and Systems
• /
• v.4 no.4
• /
• pp.551-560
• /
• 1998

This paper presents a neural network model representing complex hydro-thermo-dynamic characteristics of a steam generator in nuclear power plants. The key modeling processes include training data gathering process, analysis of system dynamics and determining of the neural network structure, training process, and the final process for validation of the trained model. In this paper, we suggest a training data gathering method from an unstable steam generator so that the data sufficiently represent the dynamic characteristics of the plant over a wide operating range. In addition, we define the inputs and outputs of neural network model by analyzing the system dimension, relative degree, and inputs/outputs of the plant. Several types of neural networks are applied to the modeling and training process. The trained networks are verified by using a class of test data, and their performances are discussed.

• Biotechnology and Bioprocess Engineering:BBE
• /
• v.11 no.1
• /
• pp.67-71
• /
• 2006

This paper compares regression and neural network modeling approaches to predict competitive biosorption equilibrium data. The regression approach is based on the fitting of modified Langmuir-type isotherm models to experimental data. Neural networks, on the other hand, are non-parametric statistical estimators capable of identifying patterns in data and correlations between input and output. Our results show that the neural network approach outperforms traditional regression-based modeling in correlating and predicting the simultaneous uptake of copper and cadmium by a microbial biosorbent. The neural network is capable of accurately predicting unseen data when provided with limited amounts of data for training. Because neural networks are purely data-driven models, they are more suitable for obtaining accurate predictions than for probing the physical nature of the biosorption process.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.8 no.4
• /
• pp.254-259
• /
• 2008

Uncertainty of result of context awareness always exists in any context-awareness computing. This falling-off in accuracy of context awareness result is mostly caused by the imperfectness and incompleteness of sensed data, because of this reasons, we must improve the accuracy of context awareness. In this article, we propose a novel approach to model the uncertain context by using ontology and context reasoning method based on Bayesian Network. Our context aware processing is divided into two parts; context modeling and context reasoning. The context modeling is based on ontology for facilitating knowledge reuse and sharing. The ontology facilitates the share and reuse of information over similar domains of not only the logical knowledge but also the uncertain knowledge. Also the ontology can be used to structure learning for Bayesian network. The context reasoning is based on Bayesian Networks for probabilistic inference to solve the uncertain reasoning in context-aware processing problem in a flexible and adaptive situation.

• Transactions on Electrical and Electronic Materials
• /
• v.7 no.4
• /
• pp.210-214
• /
• 2006

In this paper, two sampling methods which are Latin hypercube sampling (LHS) and simple random sampling were. compared to improve the modeling speed of neural network model. Sampling method was used to generate initial weights and bias set. Electrical characteristic data for $HfO_2$ thin film was used as modeling data. 10 initial parameter sets which are initial weights and bias sets were generated using LHS and simple random sampling, respectively. Modeling was performed with generated initial parameters and measured epoch number. The other network parameters were fixed. The iterative 20 minimum epoch numbers for LHS and simple random sampling were analyzed by nonparametric method because of their nonnormality.

• Journal of Communications and Networks
• /
• v.11 no.4
• /
• pp.406-415
• /
• 2009

In this work, we consider performance modeling of a wireless sensor network with a time division multiple access (TDMA) media access protocol with slot reuse. It is assumed that all the nodes are peers of each other and they have two modes of operation, active and sleep modes. We model the sensor network as a Jackson network with unreliable nodes with on and off states. Active and sleep modes of sensor nodes are modeled with on and off states of unreliable nodes. We determine the joint distribution of the sensor node queue lengths in the network. From this result, we derive the probability distribution of the number of active nodes and blocking probability of node activation. Then, we present the mean packet delay, average sleep period of a node and the network throughput. We present numerical results as well as simulation results to verify the analysis. Finally, we discuss how the derived results may be used in the design of sensor networks.

• Journal of Pharmacopuncture
• /
• v.18 no.3
• /
• pp.11-18
• /
• 2015

Objectives: Systems biology is a novel subject in the field of life science that aims at a systems' level understanding of biological systems. Because of the significant progress in high-throughput technologies and molecular biology, systems biology occupies an important place in research during the post-genome era. Methods: The characteristics of systems biology and its applicability to traditional medicine research have been discussed from three points of view: data and databases, network analysis and inference, and modeling and systems prediction. Results: The existing databases are mostly associated with medicinal herbs and their activities, but new databases reflecting clinical situations and platforms to extract, visualize and analyze data easily need to be constructed. Network pharmacology is a key element of systems biology, so addressing the multi-component, multi-target aspect of pharmacology is important. Studies of network pharmacology highlight the drug target network and network target. Mathematical modeling and simulation are just in their infancy, but mathematical modeling of dynamic biological processes is a central aspect of systems biology. Computational simulations allow structured systems and their functional properties to be understood and the effects of herbal medicines in clinical situations to be predicted. Conclusion: Systems biology based on a holistic approach is a pivotal research methodology for understanding the mechanisms of traditional medicine. If systems biology is to be incorporated into traditional medicine, computational technologies and holistic insights need to be integrated.

• The Journal of Society for e-Business Studies
• /
• v.1 no.1
• /
• pp.141-157
• /
• 1996

Data sharing is a major challenge to implement CALS. STEP is the international standard for the product model data exchange among heterogeneous systems and plays a key role in CALS. Advances in computer networks are rapidly changing the product development processes. The network oriented modeling system premises to integrate design activities across the enterprise. To achieve goals of CALS 3-dimensional concurrent modeling that complies international standard is required since integrity and common perception of product data can be assured. This paper presents 3-dimensional concurrent geometric modeling on high speed network using STEP and methodologies.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.33B no.4
• /
• pp.191-200
• /
• 1996

This paper presetns a new fuzzy neural network for fuzzy modeling.The fuzzy neural network is composed of 4 layers and then odes of each layer represent the each step of the if-then fuzzy inference. A heuristic based on the back-propagation algorithm is proposed to ajdust the parameters of the fuzzy nerual network. We prove the feasibility of the network using the experiments on modeling a nonlinear mathematical system and the comparison with previous research.