• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1530-1533
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• 2005

In this paper, we design a Linear Quadratic Gaussian controller for the active suspension. We can improve the inherent suspension problem, trade-off between the ride quality and the suspension travel by selecting appropriate weights in the LQ-objective function. Because any definite rules for selecting weights do not exist, we use an optimization-algorithm, Evolution Strategy (ES) to find the proper control gains for selected frequencies, which have major effects on the vibrations of the vehicle's state variables. The frequencies and proper control gains are used for the neural network data. During a vehicle running, the trained on-line neural network is activated and provides the proper gains for non-trained frequencies. For the full-state feedback control, Kalman filter observes the full states and Fourier transform is used to detect the frequency of the road.

• International Commerce and Information Review
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• v.9 no.1
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• pp.259-274
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• 2007

The paradigm of the global logistics network has been rapidly changing from traditional concept of productivity in cost terms, toward the new characteristics of innovative productivity in terms of economies of scale, economies of scope, and economies of network. As the ship or airplane becomes larger and speedier, the less logistics hub required. The more diverse logistics parties integrate each other horizontally or vertically, the higher synergy effects they get. The more systematic concurrent engineering available for global supply chain management such as Korean manufacturers in China, the higher productivity they get. The paper analyzes these three paradigms of logistics into the application for the Korean manufacturers in China and concludes that the Korean government, the companies in China should be more focused on the governance of the logistics and the intermediary such as the 4th party logistics(4PL) is definitely developed its role and functions based on three paradigms above mentioned.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.459-460
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• 2001

In this paper, we analyze algorithms for diagnosing of VEP(visual evoked potential) signal. We used wavelet transform for the preprocessing of VEP signal data and back propagation neural network for the pattern recognition. We used several wavelets to study their effects and efficiency in the preprocessing of VEP. The diagnosis system led to good results. We obtained the noise reduced and compressed signal with the wavelet transform of the training VEP signal. So it is possible to train the neural network faster and exact diagnosis processing is possible in the neural network. From the experimental results, we know that the discrimination ability of the neural network is changed by the type of basis vector and the proposed system is good to the diagnosis of VEP.

• Asia pacific journal of information systems
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• v.9 no.3
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• pp.93-110
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• 1999

The previous studies regarding the stock returns have advocated that industry effects exist over entire industry. As the industry categories are more rigid, the demand for predicting the industry sectors is rapidly increasing. The advances in Artificial Intelligence and Neural Networks suggest the feasibility of a valuable computational model for stock returns prediction. We propose a sector-factor model for predicting the return on industry stock index using neural networks. As a substitute for the traditional models, neural network model may be more accurate and effective alternative when the dynamics between the underlying industry features are not well known or when the industry specific asset pricing equation cannot be solved analytically. To assess the potential value of neural network model, we simulate the resulting network and show that the proposed model can be used successfully for banks and general construction industry. For comparison, we estimate models using traditional statistical method of multiple regression. To illustrate the practical relevance of neural network model, we apply it to the predictions of two industry stock indexes from 1980 to 1995.

• Journal of Mechanical Science and Technology
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• v.18 no.3
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• pp.432-442
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• 2004

This paper presents a neural network predictor for analysing rigidity variations of hydrostatic bearing system. The designed neural network has feedforward structure with three layers. The layers are input layer, hidden layer and output layer. Two main parameter could be considered for hydrostatic bearing system. These parameters are the size of bearing pocket and the orifice dimension. Due to importancy of these parameters, it is necessary to analyse with a suitable optimisation method such as neural network. As depicted from the results, the proposed neural predictor exactly follows experimental desired results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.12
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• pp.1925-1932
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• 2016

One of the major challenges in the design of wireless sensor network (WSN) is to reduce the energy consumption of sensor nodes for prolonging the network lifetime. In the sensor network, communication is the most energy consuming event. Therefore, most of the energy saving techniques conserve energy by adjusting different parameters of the trans-receiver. Among them, one of the promising methods is the transmission power control (TPC). In this paper, we investigated the effects of the link quality based TPC scheme employed to the IEEE 802.15.4 standard for energy saving. The simulation results demonstrated that the link quality based TPC scheme works effectively in conserving energy as compared to the conventional IEEE 802.15.4.

• Korean Management Science Review
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• v.26 no.3
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• pp.169-184
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• 2009

Recently, mobile telecommunication businesses contend with each other to expand their customer base by using aggressive marketing strategies. In order to determine if there strategies are effective, customer's switching behavior needs to be studied. This study identifies and analyzes direct, indirect factors that may customer switching behavior : attractiveness of alternatives, network externality, and switching cost. Results reveals that attractiveness of alternatives, network externalities have a direct impact on customer switching behavior. These two factors also have moderating effects on customer switching behavior but the switching cost does not In short, network externalities and alternatives strategically determine the success of 3.5G service. In this regard, mobile business should improve their own attractiveness of alternatives by developing specialized service in 3.5G service.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.7
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• pp.515-526
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• 2003

Tutorial contents of kinematics and dynamics of a wheeled drive mobile robot are presented. Based on the dynamic model, simulation studies of position tracking of a mobile robot are performed. The control structure of several position control algorithms using visual feedback are proposed and their performances are compared. In order to compensate for uncertainties from unknown dynamics and ignored dynamic effects such as slip conditions, neural network based position control schemes are proposed. Experiments are conducted and the results show the performance of the vision based neural network control scheme fumed out to be the best among several proposed schemes.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1203-1210
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• 2017

Switched-inductor (SL) Z-source three-level inverter is a novel high power topology. The SL based impedance network can boost the input dc voltage to a higher value than the single LC impedance network. However, as all the neutral-point-clamped (NPC) inverters, the SL Z-source three-level inverter has to balance the neutral-point (NP) potential too. The principle of the inverter is introduced and then the effects of NP potential unbalance are analyzed. A NP balancing method is proposed. Other than the methods for conventional NPC inverter without Z-source impedance network, the upper and lower shoot-through durations are corrected by the feedforward compensation factors. With the proposed method, the NP potential is balanced and the voltage boosting ability of the Z-source network is not affected obviously. Simulations are conducted to verify the proposed method.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.4
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• pp.151-157
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• 2003

In this paper, a blackbox approach is carried out to model the nonlinear dynamic bushing model. One-axis durability test is performed to describe the mechanical behavior of typical vehicle elastomeric components. The results of the tests are used to develop an empirical bushing model with an artificial neural network. The back propagation algorithm is used to obtain the weighting factor of the neural network. Since the output for a dynamic system depends on the histories of inputs and outputs, Narendra algorithm of 'NARMAX' form is employed to consider these effects. A numerical example is carried out to verify the developed bushing model.