• Journal of Power Electronics
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• v.13 no.3
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• pp.400-408
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• 2013

This paper presents a discrete state-space controller using state feedback control and feed-forward decoupling to provide a desirable control bandwidth and control stability for dynamic voltage restorers (DVR). The paper initially discusses three typical applications of a DVR. The load-side capacitor DVR topology is preferred because of its better filtering capability. The proposed DVR controller offers almost full controllability because of the multi-feedback of state variables, including one-beat delay feedback. Feed-forward decoupling is usually employed to prevent disturbances of the load current and source voltage. Directly obtaining the feed-forward paths of the load current and source voltage in the discrete domain is a complicated process. Fortunately, the full feed-forward decoupling strategy can be easily applied to the discrete state-space controller by means of continuous transformation. Simulation and experimental results from a digital signal processor-based system are included to support theoretical analysis.

• Proceedings of the KIPE Conference
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• 2016.07a
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• pp.329-330
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• 2016

This paper deals with stability of active EMI filter. Feed-forward type voltage-sensing voltage-injection (VSVI) AEF is best topology when considering the filter size and leakage current. But it is not studied in stability. Therefore, the detailed and simplified transfer function is derived and it is used to analyze stability. The experimental results are provided to verify the effectiveness of the analysis.

• Journal of Power Electronics
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• v.19 no.4
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• pp.1011-1019
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• 2019

In this paper, a buck-flyback (fly-buck) stand-alone photovoltaic (PV) system for charge balancing with a differential power processor (DPP) circuit is proposed. Conventional feed-back DPP converters draw differential feed-back power from the output of a string converter. Therefore, the power is always through the switches and diodes of the string converter. Because of the returning conduction path, there are always power losses due to the resistance of the switch and the forward voltage of the diode. Meanwhile, the proposed feed-back DPP converter draws power from the magnetically-coupled inductor in a string converter. This shortens the power path of the DPP converter, which reduces the power losses. In addition, the extra winding in the magnetically-coupled inductor works as a charge balancer for battery-stacked stand-alone PV systems. The proposed system, which uses a single magnetically-coupled inductor, can control each of the PV modules independently to track the maximum power point. Thus, it can overcome the power loss due to the power path. It can also achieve charge balancing for each of the battery modules. The proposed topology is analyzed and verified using 120W hardware experiments.

• The Journal of Information Technology
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• v.8 no.1
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• pp.51-56
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• 2005

The Back propagation algorithm is a very effective supervised training method for multi-layer feed forward neural networks. This paper studies the decision boundary formation based on the Back propagation algorithm. The discriminating powers of several neural network topology are also investigated against five manually created data sets. It is found that neural networks with multiple hidden layer perform better than single hidden layer.

• ETRI Journal
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• v.29 no.6
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• pp.835-837
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• 2007

A multi-bit sigma-delta modulator architecture is described for low-distortion performance and a high-speed operation. The proposed architecture uses both a delayed code and a delayed differential code of analog-to-digital converter in the feedback path, thereby suppressing signal components in the integrators and relaxing the timing requirement of the analog-to-digital converter and the scrambler logic. Implemented by a 0.13 ${\mu}m$ CMOS process, the sigma-delta modulator achieves high linearity. The measured spurious-free dynamic range is 89.1 dB for -6 dBFS input signal.

• Proceedings of the KIPE Conference
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• 2012.07a
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• pp.232-233
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• 2012

This paper presents an effective control scheme for an electric vehicle battery charger where a symmetrical bridgeless power factor-corrected converter and a buck converter are cascaded. Both converters have been popular in industries because of their high efficiency, low cost, and compact size, hence combining these converters makes the overall battery charging system strongly efficient. Moreover, this charger topology can operate at universal input voltage and attain a desired battery current and voltage without ripple. In order to achieve a unity input power factor and zero input current harmonic distortion, the proposed control scheme adopts duty ratio feed-forward control technique in both current and voltage control loop. Additionally, in the current loop, its reference is created by a phase-locked loop (PLL) block, leading to a pure sinusoidal input current although the input voltage waveform is being distorted. The feasibility and practical value of the proposed approach are verified by simulation and experiment with an 110V/60Hz ac line input and 1.5kW-72V dc output of the battery charging system.

• Journal of Power Electronics
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• v.17 no.4
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• pp.1037-1047
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• 2017

The Differential Power Processing (DPP) converter is a promising multi-module photovoltaic inverter architecture recently proposed for photovoltaic systems. In this paper, a DPP converter architecture, in which each PV-panel has its own DPP converter in shunt, performs distributed maximum power point tracking (DMPPT) control. It maintains a high energy conversion efficiency, even under partial shading conditions. The system architecture only deals with the power differences among the PV panels, which reduces the power capacity of the converters. Therefore, the DPP systems can easily overcome the conventional disadvantages of PCS such as centralized, string, and module integrated converter (MIC) topologies. Among the various types of the DPP systems, the feed-forward method has been selected for both its voltage balancing and power transfer to a modified H-bridge inverter that needs charge balancing of the input capacitors. The modified H-bridge multi-level inverter had some advantages such as a low part count and cost competitiveness when compared to conventional multi-level inverters. Therefore, it is frequently used in photovoltaic (PV) power conditioning system (PCS). However, its simplified switching network draws input current asymmetrically. Therefore, input capacitors in series suffer from a problem due to a charge imbalance. This paper validates the operating principle and feasibility of the proposed topology through the simulation and experimental results. They show that the input-capacitor voltages maintain the voltage balance with the PV MPPT control operating with a 140-W hardware prototype.

• Computers and Concrete
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• v.21 no.1
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• pp.47-54
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• 2018

Artificial neural network models can be successfully used to simulate the complex behavior of many problems in civil engineering. As compared to conventional computational methods, this popular modeling technique is powerful when the relationship between system parameters is intrinsically nonlinear, or cannot be explicitly identified, as in the case of concrete behavior. In this investigation, an artificial neural network model was developed to assess the residual compressive strength of self-compacted concrete at elevated temperatures ($20-900^{\circ}C$) and various relative humidity conditions (28-99%). A total of 332 experimental datasets, collected from available literature, were used for model calibration and verification. Data used in model development incorporated concrete ingredients, filler and fiber types, and environmental conditions. Based on the feed-forward back propagation algorithm, systematic analyses were performed to improve the accuracy of prediction and determine the most appropriate network topology. Training, testing, and validation results indicated that residual compressive strength of self-compacted concrete, exposed to high temperatures and relative humidity levels, could be estimated precisely with the suggested model. As illustrated by statistical indices, the reliability between experimental and predicted results was excellent. With new ingredients and different environmental conditions, the proposed model is an efficient approach to estimate the residual compressive strength of self-compacted concrete as a substitute for sophisticated laboratory procedures.

• Journal of the Korean Institute of Intelligent Systems
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• v.1 no.1
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• pp.9-25
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• 1991

This tutorial paper has been written for biologists, physicians or beginners in fuzzy sets theory and applications. This field is introduced in the framework of medical diagnosis problems. The paper describes and illustrates with practical examples, a general methodology of special interest in the processing of borderline cases, that allows a graded assignment of diagnoses to patients. A pattern of medical knowledge consists of a tableau with linguistic entries or of fuzzy propositions. Relationships between symptoms and diagnoses are interpreted as labels of fuzzy sets. It is shown how possibility measures (soft matching) can be used and combined to derive diagnoses after measurements on collected data. The concepts and methods are illustrated in a biomedical application on inflammatory protein variations. In the case of poor diagnostic classifications, it is introduced appropriate ponderations, acting on the characterizations of proteins, in order to decrease their relative influence. As a consequence, when pattern matching is achieved, the final ranking of inflammatory syndromes assigned to a given patient might change to better fit the actual classification. Defuzzification of results (i.e. diagnostic groups assigned to patients) is performed as a non fuzzy sets partition issued from a "separating power", and not as the center of gravity method commonly employed in fuzzy control. It is then introduced a model of fuzzy connectionist expert system, in which an artificial neural network is designed to build the knowledge base of an expert system, from training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the connections: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through MIN-MAX fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feed forward network is described and illustrated in the same biomedical domain as in the first part.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.3
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• pp.55-65
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• 2004

When a neural network is used to solve a given problem it is necessary to match the complexity of the network to that of the problem because the complexity of the network significantly affects its learning capability and generalization performance. Thus, it is desirable to have an algorithm that can find appropriate network structures in a self-organizing way. This paper proposes algorithms which automatically organize feed forward multi-layer neural networks with sigmoid hidden neurons for given problems. Using both constructive procedures and pruning procedures, the proposed algorithms try to find the near optimal network, which is compact and shows good generalization performance. The performances of the proposed algorithms are tested on four function regression problems. The results demonstrate that our algorithms successfully generate near-optimal networks in comparison with the previous method and the neural networks of fixed topology.