• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.2
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• pp.63-70
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• 1999

This paper is proposed a new method to deal with the optimized auto-tuning for the Pill controller which is used to the process-control in various fields. First of all, in this method, 1st order delay system with dead time which is modelled from the unit step response of the system is Pade-approximated, then initial values are determined by the Ziegler-Nichols method. So we can find the parameters of Pill controller so as to minimize the fuzzy criterion function which includes the maximum overshoot, damping ratio, rising time and settling time. Finally, after studying the parameters of Pill controller by Backpropagation of Neural-Network, when we give new K, L, T values to Neural-Network, the optimized parameter of Pill controller is found by Neural-Network Program.rogram.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.5
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• pp.705-711
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• 2013

In this paper, we introduce an design of multi-output fuzzy neural networks based on Interval Type-2 fuzzy set. The proposed Interval Type-2 fuzzy set-based fuzzy neural networks with multi-output (IT2FS-based FNNm) comprise the network structure generated by dividing the input space individually. The premise part of the fuzzy rules of the network reflects the individuality of the division space for the entire input space and the consequent part of the fuzzy rules expresses three types of polynomial functions with interval sets such as constant, linear, and modified quadratic inference for pattern recognition. The learning of fuzzy neural networks is realized by adjusting connections of the neurons in the consequent part of the fuzzy rules, and it follows a back-propagation algorithm. In addition, in order to optimize the network, the parameters of the network such as apexes of membership functions, uncertainty factor, learning rate and momentum coefficient were automatically optimized by using real-coded genetic algorithm. The proposed model is evaluated with the use of numerical experimentation.

• Journal of Power Electronics
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• v.16 no.6
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• pp.2359-2367
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• 2016

Inductive power transfer (IPT) systems have become increasingly popular in recharging electric vehicle (EV) batteries. This paper presents an investigation of a series parallel/series (SP/S) resonant compensation network based IPT system for EVs with further optimized circular pads (CPs). After the further optimization, the magnetic coupling coefficient and power transfer capacity of the CPs are significantly improved. In this system, based on a series compensation network on the secondary side, the constant output voltage, utilizing a simple yet effective control method (fixed-frequency control), is realized for the receiving terminal at a settled relative position under different load conditions. In addition, with a SP compensation network on the primary side, zero voltage switching (ZVS) of the inverter is universally achieved. Simulations and experiments have been implemented to validate the favorable applicability of the modified optimization of CPs and the proposed SP/S IPT system.

• Journal of the Korean institute of surface engineering
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• v.43 no.3
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• pp.159-164
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• 2010

Surface roughness of deposited or etched film strongly depends on ion bombardment. Relationships between ion bombardment variables and surface roughness are too complicated to model analytically. To overcome this, an empirical neural network model was constructed and applied to a deposition process of silicon nitride (SiN) films. The films were deposited by using a pulsed plasma enhanced chemical vapor deposition system in $SiH_4$-$NH_4$ plasma. Radio frequency source power and duty ratio were varied in the range of 200-800 W and 40-100%. A total of 20 experiments were conducted. A non-invasive ion energy analyzer was used to collect ion energy distribution. The diagnostic variables examined include high (or) low ion energy and high (or low) ion energy flux. Mean surface roughness was measured by using atomic force microscopy. A neural network model relating the diagnostic variables to the surface roughness was constructed and its prediction performance was optimized by using a genetic algorithm. The optimized model yielded an improved performance of about 58% over statistical regression model. The model revealed very interesting features useful for optimization of surface roughness. This includes a reduction in surface roughness either by an increase in ion energy flux at lower ion energy or by an increase in higher ion energy at lower ion energy flux.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.6-10
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• 2021

Recognition of characters printed or marked on the PCB components from images captured using cameras is an important task in PCB components inspection systems. Previous optical character recognition (OCR) of PCB components typically consists of two stages: character segmentation and classification of each segmented character. However, character segmentation often fails due to corrupted characters, low image contrast, etc. Thus, OCR without character segmentation is desirable and increasingly used via deep neural networks. Typical implementation based on deep neural nets without character segmentation includes convolutional neural network followed by recurrent neural network (RNN). However, one disadvantage of this approach is slow execution due to RNN layers. LPRNet is a segmentation-free character recognition network with excellent accuracy proved in license plate recognition. LPRNet uses a wide convolution instead of RNN, thus enabling fast inference. In this paper, LPRNet was adapted for recognizing characters printed on PCB components with fast execution and high accuracy. Initial training with synthetic images followed by fine-tuning on real text images yielded accurate recognition. This net can be further optimized on Intel CPU using OpenVINO tool kit. The optimized version of the network can be run in real-time faster than even GPU.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.156-165
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• 2023

IEEE 802.15.4e-TSCH is recognized as a wireless industrial sensor network standard used in IoT systems. To ensure both power savings and reliable communications, the TSCH standard uses techniques including channel hopping and bandwidth reserve. In TSCH mode, scheduling is crucial because it allows sensor nodes to select when data should be delivered or received. Because a wide range of applications may necessitate energy economy and transmission dependability, we present a distributed approach that uses a cluster tree topology to forecast scheduling requirements for the following slotframe, concentrating on the Poisson model. The proposed Optimized Minimal Scheduling Function (OMSF) is interested in the details of the scheduling time intervals, something that was not supported by the Minimal Scheduling Function (MSF) proposed by the 6TSCH group. Our contribution helps to deduce the number of cells needed in the following slotframe by reducing the number of negotiation operations between the pairs of nodes in each cluster to settle on a schedule. As a result, the cluster tree network's error rate, traffic load, latency, and queue size have all decreased.

• ETRI Journal
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• v.38 no.2
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• pp.291-301
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• 2016

In this paper, we study an optimized block-diagonal zero-forcing (BD-ZF) precoder in a two-tiered cognitive network consisting of a macro cell (MC) and a small cell (SC). By exploiting multiuser multiple-input and multiple-output Vandermonde-subspace frequency-division multiplexing (VFDM) transmission, a cognitive SC can coexist with an MC. We first devise a cross-tier precoder based on the idea of VFDM to cancel the interference from the SC to the MC. Then, we propose an optimized BD-ZF intra-tier precoder (ITP) to suppress multiuser interference and maximize the throughput in the SC. In the case where the dimension of a provided null space is larger than that required by the BD-ZF ITP, the optimized BD-ZF ITP can collect all limited channel gain by optimizing rotating and selecting matrices. Otherwise, the optimized BD-ZF ITP is validated to be equivalent to the conventional BD-ZF ITP in terms of throughput. Numerical results are presented to demonstrate the throughput improvement of the proposed optimized BD-ZF ITP and to discover the impact of imperfect channel state information.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.7
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• pp.1510-1516
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• 2011

As emergence of the ubiquitous society, it is possible to access the network for services needed to us in anytime and anywhere. The phenomena has been accelerated by revitalization of the sensor network offering the sensing information and data. Currently, sensor network contributes the convenience for various services such as environment monitoring, health care and home automation. However, sensor network has a weak point compared to traditional network, which is easily exposed to attacker. For this reason, messages communicated over the sensor network, are encrypted with symmetric key and transmitted. A number of symmetric cryptography algorithms have been researched. Among of them HIGHT algorithm in hardware and software implementation are more efficient than tradition AES in terms of speed and chip size. Therefore, it is suitable to resource constrained devices including RFID tag, Sensor node and Smart card. In the paper, we present the optimized software implementation on the ultra-light symmetric cryptography algorithm, HIGHT.

• Journal of Internet Computing and Services
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• v.11 no.2
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• pp.85-98
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• 2010

An advanced and proactive response mechanism against diverse attacks on All-IP network should be proposed for enhancing its security and reliability on open network. There are two main research works related to this study. First one is the SPIE system with hash function on Bloom filter and second one is the Sinkhole routing mechanism using BGP protocol for verifying its transmission path. Therefore, advanced traceback and network management mechanism also should be necessary on All-IP network environments against DDoS attacks. In this study, we studied and proposed a new IP traceback mechanism on All-IP network environments based on existing SPIE and Sinkhole routing model when diverse DDoS attacks would be happen. Proposed mechanism has a Manager module for controlling the regional router with using packet monitoring and filtering mechanism to trace and find the attack packet's real transmission path. Proposed mechanism uses simplified and optimized memory for storing and memorizing the packet's hash value on bloom filter, with which we can find and determine the attacker's real location on open network. Additionally, proposed mechanism provides advanced packet aggregation and monitoring/control module based on existing Sinkhole routing method. Therefore, we can provide an optimized one in All-IP network by combining the strength on existing two mechanisms. And the traceback performance also can be enhanced compared with previously suggested mechanism.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1406-1420
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• 2017

Artificial neural networks (ANNs) play an important role in the fields of function approximation, prediction, and classification. ANN performance is critically dependent on the input parameters, including the number of neurons in each layer, and the optimal values of weights and biases assigned to each neuron. In this study, we apply the particle swarm optimization method, a popular optimization algorithm for determining the optimal values of weights and biases for every neuron in different layers of the ANN. Several regression models, including general linear regression, Fourier regression, smoothing spline, and polynomial regression, are conducted to evaluate the proposed method's prediction power compared to multiple linear regression (MLR) methods. In addition, residual analysis is conducted to evaluate the optimized ANN accuracy for both training and test datasets. The experimental results demonstrate that the proposed method can effectively determine optimal values for neuron weights and biases, and high accuracy results are obtained for prediction applications. Evaluations of the proposed method reveal that it can be used for prediction and estimation purposes, with a high accuracy ratio, and the designed model provides a reliable technique for optimization. The simulation results show that the optimized ANN exhibits superior performance to MLR for prediction purposes.