• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1724-1731
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• 2018

The conventional polynomial neural network (PNN) is a classical flexible neural structure and self-organizing network, however it is not free from the limitation of overfitting problem. In this study, we propose a space search-optimized polynomial neural network (ssPNN) structure to alleviate this problem. Ranking selection is realized by means of ranking selection-based performance index (RS_PI) which is combined with conventional performance index (PI) and coefficients based performance index (CPI) (viz. the sum of squared coefficient). Unlike the conventional PNN, L2-norm regularization method for estimating the polynomial coefficients is also used when designing the ssPNN. Furthermore, space search optimization (SSO) is exploited here to optimize the parameters of ssPNN (viz. the number of input variables, which variables will be selected as input variables, and the type of polynomial). Experimental results show that the proposed ranking selection-based polynomial neural network gives rise to better performance in comparison with the neuron fuzzy models reported in the literatures.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.2
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• pp.189-199
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• 1999

This paper presents an optimization algorithm for a stable Self Dynamic Neural Network(SDNN) using genetic algorithm. Optimized SDNN is applied to a problem of controlling nonlinear dynamical systems. SDNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. The real-time implementation is very important, and thus the neuro controller also needs to be designed such that it converges with a relatively small number of training cycles. SDW has considerably fewer weights than DNN. Since there is no interlink among the hidden layer. The object of proposed algorithm is that the number of self dynamic neuron node and the gradient of activation functions are simultaneously optimized by genetic algorithms. To guarantee convergence, an analytic method based on the Lyapunov function is used to find a stable learning for the SDNN. The ability and effectiveness of identifying and controlling a nonlinear dynamic system using the proposed optimized SDNN considering stability is demonstrated by case studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.3025-3047
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• 2021

This paper presents the latest Ethernet standardization of in-vehicle network and the future trends of automotive Ethernet technology. The proposed system provides design and optimization algorithms for automotive networking technology related to AVB (Audio Video Bridge) technology. We present a design of in-vehicle network system as well as the optimization of AVB for automotive. A proposal of Reduced Latency of Machine to Machine (RLMM) plays an outstanding role in reducing the latency among devices. RLMM's approach to real-world experimental cases indicates a reduction in latency of around 41.2%. The setup optimized for the automotive network environment is expected to significantly reduce the time in the development and design process. The results obtained in the study of image transmission latency are trustworthy because average values were collected over a long period of time. It is necessary to analyze a latency between multimedia devices within limited time which will be of considerable benefit to the industry. Furthermore, the proposed reliable camera and video streaming through optimized AVB device settings would provide a high level of support in the real-time comprehension and analysis of images with AI (Artificial Intelligence) algorithms in autonomous driving.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.33-38
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• 2021

This paper presents a comparative study between the parameter-optimized Pacejka model and artificial neural network model for the tire force estimation. The two different approaches are investigated and compared in this study. First, offline optimization is conducted based on Pacejka Magic Formula model to determine the proper parameter set for the minimization of tire force error between the model and test data set. Second, deep neural network model is used to fit the model to the tire test data set. The actual tire forces are measured using MTS Flat-Track test platform and the measurements are used as the reference tire data set. The focus of this study is on the applicability of machine learning technique to tire force estimation. It is shown via the regression results that the deep neural network model is more effective in describing the tire force than the parameter-optimized Pacejka model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.6B
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• pp.414-422
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• 2012

In the paper we present optimized implementation method over sensor mote for HUMMINGBIRD2 algorithm, ultra-light symmetric cryptography. For efficient implementation we maximized the register usage and used optimized addressing method to reduce the encryption and decryption time. With the optimized encryption implementation, we can utilize the efficient secure network over resource constrained sensor mote.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2891-2893
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• 2005

In this paper, we introduce a new category of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) that is based on a genetically optimized multi-layer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. The conventional SOFPNN algorithm leads to a tendency to produce overly complex networks as well as a repetitive computation load by the trial and error method and/or the a repetitive parameter adjustment by designer. In order to generate a structurally and parametrically optimized network, such parameters need to be optimal. In this study, in solving the problems with the conventional SOFPNN, we introduce a new design approach of evolutionary optimized SOFPNN. Optimal parameters design available within FPN (viz. the no. of input variables, the order of the polynomial, input variables, and the no. of membership function) lead to structurally and parametrically optimized network which is more flexible as well as simpler architecture than the conventional SOFPNN. In addition, we determine the initial apexes of membership functions by genetic algorithm.

• Journal of Communications and Networks
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• v.14 no.1
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• pp.91-103
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• 2012

Network mobility (NEMO) based on mobile IP version 6 has been proposed for networks that move as a whole. Route optimization is one of the most important topics in the field of NEMO. The current NEMO basic support protocol defines only the basic working mode for NEMO, and the route optimization problem is not mentioned. Some optimization schemes have been proposed in recent years, but they have limitations. A new NEMO route optimization scheme-involving a combination of the optimized route cache protocol (ORC) and reverse routing header (RRH) and the use of a quota mechanism for optimized sessions (OwR)-is proposed. This scheme focuses on balanced performance in different aspects. It combines the ORC and RRH schemes, and some improvements are made in the session selection mechanism to avoid blindness during route optimization. Simulation results for OwR show great similarity with those for ORC and RRH. Generally speaking, the OwR's performance is at least as good as that of the RRH, and besides, the OwR scheme is capable of setting up optimal routing for a certain number of sessions, so the performance can be improved and the cost of optimal routing in nested NEMO can be decreased.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.267-271
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• 2000

Traffic analysis during past years used the Poisson distribution or Markov model, assuming an exponential distribution of packet queue arrival. Recent studies, however, have shown aperiodic and burst characteristics of network traffics. Such characteristics of data traffic enable the scalability of network, QoS, optimized design, when we analyze new traffic model having a self-similar characteristic. This paper analyzes the self-similar characteristics of a small-scale mixed traffic in a network simulation, the real network Traffic.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.7
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• pp.1881-1897
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• 1994

This paper presents a neural network-based computational scheme to generate the optimized robotic assembly sequence for an assembly product consisting of a number of parts. An assembly sequence is considered to be optimal when it meets a number of conditions : it must satisfy assembly constraints, keep the stability of in-process subassemblies, and minimize assembly cost. To derive such an optimal sequence, we propose a scheme using both the Hopfield neural network and the expert system. Based upon the inferred precedence constraints and the assembly costs from the expert system, we derive the evolution equation of the network. To illustrate the suitability of the proposed scheme, a case study is presented for industrial product of an electrical relay. The result is compared with that obtained from the expert system.

• The Journal of Korea Robotics Society
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• v.18 no.1
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• pp.88-92
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• 2023

Lithium-ion batteries are actively used in various industrial sites such as field robots, drones, and electric vehicles due to their high energy efficiency, light weight, long life span, and low self-discharge rate. When using a lithium-ion battery in a field, it is important to accurately estimate the SoC (State of Charge) of batteries to prevent damage. In recent years, SoC estimation using data-based artificial neural networks has been in the spotlight, but it has been difficult to deploy in the embedded board environment at the actual site because the computation is heavy and complex. To solve this problem, neural network lightening technologies such as network pruning have recently attracted attention. When pruning a neural network, the performance varies depending on which layer and how much pruning is performed. In this paper, we introduce an optimized pruning technique by improving the existing pruning method, and perform a comparative experiment to analyze the results.