• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.39-42
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• 1996

Modeling techniques such as linear regression have been used to predict hurricane activity many months in advance of the start of the hurricane season with some success. In this paper, we construct feedforward neural networks to model Atlantic basin hurricane activity and compare the predictions of our neural network models to the predictions produced by statistical models found in the weather forecasting literature. We find that our neural network models produce reasonably accurate predictions that, for the most part, compare favorably to the predictions of statistical models.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.174-178
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• 2002

This paper discusses a blind equalization technique for FIR channel system, that might be minimum phase or not, in digital communication. The proposed techniques consist of two parts. One is to estimate the original channel coefficients based on fourth-order cumulants of the channel output, the other is to employ RBF neural network to model an inverse system fur the original channel. Here, the estimated channel is used as a reference system to train the RBF. The proposed RBF equalizer provides fast and easy teaming, due to the structural efficiency and excellent recognition-capability of R3F neural network. Throughout the simulation studies, it was found that the proposed blind RBF equalizer performed favorably better than the blind MLP equalizer, while requiring the relatively smaller computation steps in tranining.

• The KIPS Transactions:PartA
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• v.8A no.1
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• pp.56-62
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• 2001

This paper presents a neural network based variable structure control scheme for nonlinear systems. In this scheme, a set of local variable structure control laws are designed on the basis of the linear models about preselected representative points which cover the range of the system operation of interest. From the combination of the set of local variable structure control laws, neural networks infer the approximate control input in between the operating points. The neural network based variable structure control alleviates the effects of model uncertainties, which cannot be compensated by the control techniques using feedback linearization. It also relaxes the discontinuity in the system’s behavior that appears when the control schemes based on the family of the linear models are applied to nonlinear systems. Simulation results of a ball and beam system, to which feedback linearization cannot be applied, demonstrate the feasibility of the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.1
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• pp.14-24
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• 2002

In this paper, a design of the pulsewidth-modulation(PWM) neural network with both retrieving and learning function is proposed. In the designed PWM neural system, the input and output signals of the neural network are represented by PWM signals. In neural network, the multiplication is one of the most commonly used operations. The multiplication and summation functions are realized by using the PWM technique and simple mixed-mode circuits. Thus, the designed neural network only occupies the small chip area. By applying some circuit design techniques to reduce the nonideal effects, the designed circuits have good linearity and large dynamic range. Moreover, the delta learning rule can easily be realized. To demonstrate the learning capability of the realized PWM neural network, the delta learning nile is realized. The circuit with one neuron, three synapses, and the associated learning circuits has been designed. The HSPICE simulation results on the two learning examples on AND function and OR function have successfully verified the function correctness and performance of the designed neural network.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.401-403
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• 1992

For the control of robotic manipulators with unknown or uncertain dynamics, leaming control schemes are very effective control schemes for repeated trajectory following tasks. In this class of controllers, control techniques using neural networks have been gaining much attention in recent years.. In this note, we discuss the leaming control techniques using neural networks and propose a new model-based control scheme using multilayered neural networks. Three-layerd neural network is used as a feedback controller to compensate the mismatched terms between model plant and real plant. Computer simulations are performed to show the applicability and the limitation of the proposed controller.

• Advances in concrete construction
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• v.10 no.3
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• pp.247-256
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• 2020

In this paper the possibility of using different regression models to predict the mechanical properties of limestone concrete after exposure to high temperatures, based on the results of non-destructive techniques, that could be easily used in-situ, is discussed. Extensive experimental work was carried out on limestone concrete mixtures, that differed in the water to cement (w/c) ratio, the type of cement and the quantity of superplasticizer added. After standard curing, the specimens were exposed to various high temperature levels, i.e., 200℃, 400℃, 600℃ or 800℃. Before heating, the reference mechanical properties of the concrete were determined at ambient temperature. After the heating process, the specimens were cooled naturally to ambient temperature and tested using non-destructive techniques. Among the mechanical properties of the specimens after heating, known also as the residual mechanical properties, the residual modulus of elasticity, compressive and flexural strengths were determined. The results show that residual modulus of elasticity, compressive and flexural strengths can be reliably predicted using an artificial neural network approach based on ultrasonic pulse velocity, residual surface strength, some mixture parameters and maximal temperature reached in concrete during heating.

• Journal of IKEEE
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• v.2 no.1 s.2
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• pp.134-138
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• 1998

Because of the statistical fluctuations and the high 'time-variability' nature of the traffic, managing the resources of the network require highly dynamic techniques with minimal Intervention and reaction times, and adaptive and learning capabilities. The neural networks normalizes the ATM cell arrival rate and queue length and has the adaptive learning algorithm, and experimentally investigated the method to prevent the congestion generated in ATM networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.5159-5178
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• 2018

Network Intrusion detection is a rapidly growing field of information security due to its importance for modern IT infrastructure. Many supervised and unsupervised learning techniques have been devised by researchers from discipline of machine learning and data mining to achieve reliable detection of anomalies. In this paper, a deep convolutional neural network (DCNN) based intrusion detection system (IDS) is proposed, implemented and analyzed. Deep CNN core of proposed IDS is fine-tuned using Randomized search over configuration space. Proposed system is trained and tested on NSLKDD training and testing datasets using GPU. Performance comparisons of proposed DCNN model are provided with other classifiers using well-known metrics including Receiver operating characteristics (RoC) curve, Area under RoC curve (AuC), accuracy, precision-recall curve and mean average precision (mAP). The experimental results of proposed DCNN based IDS shows promising results for real world application in anomaly detection systems.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.145-152
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• 2022

One of the impacts of Covid-19 on education systems has been the shift to online education. This shift has changed the way education is consumed and perceived by students. However, the exact nature of student perception about online education is not known. The aim of this study was to understand the perceptions of Saudi higher education students (e.g., post-school students) about online education during the Covid-19 pandemic. Various aspects of online education including benefits, features and cybersecurity were explored. The data collected were analysed using statistical techniques, especially artificial neural networks, to address the research aims. The key findings were that benefits of online education was perceived by students with positive experience or when ensured of safe use of online platforms without the fear cyber security breaches for which recruitment of a cyber security officer was an important predictor. The issue of whether perception of online education as a necessity only for Covid situation or a lasting option beyond the pandemic is a topic for future research.

• Journal of the Chungcheong Mathematical Society
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• v.34 no.3
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• pp.221-234
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• 2021

We introduce high-order Hopfield neural networks with Leakage delays. Furthermore, we study the uniqueness and existence of Hopfield artificial neural networks having the weighted pseudo almost periodic forcing terms on finite delay. Our analysis is based on the differential inequality techniques and the Banach contraction mapping principle.