• ETRI Journal
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• v.37 no.3
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• pp.502-511
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• 2015

Intrusion detection plays a key role in detecting attacks over networks, and due to the increasing usage of Internet services, several security threats arise. Though an intrusion detection system (IDS) detects attacks efficiently, it also generates a large number of false alerts, which makes it difficult for a system administrator to identify attacks. This paper proposes automatic fuzzy rule generation combined with a Wiener filter to identify attacks. Further, to optimize the results, simplified swarm optimization is used. After training a large dataset, various fuzzy rules are generated automatically for testing, and a Wiener filter is used to filter out attacks that act as noisy data, which improves the accuracy of the detection. By combining automatic fuzzy rule generation with a Wiener filter, an IDS can handle intrusion detection more efficiently. Experimental results, which are based on collected live network data, are discussed and show that the proposed method provides a competitively high detection rate and a reduced false alarm rate in comparison with other existing machine learning techniques.

• Structural Engineering and Mechanics
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• v.26 no.3
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• pp.251-262
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• 2007

This paper examines the application of artificial neural networks (ANN) to the response prediction of geometrically nonlinear truss structures. Two types of analysis (deterministic and probabilistic analyses) are considered. A three-layer feed-forward backpropagation network with three input nodes, five hidden layer nodes and two output nodes is firstly developed for the deterministic response analysis. Then a back propagation training algorithm with Bayesian regularization is used to train the network. The trained network is then successfully combined with a direct Monte Carlo Simulation (MCS) to perform a probabilistic response analysis of geometrically nonlinear truss structures. Finally, the proposed ANN is applied to predict the response of a geometrically nonlinear truss structure. It is found that the proposed ANN is very efficient and reasonable in predicting the response of geometrically nonlinear truss structures.

• Korean Journal of Bronchoesophagology
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• v.7 no.1
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• pp.76-79
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• 2001

A 60-year-old male was admitted due to cervical spine injury (C7-T1 fracture dislocation) and quadriparesis after slip down. During conservative management in department of neurologic surgery, he complainted of fever, dyspnea, neck swelling. Follow up cervicothoracic CT revealed abscess pocket in paraglottic, retropharyngeal, anterior cervical spaces and mediastinum. Also noted bilateral pleural effusions. Under impression of descending necrotizing mediastinitis (DNM). cervical drainage and bilateral chest tube insertion was performed immediately. On next day. mediastinal drainage through mediastinotomy was performed with careful handling of cervical spine. Escherichia coli was identified in bacteriologic culture. Wire fixation of dislocated C7-T1 spine through Posterior approach was performed on 30th days after mediastinotomy. Right chest tube was removed on 40th days. At now, the patient is on rehabilitation and physical training program. DNM is relatively rare, but lethal disease with high mortality. Immedate and sufficient mediastinal drainage is essential in treatment.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.4
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• pp.875-880
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• 2018

After the Fourth Industrial Revolution, there has been a software revolution around the world in which software is combined with industry and society to create new value. In order to prepare this, developed countries provide software education that includes basic principles of computer science and coding instead of computer application education. This paper proposes a block program using a drone. Throughout this research, various education for creativity thinking and problem solving ability is possible in connection with the creativity and convergence education.

• Journal of Power Electronics
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• v.12 no.5
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• pp.842-850
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• 2012

Learning Power Electronics is essential in both electrical and electronic engineering fields and the introductory courses are similar in many universities. Taking this premise into account, an educational computer-aided platform for power electronics will be presented in this paper. This educational platform includes an e-book, a set of power electronics animations, Java simulations, as well as several hands-on training sessions. The main advantages of this platform are twofold. First, all necessary teaching tools are combined on a single platform. And secondly, access to this platform is available free of charge and with no complicated registration requirements. In addition to traditional teaching techniques, the use of this platform has demonstrated an increase in student participation and has consistently improved their academic performance. Data consist of surveys, which guarantee both reliability and validity through psychometric techniques.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1269-1271
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• 1996

This paper presents a visual servoing combined by evolutionary algorithms and neural network for a robotic manipulators to control position and orientation of the end-effector. Using the multi layer feedforward neural network that permits the connection of other layers, evolutionary programming(EP) that search the structure and weight of the neural network, and evolution strategies(ES) which training the weight of neuron, we optimized the net structure of control scheme. Using the four feature image information from CCD camera attached to end-effector of RV-M2 robot manipulator having 5 dof, we generate the control input to agree the target image, to realize the visual servoing. The validity and effectiveness of the proposed control scheme will be verified by computer simulations.

• Journal of Marine Bioscience and Biotechnology
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• v.3 no.1
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• pp.48-53
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• 2008

Tanzania is a coastal state on the western Indian Ocean in Africa. In an artisanal or small scale fishery, the combination of large numbers of fishers and landing places, mixed gears and migrant fishers makes fisheries management an often complex task. Lack of capital, low level of technology, poverty and high cost of transport are major socio-economic problems in Tanzanian fisheries. The combined approach of community-based management and provision of education and training for extension workers and fishers themselves are required. It is also necessary to build the capacity of fisheries institutions to meet the human resources development challenge.

• Asia pacific journal of information systems
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• v.1 no.1
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• pp.63-86
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• 1991

In this paper, our concern is the artificial neural network-based patten classification, when can resolve the difficulties in the Autoregressive Moving Average(ARMA) model identification problem To effectively classify a time series into an approriate ARMA model, we adopt the Multi-layered Backpropagation Network (MLBPN) as a pattern classifier, and Extended Sample Autocorrelation Function (ESACF) as a feature extractor. To improve the classification power of MLBPN's we suggest an integrated neural network system which consists of an AR Network and many small-sized MA Networks. The output of AR Network which will gives the MA order. A step-by-step training strategy is also suggested so that the learned MLBPN's can effectively ESACF patterns contaminated by the high level of noises. The experiment with the artificially generated test data and real world data showed the promising results. Our approach, combined with a statistical parameter estimation method, will provide a way to the automation of ARMA modeling.

• Proceedings of the KIEE Conference
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• summer
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• pp.384-387
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• 2004

The direction and the type of a fault on a transmission line need to be identified rapidly and correctly, The work described in this paper addresses the problem encountered by a conventional algorithm in a fault type classification in double circuit line, this arises due to a mutual coupling and CT saturation under the fault condition. We present an approach to identify fault type with novel neural network on double circuit transmission line. The neural network based on combined unsupervised training method provides the ability classify the fault type by different patterns of the associated voltages and currents.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.5
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• pp.179-186
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• 2021

For stereo matching based on deep learning, the design of network structure is crucial to the calculation of matching cost, and the time-consuming problem of convolutional neural network in image processing also needs to be solved urgently. In this paper, a method of stereo matching using sparse loss volume in parallax dimension is proposed. A sparse 3D loss volume is constructed by using a wide step length translation of the right view feature map, which reduces the video memory and computing resources required by the 3D convolution module by several times. In order to improve the accuracy of the algorithm, the nonlinear up-sampling of the matching loss in the parallax dimension is carried out by using the method of multi-category output, and the training model is combined with two kinds of loss functions. Compared with the benchmark algorithm, the proposed algorithm not only improves the accuracy but also shortens the running time by about 30%.