• Smart Structures and Systems
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• v.30 no.4
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• pp.371-383
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• 2022

A novel impact localization method is presented based on impact induced elastic waves in sensorized composite structure subjected to temperature fluctuations. In real practices, environmental and operational conditions influence the acquired signals and consequently make the feature (particularly Time of Arrival (TOA)) extraction process, complicated and troublesome. To overcome this complication, a robust TOA estimation method is proposed based on the times in which the absolute amplitude of the signal reaches to a specific amplitude value. The presented method requires prior knowledge about the normalized wave velocity in different directions of propagation. To this aim, a finite element model of the plate was built in ABAQUS/CAE. The impact location is then highlighted by calculating an error value at different points of the structure. The efficiency of the developed impact localization technique is experimentally evaluated by dropping steel balls with different energies on a carbon fiber composite plate with different temperatures. It is demonstrated that the developed technique is able to localize impacts with different energies even in the presence of noise and temperature fluctuations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.65-73
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• 2021

This study aimed to provide the basic data for the development of a simulation training program for new infectious diseases by analyzing the knowledge and educational needs of 119 paramedics regarding COVID-19 infection control. Data was obtained through a structured questionnaire survey of 186 paramedics from November 15th to 30th 2020. The study showed that 98 of the 119 paramedic subjects (52.7%) had not been educated regarding COVID-19. The knowledge level was 18.21±1.98 out of 25 points, with environmental control securing the lowest correct answers. The highest need for education was in the areas of prevention of transmission and spread, and employee safety control. The total average for educational needs was 3.81±.28 (4 point scale) with the highest educational need in the area of prevention of the spread and dispersion of the disease and employee safety management. There was a statistically significant difference in the knowledge of the general characteristics according to gender (t=-1.999, p=.047) and the educational demand was related to career (t=-1.999, p=.047) and, education levels (t=2.336, p=.021). Accordingly, it is necessary to plan a new infectious disease simulation education program that addresses the low-scoring areas and items with high educational needs which include the propagation path and spread prevention, environmental management, and employee safety management as per the findings of this study.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.1
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• pp.83-90
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• 2002

This paper proposed the strategy learning method by means of the fusion of Back-Propagation neural network and Q learning algorithm for two-person, deterministic janggi board game. The learning process is accomplished simply through the playing each other. The system consists of two parts of move generator and search kernel. The one consists of move generator generating the moves on the board, the other consists of back-propagation and Q learning plus $\alpha$$\beta$ search algorithm in an attempt to learn the evaluation function. while temporal difference learns the discrepancy between the adjacent rewards, Q learning acquires the optimal policies even when there is no prior knowledge of effects of its moves on the environment through the learning of the evaluation function for the augmented rewards. Depended on the evaluation function through lots of games through the learning procedure it proved that the percentage won is linearly proportional to the portion of learning in general.

• Korean Journal of Cognitive Science
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• v.13 no.1
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• pp.13-21
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• 2002

Fuzzy logic ignores some information in the reasoning process. Neural network is powerful tools for the pattern processing, but, not appropriate for the logical reasoning. To model human knowledge, besides pattern processing capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct fuzzy inference network based on the neural logic network, extending the existing rule-inference network. And the traditional propagation rule is modified. Experiments are performed to compare search costs by sequential searching and searching by priority. The experimental results show that the searching by priority is more efficient than the sequential searching as the size of the fuzzy inference network becomes larder and an the number of searching increases.

• Structural Engineering and Mechanics
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• v.42 no.6
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• pp.831-847
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• 2012

Impact from water-borne debris during tsunami and flood events pose a potential threat to structures. Debris impact forces specified by current codes and standards are based on rigid body dynamics, leading to forces that are dependent on total debris mass. However, shipping containers and other debris are unlikely to be rigid compared to the walls, columns and other structures that they impact. The application of a simple one-dimensional model to obtain impact force magnitude and duration, based on acoustic wave propagation in a flexible projectile, is explored. The focus herein is on in-air impact. Based on small-scale experiments, the applicability of the model to predict actual impact forces is investigated. The tests show that the force and duration are reasonably well represented by the simple model, but they also show how actual impact differs from the ideal model. A more detailed three-dimensional finite element model is also developed to understand more clearly the physical phenomena involved in the experimental tests. The tests and the FE results reveal important characteristics of actual impact, knowledge of which can be used to guide larger scale experiments and detailed modeling. The one-dimensional model is extended to consider water-driven debris as well. When fluid is used to propel the 1-D model, an estimate of the 'added mass' effect is possible. In this extended model the debris impact force depends on the wave propagation in the two media, and the conditions under which the fluid increases the impact force are discussed.

• Journal of Plant Biotechnology
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• v.48 no.4
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• pp.255-263
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• 2021

Paederia foetida L. is an important medicinal plant that has been used for the treatment of various gastrointestinal related ailments by different tribal communities in India. This plant is also known for its use as a food. Due to overexploitation, P. foetida has been classified as a vulnerable plant in some states of India. The propagation of P. foetida by conventional methods is easy but very slow. Synthetic seed technology offers incredible potential for in vitro propagation of threatened and commercially valuable plants, and can also facilitate the storage and exchange of axenic plant material between laboratories. However, synthetic seed production for P. foetida has not yet been reported. Thus, to the best of our knowledge, the present study is the first attempt to produce synthetic seeds of P. foetida by calcium alginate encapsulation of in vitro regenerated axenic nodal segments. Sodium alginate (3%) and CaCl₂ (100 mM) were found to be the optimal materials for the preparation of ideal synthetic seeds, both in terms of morphology and germination ability. The synthetic seeds showed the best germination (formation of both shoot as well as root; 83.3%) on ½ MS medium augmented with 0.5 mg/L indole-3-acetic acid. The plantlets obtained from these synthetic seeds could be successfully acclimatized under field conditions. We also studied the storage of these synthetic seeds at low temperature and their subsequent sprouting/germination. The seeds showed a germination rate of 63.3% even after 21 days of storage at 4 ℃; thus, they could be useful for transfer and exchange of P. foetida germplasm.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.185-196
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• 2022

The Internet of Things (IoT) has become more and more widespread in recent years, thus attackers are placing greater emphasis on IoT environments. The IoT connects a large number of smart devices via wired and wireless networks that incorporate sensors or actuators in order to produce and share meaningful information. Attackers employed IoT devices as bots to assault the target server; however, because of their resource limitations, these devices are easily infected with IoT malware. The Distributed Denial of Service (DDoS) is one of the many security problems that might arise in an IoT context. DDOS attempt involves flooding a target server with irrelevant requests in an effort to disrupt it fully or partially. This worst practice blocks the legitimate user requests from being processed. We explored an intelligent intrusion detection system (IIDS) using a particular sort of machine learning, such as Artificial Neural Networks, (ANN) in order to handle and mitigate this type of cyber-attacks. In this research paper Feed-Forward Neural Network (FNN) is tested for detecting the DDOS attacks using a modified version of the KDD Cup 99 dataset. The aim of this paper is to determine the performance of the most effective and efficient Back-propagation algorithms among several algorithms and check the potential capability of ANN- based network model as a classifier to counteract the cyber-attacks in IoT environments. We have found that except Gradient Descent with Momentum Algorithm, the success rate obtained by the other three optimized and effective Back- Propagation algorithms is above 99.00%. The experimental findings showed that the accuracy rate of the proposed method using ANN is satisfactory.

• The Journal of the Acoustical Society of Korea
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• v.25 no.6
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• pp.305-311
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• 2006

In this paper the performance of a array invariant method is evaluated for source-range estimation in horizontally stratified shallow water ocean waveguide. The method has advantage of little computationally effort over existing source-localization methods. such as matched field processing or the waveguide invariant and array gain is fully exploited. And. no knowledge of the environment is required except that the received field should not be dominated by purely interference This simple and instantaneous method is applied to simulated acoustic propagation filed for testing range estimation performance. The result of range estimation according to the SNR for the underwater impulsive source with broadband spectrum is demonstrated. The spatial smoothing method is applied to suppress the effect of mutipath propagation by high frequency signal. The result of performance test for range estimation shows that the error rate is within 20% at the SNR above 10dB.

• Journal of Korean Tunnelling and Underground Space Association
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• v.4 no.4
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• pp.277-286
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• 2002

Most of the problems in dealing with the tunnel construction are the uncertainties and complexities of the stress conditions and rock strengths in ahead of the tunnel excavation. The limitations on the investigation technology, inaccessibility of borehole test in mountain area and public hatred also restrict our knowledge on the geologic conditions on the mountainous tunneling area. Nevertheless an extensive and superior geophysical exploration data is possibly acquired deep within the mountain area, with up to the tunnel locations in the case of alternative design or turn-key base projects. An appealing claim in the use of artificial neural networks (ANN) is that they give a more trustworthy results on our data based on identifying relevant input variables such as a little geotechnical information and biological learning principles. In this study, error back-propagation algorithm that is one of the teaching techniques of ANN is applied to presupposition on Rock Mass Ratings (RMR) for unknown tunnel area. In order to verify the applicability of this model, a 4km railway tunnel's field data are verified and used as input parameters for the prediction of RMR, with the learned pattern by error back propagation logics. ANN is one of basic methods in solving the geotechnical uncertainties and helpful in solving the problems with data consistency, but needs some modification on the technical problems and we hope our study to be developed in the future design work.

• Korean Chemical Engineering Research
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• v.49 no.1
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• pp.41-46
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• 2011

We investigated experimentally the properties of dust explosion through lycopodium particle clouds in a duct to provide the fundamental knowledge. Propagating dust flames in the vertical duct of 120 cm height and 12 cm square cross-section were observed by digital video camera and flame front is visualized using by PIV(Particle Image Velocimetry) system. As the result, when the same average dust concentration existed in the vertical duct, downward flame propagation was faster than the upward flame propagation, its rate increased with dust concentration in 300g/$m^3$. Post flames were caused by the ignition of unburned particles which flowed into the rear region of flame from passage between flame and duct wall, and they generated regardless of duct condition. Also, it was found that appearance frequency of post flames during dust flame propagations increased with the increase of dust concentration.