• Structural Engineering and Mechanics
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• v.55 no.6
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• pp.1121-1137
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• 2015

High powered computers and engineering computer systems allow designers to routinely simulate complex physical phenomena. The presented work deals with the analysis of two finite element method optimization techniques (First Order Method-FOM and Subproblem Approximation Method-SAM) implemented in the individual Design Optimization module in the Ansys software to analyze the behavior of real problems. A design optimization is a difficult mathematical process, intended to find the minimum or maximum of an objective function, which is mostly based on iterative procedure. Using optimization techniques in engineering designs requires detailed knowledge of the analyzed problem but also an ability to select the appropriate optimization method. The methods embedded in advanced computer software are based on different optimization techniques and their efficiency is significantly influenced by the specific character of a problem. The efficiency, robustness and accuracy of the methods are studied through strictly convex two-dimensional optimization problem, which is represented by volume minimization of two bars' plane frame structure subjected to maximal vertical displacement limit. Advantages and disadvantages of the methods are described and some practical tips provided which could be beneficial in any efficient engineering design by using an optimization method.

• Structural Engineering and Mechanics
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• v.71 no.1
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• pp.57-63
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• 2019

During the last two decades, much joint research regarding vibration based methods has been done, leading to developing various algorithms and techniques. These algorithms and techniques can be divided into modal methods and signal methods. Although modal methods have been widely used for health monitoring and damage detection, signal methods due to higher efficiency have received considerable attention in various fields, including aerospace, mechanical and civil engineering. Signal-based methods are derived directly from the recorded responses through signal processing algorithms to detect damage. According to different signal processing techniques, signal-based methods can be divided into three categories including time domain methods, frequency domain methods, and time-frequency domain methods. The frequency domain methods are well-known and interest in using them has increased in recent years. To determine dynamic behaviours, to identify systems and to detect damages of bridges, different methods and algorithms have been proposed by researchers. In this study, a new algorithm to detect seismic damage in the bridge's piers is suggested. To evaluate the algorithm, an analytical model of a bridge with simple spans is used. Based on the algorithm, before and after damage, the bridge is excited by a sine force, and the piers' responses are measured. The dynamic specifications of the bridge are extracted by Power Spectral Density function. In addition, the Least Square Method is used to detect damage in the bridge's piers. The results indicate that the proposed algorithm can identify the seismic damage effectively. The algorithm is output-only method and measuring the excitation force is not needed. Moreover, the proposed approach does not need numerical models.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.2
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• pp.278-286
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• 2019

Conventional methods for selecting jamming techniques in electronic warfare are based on libraries in which a list of jamming techniques for radar signals is recorded. However, the choice of jamming techniques by the library is limited when modified signals are received. In this paper, we propose a method to predict the jamming technique for radar signals by using deep learning methods. Long short-term memory(LSTM) is a deep running method which is effective for learning the time dependent relationship in sequential data. In order to determine the optimal LSTM model structure for jamming technique prediction, we test the learning parameter values that should be selected, such as the number of LSTM layers, the number of fully-connected layers, optimization methods, the size of the mini batch, and dropout ratio. Experimental results demonstrate the competent performance of the LSTM model in predicting the jamming technique for radar signals.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.342-347
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• 2022

On Cloud, the important data of the user that is protected on remote servers can be accessed via internet. Due to rapid shift in technology nowadays, there is a swift increase in the confidential and pivotal data. This comes up with the requirement of data security of the user's data. Data is of different type and each need discrete degree of conservation. The idea of data security data science permits building the computing procedure more applicable and bright as compared to conventional ones in the estate of data security. Our focus with this paper is to enhance the safety of data on the cloud and also to obliterate the problems associated with the data security. In our suggested plan, some basic solutions of security like cryptographic techniques and authentication are allotted in cloud computing world. This paper put your heads together about how machine learning techniques is used in data security in both offensive and defensive ventures, including analysis on cyber-attacks focused at machine learning techniques. The machine learning technique is based on the Supervised, UnSupervised, Semi-Supervised and Reinforcement Learning. Although numerous research has been done on this topic but in reference with the future scope a lot more investigation is required to be carried out in this field to determine how the data can be secured more firmly on cloud in respect with the Machine Learning Techniques and cryptographic methods.

• Asian-Australasian Journal of Animal Sciences
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• v.20 no.2
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• pp.283-294
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• 2007

Conventional culture-based methods of enumerating rumen microorganisms (bacteria, archaea, protozoa, and fungi) are being rapidly replaced by nucleic acid-based techniques which can be used to characterise complex microbial communities without incubation. The foundation of these techniques is 16S/18S rDNA sequence analysis which has provided a phylogenetically based classification scheme for enumeration and identification of microbial community members. While these analyses are very informative for determining the composition of the microbial community and monitoring changes in population size, they can only infer function based on these observations. The next step in functional analysis of the ecosystem is to measure how specific and, or, predominant members of the ecosystem are operating and interacting with other groups. It is also apparent that techniques which optimise the analysis of complex microbial communities rather than the detection of single organisms will need to address the issues of high throughput analysis using many primers/probes in a single sample. Nearly all the molecular ecological techniques are dependant upon the efficient extraction of high quality DNA/RNA representing the diversity of ruminal microbial communities. Recent reviews and technical manuals written on the subject of molecular microbial ecology of animals provide a broad perspective of the variety of techniques available and their potential application in the field of animal science which is beyond the scope of this treatise. This paper will focus on nucleic acid based molecular methods which have recently been developed for studying major functional groups (cellulolytic bacteria, protozoa, fungi and methanogens) of microorganisms that are important in nutritional studies, as well as, novel methods for studying microbial diversity and function from a genomics perspective.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.13 no.5
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• pp.38-50
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• 2010

Expressways are often upgraded by widening the lanes and/or by reshaping the roads to be more linear for faster travel time. However, during the process of improving the route of the expressways, the areas where the old expressways used to be are often unused and abandoned. When these neglected sites are left alone, they often become dump sites causing pollution and impacting the surrounding environment. Therefore, it is important to restore the abandoned expressway sites to its full natural beauty. In this study, the abandoned expressway at the Soksa interchange in Pyungchang county, located in Kangwon province was studied for establishing the model of ecological restoration project. Considering the characteristics of the site, the target flora species was chosen to be Quercus species and the target fauna species as amphibians. After the target species were carefully chosen, each species' habitat requirements were studied in order to figure out the appropriate methods toward habitat restoration specifically for these species. In addition, to determine the most efficient method toward restoration of abandoned expressways, the study utilized the planting hole techniques, the crack techniques, and the colonization techniques. In terms of the spatial organization, public education program is incorporated at the main entrance area and the programs for experimenting, and developing vegetation and habitat restoration techniques are placed in the vicinity. In the master plan-to provide natural ecosystem at the site-ASCON (asphalt concrete) was removed first, then plans for restoration including species' habitat restoration were established. Furthermore, the project included plans for improving water quality polluted through non-point source considering the surrounding nearby road and farm lands. Finally, the study established a planning process that will experimentally apply to the abandoned expressway restoration method. In the future, there will be a continuous monitoring of the methods applied to verify if the restoration methods are effective. Also, new restoration techniques should be available according to a variety of abandoned expressways' characteristics.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.1
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• pp.55-65
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• 2020

With the development of display hardware, image interpolation techniques have been used in various fields such as image zooming and medical imaging. Traditional image interpolation methods, such as bi-linear interpolation, bi-cubic interpolation and edge direction-based interpolation, perform interpolation in the spatial domain. Recently, interpolation techniques in the discrete cosine transform or wavelet domain are also proposed. Using these various existing interpolation methods and machine learning, we propose decision tree classification-based image interpolation methods. In other words, this paper is about the method of adaptively applying various existing interpolation methods, not the interpolation method itself. To obtain the decision model, we used Weka's J48 library with the C4.5 decision tree algorithm. The proposed method first constructs attribute set and select classes that means interpolation methods for classification model. And after training, interpolation is performed using different interpolation methods according to attributes characteristics. Simulation results show that the proposed method yields reasonable performance.

• Geophysics and Geophysical Exploration
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• v.23 no.2
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• pp.72-88
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• 2020

Recent rapid advances in computer hardware performance have led to relatively low computational costs, increasing the number of applications of machine-learning techniques to geophysical problems. In particular, deep-learning techniques are gaining in popularity as the number of cases successfully solving complex and nonlinear problems has gradually increased. In this paper, applications of seismic data denoising methods using deep-learning techniques are introduced and investigated. Depending on the type of attenuated noise, these studies are grouped into denoising applications of coherent noise, random noise, and the combination of these two types of noise. Then, we investigate the deep-learning techniques used to remove the corresponding noise. Unlike conventional methods used to attenuate seismic noise, deep neural networks, a typical deep-learning technique, learn the characteristics of the noise independently and then automatically optimize the parameters. Therefore, such methods are less sensitive to generalized problems than conventional methods and can reduce labor costs. Several studies have also demonstrated that deep-learning techniques perform well in terms of computational cost and denoising performance. Based on the results of the applications covered in this paper, the pros and cons of the deep-learning techniques used to remove seismic noise are analyzed and discussed.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.15 no.3
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• pp.481-490
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• 2002

The purpose of this paper is to evaluate the efficiency of various solution techniques and propose new efficient solution techniques for space trusses. Solution techniques used in this study are three load control methods (Newton-Raphson Method, modified Newton-Raphson Method, Secant-Newton Method), two load-displacement control methods(Arc-length Method, Work Increment Control Method) and three combined load-displacement control methods(Combined Arc-length Method I , Combined Arc-length MethodⅡ, Combined Work Increment Control Method). To evaluate the efficiency of these solution techniques, we must examine accuracy of their solutions, convergences and computing times of numerical examples. The combined load-displacement control methods are the most efficient in the geometric nonlinear solution techniques and in tracing post-buckling behavior of space truss. The combined work increment control method is the most efficient in tracing the buckling load of spate trusses with high degrees of freedom.

• Journal of the Korea Fashion and Costume Design Association
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• v.23 no.1
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• pp.47-59
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• 2021

This study aims to provide inspiration and methods for menswear design by analyzing elements for men's fashion using flower images. The results are as follows: Men's fashion types with flower images were categorized as classic tailored, casual tailored, casual wear, sports-outdoor. The order of frquency was casual tailored, casual, classic tailored, and sports outdoor. For the classic tailored type, the flower images are related with an X-line silhouette, and the arrangement methods, such as a scattered patterns, one-point patterns, and surface techniques, such as printing and embroidery were used, and similar color or monochromatic schemes appeared sequentially. For the casual tailored type, the flower images are related to an H-line silhouette, arrangement methods such as a scattered pattern, panel pattern, and surface techniques, such as print, embroidery, and jacquard were used, similar color and accent color schemes appeared sequentially. For the casual type, the flower images are related to H-line and Y-line silhouettes, and arrangement methods, such as a scattered pattern, all-round continuous pattern, and panel pattern, and surface techniques, such as print, jacquard, embroidery, and patchwork were used, similar color and contrast color schemes appeared sequentially. For the sports outdoor type, the flower image were related to A-line and H-line silhouettes, arrangement methods, such as a scattered pattern and all-round continuous pattern, and surface techniques, such as print and jacquard were used, monochromatic scheme and contrast color schemes appeared sequentially. Therefore, the flower images in men's fashion were applied to various design elements, and displayed an interesting result, different from conventional design approach.