• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.2948-2963
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• 2015

In the field of computer vision, visual surveillance systems have recently become an important research topic. Growth in this area is being driven by both the increase in the availability of inexpensive computing devices and image sensors as well as the general inefficiency of manual surveillance and monitoring. In particular, the ultimate goal for many visual surveillance systems is to provide automatic activity recognition for events at a given site. A higher level of understanding of these activities requires certain lower-level computer vision tasks to be performed. So in this paper, we propose an intelligent activity recognition model that uses a structure learning method and a classification method. The structure learning method is provided as a K2-learning algorithm that generates Bayesian networks of causal relationships between sensors for a given activity. The statistical characteristics of the sensor values and the topological characteristics of the generated graphs are learned for each activity, and then a neural network is designed to classify the current activity according to the features extracted from the multiple sensor values that have been collected. Finally, the proposed method is implemented and tested by using PETS2013 benchmark data.

• Biomaterials and Biomechanics in Bioengineering
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• v.4 no.1
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• pp.33-44
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• 2019

This paper presents a novel structural composition for artificial bone scaffolds with an appropriate biocompatibility and biodegradability capability. To achieve this aim, carbon nanotubes, due to their prominent mechanical properties, high biocompatibility with the body and its structural similarities with the natural bone structure are selected in component of the artificial bone structure. Also, according to the piezoelectric properties of natural bone tissue, the barium titanate, which is one of the biocompatible material with body and has piezoelectric property, is used to create self-healing ability. Furthermore, due to the fact that, most of the bone tissue is consists of hydroxyapatite, this material is also added to the artificial bone structure. Finally, polycaprolactone is used in synthetic bone composition as a proper substrate for bone growth and repair. To demonstrate, performance of the presented composition, the mechanical behaviour of the bone scaffold is simulated using ANSYS Workbench software and three dimensional finite element modelling. The obtained results are compared with mechanical behaviour of the natural bone and the previous bone scaffold compositions. The results indicated that, the modulus of elasticity, strength and toughness of the proposed composition of bone scaffold is very close to the natural bone behaviour with respect to the previous bone scaffold compositions and this composition can be employed as an appropriate replacement for bone implants.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.88.2-88
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• 2001

In this paper Author introduce a new field named Artificial Life and its main directions of research. That is the research of evolutionary robot and artificial brain. Then author explored the advanced scientific thought hidden in them. Furthermore, the author tries intuitively to show a new type of control that is heuristically raised from artificial life research. It could be named as evolutionary control. This type of control is more like human body´s structure, and it is self-organized.

• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.262-265
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• 2018

Deep learning, a sub-field of machine learning changing the prospects of artificial intelligence (AI) because of its recent advancements and application in various field. Deep learning deals with algorithms inspired by the structure and function of the brain called artificial neural networks. This works reviews basic architecture and recent advancement of deep structured learning. It also describes contemporary applications of deep structured learning and its advantages over the treditional learning in artificial interlligence. This study is useful for the general readers and students who are in the early stage of deep learning studies.

• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.2
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• pp.36-46
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• 2009

The objective of this study is to analyze the effect of artificial soil ground on a structure. When the artificial soil ground is planted, the technical factors to be considered will be the load for buildings and the growth of plants. There are no current studies of the effect of artificial soil ground on a structure and this study will analyze the load effects of artificial soil ground, which mixes both pearlite and natural soil on structures. The load affecting the structures due to artificial soil ground will be maximized when the artificial soil ground becomes saturated, and which would occur when the rainfall intensity exceeds the infiltration capacity of the artificial soil ground. In order to determine whether the artificial soil ground has reached saturation or not, a 10 years frequency and 10 minutes rainfall intensity which is used for in urban drain design, is utilized. The hydraulic conductivity of artificial soil and mixed soil has been changed depending on the proportion of the mix, It has a range of fluctuation in the degree of hardening, in particular, but does not exceed the 10 minutes rainfall intensity over 10 years frequency in the most cases. Therefore, it would be efficient to apply the saturated unit weight of artificial soil ground as the design load of a structure.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.9 no.4
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• pp.164-167
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• 2010

In this paper, an artificial transmission line of triangular-patch type with slots is proposed. This artificial transmission line is useful for miniaturizing the electrical lengths of conventional transmission lines. Also, this structure is easy to regulate the characteristic impedance and electrical lengths. The electrical lengths and the characteristic impedances of the proposed artificial transmission line with some parameters are researched and summarized The proposed artificial transmission line could be useful for compact designs for most of the passive microwave components. The optional-artificial transmission line is fabricated and measured at 2.45GHz.

• Proceedings of KOSOMES biannual meeting
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• 2007.11a
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• pp.9-14
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• 2007

To illusσ'ate the variation of current around artificial upwelling structure which is located in the South sea of Korea, current measurements using ADCP (Acoustic Doppler Current Profiler) during neap and spring tides were carried out on 27th July(summer)， 14th October and 30th November(Autumn), 2006. Current after the set up of artificial upwelling structure were shown different in the upper and lower layer, the boundary between the upper and lower layer was at $27{\sim}30m$ depth in summer. And the boundary layer was formed structure of three layer in Autumn. Upwelling and downwelling flow were occurred around the seamount， and these vertical flows were connected from surface to bottom The distribution of vertical shear and relative vorticity support the vertical flow around the seamount. The strength of vertical shear was higher and the direction of relative vorticity was anticlockwise (+) around the upwelling area.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.136-141
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• 2007

This paper introduces an artificial neuron which is a nano composite continuous sensor. The continuous nano sensor is fabricated as a thin and narrow polymer film sensor that is made of carbon nanotubes composites with a PMMA or a silicone matrix. The sensor can be embedded onto a structure like a neuron in a human body and it can detect deteriorations of the structure. The electrochemical impedance and dynamic strain response of the neuron change due to deterioration of the structure where the sensor is located. A network of the long nano sensor can form a structural neural system to provide large area coverage and an assurance of the operational health of a structure without the need for actuators and complex wave propagation analyses that are used with other methods. The artificial neuron is expected to effectively detect damage in large complex structures including composite helicopter blades and composite aircraft and vehicles.

• Structural Engineering and Mechanics
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• v.29 no.2
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• pp.223-235
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• 2008

This paper focuses on the application of artificial neural networks (ANN) for optimal design of tensegrity grid as light-weight roof structures. A tensegrity grid, 2 m ${\times}$ 2 m in size, is fabricated by integrating four single tensegrity modules based on half-cuboctahedron configuration, using galvanised iron (GI) pipes as struts and high tensile stranded cables as tensile elements. The structure is subjected to destructive load test during which continuous monitoring of the prestress levels, key deflections and strains in the struts and the cables is carried out. The monitored structure is analyzed using finite element method (FEM) and the numerical model verified and updated with the experimental observations. The paper then explores the possibility of applying ANN based on multilayered feed forward back propagation algorithm for designing the tensegrity grid structure. The network is trained using the data generated from a finite element model of the structure validated through the physical test. After training, the network output is compared with the target and reasonable agreement is found between the two. The results demonstrate the feasibility of applying the ANNs for design of the tensegrity structures.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.3
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• pp.474-484
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• 2017

This paper studies the applicability of an efficient numerical model based on artificial neural networks (ANNs) to predict the dynamic responses of the wing structure of an airplane due to atmospheric turbulence in the time domain. The turbulence velocity is given in the form of a stationary Gaussian random process with the von Karman power spectral density. The wing structure is modeled by a classical beam considering bending and torsional deformations. An unsteady vortex-lattice method is applied to estimate the aerodynamic pressure distribution on the wing surface. Initially, the trim condition is obtained, then structural dynamic responses are computed. The numerical solution of the wing structure's responses to a random turbulence profile is used as a training data for the ANN. The current ANN is a three-layer network with the output fed back to the input layer through delays. The results from this study have validated the proposed low-cost ANN model for the predictions of dynamic responses of wing structures due to atmospheric turbulence. The accuracy of the predicted results by the ANN was discussed. The paper indicated that predictions for the bending moments are more accurate than those for the torsional moments of the wing structure.