• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권8호
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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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• 제4권1호
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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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• 제어로봇시스템학회 2001년도 ICCAS
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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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• 제6권4호
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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.

• 한국조경학회지
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• 제37권2호
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• pp.36-46
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• 2009

이 연구의 목적은 녹화를 위한 인공지반이 구조물에 미치는 영향을 분석하는 것이다. 인공지반 녹화 시 고려해야 할 기술적 사항은 건물에 미치는 하중과 식물의 생육에 관한 것이다. 인공지반이 구조물에 미치는 영향에 대한 연구는 거의 이루어지지 않고 있다. 본 연구에서는 펄라이트와 자연토양이 혼합된 인공지반이 구조물에 미치는 하중으로 인한 영향을 분석하였다. 인공지반이 구조물에 미치는 하중은 인공지반이 포화되었을 때 최대가 되며, 강우강도가 인공지반의 침투능을 증가할 때 인공지반이 포화된다. 인공지반의 포화 여부를 판정하기 위하여 강우 강도는 도시배수시설물 설계에 이용되는 10년 빈도 10분 강우 강도를 이용하였고, 침투능은 정수위투수계수 측정법을 이용하여 산정하였다. 인공토 및 혼합토는 그 비율에 따라 투수계수가 변하며, 특히 다짐 정도에 따라 변동이 큰 것으로 나타났으나, 대부분의 경우 재현기간 10년의 10분 강우강도를 초과하지는 않았다. 따라서 인공지반의 포화단위중량을 구조물 설계의 설계하중으로 적용하여야 함을 알 수 있다.

• 정보통신설비학회논문지
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• 제9권4호
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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.

• 해양환경안전학회:학술대회논문집
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• 해양환경안전학회 2007년도 추계학술발표회
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• pp.9-14
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• 2007

한국 남해안 거제도 앞바다에 구축된 인공용승구조물로 인하여 발생하는 유동 변화를 파악하기 위해 ADCP(Acoustic Doppler Current Profiler)를 이용한 정선관측을 2003년 하계 및 추계에 실시하였다. 관측 결과 하계에는 수심 약 $30{\sim}40$ m를 기준으로 상층과 하층의 유동분포가 상이한 경계수심이 존재하였으며, 추계에는 이러한 현상이 3층 구조로 나타났다. 또한 연직유동성분은 수심과 지역에 따라 상승류와 하강류가 번갈아 분포하였으며 이러한 현상은 관측범위를 확장하였을 때도 유사한 유동분포를 보이고 있다. 한편 ADCP의 수평유동성분을 이용하여 계산된 연직전단(vertical shear) 및 상대와도(relative vorticity)는 상승류가 발생하는 지역에서 연직전단은 큰 값을 나타내었고, 상대와도는 +값으로 상승류의 방향을 나타내었다. 이러한 결과는 유동성분의 상승류 분포역과 유사하게 나타났다.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회A
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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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• 제29권2호
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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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• 제18권3호
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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.