• Title/Summary/Keyword: Nano-Network

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Modeling the compressive strength of cement mortar nano-composites

  • Alavi, Reza;Mirzadeh, Hamed
    • Computers and Concrete
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    • v.10 no.1
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    • pp.49-57
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    • 2012
  • Nano-particle-reinforced cement mortars have been the basis of research in recent years and a significant growth is expected in the future. Therefore, optimization and quantification of the effect of processing parameters and mixture ingredients on the performance of cement mortars are quite important. In this work, the effects of nano-silica, water/binder ratio, sand/binder ratio and aging (curing) time on the compressive strength of cement mortars were modeled by means of artificial neural network (ANN). The developed model can be conveniently used as a rough estimate at the stage of mix design in order to produce high quality and economical cement mortars.

A Environment Data Monitoring System of Exhibition using Sensor Network and Nano-Q+ (센서네트워크와 Nano-Q+를 이용한 전시물 환경 정보 모니터링 시스템)

  • Kim, Gang-Seok;Huh, Jee-Wan;Song, Wang-Cheol
    • Proceedings of the Korea Information Processing Society Conference
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    • 2007.05a
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    • pp.867-869
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    • 2007
  • TinyOS기반 무선센서노드를 사용한 실시간 계측 데이터 측정 및 제어기술은 유비쿼터스 센서 네트워크 분야에 주로 사용되어 왔으나 다양한 응용이나 신뢰성 있는 무선 네트워크 연구 개발에 한계가 있는 것으로 알려져 있다. 본 논문에서는 ATMega128L을 장착한 최소 8대 이상의 nano-24 센서 노드모듈과 메모리 용량이 매우 제한적인 무선 센서 노드에 적합하다고 평가받고 있는 Nano-Q+를 사용하여 전시물 주변의 환경 정보를 실시간 측정하는 시스템을 구현하였다. 전시물 주변의 센서들은 하나의 PAN Coordinator를 중심으로 Start-Mesh 네트워크를 구성하여 환경 정보를 측정한다. 환경 정보를 전송 하기 위해 측정된 계측 데이터를 센서 네트워크의 PAN Coordinator 노드로부터 환경 모니터링 서버로 효율적으로 전송하기 위한 TCP기반 전송 프로그램을 구현하였다. 실험 결과 센서 노드 수와 관련된 PAN의 크기 및 샘플링 주기에 상관없이 안정적으로 계측 데이터 수신이 이루어짐을 확인 하였다.

Developing Artificial Neurons Using Carbon Nanotubes Smart Composites (탄소나노튜브 스마트 복합소재를 이용한 인공뉴런 개발 연구)

  • Kang, In-Pil;Baek, Woon-Kyung;Choi, Gyeong-Rak;Jung, Joo-Young
    • 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.

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Determination of Deformation Behavior of Coating Layer on Electronic galvanized Sheet Steel using Nano-indentation and FEM (나노 인덴테이션 실험과 유한요소해석을 이용한 전기아연도금강판의 코팅층 체적 거동 결정)

  • Ko, Young-Ho;Lee, Jung-Min;Kim, Byung-Min
    • Journal of the Korean Society for Precision Engineering
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    • v.22 no.10 s.175
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    • pp.186-194
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    • 2005
  • This study was designed to investigate the mechanical properties of the coating layer on electronic galvanized sheet steel as a part of the ongoing research on the coated steel. Those properties were determined using nano-indentation, the finite element method, and artificial neural networks. First and foremost, the load-displacement curve (the loading-unloading curve) of coatings was derived from a nano-indentation test by CSM (continuous stiffness measurement) and was used to measure the elastic modulus and hardness of the coating layer. The properties derived were applied in FE simulations of a nano-indentation test, and the analytical results were compared with the experimental result. A numerical model for FE simulations was established for the coating layer and the substrate separately. Finally, to determine the mechanical properties of the coating, such as the stress-strain curve, functional equations of loading and unloading curves were introduced and computed using the neural networks method. The results show errors within $5\%$ in comparison with the load-displacement measured by a nano-indentation test.

Black Ice Detection Platform and Its Evaluation using Jetson Nano Devices based on Convolutional Neural Network (CNN)

  • Sun-Kyoung KANG;Yeonwoo LEE
    • Korean Journal of Artificial Intelligence
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    • v.11 no.4
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    • pp.1-8
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    • 2023
  • In this paper, we propose a black ice detection platform framework using Convolutional Neural Networks (CNNs). To overcome black ice problem, we introduce a real-time based early warning platform using CNN-based architecture, and furthermore, in order to enhance the accuracy of black ice detection, we apply a multi-scale dilation convolution feature fusion (MsDC-FF) technique. Then, we establish a specialized experimental platform by using a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Experimental results of a real-time black ice detection platform show the better performance of our proposed network model compared to conventional image segmentation models. Our proposed platform have achieved real-time segmentation of road black ice areas by deploying a road black ice area segmentation network on the edge device Jetson Nano devices. This approach in parallel using multi-scale dilated convolutions with different dilation rates had faster segmentation speeds due to its smaller model parameters. The proposed MsCD-FF Net(2) model had the fastest segmentation speed at 5.53 frame per second (FPS). Thereby encouraging safe driving for motorists and providing decision support for road surface management in the road traffic monitoring department.

A Study on the Classification of Fault Motors using Sound Data (소리 데이터를 이용한 불량 모터 분류에 관한 연구)

  • Il-Sik, Chang;Gooman, Park
    • Journal of Broadcast Engineering
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    • v.27 no.6
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    • pp.885-896
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    • 2022
  • Motor failure in manufacturing plays an important role in future A/S and reliability. Motor failure is detected by measuring sound, current, and vibration. For the data used in this paper, the sound of the car's side mirror motor gear box was used. Motor sound consists of three classes. Sound data is input to the network model through a conversion process through MelSpectrogram. In this paper, various methods were applied, such as data augmentation to improve the performance of classifying fault motors and various methods according to class imbalance were applied resampling, reweighting adjustment, change of loss function and representation learning and classification into two stages. In addition, the curriculum learning method and self-space learning method were compared through a total of five network models such as Bidirectional LSTM Attention, Convolutional Recurrent Neural Network, Multi-Head Attention, Bidirectional Temporal Convolution Network, and Convolution Neural Network, and the optimal configuration was found for motor sound classification.

On-chip Decoupling Capacitor for Power Integrity (전력 무결성을 위한 온 칩 디커플링 커패시터)

  • Cho, Seungbum;Kim, Sarah Eunkyung
    • Journal of the Microelectronics and Packaging Society
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    • v.24 no.3
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    • pp.1-6
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    • 2017
  • As the performance and density of IC devices increase, especially the clock frequency increases, power grid network integrity problems become more challenging. To resolve these power integrity problems, the use of passive devices such as resistor, inductor, and capacitor is very important. To manage the power integrity with little noise or ripple, decoupling capacitors are essential in electronic packaging. The decoupling capacitors are classified into voltage regulator capacitor, board capacitor, package capacitor, and on-chip capacitor. For next generation packaging technologies such as 3D packaging or wafer level packaging on-chip MIM decoupling capacitor is the key element for power distribution and delivery management. This paper reviews the use and necessity of on-chip decoupling capacitor.

Using nanotechnology for improving the mechanical behavior of spherical impactor in sport problem via complex networks

  • Bo Jin Cheng;Peng Cheng;Lijun Wang
    • Steel and Composite Structures
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    • v.49 no.1
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    • pp.31-45
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    • 2023
  • The network theory studies interconnection between discrete objects to find about the behavior of a collection of objects. Also, nanomaterials are a collection of discrete atoms interconnected together to perform a specific task of mechanical or/and electrical type. Therefore, it is reasonable to use the network theory in the study of behavior of super-molecule in sport nano-scale. In the current study, we aim to examine vibrational behavior of spherical nanostructured composite with different geometrical and materials properties. In this regard, a specific shear deformation displacement theory, classical elasticity theory and analytical solution to find the natural frequency of the spherical nano-composite sport structure equipment. The analytical results are validated by comparison to finite element (FE). Further, a detail comprehensive results of frequency variations are presented in terms of different parameters. It is revealed that the current methodology provides accurate results in comparison to FE results. On the other hand, different geometrical and weight fraction have influential role in determining frequency of the structure.

A Design and Implementation of modified ZigBee using the Directed-Messaging for Energy Efficiency Improvement (에너지 효율성 향상을 위하여 방향성 메시징을 사용하는 수정된 지그비의 설계 및 구현)

  • Khil, A-Ra
    • Journal of the Korea Society of Computer and Information
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    • v.17 no.10
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    • pp.99-105
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    • 2012
  • ZigBee is the low power, low cost and low data rate wireless personal area network(LR-WPAN) standard. The Directed-Messaging is the protocol which improves the energy efficiency through reducing the redundant message transmission by transmitting messages with directional information toward the specified sub-network area in wireless sensor network using broadcasting. In this paper, we design and implement the experimental grid sensor network using ZigBee modified by the Directed-Messaging for the energy efficiency improvement. The experimental sensor network in this paper is configured with Nano24 supporting the ADV message and the routing management module modified to use the directional information. The energy efficiency improvement of the experimental sensor-network by evaluating the experimental results according to transmitting ADV message.

An Artificial Neural Networks Model for Predicting Permeability Properties of Nano Silica-Rice Husk Ash Ternary Blended Concrete

  • Najigivi, Alireza;Khaloo, Alireza;zad, Azam Iraji;Rashid, Suraya Abdul
    • International Journal of Concrete Structures and Materials
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    • v.7 no.3
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    • pp.225-238
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    • 2013
  • In this study, a two-layer feed-forward neural network was constructed and applied to determine a mapping associating mix design and testing factors of cement-nano silica (NS)-rice husk ash ternary blended concrete samples with their performance in conductance to the water absorption properties. To generate data for the neural network model (NNM), a total of 174 field cores from 58 different mixes at three ages were tested in the laboratory for each of percentage, velocity and coefficient of water absorption and mix volumetric properties. The significant factors (six items) that affect the permeability properties of ternary blended concrete were identified by experimental studies which were: (1) percentage of cement; (2) content of rice husk ash; (3) percentage of 15 nm of $SiO_2$ particles; (4) content of NS particles with average size of 80 nm; (5) effect of curing medium and (6) curing time. The mentioned significant factors were then used to define the domain of a neural network which was trained based on the Levenberg-Marquardt back propagation algorithm using Matlab software. Excellent agreement was observed between simulation and laboratory data. It is believed that the novel developed NNM with three outputs will be a useful tool in the study of the permeability properties of ternary blended concrete and its maintenance.