• 대한기계학회논문집A
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• 제33권11호
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• pp.1209-1216
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• 2009

Nano-micro hierarchical structures that nanoprotrusions were formed on the network-type microstructures were fabricated using an ultrasonic vibration forming technology. A commercial ultrasonic welding system was used to apply ultrasonic vibration energy. To evaluate the formability of ultrasonic vibration forming, nickel nano-micro hierarchical mold was fabricated and polyethylene (PE) was used as the replication material. The optimal molding time was 3.5 sec for PE nano-micro hierarchical structures. The molding process was conducted at atmospheric pressure.

• Computers and Concrete
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• 제10권1호
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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.

• 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.

• 한국정밀공학회지
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• 제22권10호
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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.

• 한국인공지능학회지
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• 제11권4호
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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.

• 방송공학회논문지
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• 제27권6호
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• pp.885-896
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• 2022

제조에서의 모터 불량은 향후 A/S 및 신뢰성에 중요한 역활을 한다. 모터의 불량 구분은 소리, 전류, 진동등의 측정을 통해 검출한다. 본 논문에서 사용한 데이터는 자동차 사이드미러 모터 기어박스의 소리를 사용하였다. 모터 소리는 3가지의 클래스로 구성되어 있다. 소리 데이터는 멜스펙트로그램을 통한 변환 과정을 거쳐 네트워크 모델에 입력된다. 본 논문에서는 불량 모터 구분 성능을 올리기 위한 데이터 증강, 클래스 불균형에 따는 다양한 데이터 재샘플링, 재가중치 조절, 손실함수의 변경, 표현 학습과 클래스 구분의 두 단계 분리 방법 등 다양한 방법을 적용하였으며, 추가적으로 커리큘럼 러닝 방법, 자기 스페이스 학습 방법 등을 Bidirectional LSTM Attention, Convolutional Recurrent Neural Network, Multi-Head Attention, Bidirectional Temporal Convolution Network, Convolution Neural Network 등 총 5가지 네트워크 모델을 통하여 비교하고, 모터 소리 구분에 최적의 구성을 찾을 수 있었다.

• 마이크로전자및패키징학회지
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• 제24권3호
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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.

• Steel and Composite Structures
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• 제49권1호
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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.

• 한국컴퓨터정보학회논문지
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• 제17권10호
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• pp.99-105
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• 2012

지그비는 저 전력, 저 비용, 낮은 데이터 전송 속도의 특징을 가지는 무선 개인 망(Low Rate Wireless Personal Area Networ: LR-WPAN)의 표준이다. 방향성 메시징 기법은 방송을 사용하는 무선 센서 네트워크에서 방향정보를 가지는 메시지를 특정 영역으로 전송함으로써 중복 메시지 전송을 줄이는 것을 통하여 에너지 효율성을 높이는 프로토콜이다. 본 논문에서는 에너지 효율성 향상을 위하여 방향성 메시징을 지원하도록 수정한 지그비를 사용하는 실험용 격자형 센서 네트워크를 설계하고 구현한다. 본 논문의 실험용 센서 네트워크는 방향성 정보를 사용하도록 수정한 ADV 메시지와 라우팅 관리 모듈을 지원하는 Nano-24 노드로 구성한다. 실험용 센서 네트워크의 에너지 효율 향상성은 실제 ADV 메시지 전송에 따른 실험 결과 분석울 통하여 나타내 보인다.

• International Journal of Concrete Structures and Materials
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• 제7권3호
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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.