• Smart Structures and Systems
• /
• v.30 no.3
• /
• pp.245-253
• /
• 2022

A two-layer beam consisting of an elastoplastic layer and a functional layer made of shape memory alloy (SMA) TiNi is considered. Constitutive relations for SMA are set by a microstructural model capable to calculate strain increment produced by arbitrary increments of stress and temperature. This model exploits the approximation of small strains. The equations to calculate the variations of the strain and the internal variables are based on the experimentally registered temperature kinetics of the martensitic transformations with an account of the crystallographic features of the transformation and the laws of equilibrium thermodynamics. Stress and phase distributions over the beam height are calculated by steps, by solving on each step the boundary-value problem for given increments of the bending moment (or curvature) and the tensile force (or relative elongation). Simplifying Bernoulli's hypotheses are applied. The temperature is considered homogeneous. The first stage of the numerical experiment is modeling of preliminary deformation of the beam by bending or stretching at a temperature corresponding to the martensitic state of the SMA layer. The second stage simulates heating and subsequent cooling across the temperature interval of the martensitic transformation. The curvature variation depends both on the total thickness of the beam and on the ratio of the layer's thicknesses.

• ETRI Journal
• /
• v.45 no.5
• /
• pp.795-810
• /
• 2023

Unmanned aerial vehicles (UAV) and ground vehicles (UGV) require advanced video analytics for various tasks, such as moving object detection and segmentation; this has led to increasing demands for these methods. We propose a zero-shot video object segmentation method specifically designed for UAV and UGV applications that focuses on the discovery of moving objects in challenging scenarios. This method employs a background memory model that enables training from sparse annotations along the time axis, utilizing temporal modeling of the background to detect moving objects effectively. The proposed method addresses the limitations of the existing state-of-the-art methods for detecting salient objects within images, regardless of their movements. In particular, our method achieved mean J and F values of 82.7 and 81.2 on the DAVIS'16, respectively. We also conducted extensive ablation studies that highlighted the contributions of various input compositions and combinations of datasets used for training. In future developments, we will integrate the proposed method with additional systems, such as tracking and obstacle avoidance functionalities.

• Smart Structures and Systems
• /
• v.2 no.4
• /
• pp.357-363
• /
• 2006

Equations describing deformation defects, damage accumulation, and fracture condition have been suggested. Analytical and numerical solutions have been obtained for defects produced by a shear in a fixed direction. Under cyclic loading the number of cycles to failure well fits the empirical Koffin-Manson law. The developed model is expanded to the case of the micro-plastic deformation, which accompanies martensite accommodation in shape memory alloys. Damage of a shape memory specimen has been calculated for two regimes of loading: a constant stress and cyclic variation of temperature across the interval of martensitic transformations, and at a constant temperature corresponding to the pseudoelastic state and cyclic variation of stress. The obtained results are in a good qualitative agreement with available experimental data.

• Transactions on Electrical and Electronic Materials
• /
• v.15 no.1
• /
• pp.37-40
• /
• 2014

A novel memory is reported, in which $Ge_2Sb_2Te_5$ (GST) has been used as a floating gate. The threshold voltage was shifted due to the phase transition of the GST layer, and the hysteretic behavior is opposite to that arising from charge trapping. Finite Element Modeling (FEM) was adapted, and a new simulation program was developed using c-interpreter, in order to analyze the small shift of threshold voltage. The results show that GST undergoes a partial phase transformation during the process of RESET or SET operation. A large $V_{TH}$ shift was observed when the thickness of the GST layer was scaled down from 50 nm to 25 nm. The novel 1 transistor PCM (1TPCM) can achieve a faster write time, maintaining a smaller cell size.

• Nuclear Engineering and Technology
• /
• v.29 no.6
• /
• pp.433-443
• /
• 1997

In this work, an analytic approach to the dependability of software in the operational phase is suggested with special attention to the hardware fault effects on the software behavior : The hardware faults considered are memory faults and the dependability measure in question is the reliability. The model is based on the simple reliability theory and the graph theory which represents the software with graph composed of nodes and arcs. Through proper transformation, the graph can be reduced to a simple two-node graph and the software reliability is derived from this graph. Using this model, we predict the reliability of an application software in the digital system (ILS) in the nuclear power plant and show the sensitivity of the software reliability to the major physical parameters which affect the software failure in the normal operation phase. We also found that the effects of the hardware faults on the software failure should be considered for predicting the software dependability accurately in operation phase, especially for the software which is executed frequently. This modeling method is particularly attractive for the medium size programs such as the microprocessor-based nuclear safety logic program.

• Journal of Communications and Networks
• /
• v.16 no.4
• /
• pp.436-446
• /
• 2014

The high intensity of research and modeling in fields of mathematics, physics, biology and chemistry requires new computing resources. For the big computational complexity of such tasks computing time is large and costly. The most efficient way to increase efficiency is to adopt parallel principles. Purpose of this paper is to present the issue of parallel computing with emphasis on the analysis of parallel systems, the impact of communication delays on their efficiency and on overall execution time. Paper focuses is on finite algorithms for solving systems of linear equations, namely the matrix manipulation (Gauss elimination method, GEM). Algorithms are designed for architectures with shared memory (open multiprocessing, openMP), distributed-memory (message passing interface, MPI) and for their combination (MPI + openMP). The properties of the algorithms were analytically determined and they were experimentally verified. The conclusions are drawn for theory and practice.

• ETRI Journal
• /
• v.28 no.3
• /
• pp.347-354
• /
• 2006

The characteristic variation of 3-dimensional (3-D) solenoid-type embedded inductors is investigated. Four different structures of a 3-D inductor are fabricated by using a low-temperature co-fired ceramic (LTCC) process, and their s-parameters are measured between 50 MHz and 5 GHz. The circuit model parameters of each building block are optimized and extracted using the partial element equivalent circuit method and an HSPICE circuit simulator. Based on the model parameters, the characteristics of the test structures such as self-resonant frequency, inductance, and quality (Q) factor are analyzed, and predictive modeling is applied to the structures composed of a combination of the modeled building blocks. In addition, characteristic variations of the 3-D inductors with different structures using extracted building blocks are also investigated. This approach can provide a characteristic estimation of 3-D solenoid embedded inductors for structural variations.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.13 no.3
• /
• pp.200-205
• /
• 2000

This paper describes the structure modeling and operation characteristics of MFMIS(metal-ferroelectric-metal-insulator-semiconductor) device using the Tsuprem4 which is a semiconductor device tool by Avanti. MFMIS device is being studied for nonvolatile memory application at various semiconductor laboratory but it is difficult to fabricate and analyze MFMIS devices using the semiconductor simulation tool: Tsuprem4, medici and etc. So the new library and new materials parameters for adjusting ferroelectric material and platinum electrodes in the tools are studied. In this paper structural model and operation characteristics of MFMIS devices are measured, which can be easily adopted to analysis of MFMIS device for nonvolatile memory device application.

• Journal of Integrative Natural Science
• /
• v.11 no.4
• /
• pp.204-208
• /
• 2018

Comparison of different optimizer performance in photovoltaic power modeling using artificial neural deep learning techniques is described in this paper. Six different deep learning optimizers are tested for Long-Short-Term Memory networks in this study. The optimizers are namely Adam, Stochastic Gradient Descent, Root Mean Square Propagation, Adaptive Gradient, and some variants such as Adamax and Nadam. For comparing the optimization techniques, high and low fluctuated photovoltaic power output are examined and the power output is real data obtained from the site at Mokpo university. Using Python Keras version, we have developed the prediction program for the performance evaluation of the optimizations. The prediction error results of each optimizer in both high and low power cases shows that the Adam has better performance compared to the other optimizers.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.19 no.11
• /
• pp.1254-1264
• /
• 2008

This paper suggests an extended memory polynomial model that improves accuracy in modeling memory effects of RF power amplifiers(PAs), and verifies effectiveness of the suggested method. The extended memory polynomial model includes cross-terms that are products of input terms that have different delay values to improve the limited accuracy of basic memory polynomial model that includes the diagonal terms of Volterra kernels. The complexity of the memoryless model, memory polynomial model, and the suggested model are compared. The extended memory polynomial model is represented with a matrix equation, and the Volterra kernels are extracted using least square method. In addition, the structure of digital predistorter and digital signal processing(DSP) algorithm based on the suggested model and indirect learning method are proposed to implement a digital predistortion linearization. To verify the suggested model, the predicted output of the model is compared with the measured output for a 10W GaN HEMT RF PA and 30 W LDMOS RF PA using 2.3 GHz WiBro input signal, and adjacent-channel power ratio(ACPR) performance with the proposed digital predistortion is measured. The proposed model increases model accuracy for the PAs, and improves the linearization performance by reducing ACPR.