87,615 research outputs found
Implementation of Arithmetic Mean Method on Determination of Peak Junction Temperature of Semiconductor Device on Printed Circuit Board
High reliability users of microelectronic devices have been derating junction temperature and other critical stress parameters to improve device reliability and extend operating life. The junction temperature is what really matters for component functionality and reliability. This study presents a useful analysis on mathematical approach which can be implemented to predict thermal behavior in Integrated Circuit (IC). The problem could be modeled as heat conduction equation. In this study, numerical approaches based on implicit scheme and Arithmetic Mean (AM) iterative method will be applied to solve the governing heat conduction equation. From the numerical results obtained, it shows that AM method solves the governing heat conduction equation with minimum number of iterations and fastest computational time compared to the Gauss-Seidel (GS) method. It is in design phase when simulations and modeling are carried out to ensure high performance and reliability. The availability of thermal analysis tool for maximum temperature prediction would be of great value to designers of power device ICs
Implementation of Arithmetic Mean Method on Determination of Peak Junction Temperature of Semiconductor Device on Printed Circuit Board
High reliability users of microelectronic devices have been derating junction temperature and other critical stress parameters to improve device reliability and extend operating life. The junction temperature is what really matters for component functionality and reliability. This study presents a useful analysis on mathematical approach which can be implemented to predict thermal behavior in Integrated Circuit (IC). The problem could be modeled as heat conduction equation. In this study, numerical approaches based on implicit scheme and Arithmetic Mean (AM) iterative method will be applied to solve the governing heat conduction equation. From the numerical results obtained, it shows that AM method solves the governing heat conduction equation with minimum number of iterations and fastest computational time compared to the Gauss-Seidel (GS) method. It is in design phase when simulations and modeling are carried out to ensure high performance and reliability. The availability of thermal analysis tool for maximum temperature prediction would be of great value to designers of power device ICs
A Survey of Prediction and Classification Techniques in Multicore Processor Systems
In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems
A practical degradation based method to predict long-term moisture incursion and colour change in high power LEDs
The effect of relative humidity on LEDs and how the moisture incursion is associated to the color shift is studied. This paper proposes a different approach to describe the lumen degradation of LEDs due to the long-term effects of humidity. Using the lumen degradation data of different types of LEDs under varying conditions of relative humidity, a humidity based degradation model (HBDM) is developed. A practical estimation method from the degradation behaviour is proposed to quantitatively gauge the effect of moisture incursion by means of a humidity index. This index demonstrates a high correlation with the color shift indicated by the LED's yellow to blue output intensity ratio. Physical analyses of the LEDs provide a qualitative validation of the model, which provides good accuracy with longer periods of moisture exposure. The results demonstrate that the HBDM is an effective indicator to predict the extent of the long-term impact of humidity and associated relative color shift
Stochastic RUL calculation enhanced with TDNN-based IGBT failure modeling
Power electronics are widely used in the transport and energy sectors. Hence, the reliability of these power electronic components is critical to reducing the maintenance cost of these assets. It is vital that the health of these components is monitored for increasing the safety and availability of a system. The aim of this paper is to develop a prognostic technique for estimating the remaining useful life (RUL) of power electronic components. There is a need for an efficient prognostic algorithm that is embeddable and able to support on-board real-time decision-making. A time delay neural network (TDNN) is used in the development of failure modes for an insulated gate bipolar transistor (IGBT). Initially, the time delay neural network is constructed from training IGBTs' ageing samples. A stochastic process is performed for the estimation results to compute the probability of the health state during the degradation process. The proposed TDNN fusion with a statistical approach benefits the probability distribution function by improving the accuracy of the results of the TDDN in RUL prediction. The RUL (i.e., mean and confidence bounds) is then calculated from the simulation of the estimated degradation states. The prognostic results are evaluated using root mean square error (RMSE) and relative accuracy (RA) prognostic evaluation metrics
Context-Awareness Enhances 5G Multi-Access Edge Computing Reliability
The fifth generation (5G) mobile telecommunication network is expected to
support Multi- Access Edge Computing (MEC), which intends to distribute
computation tasks and services from the central cloud to the edge clouds.
Towards ultra-responsive, ultra-reliable and ultra-low-latency MEC services,
the current mobile network security architecture should enable a more
decentralized approach for authentication and authorization processes. This
paper proposes a novel decentralized authentication architecture that supports
flexible and low-cost local authentication with the awareness of context
information of network elements such as user equipment and virtual network
functions. Based on a Markov model for backhaul link quality, as well as a
random walk mobility model with mixed mobility classes and traffic scenarios,
numerical simulations have demonstrated that the proposed approach is able to
achieve a flexible balance between the network operating cost and the MEC
reliability.Comment: Accepted by IEEE Access on Feb. 02, 201
Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization
We propose a data-driven method to solve a stochastic optimal power flow
(OPF) problem based on limited information about forecast error distributions.
The objective is to determine power schedules for controllable devices in a
power network to balance operation cost and conditional value-at-risk (CVaR) of
device and network constraint violations. These decisions include scheduled
power output adjustments and reserve policies, which specify planned reactions
to forecast errors in order to accommodate fluctuating renewable energy
sources. Instead of assuming the uncertainties across the networks follow
prescribed probability distributions, we assume the distributions are only
observable through a finite training dataset. By utilizing the Wasserstein
metric to quantify differences between the empirical data-based distribution
and the real data-generating distribution, we formulate a distributionally
robust optimization OPF problem to search for power schedules and reserve
policies that are robust to sampling errors inherent in the dataset. A simple
numerical example illustrates inherent tradeoffs between operation cost and
risk of constraint violation, and we show how our proposed method offers a
data-driven framework to balance these objectives
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