There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

A diverse spectrum of technology drivers such as improved thermal barriers, higher efficiency thermoelectric energy conversion, phase-change memory, heat-assisted magnetic recording, thermal management of nanoscale electronics, and nanoparticles for thermal medical therapies are motivating studies of the applied physics of thermal transport at the nanoscale. This review emphasizes developments in experiment, theory, and computation in the past ten years and summarizes the present status of the field. Interfaces become increasingly important on small length scales. Research during the past decade has extended studies of interfaces between simple metals and inorganic crystals to interfaces with molecular materials and liquids with systematic control of interface chemistry and physics. At separations on the order of ∼1 nm, the science of radiative transport through nanoscale gaps overlaps with thermal conduction by the coupling of electronic and vibrational excitations across weakly bonded or rough interface...

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Nanoscale thermal transport. II. 2003–2012

The smart grid concept continues to evolve and various methods have been developed to enhance the energy efficiency of the electricity infrastructure. Demand Response (DR) is considered as the most cost-effective and reliable solution for the smoothing of the demand curve, when the system is under stress. DR refers to a procedure that is applied to motivate changes in the customers' power consumption habits, in response to incentives regarding the electricity prices. In this paper, we provide a comprehensive review of various DR schemes and programs, based on the motivations offered to the consumers to participate in the program. We classify the proposed DR schemes according to their control mechanism, to the motivations offered to reduce the power consumption and to the DR decision variable. We also present various optimization models for the optimal control of the DR strategies that have been proposed so far. These models are also categorized, based on the target of the optimization procedure. The key aspects that should be considered in the optimization problem are the system's constraints and the computational complexity of the applied optimization algorithm.

A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms

Introduction to heat transfer

A technique based on the sensitivity of the output to input waveform is presented for accurate propagation of delay information through a gate for the purpose of static timing analysis (STA) in the presence of noise. Conventional STA tools represent a waveform by its arrival time and slope. However, this is not an accurate way of modeling the waveform for the purpose of noise analysis. The key contribution of our work is the development of a method that allows efficient propagation of equivalent waveforms throughout the circuit. Experimental results demonstrate higher accuracy of the proposed sensitivity-based gate delay propagation technique, SGDP, compared to the best of existing approaches. SGDP is compatible with the current level of gate characterization in conventional ASIC cell libraries, and as a result, it can be easily incorporated into commercial STA tools to improve their accuracy.

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Modeling and Propagation of Noisy Waveforms in Static Timing Analysis

In this paper, we propose an efficient predefined structured sparsity-based ex-situ training framework for a hybrid CMOS-memristive neuromorphic hardware for deep neural network to significantly lower the power consumption and computational complexity and improve scalability. The structure is verified on a wide range of datasets including MNIST handwritten recognition, breast cancer prediction, and mobile health monitoring. The results of this study show that compared to its fully connected version, the proposed structure provides significant power reduction while maintaining high classification accuracy.

SpRRAM: A Predefined Sparsity Based Memristive Neuromorphic Circuit for Low Power Application

The shrinking of transistor geometries as well as the increasing complexity of integrated circuits, significantly aggravate nonlinear design behavior. This demands accurate and fast circuit simulation to meet the design quality and time-to-market constraints. The existing circuit simulators which utilize lookup tables and/or closed-form expressions are either slow or inaccurate in analyzing the nonlinear behavior of designs with billions of transistors. To address these shortcomings, we present NN-PARS, a neural network (NN) based and parallelized circuit simulation framework with optimized event-driven scheduling of simulation tasks to maximize concurrency, according to the underlying GPU parallel processing capabilities. NN-PARS replaces the required memory queries in traditional techniques with parallelized NN-based computation tasks. Experimental results show that compared to a state-of-the-art current-based simulation method, NN-PARS reduces the simulation time by over two orders of magnitude in large circuits. NN-PARS also provides high accuracy levels in signal waveform calculations, with less than 2% error compared to HSPICE.

NN-PARS: A Parallelized Neural Network Based Circuit Simulation Framework

Since the development of semiconductor technologies, exascale computing and its associated applications have required increasing degrees of efficiency. Semiconductor-transistor-based circuits (STbCs) have struggled in increasing the GHz frequency. Emerg-ing as an alternative to STbC, the superconducting electrons (SCE) technology promises higher-speed clock frequencies at ultra-low power consumption. The rapid single flux quantum (RSFQ) circuits have a theoretical potential for three orders of magnitude reduc-tion in power while operating at clock frequencies higher than 100 GHz. Although the security in semiconductor technology has been extensively researched and developed, the security design in the superconducting field requires field demands attention. In this paper, C-SAR is presented that aims to protect the superconducting circuit electronics from Boolean satisfiability (SAT) based attacks. The SAT attack is an attack that can break all the existing combinational logic locking techniques. C-SAR can immunize against SAT attacks by increasing the key search space and prolonging the clock cycles of attack inputs. Even in the worst case of C-SAR, in face of S-SAT a specially designed SAT attack, C-SAR can also soar the attack cost exponentially with key bits first, then linearly with the length of camouflaged DFF array. We have shown in this work that the cost of C-SAR is manageable as it only linearly increases as a function of key bits.

C-SAR: SAT Attack Resistant Logic Locking for RSFQ Circuits

Plug-in electric vehicles (PEVs) are considered the key to reducing fossil fuel consumption and an important part of the smart grid. The plug-in electric vehicle-to-grid (V2G) technology in the smart grid infrastructure enables energy flow from PEV batteries to the power grid so that the grid stability is enhanced and the peak power demand is shaped. PEV owners will also benefit from V2G technology, as they will be able to reduce energy cost through proper PEV charging and discharging scheduling. Moreover, power regulation service (RS) reserves have been playing an increasingly important role in modern power markets. It has been shown that by providing RS reserves, the power grid achieves a better match between energy supply and demand in presence of volatile and intermittent renewable energy generation. This article starts with the problem of PEV charging under dynamic energy pricing, properly taking into account the degradation of battery state-of-health (SoH) during V2G operations as well as RS provisioning. An overall optimization throughout the whole parking period is proposed for the PEV and an adaptive control framework is presented to dynamically update the optimal charging/discharging decision at each hour to mitigate the effect of RS tracking error.As more and more PEVs are being plugged into the power grid, the control or management issue of PEV charging arises, since mass unregulated charging processes of PEVs may result in degradation of power quality and damage utility equipments and customer appliances. To solve this problem, this article also presents an SoH-aware charging aggregator design, which decides the control sequences of a group of PEVs. An energy storage system is used in the charging aggregator to do a peak power shaving, and future parking PEVs are properly taken care of. Experimental results show that the proposed optimal charging algorithm minimizes the combination of electricity cost and battery aging cost in the RS provisioning power market. Experimental results also show that the introduction of charging aggregator can significantly reduce the peak power consumption caused by simultaneous PEV charging.

https://dl.acm.org/doi/pdf/10.1145/3086507

Shahin Nazarian

Papers

Modeling and Propagation of Noisy Waveforms in Static Timing Analysis

SpRRAM: A Predefined Sparsity Based Memristive Neuromorphic Circuit for Low Power Application

NN-PARS: A Parallelized Neural Network Based Circuit Simulation Framework

C-SAR: SAT Attack Resistant Logic Locking for RSFQ Circuits

Optimal Control of PEVs with a Charging Aggregator Considering Regulation Service Provisioning