Journal of Electronic Science and Technology最新文献

This paper explores a way of deploying the classical algorithm named genetic algorithm (GA) with the memristor. The memristor is a type of circuit device with both characteristics of storage and computing, which provides the similarity between electronic devices and biological components, such as neurons, and the structure of the memristor-based array is similar to that of chromosomes in genetics. Besides, it provides the similarity to the image gray-value matrix that can be applied to image restoration with GA. Thus, memristor-based GA is proposed and the experiment about image restoration using memristor-based GA is carried out in this paper. And parameters, such as the size of initial population and the number of iterations, are also set different values in the experiment, which demonstrates the feasibility of implementing GA with memristors.¹

In this paper, a salp swarm algorithm (SSA) is proposed for solving the optimal power flow (OPF) problem of a power system with the incorporation of the thyristor-controlled series capacitor (TCSC). The proposed methodology is implemented for determining the optimal setting of control variables for the OPF problem, which includes the real power of generators buses, voltages of generator buses, reactive power injected by shunt compensators, and tap changing transformer ratios. The performance of the proposed approach is validated and tested on the standard IEEE-30 bus system and single-objective functions, including transmission line losses. The severity factor has been minimized and the result obtained is compared with the existing algorithms. Simulation results achieved with the proposed SSA approach demonstrate that it results in an effective and better solution for the OPF problem.

The knowledge graph (KG) that represents structural relations among entities has become an increasingly important research field for knowledge-driven artificial intelligence. In this survey, a comprehensive review of KG and KG reasoning is provided. It introduces an overview of KGs, including representation, storage, and essential technologies. Specifically, it summarizes several types of knowledge reasoning approaches, including logic rules-based, representation-based, and neural network-based methods. Moreover, this paper analyzes the representation methods of knowledge hypergraphs. To effectively model hyper-relational data and improve the performance of knowledge reasoning, a three-layer knowledge hypergraph model is proposed. Finally, it analyzes the advantages of three-layer knowledge hypergraphs through reasoning and update algorithms which could facilitate future research.

Total transmission plays an important role in efficiency improvement and wavefront control, and has made great progress in many applications, such as the optical film and signal transmission. Therefore, many traditional physical methods represented by transformation optics have been studied to achieve total transmission. However, these methods have strict limitations on the size of the photonic structure, and the calculation is complex. Here, we exploit deep learning to achieve this goal. In deep learning, the data-driven prediction and design are carried out by artificial neural networks (ANNs), which provide a convenient architecture for large dataset problems. By taking the transmission characteristic of the multi-layer stacks as an example, we demonstrate how optical materials can be designed by using ANNs. The trained network directly establishes the mapping from optical materials to transmission spectra, and enables the forward spectral prediction and inverse material design of total transmission in the given parameter space. Our work paves the way for the optical material design with special properties based on deep learning.

In the real-time scheduling theory, schedulability and synchronization analyses are used to evaluate scheduling algorithms and real-time locking protocols, respectively, and the empirical synthesis experiment is one of the major methods to compare the performance of such analyses. However, since many sophisticated techniques have been adopted to improve the analytical accuracy, the implementation of such analyses and experiments is often time-consuming. This paper proposes a schedulability experiment toolkit for multiprocessor real-time systems (SET-MRTS), which provides a framework with infrastructures to implement the schedulability and synchronization analyses and the deployment of empirical synthesis experiments. Besides, with well-designed peripheral components for the input and output, experiments can be conducted easily and flexibly on SET-MRTS. This demonstration further proves the effectiveness of SET-MRTS in both functionality and availability.

We demonstrated the ∼2.8-μm and ∼3.5-μm linearly polarized continuous wave (CW) laser outputs from a polarization-maintaining (PM) Er³⁺-doped fluoride fiber laser. By introducing a film polarizer into the cavity to select the laser polarization orientation, the ∼2.8-μm linearly polarized CW laser with a high polarization extinction ratio (PER) of ∼23 ​dB and maximum output power of 2.37 ​W was achieved under double-end pumping at 976 ​nm. By adding another 1981-nm pump source simultaneously, the ∼3.5-μm linearly polarized CW laser was also obtained, giving higher PER of ∼27 ​dB and maximum output power of 307 ​mW which is only limited by the available power of 1981-nm pump. To the best of our knowledge, this is the first report on a mid-infrared linearly polarized CW PM fiber laser in the >2.5-μm mid-infrared region. This work not only opens up opportunities for some new mid-infrared applications, but also provides a promising platform for developing high-stability and versatile mid-infrared laser sources.

The simulation mechanism of surface plasmon polaritons (SPPs) and localized surface plasmon (LSP) in different structures was studied, including the Au reflection grating (Au grating), Au substrate with dielectric ribbons grating (Au substrate grating), and pure electric conductor (PEC) substrate with Au ribbons grating (Au ribbons grating). And the characteristics of the Smith-Purcell radiation in these structures were presented. Simulation results show that SPPs are excited on the bottom surface of Au substrate grating grooves and LSP is stimulated on the upper surface both of Au ribbons grating grooves and Au grating grooves. Owing to the irreconcilable contradiction between optimizing the grating diffraction radiation efficiency and optimizing the SPPs excitation efficiency in the Au substrate grating, only 40-times enhancement of the radiation intensity was obtained by excited SPPs. However, the LSP enhanced structure overcomes the above problem and gains much better radiation enhancement ability, with about 200-times enhancement obtained in the Au ribbons grating and more than 500-times enhancement obtained in the Au grating. The results presented here provide a way of developing miniature, integratable, tunable, high-power-density radiation sources from visible light to ultraviolet rays at room temperature.

The R-2R resistor ladder is one of the best topologies for implementing compact-sized digital-to-analog converter (DAC) arrays in implantable neuro-stimulators. However, it has a limited resolution and considerable inter-channel variation due to component mismatches. To avoid losing analog information, we present sub-radix-2 DAC implemented by the R-βR resistor ladder in this paper. The digital successive approximation register (DSAR) algorithm corrects the transfer function of DACs based on their actual bit weights. Furthermore, a low-cost in situ adaptive bit-weight calibration (ABC) algorithm drives the analog output error between two DACs to zero by adjusting their bit weights automatically. The simulation results show that the proposed algorithm can calibrate the non-linear transfer function of each DAC and the gain error among multiple channels in the background.

According to current solar power research, both the generating unit's minimum start-up speed and power generation system's minimum flow rate for operation decrease with the increase in the impeller solidity. Ideally, a high solidity should be achieved, as this translates more power for a solar power system in the start-up and shut-down cycles. However, increasing the number of blades does not increase the impeller solidity; therefore, there is an optimal number of blades needed to achieve the preferred solidity. This paper begins by selecting the blade airfoil and then performs a theoretical analysis based on the relationship between the blade number and chord length. Experiments are conducted to measure the starting and stopping wind speeds and power characteristics for different numbers of blades. The results show that a maximum impeller solidity of 0.2862 is achieved, as well as the minimum flow speed at the start-up, and the maintenance of the solar chimney power generation system is optimized when there are four blades.

An increase in the cache capacity is usually accompanied by a decrease in access speed. To balance the capacity and performance of caches, this paper proposes an instruction cache (ICache) architecture based on divide-by-2 memory banks (D2MB-ICache). The control circuit and memory banks of D2MB-ICache work at the central processing unit (CPU) frequency and the divide-by-2 CPU frequency, respectively, so that the capacity of D2MB-ICache can be expanded without lowering its frequency. For sequential access, D2MB-ICache can output the required instruction from memory banks per CPU cycle by dividing the memory banks with a partition mechanism and employing an inversed clock technique. For non-sequential access, D2MB-ICache will fetch certain jump instructions one or two more times, so that it can catch the jump of the request address in time and send the correct instruction to the pipeline. Experimental results show that, compared with conventional ICache, D2MB-ICaches with the same and double capacities show a maximum frequency increase by an average of 14.6% and 6.8%, and a performance improvement by an average of 10.3% and 3.8%, respectively. Moreover, the energy efficiency of 64-kB D2MB-ICache is improved by 24.3%.