Blended Oral English Teaching Based on Core Competence Training Model

The traditional mixed oral English teaching model has many obvious shortcomings, such as the inability to correct the students’ oral pronunciation errors and feed them back in time, which leads to the slow improvement of students’ English learning level. For this reason, this paper proposes a guided teaching model based on core literacy. According to the structure of the oral English mixed teaching model, determine the application plan of the oral English mixed teaching model, design the development environment, obtain the corpus, design the oral training model, extract the oral features, identify the wrong pronunciation and correct it in time, clarify the evaluation purpose, obtain preliminary evaluation indicators, reduce evaluation indicators and determine indicator weights, obtain indicator feature information, generate fuzzy rules, obtain fuzzy matrices, achieve quantitative evaluation, and synthesize all evaluation scores to construct a result vector matrix to realize the study of mixed spoken language teaching mode. Research shows that the mixed teaching method is effective and feasible and can effectively improve the accuracy of the evaluation results of the mixed oral English teaching model.

Quantum inspiration to build a neural model based on the Day-Ahead Market of the Polish Power Exchange

The article is an attempt of the methodological approach to the proposed quantum-inspired method of neural modeling of prices quoted on the Day-Ahead Market operating at TGE S.A. In the proposed quantum-inspired neural model it was assumed, inter alia, that it is composed of 12 parallel Perceptron ANNs with one hidden layer. Moreover, it was assumed that weights and biases as processing elements are described by density matrices, and the values flowing through the Artificial Neural Network of Signals are represented by qubits. Calculations checking the correctness of the adopted method and model were carried out with the use of linear algebra and vector-matrix calculus in MATLAB and Simulink environments. The obtained research results were compared to the results obtained from the neural model with the use of a comparative model.

From Photon to Oganesson: Lie Algebra Realization of the Standard Model Extending over the Periodic Table

As reported in a series of previous PIRT conferences, a direct SU(3) structural realization of the Standard Model has been developed based upon Marius Sophus Lie’s original Norwegian Ph.D. thesis Over en Classe Geometriske Transformationer from 1871 (and thus due for a most deserved 150-year anniversary). It elucidates how “the theory of main tangential curves can be brought back to that of rounded curves”, anticipating a coherent linear representation of the elementary particles instead of the rotational chosen since they were considered point-like and amorphous when they many years later entered the stage. Under these premises the Standard Model has built a magnificent, undoubtedly true but congested multi-particle system whereas the Lie continuous transformation element, the partial derivative ’straight line of length equal to zero’ outlines an isotropic vector matrix lattice of crystallographic Killing root space diagram A3 form which from the Nucleon and inwards can backtrack the Standard Model geometrically, as well as continue outward iterating to a space-filling solid state R3×SO(3) wave-packet complex tessellating the whole periodic table with electron shells and subshells, isotope spectrum, neutron captures, radiative channels, oxidation states, molecular binding sites etc. in successive layers also including the Lanthanides in the sixth period and the Actinides in the seventh, in which now the concluding Oganesson has been reached in perfectly well-built saturated noble gas shape and condition.

A Parallel Algorithm for Dividing Octonions

The article presents a parallel hardware-oriented algorithm designed to speed up the division of two octonions. The advantage of the proposed algorithm is that the number of real multiplications is halved as compared to the naive method for implementing this operation. In the synthesis of the discussed algorithm, the matrix representation of this operation was used, which allows us to present the division of octonions by means of a vector–matrix product. Taking into account a specific structure of the matrix multiplicand allows for reducing the number of real multiplications necessary for the execution of the octonion division procedure.

A Computer Model of the Spread of the Pandemic and its Analysis

The paper describes and analyzes a mathematical model of the variable state of the incidence of epidemic diseases, which is of great importance for determining the quantity of vaccines and antiviral drugs to be produced. The information model according to the system of differential equations of the spread of the pandemic is illustrated in a structural diagram. The model is presented in a vector-matrix form and the state of equilibrium of the model in the spatial state is proved.The model of the spread of the pandemic was developed, whose implementation with a Matlab software package resulted in obtaining the curves of variation of the state. The developed computer model of the incidence of epidemic diseases can be used to make a projection of the number of infected people, as well as intensity of the process of disseminating information and ideas in the community.

Mitigating State-Drift in Memristor Crossbar Arrays for Vector Matrix Multiplication

In this Chapter, we review the recent progress on resistance drift mitigation techniques for resistive switching memory devices (specifically memristors) and its impact on the accuracy in deep neural network applications. In the first section of the chapter, we investigate the importance of soft errors and their detrimental impact on memristor-based vector–matrix multiplication (VMM) platforms performance specially the memristance state-drift induced by long-term recurring inference operations with sub-threshold stress voltage. Also, we briefly review some currently developed state-drift mitigation methods. In the next section of the chapter, we will discuss an adaptive inference technique with low hardware overhead to mitigate the memristance drift in memristive VMM platform by using optimization techniques to adjust the inference voltage characteristic associated with different network layers. Also, we present simulation results and performance improvements achieved by applying the proposed inference technique by considering non-idealities for various deep network applications on memristor crossbar arrays. This chapter suggests that a simple low overhead inference technique can revive the functionality, enhance the performance of memristor-based VMM arrays and significantly increases their lifetime which can be a very important factor toward making this technology as a main stream player in future in-memory computing platforms.

Mathematical model of an asynchronous machine in real coordinates of state

A mathematical description of an asynchronous machine with unreduced parameters in the space of real phase coordinates is given. When studying an asynchronous motor in a dual power supply system with controlled converters in rotor and stator circuits, there is a need for an AM model without parameters, in which the processes in the stator and rotor circuits will correspond to reality in magnitude. In addition, such a model is necessary when analyzing the energy parameters of the whole electric drive controlled by the rotor. To observe real processes in the stator and rotor, the model should be designed in separate spatial coordinate systems. In reference books for AM with a wound rotor (WR), as a rule, the real parameters (resistances, currents and voltages) of the stator and rotor and the voltage reduction coefficient ( ke ) are given. Bringing currents and resistances (inductances) is carried out by coefficients ki=ke and kr=ke2 respectively. Let's consider the description of the machine without reduction of parameters. For the convenience of further consideration, let's introduce the general value of the mutual inductance. We obtain an equation for the electromagnetic moment in real coordinates. Let's design a model in the MATLAB dynamic modeling environment using vector-matrix representation. Matrix algebraic operations with vector variables are implemented by Matlab Fn blocks, which are the calls to userdefined functions described in the form of M-files. The content of Matlab Fn functions is considered. On the model, the processes of starting of an AM with a phase rotor of AK-52-6 type were. Shows the graphs of start-up transients. The processes in the obtained model coincided with the results of modeling of this engine in the model with the coordinates reduced to the rotor. Thus, the energy processes described by this model correspond to the processes to the model with the given parameters, and the processes of currents and flux linkages changing of the stator and rotor are real. The model designed in the MATLAB/Simulink dynamic modeling environment can be used to study double-powered asynchronous electric drives.

The challenges of modern computing and new opportunities for optics

In recent years, the explosive development of artificial intelligence implementing by artificial neural networks (ANNs) creates inconceivable demands for computing hardware. However, conventional computing hardware based on electronic transistor and von Neumann architecture cannot satisfy such an inconceivable demand due to the unsustainability of Moore’s Law and the failure of Dennard’s scaling rules. Fortunately, analog optical computing offers an alternative way to release unprecedented computational capability to accelerate varies computing drained tasks. In this article, the challenges of the modern computing technologies and potential solutions are briefly explained in Chapter 1. In Chapter 2, the latest research progresses of analog optical computing are separated into three directions: vector/matrix manipulation, reservoir computing and photonic Ising machine. Each direction has been explicitly summarized and discussed. The last chapter explains the prospects and the new challenges of analog optical computing.

A Novel Ultra-Low Power 8T SRAM-Based Compute-in-Memory Design for Binary Neural Networks

We propose a novel ultra-low-power, voltage-based compute-in-memory (CIM) design with a new single-ended 8T SRAM bit cell structure. Since the proposed SRAM bit cell uses a single bitline for CIM calculation with decoupled read and write operations, it supports a much higher energy efficiency. In addition, to separate read and write operations, the stack structure of the read unit minimizes leakage power consumption. Moreover, the proposed bit cell structure provides better read and write stability due to the isolated read path, write path and greater pull-up ratio. Compared to the state-of-the-art SRAM-CIM, our proposed SRAM-CIM does not require extra transistors for CIM vector-matrix multiplication. We implemented a 16 k (128 × 128) bit cell array for the computation of 128× neurons, and used 64× binary inputs (0 or 1) and 64 × 128 binary weights (−1 or +1) values for the binary neural networks (BNNs). Each row of the bit cell array corresponding to a single neuron consists of a total of 128 cells, 64× cells for dot-product and 64× replicas cells for ADC reference. Additionally, 64× replica cells consist of 32× cells for ADC reference and 32× cells for offset calibration. We used a row-by-row ADC for the quantized outputs of each neuron, which supports 1–7 bits of output for each neuron. The ADC uses the sweeping method using 32× duplicate bit cells, and the sweep cycle is set to 2N−1+1, where N is the number of output bits. The simulation is performed at room temperature (27 °C) using 45 nm technology via Synopsys Hspice, and all transistors in bitcells use the minimum size considering the area, power, and speed. The proposed SRAM-CIM has reduced power consumption for vector-matrix multiplication by 99.96% compared to the existing state-of-the-art SRAM-CIM. Furthermore, because of the decoupled reading unit from an internal node of latch, there is no feedback from the reading unit, with read static noise, and margin-free results.