# Computational Simulation Analysis for Physical Identifiable Teaching Technology

Authors: Aihui Du
Journal: Security and Communication Networks
 (2022)

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## Abstract

In order to improve the identifiable teaching effect of physics, this paper studies the identifiable teaching technology of physics combined with computer simulation analysis technology, changes the traditional physics teaching method, and studies the identifiable teaching effect of computer simulation algorithm. The advantages are obvious; that is, it overcomes scaling, translation, and rotation and does not require a lot of time to learn samples, etc. In addition, this paper combines the computer simulation analysis technology and the actual needs of physics teaching to construct an intelligent and identifiable teaching system. The experimental research results show that the physics identifiable teaching system based on computer simulation analysis proposed in this paper can play an important role in physics teaching.


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## Body

## 1. Introduction

Information teaching software is a software, tool, and resource platform that can help teachers make better use of information technology to solve teaching problems under the theoretical guidance of information technology and curriculum integration. In the process of organizing and implementing teaching activities, the key to the integration of information technology and physics courses lies in how to apply information-based teaching software to achieve the learning objectives of the course, complete effective teaching, and achieve the purpose of cultivating students’ information literacy and scientific thinking.Teachers and educational researchers are increasingly focusing on the integration of information technology and physics course instruction as current educational technology advances. Furthermore, current educational technology is employed in the teaching of middle school physics, and it is used in numerous teaching connections such as lesson preparation, concept explanation, experimental inquiry, and teacher assessment, therefore disrupting the conventional teaching mode [1]. The objective of teaching physics exercises is to help students break through difficult concepts, reinforce essential concepts, and improve their ability to analyze and solve issues. Simultaneously, exercise class hours account for around one-third of total class hours in the daily teaching process, and they play a significant role in middle school physics instruction [2]. The old teaching style is no longer able to address the exercise demands of both instructors and pupils. Furthermore, it is difficult to increase the teaching effectiveness of the practice course using this kind of teaching style. It is tough for professors to teach and pupils to learn since there are so many questions. As a result, modern educational technology must be applied to daily physics exercises and physical problem solving in order to assist teachers in changing the traditional teaching method, which is limited to teaching exercises, and assisting students in better understanding, analyzing, and solving physical problems, thereby improving classroom teaching efficiency and cultivating students’ core literacy in physics [3].This paper combines the computer simulation analysis technology to carry out the research on the identifiable teaching technology of physics, changes the traditional physics teaching method, and improves the quality of modern physics teaching.


## 2. Related Work

With the continuous development of modern educational technology, teachers and educational researchers pay more and more attention to the integration of information technology and physics curriculum teaching. Modern educational technology is used in middle school physics teaching and is implemented in lesson preparation, concept explanation, experimental inquiry, and teaching evaluation. In many teaching links, the traditional teaching mode is changed. The teaching of physics exercises has the purpose of breaking through difficulties, strengthening key points, and improving students’ ability to analyze and solve problems. The class hours of exercises are generally not less than one-third of the total class hours in the daily teaching process. In the whole middle school, physics teaching occupies an important position [4]. The traditional lecture-based teaching method can no longer meet the needs of teachers and students for practice courses, and it is also difficult to improve the teaching efficiency of practice courses. Apply modern educational technology to daily physics exercises and solve physics problems, help teachers change the traditional teaching method that is limited to teaching exercises, help students better understand, analyze, and solve physics problems, thereby improving classroom teaching efficiency, to cultivate the core literacy of students in physics [5].Physics is the theoretical foundation of all scientific and engineering degrees since it is an experimental science. The development of fundamental experimental abilities in learners via the study of physics is also important for the development of inventive thinking, innovative ability, and scientific quality [6]. College physics experiment may successfully foster students’ practical ability and innovative capacity as a required fundamental course for students majoring in science and engineering in colleges and universities. The course covers a broad variety of topics and includes a substantial amount of material. The course itself contains theoretical physics knowledge and experimental abilities, as well as methodical and extensive experimental operation exercises that help students build a rigorous scientific mentality and a serious work style, among other things [7]. There are discrepancies in the teaching of physics experiments in universities in various nations at home and abroad due to the diverse educational levels of different countries and the different learning styles of learners. For example, according to education expert Jin Enpei, most schools in the United States do not provide experimental courses as independent courses, but rather schedule experimental courses with equivalent material immediately after the corresponding university physics theoretical courses [8]. In physics experimental courses, Japanese colleges and universities enjoy a comparatively large degree of independence. Colleges and universities may offer a variety of experimental courses, depending on the needs of their institutions. They do not need tight scheduling or the creation of a consistent teaching curriculum. As a result, students have access to experimental courses with more flexibility to choose [9].Differentiated teaching is a teaching activity organized according to students’ interests and talents in teaching activities [10]. Differentiated teaching has experienced five stages in turn: individualized education, genius education, multiple intelligences teaching, inclusive education, and differentiated education. The birth of the idea of differentiated teaching is based on the research of actual teaching needs and weighs the advantages and disadvantages of comprehensive personalized teaching, genius teaching, multiple intelligence teaching, inclusive education, and other teaching methods and the advantages and disadvantages of teaching ideas, and gradually formed the teaching idea of differential teaching [11]. Along with the adjustment of the national education policy, the education reform has also undergone repeated reorganization and regeneration, and it is still the basis for looking forward to the school in the 21st century [12]. Literature [13] summarizes its five-year-old “differentiated teaching” practical experience: in teaching, applying different levels of teaching strategies according to students’ differences and enhancing exchanges and cooperation among students are the keys to solving problems. Literature [14] specifically analyzes the important factors that affect the implementation of differentiated teaching strategies. The school finally made a comparative analysis of the results of different teaching practices and found that students have greatly improved in many aspects such as learning ability, attitude, and performance [15].


## 3. Identifiable Teaching Technology

Edge detection is the basis of contour extraction. Edges exist between adjacent regions with different gray values and are the result of discontinuous gray values. Moreover, the edge is the most basic feature of the shape of the object. In the physical knowledge recognition, edge detection is an important step in character recognition. The correctness and reliability of its detection are related to the accuracy of the recognition, so the research on edge detection technology is very important.The edge is generally the consequence of a discontinuous gray value; therefore, it may be found using the derivation approach. The first-order and second-order derivatives may both be utilized to identify the edge in most cases. Roof-like (including pulse-like) and step-like edges are the most common. The step-like edge appears when the pixel values are sufficiently varied, and the roof-like edge appears when the gray value gradient reaches a specified threshold.As shown in Figure1 with a step-like edge, when the image changes from black to white at the edge, the first derivative of the corresponding image (Figure 2) has an upward step, and other positions are 0. It can be seen from this that taking the first derivative of the image and setting the reading value can detect whether there is a step-like edge. In the same way, the second-order derivative is performed on the image. If there is an upward pulse in the step-up region of the first-order derivative, and there is a downward pulse in the step-down region of the first-order derivative, it is possible to detect whether there is a step-like edge. If an edge exists, the image edge is located at the zero-crossing point between the two steps.Figure 1
Common edges. (a) Step-like edge 1, (b) step-like edge 2, (c) star-topped edge, and (d) pulse-like edge.
(a)(b)(c)(d)Figure 2
First and second derivatives corresponding to common edges.The form of the first derivative and second derivative of the pulse-like edge is similar to that of the roof in Figure2, and the distance between the rising edge and the falling edge is reduced.The extraction of the character outline, that is, the detection of the character edge, is required for the identification of the physical knowledge number in physical knowledge. Many external adverse circumstances typically interfere with the actual application of physical knowledge identification, affecting the outcomes of edge detection. As a result, in real applications, some picture processing is required to mitigate the detrimental impacts of external unfavorable elements such as noise. The following are the stages in edge detection:The edge operator can check the pixel change rate (including change direction) of the pixel neighborhood. The edge template is usually a method of convolution based on the directional derivative template. The following is an edge template with a size of 3 × 3, which can detect edges in four directions, 0, 45, 90, and 135 degrees [16]:(1)−101−101−101,111000−1−1−1,011000−1−1−1,11010−10−1−1.0-degree template, 90-degree template, 45-degree template, and 135-degree template.For the imagef, if the pixel f (i, j) is the center point, and the corresponding position of the operator and the pixel is multiplied by the corresponding number in the operator, the accumulated value is the output value corresponding to the template.All edge templates are applied one by one to each pixel in the picture, and the template with the highest output value is determined. The point’s edge direction determines the template’s direction. There is no edge at this moment if the output value of each template is near to 0. There is no reliable edge orientation for that location if each template output value is about equal.The Sobel edge detection operator is as follows:(2)−1−2−1000121,10−1+20−210−1.The two convolution kernels of the Sobel detection operator perform convolution operations on the pixels in the image(3)G1=fi−1,j+1+2fi,j+1+fi+1,j+1−fi−1,j−1−2fi,j−1−fi+1,j+1,G2=fi−1,j−1+2fi−1,j+fi−1,j+1−fi+1,j−1−2fi+1,j−fi+1,j+1.We take the larger valueMaxG1,G2 of the output values among them. If the output value MaxG1,G2 is greater than the corresponding reading value, the point is the pixel point at the edge position; otherwise, it is a smooth area [17].In addition, in order to detect edges in a specific direction, other direction operators can be used as follows:(4)0−1−210−1210,21010−10−1−2.This operator can detect pixels at 45-degree or 135-degree edges.The principle of the Roberts detection operator is very simple. It uses the difference between two adjacent pixels in the diagonal direction. It is an operator that uses a local difference operator to find edges in an image. Its formula is as follows:(5)Gi,j=fi,j−fi+1,j+12+fi+1,j−fi,j+121/2,Gi,j represents the value of the pixel in the image after processing, and f is the pixel value of the pixel in the original image. The operator can be expressed as follows:(6)01−10100−1.The Roberts edge detection operator is used to operate each pixel in the image in turn, and the value of the corresponding operator in each pixel in the image is calculated, and then, the value of each pixel is summed and then squared. According to the set threshold, the pixel point at the edge position is obtained.Analyzing the actual effect of image processing, the Roberts edge detection operator is relatively accurate in positioning the edge in the image, is sensitive to noise to a certain extent, and is easily affected by snowflake noise.The acquired pictures in practical applications frequently include a certain degree of noise owing to the weather and other conditions, as well as faults in the acquisition equipment itself. As a result, it is crucial to discover a detection technique that can not only precisely identify the edge but also efficiently overcome picture noise [18].The finite difference of the first-order partial derivatives is used to compute the gradient’s magnitude and direction. The gradient magnitude picture and the gradient direction image are created by computing the gradient value and direction of each pixel in the image generated after smoothing with the filter. The intensity of the edge at the location (x, y) on the picture and the direction perpendicular to the edge are computed as follows:(7)Bx,y=gx′2x,y+gy′2x,y,θx,y=arctangx′2x,ygy′2x,y.Among them,(8)gx′2x,y=gx,y+1−8x,y+gx+1,y+1−gx+1,y2,gy′2x,y=gx,y−8x+1,y+gx,y+1−gx+1,y+12.Nonmaximum suppression of gradient amplitude: after calculating the amplitude and direction of the gradient, a large number of gradient amplitude and direction values are obtained, and what is useful for edge detection is the point with the largest local change inB(x, y). Therefore, in order to detect edge points, nonmaximum values must be suppressed (set to zero for those that are not edge points). The process of nonmaximum suppression of the gradient amplitude is as follows: the direction of the gradient is divided into 0 degrees, 60 degrees, and 120 degrees.The amplitude value corresponding to the center point is compared with the neighboring points in three different directions, and the point with the largest amplitude value is kept unchanged, and the other values are set to 0. Nonmaximum suppression of gradient magnitudes can output only a thin line when outputting edges.Each point on the plane image only includes two coordinate points, and the Cartesian coordinate system (x, y) is usually used to represent the position of the pixel of the digital image. For example, a grayscale digital image of MN is saved in the form of a matrix in the computer(9)f0,0⋯f0,N−1⋮fM−1,0⋯fM−1,N−1.The position of the image can also be represented by the polar coordinate systemρ,θ and can be converted to each other. If it is assumed that the midpoint x0,y0 of the Cartesian coordinate system is the origin of the polar coordinate system, the midpoint (x, y) of the Cartesian coordinate system can be expressed as polar coordinates as follows:(10)r=x−x02+y−y02,θ=arctany−y0x−x0.The Cartesian coordinate system corresponds to the complex plane, and the Cartesian coordinate pointx0,y0 is the origin of the complex plane, which can be expressed as a complex number(11)z=x−x0+iy−y0=rcosθ+isinθ=reiθ.Whenω=lnz=lnr+iθ, it is the corresponding equation for converting Cartesian coordinates to log-polar coordinates. Then, the corresponding equation for converting the polar coordinate system to the logarithmic coordinate system is as follows:(12)pr,θ=lnr,qr,θ=θ.When the image in Cartesian space is rotated or scaled, that is, if the image is scaledn times and rotated by an angle of β, the corresponding polar coordinate r,θ changes to n∗r,β+θ. At this point, the log polar coordinates change from pr,θ=lnr, qr,θ=θ to(13)pn∗r,β+θ=lnn∗r=lnr+lnn,qn∗r,β+θ=β+θ.Many features of representing an image from polar coordinate space to logarithmic coordinate space are obvious.(1) When a digital image in Cartesian coordinate space is translated, the image’s corresponding logarithmic polar coordinate in the logarithmic polar coordinate space remains unchanged; (2) when a digital image in Cartesian coordinate space is scaled, the image’s corresponding translation of the horizontal position in the log-polar coordinate space is expressed as the image’s log-polar coordinate [19]; (3) when a digital image in Cartesian coordinate space is scaled, the image’s corresponding translation.These coordinate spaces’ transformation properties allow analyzing the effects of translation, tilt, and scale in character and object image identification much simpler.The shape of the object can be described by edge lines, and the edge lines can be replaced by certain discrete points, so some point sets can be used to represent the shape of the object. The basic idea of shape context is to usep = {p1, p2, …, pn} to represent n points evenly distributed on the edge of the object’s outline, and the positional relationship between each point and the remaining n-l points can form n-1 vectors, as shown in Figure 3 below. The n-1 vectors corresponding to each point describe the relative position of the point in the entire shape. These vector sets can well reflect the shape, size, and direction of the object and are called shape descriptors. For example, for the shape of an object, by taking its contour edge line, a certain number of discrete points p = {pl, p2, …, pn} with uniform spacing are collected, which can be expressed as follows:(14)Shape=s11⋯s1i⋯s1n⋮sj1⋯sji⋯sjn⋮sn1⋯sni⋯snn.Figure 3
(a) Character A; (b) the vector from a point to the rest.
(a)(b)To count the number of points in each bin, the histogram of this point can be obtained by the following definition. The point setp = {p1, p2, …, pn} represents the contour, and each point pi in the point set can form a shape histogram with the remaining n-1 points. The formula is as follows:(15)hik=#q≠pi&q∈bink,where pi represents the point, k represents that the vector formed by pi and other points belongs to the kth component, and hi(k) represents the number of points.For each point in the point setp = {p1, p2, …, pn}, according to the above formula, a shape histogram composed of n-1 points can be obtained, and there are a total of n such shape histograms. Therefore, the shape of each target object can also be represented by a shape histogram represented by n matrices.First, the corresponding point in the contrasting shape is found, that is, the origin of the polar coordinate system. In order to distinguish the real shape as much as possible, the center of mass of the matching object is selected as the coordinate origin. The formula for calculating the centroid is as follows:(16)x0=∑i,j∈Si∗Ii,j∑i,j∈SIi,j,y0=∑i,j∈Sj∗Ii,j∑i,j∈SIi,j.Among them, S represents the binarized contour point set, andI(i, j) is the image pixel value, that is,(17)Ii,j=1,i,j∈S,0,other.With shape descriptors, it is still impossible to distinguish objects of similar shapes. At this time, an evaluation function is needed to describe the degree of similarity between objects, that is, to describe the similarity between shape histograms. The similarity evaluation method is as follows:The similarity evaluation function between the shape histogram of a target object and the shape histogram of another object is defined as follows [20]:(18)cij=cpi,qj=12∗∑k=1Khik−hjk2hik+hjk.Hi(k) is the shape histogram with the log-polar coordinate origin of the target object pi as p, hj(k) is the shape histogram with the log-polar coordinate origin of the object qi as qj, and cij is the degree of similarity among them.This method may be used to determine the centroid of the sample object, which is pi, and then the centroid of the target object, which isqi. The evaluation function is then used to determine whether the items are identical or similar.Only the centroid to a log-polar coordinate histogram for each sampled shape contour point in turn is retrieved from the original log-polar coordinate histogram. The evaluation function cij of each point in the target object’s outline point set and the points in the sample object’s outline point set may be computed, and the evaluation matrix can be produced, according to the following equation:(19)C=c11⋯c1i⋯c1n⋮cj1⋯cji⋯cjn⋮cn1⋯cni⋯cnn.According to the evaluation matrix C, the matching point set pairs between the target object and the sample can be found, so as to know whether the two objects are similar.The process of using the improved shape context algorithm in physical knowledge image recognition is as follows:(1)
The algorithm detects the contour of the cut target character and extracts a certain number of points with a uniform step size(2)
The algorithm calculates the shape histogram of each point in the target character outline point set in turn(3)
The algorithm sequentially calculates the value of the evaluation function of each point in the target character outline point set and all points in the sample outline point set (formula (18)), and obtains an evaluation matrix (formula (19))(4)
The algorithm finds the matching points between the target character outline point set and the sample character outline point set according to the evaluation matrix(5)
The algorithm sets the threshold according to the matching situation and gives the recognition result of the target character.


## 4. Physical Identifiable Teaching Technology Based on Computer Simulation Analysis

It preprocesses the audio signal, digitizes it, pre-emphasizes it, splits the frame, inserts windows, and identifies the endpoint in the physical visualization teaching video speech recognition system and then extracts the characteristics of the cepstral coefficient of the audio signal. Finally, the hidden Markov model of voice recognition is utilized to locate and output the appropriate words from the dictionary. The XML file matching to the video is then processed using the DOM4J technique, and the text output of the voice recognition is written into the associated XML file node. The system’s program flow is shown in Figure4.Figure 4
The program flow of the speech recognition system for physics teaching video.The overall workflow of the physics visualization teaching system is shown in Figure5.Figure 5
Flow chart of software design of physics visualization teaching system.In the experimental scenario designed in this section, the 3D data collected by multiple Kinects refer to the coordinate system of their respective devices. In this paper, the system needs to know the position information of each person in space, and control the rotation and focus of the PTZ camera through this position information. Therefore, it is necessary to convert the collected body position coordinates to the same coordinate system. The coordinate transformation method in three-dimensional space is as follows. We need to map the four Kinect coordinate systems to the same coordinate system, and in this article, we convert to the three-dimensional coordinate system of the classroom with the lower left corner of the classroom podium as the origin, as shown in Figure6.Figure 6
Plan of the experimental scene.The teaching of Newton’s rings experiment in college physics is seen in Figure7. Figure 7 shows how the identifiable physics teaching technique presented in this research, based on computer simulation analysis, may play a major role in college physics.Figure 7
Newton’s ring identifiable teaching example. (a) Newton’s ring principle, (b) example of Newton’s rings.
(a)(b)On the basis of the above research, starting from two parts of theoretical teaching and practical teaching, this paper verifies the effect of the physical identifiable teaching system based on computer simulation analysis proposed in this paper and obtains the teaching effects shown in Table1 and Table 2.Table 1
The effect of theoretical teaching.
NumberTheoretical teaching183.71278.00380.62480.90579.05685.33774.52874.99982.181081.691181.031280.391385.601477.881581.771678.201779.711882.231985.012082.102175.882282.342387.152479.652575.912685.092774.672884.712986.433086.763187.343274.873378.103475.173580.233687.663778.853877.313975.524074.654185.914282.934376.344480.834577.504682.624778.874874.474978.605076.725182.975283.83Table 2
The effect of experimental teaching.
NumberExperimental teaching187.46281.56380.97479.11589.59682.21790.69886.97981.301084.911179.831279.631379.951491.861584.291680.181784.621879.951986.052089.072187.052290.722387.492488.772584.932689.342791.432880.382985.303081.843191.923282.643381.893488.953581.813684.853780.013880.753979.154083.974184.994283.084390.524488.334588.084684.984786.114886.614988.105090.985185.535288.46It can be seen from the above research that the physics identifiable teaching system based on computer simulation analysis proposed in this paper can play an important role in physics teaching.


## 5. Conclusion

The successful application of conventional experimental education is often limited by objective constraints. The virtual laboratory that evolves as the times demand may realistically answer the current difficulties, thanks to the growth of virtual reality technology. Furthermore, students may construct the same setting as in the experiment using virtual reality technology, enabling them to have a more realistic situational experience, imitate the actual scientific processes, and witness the related experimental phenomena. At the same time, students may use their own reasoning and inquiry to develop physical ideas and principles. The DT (data technology) age, not the IT era, is the period of the future. Big data technology offers technological assistance for educational model, method, and means transformation. This paper combines computer simulation analysis technology to study the identifiable teaching technology of physics and changes the traditional teaching method of physics. The research results show that the physics identifiable teaching system based on computer simulation analysis proposed in this paper can play an important role in physics teaching.



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