Application of Artificial Neural Networks in Analysis of Time-Variable Optical Reflectance Spectra in Digital Light Projection Spectroscopy

This article undertakes the subject matter of applying artificial neural networks to analyze optical reflectance spectra of objects exhibiting a change of optical properties in the domain of time. A compact Digital Light Projection NIRscan Nano Evaluation Module spectrometer was used to record spectra. Due to the miniature spectrometer’s size and its simplicity of measurement, it can be used to conduct tests outside of a laboratory. A series of plant-derived objects were used as test subjects with rapidly changing optical properties in the presented research cycle. The application of artificial neural networks made it possible to determine the aging time of plants with a relatively low mean squared error, reaching 0.56 h for the Levenberg–Marquardt backpropagation training method. The results of the other ten training methods for artificial neural networks have been included in the paper.

Attitude Perception of Badminton Players Based on Mobile Edge Computing

In order to help badminton players make reasonable training plans and realize a comprehensive grasp of the training process, this paper mainly recognizes and perceives the posture of badminton athletes based on the method of moving edge calculation. Firstly, from the perspective of moving edge motion analysis, considering the vector field formed by moving edge vector as movable spatial distribution information, the spatial distribution model of moving edge field is realized. Secondly, while athletes interact with the computer through limb movement, the overall posture of athletes is divided into several parts, and each part is perceived separately. Finally, in the human posture evaluation module, an algorithm for human posture evaluation in the image pixel plane is proposed. Through comparative experiments, the motion recognition algorithm can effectively recognize the three typical swing movements of badminton players in the video and improve the overall performance of the existing recognition algorithms.

Prediksi Tingkat Kepuasan dalam Pembelajaran Daring Menggunakan Algoritma Naïve Bayes

The growth of learning at this time is influenced by advances in data and communication technology. One of the data technologies that functioned in the world of learning during the COVID-19 pandemic was online education. Online education is used as a liaison between lecturers and students in an internet network that can be accessed at any time. The online media used are Whatsapp, Google Classroom, Google Meet, Cloud x and the Zoom application. This research aims to predict the level of student satisfaction in online education as well as to distribute donations to large academies in making policies related to improving the quality of education online. The information used was obtained by distributing questionnaires to 110 students of the 2020/2021 class. The parameters in the questionnaire are lecturer communication, online education atmosphere, student evaluation, module delivery. Naïve Bayes is a prediction method for finding simple probabilities based on the Bayes theorem with a strong assumption of independence. Rapid Miner is one of the tools used for testing information and viewing the results of accuracy based on revolutionary information. The results of the test using 80 training information and 30 testing information show very good accuracy.

HANDWRITTEN MATHEMATICAL EXPRESSION RECOGNITION AND GRADING SYSTEM

Handwritten Mathematical Expression recognition and grading system is a challenging task in the field of pattern recognition. A lot of researchers have already worked on Handwritten Mathematical Expression recognition and have used various classifiers. In past, Convolutional Neural Network, also called CNN, has been highly used for recognizing patterns. In this paper, We propose an idea to recognize HME and evaluate offline using CNN for classification. The steps included are, first the worksheet is scanned and is sent to the work-spaces detection module where it will return all the rectangular work-spaces from the given worksheet, then the workspaces are sent to the line extraction module to extract all the lines. The extracted lines are then passed to the character segmentation module where it will segment the character and then characters will be classified using deep learning model DCCNN. Finally, the evaluation module will assess the line and draw a green/red bounding box depending on whether the line is correct or not.

Recognition and Classification Model of Music Genres and Chinese Traditional Musical Instruments Based on Deep Neural Networks

The teaching of ideological and political theory courses and daily ideological and political education are two important parts of education for college students. With the iterative update of information technology, the individualized development of students, and the reform and innovation of ideological and political education, higher goals and requirements have been put forward for ideological and political education. Some universities have developed new paths in the teaching model, but they have not considered the evaluation module and paid little attention to their own development. They only paid attention to the fact that it injected fresh blood into the reform of education model and ideological education but ignored the improvement of their own quality. Therefore, with these limitations, the learning effect is not satisfactory. Keeping in view these issues, this article defines the concept of deep learning and ideological and political education of college students as the starting point and then analyzes the new precise and personalized concepts, new forms of intelligent teaching and evaluation, and new models of intelligent learning that deep learning brings to college students’ ideological and political education. This is a new path of intelligent linkage with the subject, object, and mediator. It can deepen the reform of the education and teaching mode of individualization, accuracy, interactivity, and vividness of college students’ ideological and political education and improve the evaluation and management of college students’ ideological and political education. The experimental results of the study showed the effectiveness of the proposed study.

Integrating k-means clustering into automatic programming assessment tool for student performance analysis

Computer programming is a subject involving a large number of logic programming activities. A programmer is compulsory to master skills of algorithms, logic, and programming language to conduct programming. An automatic programming assessment tool is an automated tool used to assist instructors in assessing programming tasks. The technology used in this application is open-source based with an evaluation module that will evaluate the sent program code, assessment, and classification. The evaluation results were then processed in the assessment module, where a comparison process with the test case was performed along with the point calculation. The classification module was used to divide students into five groups based on the point of each practicum. This study used k-means clustering classification method. The entities included were lecturers, assistants, students, and compilers. This application had 2 levels of users namely admin and students. Scoring results were then used in the process of determining the classification of student’s performance based on the k-means clustering method. In connection with the classification test results with three iterations, three practicum scores resulted that the classification process was successfully carried out with student’s performance divided into five groups covering very good, good, sufficient, less, and very less. The data used in the clustering process consisted of 41 students with 10 attributes which were then grouped into 3 groups (clusters).

Roll-to-Roll In-Line Implementation of Microwave Free-Space Non-Destructive Evaluation of Conductive Composite Thin Layer Materials

A free-space microwave nondestructive testing and evaluation module is developed for the low-power, non-ionizing, contactless, and real-time characterization of doped composite thin-film materials in an industrial context. The instrumentation proposed is built up with a handled vector network analyzer interfaced with corrugated horn antennas to measure the near-field complex reflection S11 of planar prepreg composite materials in a roll-to-roll in-line production line. Dedicated modeling and calibrations routines are developed to extract the microwave conductivity from the measured microwave signal. Practical extraction of the radiofrequency (RF) conductivity of thin film prepreg composite materials doped with nano-powders is exemplary shown at the test frequency of 10 GHz.

Statistical Analysis of the Results of Test Artillery Tracers

A statistical analysis of multiyear laboratory test results of artillery tracers number 8 is presented in this article. This analysis was aimed at testing the impact of a natural ageing process on quality indicators during the long-time storage of these tracers. The influence of storage time on taking a diagnostic decision, relating to quality of lots after the conducted laboratory tests and on different classes of inconsistencies that occurred during these tests, was analysed. A detailed analysis of the impact of the storage time on diagnostic shooting decisions taken was also presented. The conducted statistical analysis suggests an assumption, that it is possible to change an evaluation module in the previous test’s methodology. Modification of this evaluation module will not negatively impact on the quality of further diagnostic tests. It will not negatively impact on correct evaluation of the prediction process of the tested elements of ammunition such as artillery tracers. The statistical analysis, carried out in the article, may have a significant impact on the modification of test methodology of the artillery tracers.