Evaluation of scientific and technological achievements in colleges based on machine learning and image feature retrieval

2020 ◽  
pp. 1-11
Author(s):  
Gaiqin Zhang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Science And Technology ◽  
System Optimization ◽  
Colleges And Universities ◽  
Image Feature ◽  
High Tech ◽  
Stable Model ◽  
Achievement Evaluation ◽  
University Science

At present, experts and scholars have conducted more research on the ability of colleges and universities to transform scientific and technological achievements. However, they pay more attention to the holistic research on the transformation of scientific and technological achievements in colleges and universities across the country, while rarely divide the research objects in detail. In order to improve the evaluation effect of scientific and technological achievements in colleges and universities, this paper builds a university science and technology achievement evaluation system based on machine learning and image feature retrieval on the basis of analyzing the needs of high-tech achievement evaluation. The system has certain flexibility. Moreover, this study selects the appropriate network architecture based on the actual data and mission objectives of the high-tech achievement evaluation. In addition, this paper proposes a FT-GRU model of a gated recurrent unit network incorporating N nearest neighbor text, and a more stable model structure is obtained through system optimization. Finally, this study designs experiments to verify the performance of the model. The research results show that the university science and technology achievement evaluation system based on machine learning and image feature retrieval constructed in this study meets the expected goals and has certain practical significance.

PLC course performance evaluation based on machine learning and image feature retrieval

2020 ◽  
pp. 1-11
Author(s):  
Min Xu
Keyword(s):  
Machine Learning ◽  
Performance Evaluation ◽  
Evaluation System ◽  
Course Evaluation ◽  
Image Feature ◽  
Course Performance ◽  
Performance Evaluation System ◽  
Image Blur ◽  
Blur Kernel ◽  
Realization Process

PLC is an indispensable technology for modern automation. The future social development will require a large number of PLC technical talents, so higher requirements are put forward for the teaching of PLC courses in colleges and universities. The intelligence and practical effect of the PLC course evaluation system are particularly important. Based on this, this article combines machine learning and image feature retrieval to construct a PLC course performance evaluation system. Moreover, this paper introduces the smoothness of the multispectral image, the smoothness of the blur function and the smoothness between the blur functions of adjacent spectral images as constraints and uses the gradient of the image blur kernel to express the smoothness of the image blur kernel itself. In addition, this article constructs the model system architecture according to the teaching requirements of the course and analyzes its realization process. Finally, in order to verify the performance of the model, this paper conducts system performance verification experiments through practical teaching methods and analyzes the results with statistical methods. The research results show that the PLC performance evaluation system constructed in this paper has a certain effect.

scholarly journals Design Index and Empirical Analysis of the Evaluation Index System for the Transformation of Scientific and Technological Achievements of Universities in Beijing, Tianjin and Hebei

2018 ◽  
Vol 2 (3) ◽  
pp. 70
Author(s):  
Guoru Yang ◽  
Yuenan Xu
Keyword(s):  
Development Strategy ◽  
Empirical Analysis ◽  
Evaluation System ◽  
Technological Development ◽  
Science And Technology ◽  
Evaluation Index System ◽  
Coordinated Development ◽  
Performance Evaluation System ◽  
Research Activities ◽  
University Science

Promoting the transformation of scientific and technological achievements of universities and colleges in Beijing, Tianjin, and Hebei is an important measure to enhance the level of scientific and technological development in universities, enhance the scientific and technological synergy of Beijing-Tianjin-Hebei urban agglomeration, practice the coordinated development strategy of Beijing, Tianjin and Hebei, and promote the construction of Xiong’an New District. Based on the scientific and technological input of colleges and universities, the development of science and technology and the output of science and technology, this paper uses Delphi and AHP to construct a Beijing, Tianjin, Hebei University Science and Technology Achievement Transformation Performance Evaluation System from the perspectives of transformation potentials, scientific research activities and achievements transformation of university scientific and technological achievements. An empirical analysis was carried out to provide reference for the government’s efficient decision-making and improvement of strategies for transforming scientific and technological achievements in universities.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

scholarly journals Automobile tire life prediction based on image processing and machine learning technology

2021 ◽  
Vol 13 (3) ◽  
pp. 168781402110027
Author(s):  
Jianchen Zhu ◽  
Kaixin Han ◽  
Shenlong Wang
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Life Prediction ◽  
Traffic Accidents ◽  
Confusion Matrix ◽  
Small Sample ◽  
Image Feature ◽  
Engineering Practice ◽  
Learning Technology ◽  
K Nearest Neighbor

With economic growth, automobiles have become an irreplaceable means of transportation and travel. Tires are important parts of automobiles, and their wear causes a large number of traffic accidents. Therefore, predicting tire life has become one of the key factors determining vehicle safety. This paper presents a tire life prediction method based on image processing and machine learning. We first build an original image database as the initial sample. Since there are usually only a few sample image libraries in engineering practice, we propose a new image feature extraction and expression method that shows excellent performance for a small sample database. We extract the texture features of the tire image by using the gray-gradient co-occurrence matrix (GGCM) and the Gauss-Markov random field (GMRF), and classify the extracted features by using the K-nearest neighbor (KNN) classifier. We then conduct experiments and predict the wear life of automobile tires. The experimental results are estimated by using the mean average precision (MAP) and confusion matrix as evaluation criteria. Finally, we verify the effectiveness and accuracy of the proposed method for predicting tire life. The obtained results are expected to be used for real-time prediction of tire life, thereby reducing tire-related traffic accidents.

Research on the expression of new visual intelligence system based on machine learning technology

2021 ◽  
pp. 1-9
Author(s):  
Zuoshan Li
Keyword(s):  
Machine Learning ◽  
Intelligent System ◽  
Science And Technology ◽  
Learning Technology ◽  
3D Animation ◽  
Motion Data ◽  
Intelligence System ◽  
New Science ◽  
Continuous Progress ◽  
Internet Of Things Technology

With the continuous progress of society, the level of science and technology of the country has made a leap forward development, the research energy of various industries on new science and technology continues to deepen, greatly promoting the promotion of science and technology. At the same time, with the increase in social pressure, more and more people pursue spiritual relaxation, and appropriate leisure and entertainment activities have gradually become a part of people’s life. Film plays an irreplaceable role in leisure and entertainment. Mainly from the background of the development of the film industry towards intelligent direction, and then use machine learning technology to study the application of film animation production and film virtual assets analysis and investigation. Based on the Internet of things technology, we also vigorously develop the ways and methods of visual expression of movies, and at the same time introduce new expression modes to promote the expression effect of the intelligent system. Finally, by comparing various algorithms in machine learning technology, the results of intelligent expression of random number forest algorithm in machine learning technology are more accurate. The system is also applied to 3D animation production to observe the measurement error of 3D motion data and facial expression data.

scholarly journals Research on the Evaluation Index System and the Grey Clustering Analysis Model of Teaching Quality of Physical Education in Higher Education

2015 ◽  
Vol 10 (8) ◽  
pp. 59 ◽  
Author(s):  
Baoquan Wu
Keyword(s):  
Physical Education ◽  
Clustering Analysis ◽  
Evaluation System ◽  
Quality Evaluation ◽  
Teaching Quality ◽  
Colleges And Universities ◽  
Evaluation Index System ◽  
Analysis Model ◽  
Grey Clustering

Teaching quality evaluation of physical education usually involves multiple influence factors with grey and uncertain information. This brings about limitations to effective evaluation of teaching quality of physical education in colleges and universities. Thus, this paper draws merits from previous research and proposes a teaching quality evaluation system and model of physical education in colleges and universities. First, based on real situations, grey categories of evaluation state for physical education teaching quality are established. The definite weighted functions of grey category of evaluation state are confirmed. Specific steps of the teaching quality evaluation model based on grey clustering analysis are accounted for. Finally, a case study is introduced to verify the model. This model enlightens a new way to evaluate teaching quality of physical education in colleges and universities.

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