Image detection and basketball training performance simulation based on improved machine learning

2020 ◽  
pp. 1-12
Author(s):  
Wang Pengyu ◽  
Gao Wanna
Keyword(s):  
Machine Learning ◽  
Feature Recognition ◽  
Controlled Trial ◽  
Detection Methods ◽  
Basketball Player ◽  
Learning Technology ◽  
Advantages And Disadvantages ◽  
Detection Technology ◽  
Starting Point ◽  
Bounding Boxes

Basketball player detection technology is an important subject in the field of computer vision and the basis of related image processing research. This study uses machine learning technology to build a basketball sport feature recognition model. Moreover, this research mainly takes the characteristic information of basketball in the state of basketball goals as the starting point and compares and analyzes the detection methods by detecting the targets in the environment. By comprehensively considering the advantages and disadvantages of various methods, a method suitable for the subject is proposed, namely, a fast skeleton extraction and model segmentation method. The fitting effect of this method, whether in terms of compactness or quantity, has greater advantages than traditional bounding boxes, and realizes the construction of dynamic ellipsoidal bounding boxes in a moving state. In addition, this study designs a controlled trial to verify the analysis of this research model. The research results show that the model proposed in this paper has certain effects and can improve practical guidance for competitions and basketball players training.

scholarly journals Review of Dissolved Oxygen Detection Technology: From Laboratory Analysis to Online Intelligent Detection

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3995 ◽  
Author(s):  
Yaoguang Wei ◽  
Yisha Jiao ◽  
Dong An ◽  
Daoliang Li ◽  
Wenshu Li ◽  
...  
Keyword(s):  
Dissolved Oxygen ◽  
Real Time ◽  
Optical Detection ◽  
Oxygen Sensors ◽  
Detection Methods ◽  
Future Research ◽  
Advantages And Disadvantages ◽  
Oxygen Detection ◽  
Detection Technology ◽  
Intelligent Technology

Dissolved oxygen is an important index to evaluate water quality, and its concentration is of great significance in industrial production, environmental monitoring, aquaculture, food production, and other fields. As its change is a continuous dynamic process, the dissolved oxygen concentration needs to be accurately measured in real time. In this paper, the principles, main applications, advantages, and disadvantages of iodometric titration, electrochemical detection, and optical detection, which are commonly used dissolved oxygen detection methods, are systematically analyzed and summarized. The detection mechanisms and materials of electrochemical and optical detection methods are examined and reviewed. Because external environmental factors readily cause interferences in dissolved oxygen detection, the traditional detection methods cannot adequately meet the accuracy, real-time, stability, and other measurement requirements; thus, it is urgent to use intelligent methods to make up for these deficiencies. This paper studies the application of intelligent technology in intelligent signal transfer processing, digital signal processing, and the real-time dynamic adaptive compensation and correction of dissolved oxygen sensors. The combined application of optical detection technology, new fluorescence-sensitive materials, and intelligent technology is the focus of future research on dissolved oxygen sensors.

Development of Detection Research on Fatigue Driving

2014 ◽  
Vol 641-642 ◽  
pp. 813-817
Author(s):  
Xiao Jun He ◽  
Jing Liu ◽  
Zhen Di Yi ◽  
Yuan Quan Yang
Keyword(s):  
Fatigue Testing ◽  
Detection Methods ◽  
Advantages And Disadvantages ◽  
Efficient Detection ◽  
Detection Technology ◽  
Driving Fatigue ◽  
Cost Efficient ◽  
Fatigue Driving ◽  
The Cost ◽  
Fatigue Detection

This paper presents the current most common fatigue-driving detection methods. The advantages and disadvantages of these detection methods are compared with. Moreover, several major products of the current fatigue detection are listed briefly. Furthermore, the development trends of driving-fatigue detection technology are prospected. The author believes that driver fatigue testing standards need to be further clarified and the non-contact detection method of driving-fatigue needs to be developed deeply. Information fusion is an important orientation for driving fatigue and we should design the cost-efficient detection products for fatigue-driving.

scholarly journals Plant diseases and pests detection based on deep learning: a review

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Jun Liu ◽  
Xuewei Wang
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Digital Image Processing ◽  
Digital Image ◽  
Future Trend ◽  
Plant Diseases ◽  
Detection Methods ◽  
Learning Technology ◽  
Practical Applications ◽  
Advantages And Disadvantages

AbstractPlant diseases and pests are important factors determining the yield and quality of plants. Plant diseases and pests identification can be carried out by means of digital image processing. In recent years, deep learning has made breakthroughs in the field of digital image processing, far superior to traditional methods. How to use deep learning technology to study plant diseases and pests identification has become a research issue of great concern to researchers. This review provides a definition of plant diseases and pests detection problem, puts forward a comparison with traditional plant diseases and pests detection methods. According to the difference of network structure, this study outlines the research on plant diseases and pests detection based on deep learning in recent years from three aspects of classification network, detection network and segmentation network, and the advantages and disadvantages of each method are summarized. Common datasets are introduced, and the performance of existing studies is compared. On this basis, this study discusses possible challenges in practical applications of plant diseases and pests detection based on deep learning. In addition, possible solutions and research ideas are proposed for the challenges, and several suggestions are given. Finally, this study gives the analysis and prospect of the future trend of plant diseases and pests detection based on deep learning.

scholarly journals Detection techniques for monitoring dioxin-like compounds: latest techniques and the comparison

2021 ◽  
Vol 2045 (1) ◽  
pp. 012024
Author(s):  
X Y Du ◽  
X Y Liu ◽  
Z Y Shang ◽  
W S Han ◽  
H Zhang
Keyword(s):  
Mass Spectrometry ◽  
Detection Technique ◽  
Detection Methods ◽  
Laser Mass Spectrometry ◽  
Detection Techniques ◽  
Advantages And Disadvantages ◽  
Flight Mass Spectrometry ◽  
Detection Technology ◽  
Mass Spectrometry Technique ◽  
Spectrometry Technique

Abstract In order to clarify the necessity and urgency of dioxin detection, the characteristics and emission sources were firstly studied in this paper. The current dioxin detection techniques were then summarized, including chemical detection technique represented by HRGC/MS, biological detection technique covering immunology and biotechnology, and laser mass spectrometry technique using fusion ionization technology and time-of-flight mass spectrometry. Then the advantages and disadvantages, representative technologies, development directions and application prospects of various detection methods were analyzed. Eventually, the future development direction of dioxin detection technology was prospected.

scholarly journals Research and application of bolt defects detection technology based on machine learning

2021 ◽  
Vol 2085 (1) ◽  
pp. 012012
Author(s):  
Zhidong Yao ◽  
Jiaqi Lu ◽  
Yesen Liu ◽  
Gang Wang
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Embedded System ◽  
High Performance ◽  
Image Data ◽  
Steel Structure ◽  
Learning Technology ◽  
Detection Technology ◽  
Training Samples ◽  
High Recognition Accuracy

Abstract With the development of computer technology, the technology based on computer machine learning plays an important role in various fields. Using drones for collecting image data and using machine learning to analyze the collected image data have become the current general method of intelligent detection technology. As the main machine learning method, deep learning is commonly used in image analysis, but it requires many high-quality training samples and high-performance embedded system. In the engineering quality and safety detection with few training samples, the detection effect of this method is not satisfactory. To solve this problem, computer vision and machine learning technology are introduced into image analysis of bolt, based on the analysis and mining of historical image samples, the recognition and judgment of new collected images can be realized by matching the newly collected image samples and historical samples. Taking the bolt on a steel structure bridge as an example, this method is used to recognize the bolt appearance image collected by UAV. The results show that the method can effectively identify the appearance state of bolts, with fast calculation speed and high recognition accuracy.

Design of English text-to-speech conversion algorithm based on machine learning

2020 ◽  
pp. 1-12
Author(s):  
Li Dongmei
Keyword(s):  
Machine Learning ◽  
Speech Synthesis ◽  
Feature Recognition ◽  
Learning Algorithm ◽  
Morphological Structure ◽  
English Text ◽  
Text To Speech ◽  
Part Of Speech ◽  
Modern Computer ◽  
Conversion Algorithm

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Review of classification studies for machine learning in the development of intelligent management decision support systems

2020 ◽  
Vol 89 ◽  
pp. 20-29
Author(s):  
Sh. K. Kadiev ◽  
◽  
R. Sh. Khabibulin ◽  
P. P. Godlevskiy ◽  
V. L. Semikov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Mathematical Models ◽  
Decision Support Systems ◽  
Support Systems ◽  
Management Decision ◽  
Classification Methods ◽  
Advantages And Disadvantages ◽  
Intelligent Management ◽  
Management Decision Support

Introduction. An overview of research in the field of classification as a method of machine learning is given. Articles containing mathematical models and algorithms for classification were selected. The use of classification in intelligent management decision support systems in various subject areas is also relevant. Goal and objectives. The purpose of the study is to analyze papers on the classification as a machine learning method. To achieve the objective, it is necessary to solve the following tasks: 1) to identify the most used classification methods in machine learning; 2) to highlight the advantages and disadvantages of each of the selected methods; 3) to analyze the possibility of using classification methods in intelligent systems to support management decisions to solve issues of forecasting, prevention and elimination of emergencies. Methods. To obtain the results, general scientific and special methods of scientific knowledge were used - analysis, synthesis, generalization, as well as the classification method. Results and discussion thereof. According to the results of the analysis, studies with a mathematical formulation and the availability of software developments were identified. The issues of classification in the implementation of machine learning in the development of intelligent decision support systems are considered. Conclusion. The analysis revealed that enough algorithms were used to perform the classification while sorting the acquired knowledge within the subject area. The implementation of an accurate classification is one of the fundamental problems in the development of management decision support systems, including for fire and emergency prevention and response. Timely and effective decision by officials of operational shifts for the disaster management is also relevant. Key words: decision support, analysis, classification, machine learning, algorithm, mathematical models.

Hybrid Design of Isonicotinic Acid Hydrazide Derivatives: Machine Learning Studies, Synthesis and Biological Evaluation of their Antituberculosis Activity

2020 ◽  
Vol 17 (3) ◽  
pp. 365-375
Author(s):  
Vasyl Kovalishyn ◽  
Diana Hodyna ◽  
Vitaliy O. Sinenko ◽  
Volodymyr Blagodatny ◽  
Ivan Semenyuta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Isonicotinic Acid ◽  
Acid Hydrazide ◽  
Predictive Ability ◽  
Antitubercular Activity ◽  
Biological Evaluation ◽  
Starting Point ◽  
Binary Classifiers ◽  
Synthesis And Biological Evaluation

Background: Tuberculosis (TB) is an infection disease caused by Mycobacterium tuberculosis (Mtb) bacteria. One of the main causes of mortality from TB is the problem of Mtb resistance to known drugs. Objective: The goal of this work is to identify potent small molecule anti-TB agents by machine learning, synthesis and biological evaluation. Methods: The On-line Chemical Database and Modeling Environment (OCHEM) was used to build predictive machine learning models. Seven compounds were synthesized and tested in vitro for their antitubercular activity against H37Rv and resistant Mtb strains. Results: A set of predictive models was built with OCHEM based on a set of previously synthesized isoniazid (INH) derivatives containing a thiazole core and tested against Mtb. The predictive ability of the models was tested by a 5-fold cross-validation, and resulted in balanced accuracies (BA) of 61–78% for the binary classifiers. Test set validation showed that the models could be instrumental in predicting anti- TB activity with a reasonable accuracy (with BA = 67–79 %) within the applicability domain. Seven designed compounds were synthesized and demonstrated activity against both the H37Rv and multidrugresistant (MDR) Mtb strains resistant to rifampicin and isoniazid. According to the acute toxicity evaluation in Daphnia magna neonates, six compounds were classified as moderately toxic (LD50 in the range of 10−100 mg/L) and one as practically harmless (LD50 in the range of 100−1000 mg/L). Conclusion: The newly identified compounds may represent a starting point for further development of therapies against Mtb. The developed models are available online at OCHEM http://ochem.eu/article/11 1066 and can be used to virtually screen for potential compounds with anti-TB activity.

Sign in / Sign up

Export Citation Format

Share Document