scholarly journals Sea-Land Clutter Classification Based on Graph Spectrum Features

2021 ◽  
Vol 13 (22) ◽  
pp. 4588
Author(s):  
Le Zhang ◽  
Anke Xue ◽  
Xiaodong Zhao ◽  
Shuwen Xu ◽  
Kecheng Mao
Keyword(s):  
Artificial Intelligence ◽  
Traditional Method ◽  
Data Representation ◽  
Support Vector ◽  
Data Sequence ◽  
Average Amplitude ◽  
Graph Spectrum ◽  
Radar Clutter ◽  
Artificial Intelligence Algorithm ◽  
Representation Method

In this paper, an approach for radar clutter, especially sea and land clutter classification, is considered under the following conditions: the average amplitude levels of the clutter are close to each other, and the distributions of the clutter are unknown. The proposed approach divides the dataset into two parts. The first data sequence from sea and land is used to train the model to compute the parameters of the classifier, and the second data sequence from sea and land under the same conditions is used to test the performance of the algorithm. In order to find the essential structure of the data, a new data representation method based on the graph spectrum is utilized. The method reveals the nondominant correlation implied in the data, and it is quite different from the traditional method. Furthermore, this representation is combined with the support vector machine (SVM) artificial intelligence algorithm to solve the problem of sea and land clutter classification. We compare the proposed graph feature set with nine exciting valid features that have been used to classify sea clutter from the radar in other works, especially when the average amplitude levels of the two types of clutter are very close. The experimental results prove that the proposed extraction can represent the characteristics of the raw data efficiently in this application.

Role of artificial intelligence algorithm for taekwondo teaching effect evaluation model

2020 ◽  
pp. 1-12
Author(s):  
Liang Hailong
Keyword(s):  
Artificial Intelligence ◽  
Feature Recognition ◽  
Teaching Practice ◽  
Evaluation Model ◽  
Simulation Method ◽  
Support Vector ◽  
Effect Evaluation ◽  
Intelligence Algorithm ◽  
Artificial Intelligence Algorithm ◽  
Set Up

The problems and disadvantages of the traditional teaching mode of Taekwondo in colleges and universities are obvious, which is not conducive to cultivating the interest of contemporary college students in learning Taekwondo. In order to improve the teaching effect of Taekwondo, based on the intelligent algorithm of human body feature recognition, this study uses support vector machine to construct a Taekwondo teaching effect evaluation model based on artificial intelligence algorithm. The model corrects the movement of the students by recognizing the movement characteristics of the students’ Taekwondo and can conduct the movement guidance and exercises through the simulation method. In order to verify the performance of the model in this study, this study set up control experiments and mathematical statistical methods to verify the performance of the model. The research results show that the model proposed in this paper has a certain effect and can be applied to teaching practice

scholarly journals SAX Breakpoints for Random Forest Based Real-Time Contrast Control Chart

2019 ◽  
Vol 33 ◽  
pp. 9959-9960
Author(s):  
In-Seok Lee ◽  
Jun-Geol Baek
Keyword(s):  
Random Forest ◽  
Real Time ◽  
Control Chart ◽  
Input Data ◽  
Data Representation ◽  
Support Vector ◽  
Monitoring Methods ◽  
Linear Discriminant ◽  
Symbolic Aggregate Approximation ◽  
Representation Method

In the manufacturing process, process monitoring is very important. Real-time contrast (RTC) control chart outperforms existing monitoring methods. However, the performance of RTC control chart depends on the classifier. The existing RTC charts use random forest (RF), support vector machine (SVM), or kernel linear discriminant analysis (KLDA) as a classifier. RF classifier can find cause of faults but the performance is lower than others. Therefore, we suggest the data representation method to improve the RF based RTC control chart. Symbolic aggregate approximation (SAX) is famous method to improve the performance of classification and clustering. We convert the input data by using SAX. We change the parameters of SAX such as alphabet size and breakpoints to improve the performance. Experiment shows that represented data is efficient method to improve the performance of RTC control chart.

scholarly journals Detection of Wood Boring Insects’ Larvae Based on the Acoustic Signal Analysis and the Artificial Intelligence Algorithm

2017 ◽  
Vol 42 (1) ◽  
pp. 61-70 ◽  
Author(s):  
Piotr Bilski ◽  
Piotr Bobiński ◽  
Adam Krajewski ◽  
Piotr Witomski
Keyword(s):  
Artificial Intelligence ◽  
Acoustic Analysis ◽  
Support Vector ◽  
Acoustic Signal Analysis ◽  
Hylotrupes Bajulus ◽  
Artificial Intelligence Algorithm ◽  
Computational Intelligence Methods ◽  
The Time Domain ◽  
Low Amplitude ◽  
Wood Boring

Abstract The paper presents an application of signal processing and computational intelligence methods to detect presence of the wood boring insects larvae in the wooden constructions (such as the furniture of buildings). Such insects are one of the main sources of the degradation in such objects, therefore they should be detected as quickly as possible, before inflicting serious damage. The presented work involved the acoustic monitoring for detecting the presence of the larvae inside pieces of wood. An accelerometer was used to record the sound, further analyzed by a computer algorithm extracting features important for artificial-intelligence (AI) based classification employed to detect the old house borer’s (Hylotrupes bajulus L.) activity. The presented task is difficult, as the sounds made by the larvae are of relatively low amplitude and the background noise caused by people, electrical appliances or other sources may significantly degrade the accuracy of detection. The classification of sounds is needed to separate sources of noise which deteriorate the proper larva detection and should be suppressed if possible. The employed classification was based on features defined in the time domain followed by the support vector machine used as the binary classifier. The results allowed us to assess the effectiveness of the old house borer’s detection by the acoustic analysis enhanced with the AI algorithm.

scholarly journals A Study of Automatic Judgment of Food Color and Cooking Conditions with Artificial Intelligence Technology

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1128
Author(s):  
Chern-Sheng Lin ◽  
Yu-Ching Pan ◽  
Yu-Xin Kuo ◽  
Ching-Kun Chen ◽  
Chuen-Lin Tien
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Color Space ◽  
Color Difference ◽  
Color Space Conversion ◽  
Before And After ◽  
Artificial Intelligence Technology ◽  
Artificial Intelligence Algorithm ◽  
Time Image ◽  
Cooking Conditions

In this study, the machine vision and artificial intelligence algorithms were used to rapidly check the degree of cooking of foods and avoid the over-cooking of foods. Using a smart induction cooker for heating, the image processing program automatically recognizes the color of the food before and after cooking. The new cooking parameters were used to identify the cooking conditions of the food when it is undercooked, cooked, and overcooked. In the research, the camera was used in combination with the software for development, and the real-time image processing technology was used to obtain the information of the color of the food, and through calculation parameters, the cooking status of the food was monitored. In the second year, using the color space conversion, a novel algorithm, and artificial intelligence, the foreground segmentation was used to separate the vegetables from the background, and the cooking ripeness, cooking unevenness, oil glossiness, and sauce absorption were calculated. The image color difference and the distribution were used to judge the cooking conditions of the food, so that the cooking system can identify whether or not to adopt partial tumbling, or to end a cooking operation. A novel artificial intelligence algorithm is used in the relative field, and the error rate can be reduced to 3%. This work will significantly help researchers working in the advanced cooking devices.

Simulation of public art communication in colleges based on smart cloud platform and artificial intelligence algorithm

2020 ◽  
pp. 1-11
Author(s):  
Zhang Yingying
Keyword(s):  
Artificial Intelligence ◽  
Public Art ◽  
Optimal Path ◽  
Colleges And Universities ◽  
Cloud Platform ◽  
Current Transmission ◽  
Artificial Intelligence Algorithm ◽  
Offset Coefficient ◽  
Network Status ◽  
Selection Of

Public art communication in colleges and universities needs to be launched with the support of artificial intelligence systems. According to the current situation of public art communication in colleges and universities, this paper builds a smart cloud platform for public art communication in colleges and universities with the support of artificial intelligence algorithms. Moreover, this paper introduces the bandwidth offset coefficient to judge the change of network throughput, introduces the slice download rate difference to first judge the consistency change trend of bandwidth, and then further proposes the calculation method of bandwidth prediction value by situation. In addition, this paper proposes a flexible transmission mechanism based on smart collaborative networks. Through in-depth perception of network status and component behavior, this mechanism implements the selection of the optimal path in the network according to the current network status and user service requirements to complete the transmission of service resources. If the current transmission path fails, the mechanism should ensure the continuity and reliability of the service. The research results show that the system constructed in this paper has good performance and can be applied to practice.

scholarly journals Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tiziana Ciano ◽  
Massimiliano Ferrara ◽  
Meisam Babanezhad ◽  
Afrasyab Khan ◽  
Azam Marjani
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Velocity Profile ◽  
Fluid Velocity ◽  
Accurate Prediction ◽  
Inference System ◽  
Porous Tube ◽  
Intelligence Algorithm ◽  
Water Based ◽  
Artificial Intelligence Algorithm

AbstractThe heat transfer improvements by simultaneous usage of the nanofluids and metallic porous foams are still an attractive research area. The Computational fluid dynamics (CFD) methods are widely used for thermal and hydrodynamic investigations of the nanofluids flow inside the porous media. Almost all studies dedicated to the accurate prediction of the CFD approach. However, there are not sufficient investigations on the CFD approach optimization. The mesh increment in the CFD approach is one of the challenging concepts especially in turbulent flows and complex geometries. This study, for the first time, introduces a type of artificial intelligence algorithm (AIA) as a supplementary tool for helping the CFD. According to the idea of this study, the CFD simulation is done for a case with low mesh density. The artificial intelligence algorithm uses learns the CFD driven data. After the intelligence achievement, the AIA could predict the fluid parameters for the infinite number of nodes or dense mesh without any limitations. So, there is no need to solve the CFD models for further nodes. This study is specifically focused on the genetic algorithm-based fuzzy inference system (GAFIS) to predict the velocity profile of the water-based copper nanofluid turbulent flow in a porous tube. The most intelligent GAFIS could perform the most accurate prediction of the velocity. Hence, the intelligence of GAFIS is tested for different values of cluster influence range (CIR), squash factor(SF), accept ratio (AR) and reject ratio (RR), the population size (PS), and the percentage of crossover (PC). The maximum coefficient of determination (~ 0.97) was related to the PS of 30, the AR of 0.6, the PC of 0.4, CIR of 0.15, the SF 1.15, and the RR of 0.05. The GAFIS prediction of the fluid velocity was in great agreement with the CFD. In the most intelligent condition, the velocity profile predicted by GAFIS was similar to the CFD. The nodes increment from 537 to 7671 was made by the GAFIS. The new predictions of the GAFIS covered all CFD results.

Sign in / Sign up

Export Citation Format

Share Document