scholarly journals Recognition efficiency of atypical cardiovascular readings on ECG devices through fogged goggles

Displays ◽  
2022 ◽  
pp. 102148
Author(s):  
Jia-Wei Ren ◽  
Jun Yao ◽  
Ju Wang ◽  
Hao-Yun Jiang ◽  
Xue-Cheng Zhao
Keyword(s):  
Recognition Efficiency

Effect of surface chemistry on bio-conjugation and bio-recognition abilities of 2D germanene materials

Nanoscale ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 1893-1903
Author(s):  
Wei Li Ang ◽  
Jiri Sturala ◽  
Nikolas Antonatos ◽  
Zdeněk Sofer ◽  
Alessandra Bonanni
Keyword(s):  
Surface Chemistry ◽  
Strong Influence ◽  
Intrinsic Fluorescence ◽  
Surface Ligands ◽  
Chemically Modified ◽  
Recognition Efficiency ◽  
Effect Of Surface

The surface ligands on chemically modified germanenes have strong influence on the intrinsic fluorescence, on the bio-conjugation ability and the bio-recognition efficiency of the material towards the detection of a specific analyte.

Simulation of athlete gait recognition based on spectral features and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Linuo Wang
Keyword(s):  
Machine Learning ◽  
Angular Velocity ◽  
Gait Recognition ◽  
Spectral Feature ◽  
Velocity Signal ◽  
Comparison Results ◽  
Computer Vision Technology ◽  
Feature Technology ◽  
Leg Movement ◽  
Recognition Efficiency

The current technology related to athlete gait recognition has shortcomings such as complicated equipment and high cost, and there are also certain problems in recognition accuracy and recognition efficiency. In order to improve the efficiency of athletes’ gait recognition, this paper studies the different recognition technologies of athletes based on machine learning and spectral feature technology and applies computer vision technology to sports. Moreover, according to the calf angular velocity signal, the occurrence of leg movement is detected in real time, and the gait cycle is accurately divided to reduce the influence of the signal unrelated to the behavior on the recognition process. In addition, this study proposes a gait behavior recognition method based on event-driven strategies. This method uses a gyroscope as the main sensor and uses a wearable sensor node to collect the angular velocity signals of the legs and waist. In addition, this study analyzes the performance of the algorithm proposed by this paper through experimental research. The comparison results show that the method proposed by this paper has improved the number of recognition action types and accuracy and has certain advantages from the perspective of computation and scalability.

Machine Vision Window Division on Self-Adaptation

2011 ◽  
Vol 460-461 ◽  
pp. 617-620
Author(s):  
Xiu Chen Wang
Keyword(s):  
Image Analysis ◽  
Machine Vision ◽  
Defect Detection ◽  
Actual Situation ◽  
Adaptive Window ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Self Adaptation ◽  
Recognition Efficiency ◽  
Division Model

Aiming at time-consuming and ineffective problem of image window division in fabric defect detection, this paper proposes a new adaptive division method after a large number of experiments. This method can quickly and exactly recognize defect feature. Firstly, a division model on adaptive window is established, secondly, the formula to anticipate generally situation of fabric image is given according to the peaks and valleys change in the model, and methods to calculate the division size and position of adaptive window are given. Finally, we conclude that the algorithm in this paper can quickly and simply select the size and position of window division according to actual situation of different fabric images, and the time of image analysis is shortened and the recognition efficiency is improved.

scholarly journals A Two-Stage Exon Recognition Model Based on Synergetic Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhehuang Huang ◽  
Yidong Chen
Keyword(s):  
Neural Network ◽  
Digital Signal ◽  
Two Stage ◽  
Recognition Model ◽  
Artificial Fish Swarm Algorithm ◽  
Proposed Model ◽  
Artificial Fish Swarm ◽  
Recognition Efficiency ◽  
Processing Techniques ◽  
Signal Processing Techniques

Exon recognition is a fundamental task in bioinformatics to identify the exons of DNA sequence. Currently, exon recognition algorithms based on digital signal processing techniques have been widely used. Unfortunately, these methods require many calculations, resulting in low recognition efficiency. In order to overcome this limitation, a two-stage exon recognition model is proposed and implemented in this paper. There are three main works. Firstly, we use synergetic neural network to rapidly determine initial exon intervals. Secondly, adaptive sliding window is used to accurately discriminate the final exon intervals. Finally, parameter optimization based on artificial fish swarm algorithm is used to determine different species thresholds and corresponding adjustment parameters of adaptive windows. Experimental results show that the proposed model has better performance for exon recognition and provides a practical solution and a promising future for other recognition tasks.

scholarly journals Vibration-Based Fault Diagnosis of Commutator Motor

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Adam Glowacz ◽  
Witold Glowacz
Keyword(s):  
Fault Diagnosis ◽  
Extraction Methods ◽  
Total Efficiency ◽  
Practical Application ◽  
Backpropagation Neural Network ◽  
Vibration Signals ◽  
Linear Discriminant ◽  
Recognition Efficiency ◽  
Signal Processing Methods ◽  
Selection Of

This paper presents a study on vibration-based fault diagnosis techniques of a commutator motor (CM). Proposed techniques used vibration signals and signal processing methods. The authors analysed recognition efficiency for 3 states of the CM: healthy CM, CM with broken tooth on sprocket, CM with broken rotor coil. Feature extraction methods called MSAF-RATIO-50-SFC (method of selection of amplitudes of frequencies ratio 50 second frequency coefficient), MSAF-RATIO-50-SFC-EXPANDED were implemented and used for an analysis. Feature vectors were obtained using MSAF-RATIO-50-SFC, MSAF-RATIO-50-SFC-EXPANDED, and sum of RSoV. Classification methods such as nearest mean (NM) classifier, linear discriminant analysis (LDA), and backpropagation neural network (BNN) were used for the analysis. A total efficiency of recognition was in the range of 79.16%–93.75% (TV). The proposed methods have practical application in industries.

scholarly journals AN INTELLIGENT CBMIR SYSTEM FOR DETECTION AND LOCALIZATION OF LUNG DISEASES

2021 ◽  
Vol 9 (08) ◽  
pp. 651-660
Author(s):  
Nora I. Yahia ◽  
◽  
Ayman I. Al-Dosouki ◽  
Sahar A. Mokhtar ◽  
Hany M. Harb ◽  
...  
Keyword(s):  
Medical Image ◽  
Lung Diseases ◽  
Medical Instrument ◽  
Detection Accuracy ◽  
Retrieval Process ◽  
Recognition Efficiency ◽  
Imaging Signs ◽  
Detection And Localization ◽  
Aided Diagnosis ◽  
Detector Model

The diagnosis of lung diseases is a complicated and time-consuming task for radiologists. Often radiologists struggle with accurately diagnosing lung diseases, They use Commonly CT imaging signs (CISs) which common appear in CT lung nodules in the diagnosis of lung diseases. Computer-aided diagnosis systems (CAD) can automatically diagnose and detect these signs by analyzing CT scans, which will reduce radiologists workload. The diagnosis and recognition efficiency and accuracy can be improved by using content-based medical image retrieval (CBMIR). This paper proposes a new intelligent CBMIR method to retrieve CISs helping in diagnosing and recognize lung diseases by using deep Convolutional Neural Network (CNN). Fine-tuned YOLOv4 (You Only Look Once) object detector model are proposed to fast detect and efficiently localize signs in real-time. The proposed CBMIR system can be applied as a useful and accurate medical instrument for diagnostics. The experimental results show an average detection accuracy of CT signs lung diseases as high as 92% and a mean average precision (MAP) of 0.92 is achieved using the proposed technique. Also, it takes only 0.1 milliseconds for the retrieval process. The proposed system presents high improvement as compared to the other system. It achieved better precision of retrieval results and the fastest run of the retrieval time.

scholarly journals The Google Lens analyzing quality: an analysis of the possibility to use in the educational process

2019 ◽  
Vol 53 (1) ◽  
pp. 219-234
Author(s):  
Viktor B. Shapovalov ◽  
Yevhenii B. Shapovalov ◽  
Zhanna I. Bilyk ◽  
Anna P. Megalinska ◽  
Ivan O. Muzyka
Keyword(s):  
Plant Species ◽  
Stem Education ◽  
Physical Characteristics ◽  
Educational Process ◽  
Mobile Software ◽  
Local Endemic ◽  
High Result ◽  
Plant Stem ◽  
Recognition Efficiency ◽  
Made In

Biology is a fairly complicated initial subject because it involves knowledge of biodiversity. Google Lens is a unique, mobile software that allows you to recognition species and genus of the plant student looking for. The article devoted to the analysis of the efficiency of the functioning of the Google Lens related to botanical objects. In order to perform the analysis, botanical objects were classified by type of the plant (grass, tree, bush) and by part of the plant (stem, flower, fruit) which is represented on the analyzed photo. It was shown that Google Lens correctly identified plant species in 92.6% cases. This is a quite high result, which allows recommending this program using during the teaching. The greatest accuracy of Google Lens was observed under analyzing trees and plants stems. The worst accuracy was characterized to Google Lens results of fruits and stems of the bushes recognizing. However, the accuracy was still high and Google Lens can help to provide the researches even in those cases. Google Lens wasn’t able to analyze the local endemic Ukrainian flora. It has been shown that the recognition efficiency depends more on the resolution of the photo than on the physical characteristics of the camera through which they are made. In the article shown the possibility of using the Google Lens in the educational process is a simple way to include principles of STEM-education and “New Ukrainian school” in classes.

Recognition Method of Abnormal Power Consumption State of Power Users Based on Strategy Gradient Algorithm

2021 ◽  
Vol 16 (7) ◽  
pp. 1090-1097
Author(s):  
Fu Bao ◽  
Yudou Gao
Keyword(s):  
Power Consumption ◽  
Gradient Algorithm ◽  
Process Testing ◽  
Power Load ◽  
Recognition Result ◽  
Abnormal Increase ◽  
Recognition Efficiency ◽  
State Of Power ◽  
Status Recognition ◽  
And Training

Because the traditional method ignores the problem of power load data preprocessing, the accuracy of the recognition result of the power consumption status is not high, the recognition efficiency is not high, and the recognition effect is not good. For this reason, a method for identifying the abnormal power consumption status of power users based on the strategy gradient algorithm is proposed. The preprocessing of power load data mainly includes the completion of missing data and the feature extraction of power load data. Based on the results of the preprocessing, the abnormal increase in user power consumption is detected. Finally, the strategy gradient algorithm is used for initial training and training process testing to complete the identification of the abnormal state of power users. The experimental results show that the accuracy of the power status recognition result of the proposed method is higher, and the recognition time is always less than 2.0 s, indicating that the recognition effect of the method is better.

scholarly journals Improved Anti-Collision Algorithm for the Application on Intelligent Warehouse

2019 ◽  
Vol 9 (8) ◽  
pp. 1596
Author(s):  
Yishu Qiu ◽  
Yezi Xu ◽  
Lvqing Yang ◽  
Jinsheng Lu ◽  
Dingzhao Li
Keyword(s):  
Radio Frequency Identification ◽  
Data Transmission ◽  
Rfid Tags ◽  
Small Data ◽  
Related Information ◽  
Frequency Identification ◽  
Stable Performance ◽  
Warehouse Operation ◽  
Recognition Efficiency ◽  
New Situation

As an important part of economic development, warehousing logistics also needs to be transformed and upgraded in order to adapt to the development of the new situation. The RFID reader records the related information of the goods to improve the efficiency of warehouse operation by identifying the RFID tags attached to the goods in batches. This paper also proposes an improved group-based anti-collision algorithm (GMQT) to solve the problem of tag collision in the process of Radio Frequency Identification (RFID) identification. The simulation results show that the GMQT algorithm improves the recognition efficiency of the system. The algorithm has the advantages of small data transmission and stable performance; in particular, the recognition efficiency is not affected by the number of tags.

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