scholarly journals Research on Oil Painting Image Extraction and Identification Method based on Intelligent Vision

CONVERTER ◽  
2021 ◽  
pp. 742-749
Author(s):  
Xianjun Yu
Keyword(s):  
High Efficiency ◽  
Visual Detection ◽  
Feature Fusion ◽  
Identification Accuracy ◽  
Identification Performance ◽  
Oil Painting ◽  
Oil Paintings ◽  
Detection Model ◽  
Identification Method ◽  
Feature Difference

A high-precision and high-efficiency oil painting identification method is the auxiliary basis for authenticating works, because it can improve the efficiency and credibility of oil painting identification. Therefore, an oil painting image extraction method based on intelligent vision was proposed.An intelligent visual detection model was constructed to obtain the characteristics of oil painting images.The oil painting feature fusion method based on intelligent vision was adopted to integrate the color and shape features of oil painting features, calculate oil painting feature difference coefficient and difference feature threshold, and realize oil painting image extraction by oil painting image extraction rules.The research results verified that the proposed method could effectively identify the authenticity of oil paintings. Compared with the expert identification method and the identification method based on deep learning, it can be seen that the method had the highest identification accuracy, the shortest identification time, the best anti-interference, and the remarkable identification performance, so it had a high application value.

Structural Damage Identification Method Based On Transmissibility in Time Domain

2021 ◽  
Author(s):  
Yunfeng Zou ◽  
Xuandong Lu ◽  
Jinsong Yang ◽  
Xuhui He ◽  
Tiantian Wang
Keyword(s):  
Time Domain ◽  
Structural Damage ◽  
High Efficiency ◽  
Damage Identification ◽  
Identification Accuracy ◽  
Reconstruction Method ◽  
Fe Model ◽  
Time Frequency ◽  
Identification Method ◽  
Structural Damage Identification

Abstract Structural damage identification technology is of great significance to improve the reliability and safety of civil structures and has attracted much attention in the study of structural health monitoring. In this paper, a novelty structural damage identification method based on the transmissibility in time domain is proposed. The method takes the discrepancy of transmissibility of structure response in time domain before and after damage as the basis of finite element model modification. The damage location and damage degree are obtained through iteration by minimizing the difference between the measurements at gauge locations and the reconstruction response extrapolated by FE model. Taking the advantage of the response reconstruction method based on empirical mode decomposition, the damage information is possible to obtain in the absence of prior knowledge on external excitation information. Moreover, this method is carried out in the time domain, without the need to identify the modal parameters and perform time-frequency analysis, which simplicity ensures the high efficiency of damage identification. The effectiveness and accuracy of the proposed method are studied by simulation, including reconstruction error and measurement noise. The identification results demonstrate that the proposed structural damage identification method improves the calculation effectiveness considerably and ensures the identification accuracy.

scholarly journals Eyewitness identification performance is not affected by time-of-day optimality

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sergii Yaremenko ◽  
Melanie Sauerland ◽  
Lorraine Hope
Keyword(s):  
Eyewitness Identification ◽  
Time Of Day ◽  
Decision Time ◽  
Identification Accuracy ◽  
Optimal Time ◽  
Identification Performance ◽  
Accurate Identification ◽  
Facial Stimuli ◽  
Time Accuracy ◽  
Accuracy Relationship

AbstractThe circadian rhythm regulates arousal levels throughout the day and determines optimal periods for engaging in mental activities. Individuals differ in the time of day at which they reach their peak: Morning-type individuals are at their best in the morning and evening types perform better in the evening. Performance in recall and recognition of non-facial stimuli is generally superior at an individual’s circadian peak. In two studies (Ns = 103 and 324), we tested the effect of time-of-testing optimality on eyewitness identification performance. Morning- and evening-type participants viewed stimulus films depicting staged crimes and made identification decisions from target-present and target-absent lineups either at their optimal or non-optimal time-of-day. We expected that participants would make more accurate identification decisions and that the confidence-accuracy and decision time-accuracy relationships would be stronger at optimal compared to non-optimal time of day. In Experiment 1, identification accuracy was unexpectedly superior at non-optimal compared to optimal time of day in target-present lineups. In Experiment 2, identification accuracy did not differ between the optimal and non-optimal time of day. Contrary to our expectations, confidence-accuracy relationship was generally stronger at non-optimal compared to optimal time of day. In line with our predictions, non-optimal testing eliminated decision-time-accuracy relationship in Experiment 1.

scholarly journals Unknown SAR Target Identification Method Based on Feature Extraction Network and KLD–RPA Joint Discrimination

2021 ◽  
Vol 13 (15) ◽  
pp. 2901
Author(s):  
Zhiqiang Zeng ◽  
Jinping Sun ◽  
Congan Xu ◽  
Haiyang Wang
Keyword(s):  
Feature Extraction ◽  
Position Angle ◽  
Automatic Target Recognition ◽  
Identification Accuracy ◽  
Training Dataset ◽  
Target Feature ◽  
Practical Applications ◽  
Feature Vectors ◽  
Identification Method ◽  
Leibler Divergence

Recently, deep learning (DL) has been successfully applied in automatic target recognition (ATR) tasks of synthetic aperture radar (SAR) images. However, limited by the lack of SAR image target datasets and the high cost of labeling, these existing DL based approaches can only accurately recognize the target in the training dataset. Therefore, high precision identification of unknown SAR targets in practical applications is one of the important capabilities that the SAR–ATR system should equip. To this end, we propose a novel DL based identification method for unknown SAR targets with joint discrimination. First of all, the feature extraction network (FEN) trained on a limited dataset is used to extract the SAR target features, and then the unknown targets are roughly identified from the known targets by computing the Kullback–Leibler divergence (KLD) of the target feature vectors. For the targets that cannot be distinguished by KLD, their feature vectors perform t-distributed stochastic neighbor embedding (t-SNE) dimensionality reduction processing to calculate the relative position angle (RPA). Finally, the known and unknown targets are finely identified based on RPA. Experimental results conducted on the MSTAR dataset demonstrate that the proposed method can achieve higher identification accuracy of unknown SAR targets than existing methods while maintaining high recognition accuracy of known targets.

scholarly journals Combination of Dual-Energy X-ray Transmission and Variable Gas-Ejection for the In-Line Automatic Sorting of Many Types of Scrap in One Measurement

2021 ◽  
Vol 11 (10) ◽  
pp. 4349
Author(s):  
Tianzhong Xiong ◽  
Wenhua Ye ◽  
Xiang Xu
Keyword(s):  
High Efficiency ◽  
Nearest Neighbor ◽  
High Energy ◽  
Dual Energy ◽  
Center Of Gravity ◽  
Low Energy ◽  
Identification Accuracy ◽  
Flow Process ◽  
X Ray ◽  
Automatic Sorting

As an important part of pretreatment before recycling, sorting has a great impact on the quality, efficiency, cost and difficulty of recycling. In this paper, dual-energy X-ray transmission (DE-XRT) combined with variable gas-ejection is used to improve the quality and efficiency of in-line automatic sorting of waste non-ferrous metals. A method was proposed to judge the sorting ability, identify the types, and calculate the mass and center-of-gravity coordinates according to the shading of low-energy, the line scan direction coordinate and transparency natural logarithm ratio of low energy to high energy (R_value). The material identification was satisfied by the nearest neighbor algorithm of effective points in the material range to the R_value calibration surface. The flow-process of identification was also presented. Based on the thickness of the calibration surface, the material mass and center-of-gravity coordinates were calculated. The feasibility of controlling material falling points by variable gas-ejection was analyzed. The experimental verification of self-made materials showed that identification accuracy by count basis was 85%, mass and center-of-gravity coordinates calculation errors were both below 5%. The method proposed features high accuracy, high efficiency, and low operation cost and is of great application value even to other solid waste sorting, such as plastics, glass and ceramics.

scholarly journals The effect of a secondary task on identification accuracy of oxygen saturation ranges using an enhanced pulse oximetry sonification

2016 ◽  
Vol 60 (1) ◽  
pp. 628-632
Author(s):  
Estrella Paterson ◽  
Penelope Sanderson ◽  
Neil Paterson ◽  
David Liu ◽  
Robert Loeb
Keyword(s):  
Oxygen Saturation ◽  
Secondary Task ◽  
Ambient Noise ◽  
Visual Display ◽  
Identification Accuracy ◽  
Future Research ◽  
Identification Performance ◽  
Arithmetic Task ◽  
Disruption Effect ◽  
The Difference

In the operating theatre, anesthesiologists monitor an anesthetized patient’s oxygen saturation (SpO2) with a visual display but also with an auditory tone, or sonification. However, if the anesthesiologist must divide their attention across tasks, they may be less effective at recognising their patient’s SpO2 level. Previous research indicates that a sonification enhanced with additional sound dimensions of tremolo and brightness more effectively supports participants’ identification of SpO2 ranges than a conventional sonification does. This laboratory study explored the effect of a secondary task on participants’ ability to identify SpO2 range when using a conventional sonification (LogLinear sonification) versus an enhanced sonification (Stepped Effects sonification). Nineteen non-clinician participants who used the Stepped Effects sonification were significantly more effective at identifying SpO2 range ( Md = 100%) than were 18 participants using the LogLinear sonification ( Md = 80%). Range identification performance of participants using the Stepped Effects sonification tended to be less disrupted by a concurrent arithmetic task (drop from Md = 100% to 95%) than it was for participants using the LogLinear sonification (drop from Md = 80% to 73%). However, the disruption effect in each case was small, and the difference in disruption across sonifications was not statistically significant. Future research will test the sonifications under more intense cognitive load and in the presence of ambient noise.

Design and experimental validation of a cam-based constant-force compression mechanism with friction considered

2018 ◽  
Vol 233 (11) ◽  
pp. 3873-3887 ◽  
Author(s):  
Ming Li ◽  
Wei Cheng ◽  
Ruili Xie
Keyword(s):  
Design Theory ◽  
High Efficiency ◽  
Rolling Friction ◽  
Constant Force ◽  
Identification Method ◽  
Compression Mechanism ◽  
Engineering Significance ◽  
Nonlinear Vibration Control ◽  
Preparatory Work ◽  
Differential Relationship

Due to the quasi-zero-stiffness and overload protection characteristics, constant-force mechanisms can be widely used in nonlinear vibration control, high-efficiency shock isolation, and other engineering fields. As a preparatory work for the further applications, this paper presents a cam-based constant-force compression mechanism and validates the quasi-static characteristics experimentally. By employing the friction considered profile identification method to design the cam and through the interaction between the cam and spring-sliders, the constant-force compression mechanism can passively output the desired constant force over a sufficiently large displacement. The design theory is firstly introduced in detail. Through establishing and solving the differential relationship between the lateral elastic force and vertical constant force, the constant-force compression mechanism under various frictional conditions can be designed. Then, constant-force compression mechanism prototypes corresponding to sliding and rolling friction are designed, fabricated and tested respectively. The results show that both the prototypes have the satisfactory characteristics as with the design requirements. Moreover, the relative generality and stronger engineering applicability of the proposed friction considered profile identification method are proved since it can not only cover the frictionless (micro-friction) cases, but keep the constant-force behavior of the constant-force compression mechanism under the nonignorable friction conditions. Therefore, compared with the existing cam-roller constant-force mechanisms that must ensure the ignoring micro-friction demand, the presented constant-force compression mechanism taking friction into consideration has important engineering significance since it can reduce this machining requirement.

scholarly journals T-S Fuzzy Systems Optimization Identification Based on FCM and PSO

2020 ◽  
Author(s):  
Yaxue Ren ◽  
Fucai Liu ◽  
Jingfeng Lv ◽  
Aiwen Meng ◽  
Yintang Wen
Keyword(s):  
Membership Function ◽  
Gaussian Function ◽  
Model Identification ◽  
Fuzzy Model ◽  
Pso Algorithm ◽  
Identification Accuracy ◽  
Fine Tuning ◽  
Recursive Least Squares ◽  
Identification Method ◽  
Two Parameters

Abstract The division of fuzzy space is very important in the identification of premise parameters and the Gaussian membership function is applied to the premise fuzzy set. However, the two parameters of Gaussian membership function, center and width, are not easy to be determined. In this paper, a novel T-S fuzzy model optimal identification method of optimizing two parameters of Gaussian function based on Fuzzy c-means (FCM) and particle swarm optimization (PSO) algorithm is presented. Firstly, we use FCM algorithm to determine the Gaussian center for rough adjustment. Then, under the condition that the center of Gaussian function is fixed, the PSO algorithm is used to optimize another adjustable parameter, the width of the Gaussian membership function, to achieve fine tuning, so as to complete the identification of prerequisite parameters of fuzzy model. In addition, the recursive least squares (RLS) algorithm is used to identify the conclusion parameters. Finally, the effectiveness of this method for T-S fuzzy model identification is verified by simulation examples, and the higher identification accuracy can be obtained by using the novel identification method described compared with other identification methods.

scholarly journals Bird Species Identification Using Spectrogram Based on Multi-Channel Fusion of DCNNs

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1507
Author(s):  
Feiyu Zhang ◽  
Luyang Zhang ◽  
Hongxiang Chen ◽  
Jiangjian Xie
Keyword(s):  
Species Identification ◽  
Feature Fusion ◽  
Bird Species ◽  
The Other ◽  
Identification Accuracy ◽  
Mean Average Precision ◽  
Training Dataset ◽  
Model Parameters ◽  
Average Precision ◽  
Fusion Mode

Deep convolutional neural networks (DCNNs) have achieved breakthrough performance on bird species identification using a spectrogram of bird vocalization. Aiming at the imbalance of the bird vocalization dataset, a single feature identification model (SFIM) with residual blocks and modified, weighted, cross-entropy function was proposed. To further improve the identification accuracy, two multi-channel fusion methods were built with three SFIMs. One of these fused the outputs of the feature extraction parts of three SFIMs (feature fusion mode), the other fused the outputs of the classifiers of three SFIMs (result fusion mode). The SFIMs were trained with three different kinds of spectrograms, which were calculated through short-time Fourier transform, mel-frequency cepstrum transform and chirplet transform, respectively. To overcome the shortage of the huge number of trainable model parameters, transfer learning was used in the multi-channel models. Using our own vocalization dataset as a sample set, it is found that the result fusion mode model outperforms the other proposed models, the best mean average precision (MAP) reaches 0.914. Choosing three durations of spectrograms, 100 ms, 300 ms and 500 ms for comparison, the results reveal that the 300 ms duration is the best for our own dataset. The duration is suggested to be determined based on the duration distribution of bird syllables. As for the performance with the training dataset of BirdCLEF2019, the highest classification mean average precision (cmAP) reached 0.135, which means the proposed model has certain generalization ability.

Sign in / Sign up

Export Citation Format

Share Document