Deep Learning Based Feature Selection Algorithm for Small Targets Based on mRMR

Small target features are difficult to distinguish and identify in an environment with complex backgrounds. The identification and extraction of multi-dimensional features have been realized due to the rapid development of deep learning, but there are still redundant relationships between features, reducing feature recognition accuracy. The YOLOv5 neural network is used in this paper to achieve preliminary feature extraction, and the minimum redundancy maximum relevance algorithm is used for the 512 candidate features extracted in the fully connected layer to perform de-redundancy processing on the features with high correlation, reducing the dimension of the feature set and making small target feature recognition a reality. Simultaneously, by pre-processing the image, the feature recognition of the pre-processed image can be improved. Simultaneously, by pre-processing the image, the feature recognition of the pre-processed image can significantly improve the recognition accuracy. The experimental results demonstrate that using the minimum redundancy maximum relevance algorithm can effectively reduce the feature dimension and identify small target features

Nonlinear Quantum-Inspired Weighting Structuring Element for Bearing Impulse Response Signal Processing

In order to solve the disadvantage of conventional structuring element (CSE) where amplitude does not change in accordance with the analyzed signal, the quantum theory is combined and a nonlinear quantum-inspired weighting structuring element (NQWSE) is proposed. The NQWSE which is utilized to extract the bearing impulse response signal can adjust its amplitude according to the mechanical signal. Firstly, after constructing the multiple quantum bits system for signals, the mapping method which is employed to map the quantum space to the real space is presented and the parameters of the mapping method are set. The nonlinear amplitude probability is calculated based on the stochastic characteristics of the bearing signals, while the dynamic amplitude is calculated based on the local feature of the bearing signals in a subwindow. Then the mathematical formula of NQWSE is derived by incorporating the mathematical expectation into the quantum theory and the mapping method. Finally, the NQWSE is applied to extract the fault information of a failure bearing. The results reveal that NQWSE can extract the bearing impulse response signals exactly

THE REVIEW OF MECHANICAL FAULT DIAGNOSIS METHODS BASED ON CONVOLUTIONAL NEURAL NETWORK

Deep learning is good at abstract features from massive data and has good generalization ability,which has attracted more and more researchers’ attention. The Convolutional Neural Network（ CNN） is a classic structure of deep learing and which is being widely and successfully used in the fields of computer vision,target detection,natural language processing,and speech recognition. Based on a detailed analysis of the current status and needs of mechanical system fault diagnosis,this paper introduces the structure of CNN,and summarizes the application of CNN in the field of mechanical faults from the aspects of input data type,network structure design and migration learning. The problems of deep feature extraction and visualization are also discussed,and finally,the difficulties in mechanical fault diagnosis are analyzed and several problems to be solved in the field of mechanical fault diagnosis based on CNN are prospected

Changes in photosynthesis and activities of enzymes involved in carbon metabolism during exposure to low light in cucumber (Cucumis sativus) seedlings

Two cucumber genotypes, S404 and S1 with low light-sensitivity and low light-tolerance, respectively were used to investigate the oxygen consumption rate of photosystem I, the oxygen evolution rate of photosystem II, cab transcript levels, and activities of enzymes involved in photosynthetic carbon reduction cycle. The results show that short term (24 h) / long term (five and 10 days) low light stress had significant effect on PSII activities while PSI’s effect was not significant. Under the low light stress, S1 cab gene transcript levels were quickly recovered while S404 cab gene transcript levels were slowly recovered. The total dry mass and leaf area of S1 was lower than S404. Low light treatment decreased C3 photosynthetic carbon cycle enzyme activities involved in ribulose-1,5-bisphosphate carboxylase oxygenase (rubisco) carboxylation and fructose-1,6-bisphosphatase (FBPase), and increased C4 photosynthetic carbon cycle enzyme activities involved in nicotinamide adenine dinucleotide phosphate malate dehydrogenase (NADP-MDH). The NADP-MDH activity in S1 leaves increased significantly compared to S404. These observations suggest that S1 photosynthetic capacity is higher than S404 under low light conditions. Photosynthetic C4-microcycle possibly would have played a role in low light stress. Therefore, the transcript levels of cab and the involvement of NADP-MDH in low light-resistance need further research.Keywords: Low light, oxygen consumption rate, oxygen evolution rate, cab gene, NADP-MD

A Quantitative Evaluation Method for Obstacle Avoidance Performance of Unmanned Ship

In response to the urgent need for quantitative evaluation of the obstacle avoidance performance of an unmanned ship, a quantitative evaluation model is established to evaluate quantitatively the obstacle perception performance, static obstacle avoidance performance and dynamic obstacle avoidance performance of an unmanned ship. The base data for calculating are derived from the shore-based database; the evaluation factor layer is evaluated by the cost function method. Based on the established evaluation model, the quantitative evaluation score of the obstacle perception performance of the unmanned ship is obtained by data analysis for the 50 to 100 m buoy and 100 m island obstacle perception test. The quantitative evaluation score of the static obstacle avoidance performance obtained by testing the performance of a single obstacle, continuous obstacle and inflection obstacle is 68.8 points. For buoys as dynamic obstacles, the dynamic obstacle avoidance performance quantitative evaluation score of 64.13 points is obtained by testing the performance of obstacle avoidance in chasing, facing and crossing encounters. The analysis of the test data saved to the database verify the rationality of the quantitative evaluation model, which can provide reference for the quantitative evaluation and improvement of the unmanned ship’s obstacle avoidance performance

A Small Target Localization Method Based on the Magnetic Gradient Tensor

Currently, many small target localization methods based on a magnetic gradient tensor have problems, such as complex solution processes, poor stability, and multiple solutions. This paper proposes an optimization method based on the Euler deconvolution localization method to solve these problems. In a simulation, the Euler deconvolution method, an improved method of the Euler deconvolution method and our proposed method are analyzed under noise conditions. These three methods are evaluated in the field with complex magnetic interference in an experiment. The simulations show that the accuracy of the proposed method is higher than that of the improved Euler deconvolution method and is slightly lower for noisy conditions. The experimental results show that the proposed method is more precise and accurate than the Euler deconvolution and enhanced methods

A Test Method for Obstacle-Avoidance Performance of Unmanned Surface Vehicles Based on Mobile-Buoy–Shore Multisource-Sensing-Data Fusion

In order to avoid the influence of the test system itself on the autonomous navigation and performance test accuracy of unmanned surface vehicles (USVs), a test method for the obstacle-avoidance performance of USVs based on mobile-buoy–shore multisource-sensing-data fusion is proposed. In this method, a mobile-buoy-integrated test system is designed (that is, the test instrument is installed on the mobile buoy). The buoy is both the carrier of the test instrument and the obstacle. The software and hardware functions of the test system are realized in modules, and the obstacle-avoidance monitoring function of the USV is realized by the trajectory-tracking method of buoy perception preprocessing and shore adaptive weighted fusion. Then, on the basis of the mobile-buoy–shore sensing-data-fusion method, performance tests and a quantitative evaluation of the obstacle perception, static-obstacle avoidance, and dynamic-obstacle avoidance of the USV were carried out. The results show that: (1) the tested USV can accurately identify the distance between buoys; (2) the three static-obstacle-avoidance performance scores of the single obstacle, continuous obstacle, and inflection-point obstacle are 74.81, 77.14, and 47.61, respectively, and the quantitative evaluation score of the static-obstacle-avoidance comprehensive performance is 66.4; (3) the obstacle-avoidance-performance scores of overtaking, encounter, and cross encounter are about 53.92, 36.51, and 6.48, respectively, and the quantitative evaluation score of the comprehensive performance of the dynamic-obstacle avoidance is 72.36. The above quantitative evaluation results show that the system can: participate in track intervention and obstacle-avoidance monitoring as an obstacle; give the static- and dynamic-obstacle-avoidance quantitative evaluation results in a predetermined way, which verifies the feasibility and effectiveness of the obstacle-avoidance-performance test system of the USV on the basis of mobile-buoy–shore multisource-sensing fusion; and be used for the testing and evaluation of the obstacle-avoidance performance of USVs

Audio-visual keyword transformer for unconstrained sentence-level keyword spotting

As one of the most effective methods to improve the accuracy and robustness of speech tasks, the audio–visual fusion approach has recently been introduced into the field of Keyword Spotting (KWS). However, existing audio–visual keyword spotting models are limited to detecting isolated words, while keyword spotting for unconstrained speech is still a challenging problem. To this end, an Audio–Visual Keyword Transformer (AVKT) network is proposed to spot keywords in unconstrained video clips. The authors present a transformer classifier with learnable CLS tokens to extract distinctive keyword features from the variable‐length audio and visual inputs. The outputs of audio and visual branches are combined in a decision fusion module. As humans can easily notice whether a keyword appears in a sentence or not, our AVKT network can detect whether a video clip with a spoken sentence contains a pre‐specified keyword. Moreover, the position of the keyword is localised in the attention map without additional position labels. Exper- imental results on the LRS2‐KWS dataset and our newly collected PKU‐KWS dataset show that the accuracy of AVKT exceeded 99% in clean scenes and 85% in extremely noisy conditions. The code is available at https://github.com/jialeren/AVKT.ISSN:2468-232