Nondestructive testing system design for biological product based on vibration signal analysis of acceleration sensor

In order to reduce the disadvantages of current biological product quality testing methods, taking the quality testing in cocoon trade markets as an example, this paper has proposed a quality nondestructive testing method for biological products based on the analysis of vibration signal from acceleration sensors. According to the wavelet transformation analysis on the random vibration signal acquired from the acceleration sensor, the random vibration signal related to the silkworm chrysalis quality has been analyzed and reconstructed; then the characteristic values such as: mean value, variance, mean square root, waveform index, pulse factor, and so on of the quality signal have been extracted also; and then the characteristic values of the quality have been optimally selected within a fuzzy clustering method; at the end, a RBF neural network testing model with characteristic values from the silkworm chrysalis quality as an input signal was built. With these procedures, this paper has established a quality nondestructive testing system for silkworm chrysalis quality based on acceleration sensor signal measurement. The results from the application experiments demonstrated the effectiveness and applicability of this quality nondestructive system for quality testing of biological products. This quality nondestructive testing system has many advantages, including shortening the testing time, avoiding sample waste from traditional testing method, increasing the accuracy and reliability, which shows many bright social and economic benefits. This paper also provides the design and application of quality nondestructive testing systems based on vibration signal analysis with a theoretical support and experimental basis

Harmonic vibration synchronization phenomenon analysis of dual excitation rotors nonlinear vibration system

Based on the electromechanical coupling nonlinear dynamic model of nonlinear vibration system that driven by dual excitation rotors, the theoretical analysis of the harmonic vibration synchronization conditions for dual excitation rotors have been conducted at the balance singularity of the nonlinear vibration system. And the harmonic vibration synchronization phenomena of dual excitation rotors in nonlinear vibration system have also been quantitatively analyzed and interpreted. Under different parameter conditions, the validity of the harmonic vibration synchronization theoretical research has been verified by the numerical simulation and practical application experiments. And research results also demonstrate that with certain systemic characteristic conditions, the harmonic vibration synchronization movement phenomenon of dual excitation rotors could realize because of the harmonic vibration response of nonlinear vibration system. But the harmonic vibration synchronization phenomena only occur at certain order ratio of p/q. Compared to the frequency multiplication harmonic vibration synchronization and fractional frequency harmonic vibration synchronization, the prime harmonic vibration synchronization of dual excitation rotors is much easier to implement. The research of this paper can used to provide the theoretical basis and experiment support for the design and application of this kind of high-efficiency, energy-saving nonlinear vibration machine that driven by multiple excitation rotors, in vibration application engineering technical field

Nondestructive testing system design for biological product based on vibration signal analysis of acceleration sensor

In order to reduce the disadvantages of current biological product quality testing methods, taking the quality testing in cocoon trade markets as an example, this paper has proposed a quality nondestructive testing method for biological products based on the analysis of vibration signal from acceleration sensors. According to the wavelet transformation analysis on the random vibration signal acquired from the acceleration sensor, the random vibration signal related to the silkworm chrysalis quality has been analyzed and reconstructed; then the characteristic values such as: mean value, variance, mean square root, waveform index, pulse factor, and so on of the quality signal have been extracted also; and then the characteristic values of the quality have been optimally selected within a fuzzy clustering method; at the end, a RBF neural network testing model with characteristic values from the silkworm chrysalis quality as an input signal was built. With these procedures, this paper has established a quality nondestructive testing system for silkworm chrysalis quality based on acceleration sensor signal measurement. The results from the application experiments demonstrated the effectiveness and applicability of this quality nondestructive system for quality testing of biological products. This quality nondestructive testing system has many advantages, including shortening the testing time, avoiding sample waste from traditional testing method, increasing the accuracy and reliability, which shows many bright social and economic benefits. This paper also provides the design and application of quality nondestructive testing systems based on vibration signal analysis with a theoretical support and experimental basis

Dynamic performance analysis of nonlinear anti-resonance vibrating machine with the fluctuation of material mass

Aimed to solve the problem of weak anti-resonance performance and poor working stability in current anti-resonance vibrating machines, this paper presents a nonlinear dynamic model that reflects the actual working state of the anti-resonance machine. Under the material mass fluctuation condition, the dynamic response of the anti-resonance vibrating system has been discussed, and the dynamic parameters selection problem of the anti-resonance vibrating system has been analyzed which could be used to improve working performance stability. In the paper, the influence of nonlinear factors of the anti-resonance vibrating machine on the driving body and the working body has been analyzed under the material fluctuation conditions also. The results can provide the theoretical support and experimental basis for improving the design and working performance of the anti-resonance vibrating machine

Accurate Single Stage Detector Using Recurrent Rolling Convolution

Most of the recent successful methods in accurate object detection and localization used some variants of R-CNN style two stage Convolutional Neural Networks (CNN) where plausible regions were proposed in the first stage then followed by a second stage for decision refinement. Despite the simplicity of training and the efficiency in deployment, the single stage detection methods have not been as competitive when evaluated in benchmarks consider mAP for high IoU thresholds. In this paper, we proposed a novel single stage end-to-end trainable object detection network to overcome this limitation. We achieved this by introducing Recurrent Rolling Convolution (RRC) architecture over multi-scale feature maps to construct object classifiers and bounding box regressors which are "deep in context". We evaluated our method in the challenging KITTI dataset which measures methods under IoU threshold of 0.7. We showed that with RRC, a single reduced VGG-16 based model already significantly outperformed all the previously published results. At the time this paper was written our models ranked the first in KITTI car detection (the hard level), the first in cyclist detection and the second in pedestrian detection. These results were not reached by the previous single stage methods. The code is publicly available.Comment: CVPR 201

Dynamic performance analysis of nonlinear anti-resonance vibrating machine with the fluctuation of material mass

Aimed to solve the problem of weak anti-resonance performance and poor working stability in current anti-resonance vibrating machines, this paper presents a nonlinear dynamic model that reflects the actual working state of the anti-resonance machine. Under the material mass fluctuation condition, the dynamic response of the anti-resonance vibrating system has been discussed, and the dynamic parameters selection problem of the anti-resonance vibrating system has been analyzed which could be used to improve working performance stability. In the paper, the influence of nonlinear factors of the anti-resonance vibrating machine on the driving body and the working body has been analyzed under the material fluctuation conditions also. The results can provide the theoretical support and experimental basis for improving the design and working performance of the anti-resonance vibrating machine

Dynamic performance analysis of nonlinear anti-resonance vibrating machine with the fluctuation of material mass

Aimed to solve the problem of weak anti-resonance performance and poor working stability in current anti-resonance vibrating machines, this paper presents a nonlinear dynamic model that reflects the actual working state of the anti-resonance machine. Under the material mass fluctuation condition, the dynamic response of the anti-resonance vibrating system has been discussed, and the dynamic parameters selection problem of the anti-resonance vibrating system has been analyzed which could be used to improve working performance stability. In the paper, the influence of nonlinear factors of the anti-resonance vibrating machine on the driving body and the working body has been analyzed under the material fluctuation conditions also. The results can provide the theoretical support and experimental basis for improving the design and working performance of the anti-resonance vibrating machine

Towards Robust Referring Video Object Segmentation with Cyclic Relational Consensus

Referring Video Object Segmentation (R-VOS) is a challenging task that aims to segment an object in a video based on a linguistic expression. Most existing R-VOS methods have a critical assumption: the object referred to must appear in the video. This assumption, which we refer to as semantic consensus, is often violated in real-world scenarios, where the expression may be queried against false videos. In this work, we highlight the need for a robust R-VOS model that can handle semantic mismatches. Accordingly, we propose an extended task called Robust R-VOS, which accepts unpaired video-text inputs. We tackle this problem by jointly modeling the primary R-VOS problem and its dual (text reconstruction). A structural text-to-text cycle constraint is introduced to discriminate semantic consensus between video-text pairs and impose it in positive pairs, thereby achieving multi-modal alignment from both positive and negative pairs. Our structural constraint effectively addresses the challenge posed by linguistic diversity, overcoming the limitations of previous methods that relied on the point-wise constraint. A new evaluation dataset, R\textsuperscript{2}-Youtube-VOSis constructed to measure the model robustness. Our model achieves state-of-the-art performance on R-VOS benchmarks, Ref-DAVIS17 and Ref-Youtube-VOS, and also our R\textsuperscript{2}-Youtube-VOS~dataset.Comment: iccv 2023, https://github.com/lxa9867/R2VO

Online Video Instance Segmentation via Robust Context Fusion

Video instance segmentation (VIS) aims at classifying, segmenting and tracking object instances in video sequences. Recent transformer-based neural networks have demonstrated their powerful capability of modeling spatio-temporal correlations for the VIS task. Relying on video- or clip-level input, they suffer from high latency and computational cost. We propose a robust context fusion network to tackle VIS in an online fashion, which predicts instance segmentation frame-by-frame with a few preceding frames. To acquire the precise and temporal-consistent prediction for each frame efficiently, the key idea is to fuse effective and compact context from reference frames into the target frame. Considering the different effects of reference and target frames on the target prediction, we first summarize contextual features through importance-aware compression. A transformer encoder is adopted to fuse the compressed context. Then, we leverage an order-preserving instance embedding to convey the identity-aware information and correspond the identities to predicted instance masks. We demonstrate that our robust fusion network achieves the best performance among existing online VIS methods and is even better than previously published clip-level methods on the Youtube-VIS 2019 and 2021 benchmarks. In addition, visual objects often have acoustic signatures that are naturally synchronized with them in audio-bearing video recordings. By leveraging the flexibility of our context fusion network on multi-modal data, we further investigate the influence of audios on the video-dense prediction task, which has never been discussed in existing works. We build up an Audio-Visual Instance Segmentation dataset, and demonstrate that acoustic signals in the wild scenarios could benefit the VIS task