查看更多>>摘要：? 2022 Elsevier LtdRegular medication intake is extremely important to get the desired therapeutic effect. However, this is quite difficult for patients, especially for the elderly and patients with Alzheimer's and other neurodegenerative diseases. In this paper, we present a sensor system for the patients to monitor the number of pills in a pharmacy bottle so that missing or repeated medicine-intake can be avoided. Polydimethylsiloxane (PDMS) with a special pattern is used to make pillars as springs between metal plates to form a capacitive sensor. Stress analysis and computer simulation are conducted to prove the feasibility of the new design. A circuit for small capacitance measurement is also designed to convert the capacitance to the voltage signal. Moreover, a sandwich of five capacitive sensing layers is fabricated into a weighing system to detect the weight of a medicine bottle with pills. In the detection system, the output voltage corresponds to the weight of pills with an average sensitivity is 20.06 mV/g. We conducted experiments examining the unused pills in the pharmacy bottles. Our results indicate the number of the pills from 0 to 100 can be measured accurately. Therefore, our detection system by using the flexible PDMS capacitive sensor can detect and record the number of pills, facilitating the monitoring of medicine intake for the elderly and patients suffering from dementia.
查看更多>>摘要：? 2022 The AuthorsThe reverberation time is one of the most important parameters used to characterize the acoustic property of an enclosure. In real-world scenarios, it is much more convenient to estimate the reverberation time blindly from recorded speech compared to the traditional acoustic measurement techniques using professional measurement instruments. However, the recorded speech is often corrupted by noise, which has a detrimental effect on the estimation accuracy of the reverberation time. To address this issue, this paper proposes a two-stage blind reverberation time estimation method based on noise-aware time–frequency masking. This proposed method has a good ability to distinguish the reverberation tails from the noise, thus improving the estimation accuracy of reverberation time in noisy scenarios. The simulated and real-world acoustic experimental results show that the proposed method significantly outperforms other methods in challenging scenarios.
查看更多>>摘要：? 2022 Elsevier LtdFor traditional infrared thermography (IRT) has a poor detection effect on deep defects, a novel infrared thermography method based on scanning laser in-depth heating was proposed to detect the deep debonding of hidden frame glass curtain walls structural adhesive which is located 6 mm underneath. The novel scanning laser in-depth heating thermography has advantages as follows: (1) scanning laser in-depth heating can heat the bonding interface of structure adhesive directly and has a greater detection capacity for deep debonding than surface heating; (2) the optimal detection scheme and parameters can get reasonable detection effect and prevent overheating which will cause structural adhesive performance degradation or damage; (3) thermal image reconstruction and integral enhancement can effectively improve detection effect and SNR of images. The test results demonstrated that the 10 mm × 8 mm debonding defect located 6 mm underneath form glass surface can be successfully detected.
查看更多>>摘要：? 2022 Elsevier LtdTraditional fault diagnosis models based on machine learning technology are difficult to apply to data samples under different working conditions. In a working environment that can only provide less computational resources, the parameter scale of algorithms is restricted, and the difficulty of transfer diagnosis is further increased. To this end, this paper proposes a transfer diagnosis method based on PMSPB-CNN (Convolutional Neural Networks Based on Parallel Multi-scale Pooling Branch) to solve the mechanical vibrational signal fault diagnosis problem under multiple working conditions with less computational cost. PMSPB-CNN introduces a parallel multi-scale pooling branch (PMSPB) structure to replace the basic convolution module used in traditional 1D-CNN. The multiple parallel paths in PMSPB structure contain the pooling layers with different scales and pooling methods to mine high-level features with different granularities. There are no network parameters that need to be trained in this structure, which greatly saves computational resources and reduces the risk of overfitting. Based on the transfer learning strategy of freezing the pretrained feature mining unit and fine-tuning the parameters of the fault identification unit, PMSPB-CNN can perform high-accuracy fault diagnosis on similar fault samples under multiple working conditions. The experimental results show that the parameter number of PMSPB-CNN is 774, and the number of parameters that need to be re-optimized for transfer diagnose is only 360. However, compared with the existing methods, even if the pre-trained network is directly used to diagnose faults under the other working conditions, the accuracy of PMSPB-CNN still maintain a high level on the two verification datasets, reaching 73.2% and 97.8% respectively. After fine-tuning the fault identification unit, PMSPB-CNN can achieve the 100% transfer diagnosis accuracy. In addition, the mechanism analysis experimental results show that when dealing with the data under different working conditions, the pooling layer in PMSPB structure with the best classification performance is not exactly the same. Furthermore, the output features of the frozen feature mining unit already highly recognizable before fine-tuning the fault identification unit. These conclusions proved that PMSPB structure provides sufficient fault tolerance and flexibility for the network, thereby improving the generalization of PMSPB-CNN.
查看更多>>摘要：? 2022 Elsevier LtdModern power system requires advanced and intelligent sensors-based protection such as a Phasor Measurement Unit that can provide faster, accurate, and real-time data acquisition. The aim is to allow accurate action-based performance for analysts in monitoring the transmission lines so that rapid actions can be taken during abnormal circumstances before the blackout occurs. Among different algorithms, this study focuses on modelling the non-recursive phasor estimation method in a power Simulink environment for a standard test system equipped with a developed algorithm to detect the fault zone. The algorithm includes an index for faulty bus classification based on the positive-sequence voltage measurements of the pre-fault and post-fault conditions, where the bus with a maximum differential percentage is identified as a faulted bus. An important differentiation of this work is that the proposed algorithm can coordinate with all phasor measurement units to accurately determine the faulty line using the index of unwrapped dynamic phase angles. Furthermore, the robustness of the indices is analyzed in the presence of sudden load change, measurement noise, and during nonlinear high-impedance faults. The performance of the comprehensive algorithm is investigated on the IEEE 9-bus and 39-bus standard test systems by applying different faults scenarios, considering several factors such as fault inception angles, line-fault resistance, ground-fault resistance. The comparative studies have shown that the proposed indices can play a significant role in segregating the fault and non-fault conditions, as they are needed to supervise the appropriate relays for enhancing the overall security of the power grid.
查看更多>>摘要：? 2022 Elsevier LtdThis paper introduces fluorescence strobo-stereoscopy (FSS) to suppress strong specular reflection and enable the full field of view (FFOV) 3D surface imaging while the part is rotating. Specular reflection off the target surface significantly degrades the image quality and becomes critical for highly reflective surface measurements. In FSS, the fluorescent dye-doped fluid applied on the machined surface is excited upon incident ultra-violet light and becomes a new light source by Stokes' Law. Thus, specular reflection off of smooth surface can be suppressed by separating the fluorescent light from the excitation light. The developed FSS comprises a pair of imaging cameras, spatial filters, and an excitation light source. As a result, FSS effectively rejected the specular reflection and improved the FFOV 3D surface image quality of the machined part by enhancing contrast in the rotating target surface. Such enhancements in 3D imaging allowed to identify manufacturing tolerance of the part and to detect the surface features. The axial and lateral accuracy errors of FSS were 2.3% and 1.4% with the target size of 4.07 mm and 0.215 mm, respectively. A whole view reconstruction of the cylindrical target sample was performed, and the corresponding cylindricity and diameter deviation were assessed. The fluid media effect and the target surface quality effect were discussed.
查看更多>>摘要：? 2022 Elsevier LtdThis research develops a novel hybrid model for multi-point deformation monitoring of super-high arch dams in operating conditions. The weighted distances are established to characterize deformation similarity degree, and then observation point groups with similar deformation regularities are produced using the bottom-up hierarchical clustering. The hybrid hydrostatic seasonal time (HHST) panel model is proposed, and the artificial fish swarm (AFS) algorithm is improved to optimize the undetermined parameters. The confidence ellipsoid criteria are established by applying multivariate statistic and principle of small probability event. According to the example analysis, the HHST panel model achieves a better fitting performance than the HST panel model; the applicability of HHST panel model is wider than that of HHST model; the optimization performance of the improved AFS is superior to that of the conventional AFS; confidence ellipsoid compared with confidence interval possesses a stricter identification for abnormal deformations and a clearer physical significance.
查看更多>>摘要：? 2022 Elsevier LtdThe authors regret ‘In the original Fig. 10, the grayscale statistical chart a) and b) should be swapped. The left one represents the dehazed image and not the raw image. The picture of the specimen in the upper right corner in Fig. 10 should remain unchanged, and only the left and right gray scale statistical chart should be swapped.’. [Fgiure presented] The authors would like to apologise for any inconvenience caused.
查看更多>>摘要：? 2022Increasing the rate of gear fault diagnosis is crucial to research on gear fault diagnosis methods. The existing signal processing methods have modal aliasing phenomena and poor adaptability. Moreover, the neural network method has serious problems, such as complex training and low accuracy. Based on these problems, this study aims to improve the shortcomings of existing gear fault diagnosis methods, thereby enhancing the adaptability and accuracy of gear fault diagnosis systems. This study proposes a method based on the combination of the parameter optimisation of variational mode decomposition (VMD) with cuckoo search (CS) and the probabilistic neural network (PNN) for intelligent identification of gearbox faults. Firstly, the energy parameters of each mode of the signals are extracted by the method of CS-improved VMD, and a feature matrix is constructed. Then, the optimal training parameters of the PNN are selected and the PNN is trained, and the performance is evaluated by the parameters RMSEC and RMSEP. Use the data set from Southeast University of China and the experimental data, and compare with the diagnosis classification effect of four other fault diagnosis models. The diagnostic results of the experimental data show that the fault diagnosis accuracy of the method proposed in this paper can reach an average of 98.5%, proving the advancement and effectiveness of this method over existing fault diagnosis technologies.
查看更多>>摘要：? 2022 Elsevier LtdThe metal structure hole edge is the most likely location for cracks in the aircraft structure. Quantitative monitoring of the of metal structure hole edge is an important research content in the field of structural health monitoring (SHM). This paper proposed a grating coating sensor for quantitatively monitoring structural cracks based on the electric potential method. First, the structural composition and working principle of the grating coating sensor were introduced. A grating coating sensor was prepared by arc ion plating technology, and the coating sensors ability to quantitatively monitor cracks was verified by fatigue crack monitoring experiment. Then, the influence mechanism of ambient temperature change on the output signal of coating sensors was analyzed and verified by experiments. Moreover, a temperature compensation technology was developed. Based on the grating coating sensor, an optimized sensor with a temperature compensation channel was proposed, and define the output signal, SC, of the optimized sensor. Finally, the fatigue crack monitoring experiment under varying ambient temperature was carried out. The results show that optimized grating coating sensors can quantitatively monitor fatigue cracks under varying ambient temperature. The research results also have great reference value for other sensors based on the electric potential method.
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