GLORIA

GEOMAR Library Ocean Research Information Access

X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • MDPI AG  (7)
  • 1
    Online Resource
    Online Resource
    Arc-Extinguishing Research on Semi-Closed Multi-Compression Tube Structures
    MDPI AG ; 2023
    In:  Energies Vol. 16, No. 3 ( 2023-01-18), p. 1057-
    Details
    In: Energies, MDPI AG, Vol. 16, No. 3 ( 2023-01-18), p. 1057-
    Abstract: Using lightning energy to extinguish the arc is a new lightning protection method. On this basis, the semi-closed multi-compression tube structures (SMTS) combined with the arc extinguishing structure studied in this paper can suppress the power frequency arc at the initial stage of arc establishment by using the coupling effect of current and gas. Firstly, through the simulation comparison method, the promotion effect of the semi-closed tube on the arc discharge was found. Furthermore, the two-dimensional impulse power frequency current coupling discharge model was established to obtain changes in physical quantities, such as temperature and conductivity. The conductivity decreased to the initial value in about 1 ms. Finally, the impulse power frequency combined arc extinguishing test was carried out. The test results show that the arc extinguishing structure can effectively extinguish power frequency freewheeling within 1 ms. It proves the effectiveness of the arc extinguishing structure.
    Type of Medium: Online Resource
    ISSN: 1996-1073
    URL: Article
    DOI: 10.3390/en16031057
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2437446-5
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 2
    Online Resource
    Online Resource
    Research on Energy Conversion in the Arc-Extinguishing Process of a Long-Gap Gas Lightning-Protection Device
    MDPI AG ; 2022
    In:  Energies Vol. 15, No. 20 ( 2022-10-12), p. 7490-
    Details
    In: Energies, MDPI AG, Vol. 15, No. 20 ( 2022-10-12), p. 7490-
    Abstract: In this paper, a two-dimensional axisymmetric module of gas arc extinguishing was simulated using energy balance theories. We used simulation to study the energy distribution change during the gas arc-extinguishing process. We built a lightning impulse current experimental platform according to the IEC standard, and experiments verified the preliminary conclusions of the simulation. Comparison curves of the experimental data and simulation calculations were drawn in the range of 20 kV to 70 kV. Simulation and experimental results showed that the arc-extinguishing ability of long-gap gas arcs is negatively correlated with voltage level and positively correlated with distance. Furthermore, within the allowable range of conditions, increasing the length of the chamber rather than shortening it helps to extinguish the arc more effectively and quickly.
    Type of Medium: Online Resource
    ISSN: 1996-1073
    URL: Article
    DOI: 10.3390/en15207490
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2437446-5
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 3
    Online Resource
    Online Resource
    Real-Time Detection Algorithm for Kiwifruit Canker Based on a Lightweight and Efficient Generative Adversarial Network
    MDPI AG ; 2023
    In:  Plants Vol. 12, No. 17 ( 2023-08-25), p. 3053-
    Details
    In: Plants, MDPI AG, Vol. 12, No. 17 ( 2023-08-25), p. 3053-
    Abstract: Disease diagnosis and control play important roles in agriculture and crop protection. Traditional methods of identifying plant disease rely primarily on human vision and manual inspection, which are subjective, have low accuracy, and make it difficult to estimate the situation in real time. At present, an intelligent detection technology based on computer vision is becoming an increasingly important tool used to monitor and control crop disease. However, the use of this technology often requires the collection of a substantial amount of specialized data in advance. Due to the seasonality and uncertainty of many crop pathogeneses, as well as some rare diseases or rare species, such data requirements are difficult to meet, leading to difficulties in achieving high levels of detection accuracy. Here, we use kiwifruit trunk bacterial canker (Pseudomonas syringae pv. actinidiae) as an example and propose a high-precision detection method to address the issue mentioned above. We introduce a lightweight and efficient image generative model capable of generating realistic and diverse images of kiwifruit trunk disease and expanding the original dataset. We also utilize the YOLOv8 model to perform disease detection; this model demonstrates real-time detection capability, taking only 0.01 s per image. The specific contributions of this study are as follows: (1) a depth-wise separable convolution is utilized to replace part of ordinary convolutions and introduce noise to improve the diversity of the generated images; (2) we propose the GASLE module by embedding a GAM, adjust the importance of different channels, and reduce the loss of spatial information; (3) we use an AdaMod optimizer to increase the convergence of the network; and (4) we select a real-time YOLOv8 model to perform effect verification. The results of this experiment show that the Fréchet Inception Distance (FID) of the proposed generative model reaches 84.18, having a decrease of 41.23 compared to FastGAN and a decrease of 2.1 compared to ProjectedGAN. The mean Average Precision (mAP@0.5) on the YOLOv8 network reaches 87.17%, which is nearly 17% higher than that of the original algorithm. These results substantiate the effectiveness of our generative model, providing a robust strategy for image generation and disease detection in plant kingdoms.
    Type of Medium: Online Resource
    ISSN: 2223-7747
    URL: Article
    DOI: 10.3390/plants12173053
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2704341-1
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 4
    Online Resource
    Online Resource
    Gearbox Fault Diagnosis Based on Optimized Stacked Denoising Auto Encoder and Kernel Extreme Learning Machine
    MDPI AG ; 2023
    In:  Processes Vol. 11, No. 7 ( 2023-06-27), p. 1936-
    Details
    In: Processes, MDPI AG, Vol. 11, No. 7 ( 2023-06-27), p. 1936-
    Abstract: The gearbox is one of the key components of many large mechanical transmission devices. Due to the complex working environment, the vibration signal stability of the gear box is poor, the fault feature extraction is difficult, and the fault diagnosis accuracy makes it difficult to meet the expected requirements. To solve this problem, this paper proposes a gearbox fault diagnosis method based on an optimized stacked denoising auto encoder (SDAE) and kernel extreme learning machine (KELM). Firstly, the particle swarm optimization algorithm in adaptive weight (SAPSO) was adopted to optimize the SDAE network structure, and the number of hidden layer nodes, learning rate, noise addition ratio and iteration times were adaptively obtained to make SDAE obtain the best network structure. Then, the best SDAE network structure was used to extract the deep feature information of weak faults in the original signal. Finally, the extracted fault features are fed into KELM for fault classification. Experimental results show that the classification accuracy of the proposed method can reach 97.2% under the condition of low signal-to-noise ratio, which shows the effectiveness and robustness of the proposed method compared with other diagnostic methods.
    Type of Medium: Online Resource
    ISSN: 2227-9717
    URL: Article
    DOI: 10.3390/pr11071936
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2720994-5
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 5
    Online Resource
    Online Resource
    Deep-Learning Image Stabilization for Adaptive Optics Ophthalmoscopy
    MDPI AG ; 2022
    In:  Information Vol. 13, No. 11 ( 2022-11-08), p. 531-
    Details
    In: Information, MDPI AG, Vol. 13, No. 11 ( 2022-11-08), p. 531-
    Abstract: An adaptive optics scanning laser ophthalmoscope (AOSLO) has the characteristics of a high resolution and a small field of view (FOV), which are greatly affected by eye motion. Continual eye motion will cause distortions both within the frame (intra-frame) and between frames (inter-frame). Overcoming eye motion and achieving image stabilization is the first step and is of great importance in image analysis. Although cross-correlation-based methods enable image registration to be achieved, the manual identification and distinguishing of images with saccades is required; manual registration has a high accuracy, but it is time-consuming and complicated. Some imaging systems are able to compensate for eye motion during the imaging process, but special hardware devices need to be integrated into the system. In this paper, we proposed a deep-learning-based algorithm for automatic image stabilization. The algorithm used the VGG-16 network to extract convolution features and a correlation filter to detect the position of reference in the next frame, and finally, it compensated for displacement to achieve registration. According to the results, the mean difference in the vertical and horizontal displacement between the algorithm and manual registration was 0.07 pixels and 0.16 pixels, respectively, with a 95% confidence interval of (−3.26 px, 3.40 px) and (−4.99 px, 5.30 px). The Pearson correlation coefficients for the vertical and horizontal displacements between these two methods were 0.99 and 0.99, respectively. Compared with cross-correlation-based methods, the algorithm had a higher accuracy, automatically removed images with blinks, and corrected images with saccades. Compared with manual registration, the algorithm enabled manual registration accuracy to be achieved without manual intervention.
    Type of Medium: Online Resource
    ISSN: 2078-2489
    URL: Article
    DOI: 10.3390/info13110531
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2599790-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 6
    Online Resource
    Online Resource
    Intelligent Fault Diagnosis Method for Gearboxes Based on Deep Transfer Learning
    MDPI AG ; 2022
    In:  Processes Vol. 11, No. 1 ( 2022-12-27), p. 68-
    Details
    In: Processes, MDPI AG, Vol. 11, No. 1 ( 2022-12-27), p. 68-
    Abstract: The complex operating environment of gearboxes and the easy interference of early fault feature information make fault identification difficult. This paper proposes a fault diagnosis method based on a combination of whale optimization algorithm (WOA), variational mode decomposition (VMD), and deep transfer learning. First, the VMD is optimized by using the WOA, and the minimum sample entropy is used as the fitness function to solve for the K value and penalty parameter α corresponding to the optimal decomposition of the VMD, and the correlation coefficient is used to reconstruct the signal. Second, the reconstructed signal after reducing noise is used to generate a two-dimensional image using the continuous wavelet transform method as the transfer learning target domain data. Finally, the AlexNet model is used as the transfer object, which is pretrained and fine-tuned with model parameters to make it suitable for early crack fault diagnosis in gearboxes. The experimental results show that the method proposed in this paper can effectively reduce the noise of gearbox vibration signals under a complex working environment, and the fault diagnosis method of using transfer learning is effective and achieves high accuracy of fault diagnosis.
    Type of Medium: Online Resource
    ISSN: 2227-9717
    URL: Article
    DOI: 10.3390/pr11010068
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2720994-5
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 7
    Online Resource
    Online Resource
    Combination of VMD Mapping MFCC and LSTM: A New Acoustic Fault Diagnosis Method of Diesel Engine
    MDPI AG ; 2022
    In:  Sensors Vol. 22, No. 21 ( 2022-10-30), p. 8325-
    Details
    In: Sensors, MDPI AG, Vol. 22, No. 21 ( 2022-10-30), p. 8325-
    Abstract: Diesel engines have a wide range of functions in the industrial and military fields. An urgent problem to be solved is how to diagnose and identify their faults effectively and timely. In this paper, a diesel engine acoustic fault diagnosis method based on variational modal decomposition mapping Mel frequency cepstral coefficients (MFCC) and long-short-term memory network is proposed. Variational mode decomposition (VMD) is used to remove noise from the original signal and differentiate the signal into multiple modes. The sound pressure signals of different modes are mapped to the Mel filter bank in the frequency domain, and then the Mel frequency cepstral coefficients of the respective mode signals are calculated in the mapping range of frequency domain, and the optimized Mel frequency cepstral coefficients are used as the input of long and short time memory network (LSTM) which is trained and verified, and the fault diagnosis model of the diesel engine is obtained. The experimental part compares the fault diagnosis effects of different feature extraction methods, different modal decomposition methods and different classifiers, finally verifying the feasibility and effectiveness of the method proposed in this paper, and providing solutions to the problem of how to realise fault diagnosis using acoustic signals.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s22218325
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2052857-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...