Search

• The gated recurrent unit algorithm with fused attention mechanism is a target positioning method in a non-line-of-sight error environment. • The introduction of the attention mechanism can analyze features, select important features, and better handle non-line-of-sight errors. • Introducing the dropout layer during training reduces the risk of overfitting during training. • Through simulation and actual measurement experiments, the effectiveness of the algorithm in this paper is verified by comparing it with other famous algorithms. Amidst the increasing reliance on indoor positioning in areas such as large shopping malls, airports, and train stations, the precision of ultra-wideband (UWB) technology stands out for its centimeter-level accuracy. However, non-line-of-sight (NLOS) errors caused by people's movements significantly reduce indoor positioning accuracy. In response, this paper presents an innovative blend of gated recurrent unit algorithm with fused attention mechanism (GRU_Attention). This algorithm adeptly captures the dynamism in UWB signals and intelligently focuses on pivotal features to actualize accurate three-dimensional positioning while effectively reducing NLOS error. It has been demonstrated that GRU_Attention algorithm can reach 5.6 cm accuracy level through experiments, which improved by 74.47 %, 87.44 %, 14.46 %, 43.82 %, and 29.07 % respectively compared with the backpropagation (BP), least square (LS), GRU, convolutional neural network (CNN), and fused attention mechanism convolutional neural network (CNN_Attention) algorithms. This algorithm charts a new pathway to overcome NLOS errors in indoor personnel movement scenes. [ABSTRACT FROM AUTHOR]

• A novel general cascaded convolutional neural network (CNN) reading method for pointer reading is presented. • Proposes PMA method, which can increase the data samples significantly without introducing redundant background information. • Proposes ADFPN for feature fusion, learns branch importance, performs fusion at various levels. Enhances fusion efficiency. • Construct a dataset for pointer meter readings that contains images of common pointer meters in substations. Pointer meters continue to serve as the primary monitoring instruments within substations. To solve the issue of poor model accuracy and low reading accuracy when reading multiple types of pointer meters, this paper proposes a universal cascaded convolutional neural network for accurate reading of various types of pointer instruments. The proposed detection method consists of two stages: instrument digital positioning and instrument pointer fitting. In the digital(figure of a number) positioning stage, an instrument data augmentation method for "large background and small target" is proposed to improve the model's sensitivity to digital position and reduce the interference of redundant background information to the model. In addition, an adaptive feature fusion pyramid structure is proposed for more effective feature fusion and enhanced fuzzy small target recognition. Compared with the baseline model, the method introduced in this study realizes a 0.47 % improvement in target detection accuracy. In our proposed dataset, the average recognition accuracy for the digital position location phase is 98.65 %,the average reading errors in square instruments, circular instruments, and instruments with uneven scales are 0.76 %, 0.73 %, and 0.67 % respectively. The proposed universal detection method in this paper achieves ultra-high precision and can be applied to reading pointer instruments of different types and scales. Compared with existing pointer meter reading methods, the proposed detection method showcases superior simplicity and effectiveness. Furthermore, it showcases a reduced measurement error, thereby bolstering its practical utility in industrial contexts. [ABSTRACT FROM AUTHOR]

• A novel methodology integrating percussion and CNN model is established to detect concrete voids. • The CNN model trained by both CWT and MFCC show satisfying categorizing performances. • The methodology proves its good noise-immune capacity in processing percussion sound signals. • The superiority of the proposed method is well demonstrated by comparison with ML methods. As a fundamental construction material, concrete commonly encounter void defects, which could significantly impair the integrity of concrete members and seriously weaken their load-bearing capacity. Worse still, void defects typically exist inside concrete members, making it difficult to detect them in real time. Therefore, conducting regular inspections for internal void defects of concrete structures is of great importance to maintain structural health. This paper investigates an integrated method to detect concrete voids based on percussion approach and a convolutional neural network (CNN). Firstly, percussion-induced sound signals were collected from 18 concrete specimens with different precast void defects. Each specimen was tapped 100 times, for a total of 1,800 percussion-induced acoustic signals. Subsequently, continuous wavelet transform (CWT) and Mel frequency cepstral coefficient (MFCC) were introduced to convert the collected acoustic signals into two types of images. The converted images were divided into testing and training datasets according to the ratio of 1:3, and 600 images were input to the CNN as the test dataset and 1200 images as the training dataset. The CNN was then trained and verified by the joint input of CWT and MFCC images to perform concrete voids classification. The result shows that the proposed method presents a satisfying classification accuracy of 100 %. Next, percussion tests under 5 levels of noisy environments were conducted, and the results prove the remarkable anti-noise performance of the methodology by achieving 88.12 % accuracy even at the strongest signal-to-noise ratio of 0 dB. Finally, the superiority of the proposed method was further demonstrated through the accuracy comparison with machine learning (ML) methods, with the accuracy consistently outperforming over 25 %. In conclusion, the proposed method has a great potential for future in-situ applications in concrete void detection. [ABSTRACT FROM AUTHOR]

• A new blind T60 estimation method using a denoising-aided multi-task learning network is proposed for accurate T60 estimation. • The noise-robustness and applicability of the proposed model are comprehensively investigated. • Audio datasets with unknown RIRs and noise recorded in real-world classrooms are used to further validate the effectiveness of the proposed method. The noise in reverberant speech severely limits the estimation accuracy of reverberation time T60 using current deep learning (DL) methods. To address this issue, this paper proposes a denoising-aided multi-task learning (DAMTL) method for blind T60 estimation. Specifically, speech denoising, as an auxiliary module, is conducted joint training with T60 estimation for more accurate prediction accuracy. These two tasks are integrated into one DL network by sharing the same encoder network, where the complex-valued spectrum is introduced to extract comprehensive high-dimensional features from noisy reverberant speech. Subsequently, complex operation of 2-D convolutional neural network (Conv2d), batch normalization and long short-term memory (LSTM) are formulated. Furthermore, the noise robustness and applicability of the DAMTL are fully discussed by comparison with state-of-the-art DL-based methods using simulated data and real-world recorded data. The results prove the effectiveness and superiority of the proposed DAMTL, especially in low signal-to-noise ratio (SNR) scenarios and practical applications. [ABSTRACT FROM AUTHOR]

• An intelligent analysis framework of the super-large diameter shield machine performance is established. • The key parameters which affect the TAS (tunnelling advance speed) of shield machine are obtained. • Compared with traditional GRU and LSTM deep learning prediction models. The tunnelling advance speed (TAS) of a super-large-diameter tunnel boring machine (TBM) significantly affects project progress and safety. Effective prediction of the TAS of a TBM is crucial for early warning and evaluation of its excavation performance. This paper proposes a model based on convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) to predict TAS. This model can more effectively extract and retain many features; additionally, it considers the temporal relationships among the operating parameters of the TBM. Additionally, a Kalman filter (KF) is incorporated into the model to effectively remove input parameter noise, leading to a more accurate prediction of TBM excavation speed. A case study indicated that the pressure of the excavation chamber and the pitch angle of the shield tunnelling machine were correlated with the advance speed. The research results show that the proposed model can accurately and effectively predict TAS. [ABSTRACT FROM AUTHOR]

• A novel spatiotemporal deep learning is proposed to detect abnormal sensor data. • The first layer of 1DCNN model is replaced by wavelet scattering transform. • The bidirectional LSTM is developed to recover abnormal sensor data. • Different sensor configurations are considered to select the optimal sensors. • Simulated and actual engineering data are adopted to validate the methodology. This paper proposes a novel deep-learning-enabled framework for abnormal data detection and recovery considering spatial-temporal correlation among sensors. The wavelet scattering transform is adopted to replace the first layer of one-dimension convolutional neural network classification model. The multiple typical sensor failure modes of sensor are identified including bias, drifting, gain, precision degradation, and three complete faults (constant; constant + noise; noise). The bidirectional long-short time memory (BLSTM) network is developed to recover abnormal data through positive and negative propagation. The BLSTM cell involving forget, input, and output gate is utilized to adaptively transmit valuable information of long-term data. Different sensor configurations are conducted to select the optimal input sensors of BLSTM model. The proposed models are then validated by three engineering cases. The results show that the failure mode diagnosis and recovery accuracy are over 90 %. Sensor selection has a large impact on the data reconstruction, and selecting sensors with higher correlation can achieve a higher accuracy. In addition, the data recovery accuracy of the proposed BLSTM model surpasses that of traditional long-short time memory models. [ABSTRACT FROM AUTHOR]

• A hybrid deep learning model, i.e., CNN-LSTM is presented for the quantitative detection of polygonal wheels. • The hybrid model makes up for the deficiency of a single model and improves the reliability and generalization performance. • The effectiveness and robustness of the proposed model are proved under different running speeds and SNRs. • Both dynamics simulations and field tests are carried out to show the proposed method has high detection accuracy. Wheel polygonization poses severe threat to the operational safety of railway vehicles. To improve running safety, condition monitoring of wheel polygonization based on axle box acceleration (ABA) signals is an effective method. However, the signals usually contain strong noise, making it difficult to diagnose polygonal wheels quantitatively. To address these issues, this paper presents a quantitative detection method for wheel polygonization based on a deep learning (DL) model that integrates Convolutional Neural Network (CNN) with Long Short-term Memory (LSTM). The CNN is responsible for automatically extracting the sensitive spatial features from the original input and it can retain effective signal components. The LSTM is introduced to fuse the local features and conduct sequence modeling for polygonal wear amplitude estimation. Both dynamics simulations and field tests are carried out to verify the feasibility of the proposed method. The detection results indicate that the proposed model can accurately estimate the wear amplitudes. [ABSTRACT FROM AUTHOR]