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In the development of smart home products, energy monitoring, and control systems, there is a significant need for precise and reliable electric current measurement. Most domestic appliances, whether residential or commercial, are powered by power cords with two wires. A reliable and accurate current probe would be required to detect current in two-wire power cables. Existing current sensors for two-wire power cables are complex, bulky (they use a magnetic core), and need full access to the power cord. In this paper a coreless, contactless current probe that requires only limited access of the space around the two-wire power cable is proposed. The sensor unit presented here measures the current accurately even for certain types of misalignments of the power cord. This makes the current measurement less sensitive to minor mechanical displacements, radius of the conductors, and the overall insulation thickness of power cord. In the proposed current probe, one-of-a-kind and highly reliable arrangement of magnetic field sensors, are used to measure the magnetic flux density caused by the current passing in the power cord. The current is computed using a derived mathematical formula which is expressed in terms of the voltage outputs of the magnetic sensors. To provide a clear understanding of the measurement process, an analysis using the Finite Element Method (FEM) is performed. The functionality of the proposed current probe is theoretically tested. Considering different sources of errors, an accuracy of less than 1.5% is obtained. [Display omitted] • The proposed current probe does not require 360° space around the power cord, this method requires only partial space by the side of the power cord. • The proposed probe enables distance-independent current measurement, varying based on power cord insulation thickness and diameter. • The probe offers efficient, simple, cost-effective current measurement in power cords with three magnetic sensors. • To keep up with the errors, mathematical analysis was done with finite length of the power cord. • In addition, FEM analysis validated in presence of ferromagnetic materials and with interference due to external magnetic field. [ABSTRACT FROM AUTHOR]

[Display omitted] • This paper proposes a fast dynamic pose estimation method for industrial robots. • The encoder-driven error-state kinematics for industrial robots is reconstructed. • This method has robust estimation performance in complex desired trajectories. • This method reduces estimation error by about 50% compared to existing methods. • This method significantly improves the trajectory tracking accuracy of robots. Vision-based robotic trajectory tracking control is considered a promising technology. However, the slow sampling rate and latency of the vision sensor enormously limit the tracking performance. To conquer the issues, this paper proposes a dual-space error-state Kalman filter (DS-ESKF). By combining the encoder measurement with the vision measurement, the end-effector's pose between adjacent vision measurements is restored, and the pose estimation cycle is synchronized with the control cycle. The critical distinguishing of DS-ESKF is that the encoder-driven error-state kinematics for the industrial robot is reconstructed. The experimental results on the Staubli TX60 industrial robot show that compared with the existing dual-rate Kalman filters (DR-KF), DS-ESKF can reduce estimation errors by about 50 % and have robust estimation performance. By applying the trajectory tracking control scheme combining DS-ESKF with a simple PID controller to Staubli TX60, the tracking accuracy is significantly improved (±0.11 mm for position and ± 0.05°for orientation). [ABSTRACT FROM AUTHOR]

Blade Tip Timing (BTT) has become a promising method for online monitoring of rotating blades due to its non-contact measurement characteristics. However, due to the undersampling and non-uniform characteristics of blade tip timing signals, there are few studies on using them to diagnose rub-impact fault. This paper focuses on this topic and proposes a rub-impact diagnosis process. The contents of this paper are as follows: (1) A review of the existing rub-impact diagnosis technology and signal processing methods, then the advantages of diagnosis rub-impact using BTT are given. (2) The digital twin model framework for rotating blades has been established, including physical entity, digital entity, and the connections between them. (3) Dynamics analysis of both single blades and entire blade disks under rub-impact states has been carried out for the digital twin model, and six signal characteristics of blade tip under rubbing condition are summarized which provide prior knowledge for subsequent diagnosis. (4) Aiming at that traditional Enhanced Sparse Decomposition (ESD) method is prone to falling into local optimal solutions for BTT undersampled data, an envelope spectrum kurtosis enhanced sparse decomposition (ESKESD) method is proposed. (5) A complete rub-impact fault diagnosis process using digital twin is proposed. Finally, the effectiveness of the proposed method and diagnosis process are verified through simulation and experimental data. • Reviewed rub-impact diagnosis methods. • Established digital twin model for rotating blades. • Analyzed dynamics response of blades under rub-impact states. • Proposed envelope spectrum kurtosis enhanced sparse decomposition method for BTT data. • Proposed complete rub-impact diagnosis with Digital Twin and BTT. [ABSTRACT FROM AUTHOR]

• Propose a new non-invasive air gap flux density measurement method for PMSLM. • Introduce GAF method to show 1D magnetic density signals features obviously. • Introduce image matching method to obtain the best TMR sensor measurement position. • Integrated design of TMR sensor hardware circuit and PMSLM mover mounting module. • Establish CNN-LSTM model to accurately calculate PMSLM air gap magnetic density. This paper presents a new non-invasive air gap flux density measurement method for permanent magnet synchronous linear motor (PMSLM) using tunneling magnetoresistance (TMR) sensor and convolutional neural networks-long short-term memory (CNN-LSTM) regression modeling. First, the analytical and finite element models of the air gap magnetic field of PMSLM are established as the data basis. Second, TMR sensor is used to measure the external stray magnetic density. Gramian Angular Field method combined with image similarity matching technology are used to obtain the optimal measurement position of the TMR sensor. Then, a new deep learning regression method as CNN-LSTM is introduced to establish a high-precision mapping model to realize non-invasive high-precision measurement of the air gap magnetic density by "substituting external to internal." Finally, PMSLM prototype experimental platform with TMR sensor hardware acquisition circuit is built. Comparison confirmatory experiments with Gauss meter can verify the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

The parachute recovery system requires continuous improvement to meet the increasingly stringent performance requirements. Measuring parachute morphology and motion parameters during descent is essential for parachute performance and stability analysis. This paper presents a vision-based method for measuring the morphology and motion parameters of parachutes and designs a self-observing system for parachute testing. The system employs four synchronized cameras, with two cameras capturing the performance of the parachute during its operation, and the other two estimating the 6D pose of the load. By integrating the data from four synchronized cameras, we can achieve a more accurate and comprehensive understanding of the parachute's working condition during descent. Through the accuracy verification experiments and outfield airdrop experiments, the system is verified to have high measurement accuracy and practicability. The experiments show that the RMSE of the feature point location of the parachute measured by our method is 2.78 mm. • A novel vision-based method for measuring parachute morphology and motion parameters. • The method effectively integrates parachute measurements with load pose information. • A system suitable for small parachute self-observing was designed. • An air-drop experiment was implemented to verify the validity of our proposed method. [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]

• An optical fiber temperature sensor employing the vernier effect is presented. • The sensor has low production cost, simple fabrication, and small volume. • The sensor has good stability and repeatability. • It provides a new approach for in-depth research on temperature monitoring. This paper introduces a optical fiber temperature sensor based on vernier effect. The sensor's design consists of two integrated interferometric structures: the Mach-Zehnder interferometer (MZI) and the Fabry-Perot interferometer (FPI). Both interferometric structures exhibit symmetry, but they employ different types of optical fibers. Specifically, the MZI employs dual side hole fiber (DSHF), while the FPI is constructed using hollow-core optical fibers.Given the MZI's superior sensitivity to temperature changes compared to the FPI, it is chosen as the sensing arm. The paper conducts theoretical research and experimental validation of the optical fiber sensor's sensing principles. The findings reveal a sensor sensitivity of −305.42 pm/°C when measuring temperatures within the 32–50 °C range. Stability testing, repeatability testing, and response time testing are carried out using a constant temperature chamber and a water bath, affirming the sensor's excellent repeatability and stability, along with a response time faster than that of conventional thermometers. [ABSTRACT FROM AUTHOR]

[Display omitted] Crystal measurement, especially on-line measurement, in industrial crystallization is always a challenging topic in the past years, which is crucial to precisely understand crystal dynamic behavior and product quality control. With the development of photoelectric sensing technology, various on-line/in situ crystal measurement technologies (CMTs) have been developed to extract crystal morphological information, detect crystal form and even microstructure. This paper critically reviews the principles, advantages, and disadvantages of various existing CMTs, and their applications in exploring new crystal forms, characterization, prediction, control of shapes and size of crystals. The future research directions are proposed from the aspects of improving measurement accuracy, developing novel technologies for complex crystal behavior measurement, and promoting the application of CMTs in industrial crystallization. On balance, this review aims to enhance the awareness of scientists and engineers across industry and academia on CMTs, further promoting the development of automation and intellectualization of industrial crystallization processes. [ABSTRACT FROM AUTHOR]

• A fast CWE entity calculation method based on the local Boolean operation is clarified. • A 2D image mapping method is proposed to achieve the rapid extraction of engagement region. • A simulation platform embed with kernel program of CWE extraction is developed. • An accurate and fast prediction of instantaneous milling force during multi-axis machining has been achieved. The paper proposes a fast extraction method for cutter-workpiece engagement (CWE), which greatly facilitates the milling force prediction during multi-axis machining. One of the key cores of the method is to reconstruct the nearby cut-off material of workpiece using a set of past neighboring cutter location points (CLPs), and obtain the CWE entity through local Boolean operation among the current tool, workpiece blank, and reconstructed entity. Moreover, by performing 2-dimensional (2D) image mapping, the engagement boundary of CWE entity will be quickly obtained. Combining with the analytical formula of instantaneous undeformed chip thicknesses (IUCTs), the composite IUCT for tool side and bottom cutting edges during actual immersion is further derived. Finally, accurate and fast prediction of milling force with consideration of tool attitude, corner feature and bottom cutting effect is achieved. The results reveal that the method can predict milling force with an excellent performance of first-order computation complexity. [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]