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Surface flaw inspection is of great importance for quality control in the field of manufacture. In this paper, a novel surface flaw inspection algorithm is proposed based on adaptive multiscale image collection (AMIC) using convolutional neural networks. First, the inspection networks are pretrained with ImageNet data set. Second, the AMIC is established, which consists of adaptive multiscale image extraction and with-contour local extraction from training images. Through the AMIC, the training data set is greatly augmented, and labels of images can be accomplished automatically without artificial consumption. Then, transfer learning is performed with the AMIC established from training data set. Finally, an automatic surface flaw inspection instrument for large-volume metal components embedded with the proposed inspection algorithm is designed. Experiments with small metal components are performed to analyze the influence of parameters, and comparative experiments are carried out. The inspecting precisions for indentation, scratch, and pitted surface of the proposed method are 97.3%, 99.5%, and 100%, respectively. The experimental results demonstrate the effectiveness of the proposed method in the detection of various surface flaws. [ABSTRACT FROM AUTHOR]

A precise robotic assembly for large-scale objects is an important application in manufacture. In this paper, we design a new assembly system for large-scale objects based on automatic guidance and alignment with two cameras and three laser distance sensors. The corresponding strategy consists of three stages. First, the object is guided from a remote position to a close one based on our proposed link-line model-based polygon detection and Computer Aided Design (CAD) - View method. Second, the object is aligned to the installation framework based on the proposed visual and laser alignment method. Finally, the object is automatically inserted into the installation framework. An accurate easily applying and close-loop hand-eye calibration method is also proposed. The proposed assembly system has high assembly precision and efficiency. In our test for a 710 \text mm \times 625 mm optical module, the maximum 6-DOF localization error of guidance and alignment is 0.53 mm in translation and 0.10° in orientation. The mechanical constraint-based controlling accuracy, i.e., the final assembly accuracy, is up to 0.2 mm in translation. [ABSTRACT FROM PUBLISHER]

In traditional Chinese pulse diagnosis (TCPD), to analyze the health condition of a patient, a practitioner should put three fingers on the wrist of the patient to adaptively feel the fluctuations in the radial pulse at the styloid processes. Thus, for comprehensive pulse signal acquisition, we should efficiently and accurately capture pulse signals at different positions and under different pressures. However, most conventional pulse signal acquisition devices can only capture signal at one position and under a fixed pressure, and thus only capture limited pulse diagnostic information. In this paper, we propose our novel solution to the problems of sensor positioning, sensor array design, pressure adjustment, and mechanical structure design, resulting in a compound system for multichannel pulse signal acquisition. Compared with the other systems, our system provides a systematic solution to sensor positioning, is effective in measuring the width of the pulse, and can capture multichannel pulse signals together with subsignals under different hold down pressures. Experimental results show that multichannel signals can achieve higher classification performance than single-channel signals. [ABSTRACT FROM PUBLISHER]

For measuring currents flowing in three-phase rectangular-shaped busbars, in this paper, a planar coil with air core is used as a current sensing unit. The vector potential of the busbar and the electromotive forces of the planar coil for single- and three-phase currents are calculated with a numerical integration arithmetic. The relative error caused by adjacent busbars was simulated when the transducers were used for the three-phase current measurement. The simulation results show that the relative error induced by the adjacent busbars is less than 2%. Planar-coil-based current transducers enclosed with silicon rubber insulator are developed. The successful field trial of the transducers for more than four years shows that the transducers can meet the field requirement. [ABSTRACT FROM PUBLISHER]

In this article, we presented a bio-impedance analysis method (BIAM) based on the human hand anatomy and propose a feasible and flexible BIAM system to obtain the bio-impedance signals for different gestures to achieve a high classification accuracy in hand gesture recognition with flexibility in electrode arrangement and fewer electrodes. To verify the proposed method, 11 gestures, including two sets: hand gestures and pinch gestures, were selected for the experiment. Based on the functional structure of the human hand, we identified appropriate electrode positions and placed five electrodes on the hand surface for bio-impedance signal measurement. Compared with the electrical impedance tomography (EIT) method, which uses a band with the same number of electrodes wrapped around the wrist, the proposed method achieved 98.7% recognition accuracies on the hand gesture set and 97.8% on the pinch gesture set, while the EIT achieved only 97.1% and 86.3%, respectively. In particular, the proposed method demonstrated the advantage of distinguishing gestures with similar muscle contractions. [ABSTRACT FROM AUTHOR]

This article presents a loosely coupled visual/inertial hybrid system for relative attitude determination on a moving platform. The inertial measurements are accomplished by two gyroscopes, of which the slave gyroscope removes the redundant motion of the platform from the total motion sensed by the master gyroscope. To correct integration drift error and compensate for the gyroscope biases, we fuse the inertial and visual measurements fused together through the error-state Kalman filter (ESKF). Furthermore, an improved ESKF is proposed by utilizing the variational Bayesian (VB) inference to adaptively estimate the process and measurement noise covariance. To realize direct estimation of the process noise covariance, a latent variable is introduced and inferred together with the noise covariances and error-state. The practical experimental setup is built up and experiments of two different motion trajectories are conducted. The ESKF, the cubature Kalman filter (CKF), the extended Kalman filter (EKF), the proposed filter, and the other two adaptive filters are implemented for comparison to verify the effectiveness of the proposed filter. The results demonstrate the proposed filter show superiority in reducing both the root mean square error (RMSE) and maximum error (ME) and improving the filter consistency. [ABSTRACT FROM AUTHOR]

Multiple ship tracking (MST) is an important task in marine surveillance and ship situational awareness systems. Considerable work has been conducted on multiple object tracking in recent years, but it has focused primarily on pedestrians and automobiles, leaving a gap in studies on MST due to the particularities of complex marine scenes, such as ship scale variations, the long-tailed distribution of ships, and long-term occlusions caused by ship movements. In this article, we present a robust deep affinity network (RoDAN) for MST. To overcome the above difficulties in MST, we start with the basic deep affinity network (DAN) and improve it in three aspects: scale, region, and motion. For the scale dimension, we integrate an atrous spatial pyramid pooling (ASPP) module to improve the modeling ability for multiscale ships. For the region dimension, we propose the joint global region modeling (JGRM) module, which further strengthens the modeling ability of DAN and exploit it to overcome the long-tailed distribution property of ships. For the motion dimension, we propose the motion-matching optimization (MMO) module to fine-tune the tracking results and make our tracker more robust, less reliant on the front-end detector, and ameliorate long-term occlusions. The experimental results demonstrate that our MST method outperforms the state-of-the-art methods. In particular, it reduces the number of ID switches (IDSs) and trajectory fragmentations (FMs), achieving holistically preferable performance. Meanwhile, our method achieves a comparable speed. [ABSTRACT FROM AUTHOR]

Multipass cell (MPC), with a long effective optical path length (OPL), is an effective method to improve measurement sensitivity in laser absorption spectroscopy for trace gas sensing. We present a space equation (SE) method to design MPC, which can solve inaccuracy caused by using paraxial-matrix (PM) method. The location and dimension of light spot pattern can be calculated accurately and intuitively through SE method. Meanwhile, actual effective OPL can be accurately calculated, SE method can reduce the interference effect, the direction of each ray can be obtained accurately, and, hence, designing the angle of outgoing ray is simple. By adjusting the mirror curvature, mirror space, incident angle, and incident position, we realized four dense spot patterns with different multiple reflections in MATLAB simulation. We calculated the shape changes of light spot after multiple reflection and its influence on the design of outgoing ray hole size accurately. We also considered the evolution of dense pattern and found a method easy to obtain multiple ring pattern. We verified the validity of the proposed model and built a compact MPC with seven rings dense spot pattern. Such MPC offers an effective OPL of 36.48 m with 198 times of reflection. We verified the tunable diode laser absorption spectroscopy (TDLAS) technology to measure 610-ppmv standard methane. In contrast to PM method, the improvement of the signal-to-noise ratio (SNR) of 1.36 times is achieved when the volume of MPC is reduced by 3 times. In addition, the SNR can be improved to 6.6 times, while the volume of MPC is the same. Results validated the ability of the SE method to design MPC. [ABSTRACT FROM AUTHOR]

Deep learning (DL) architecture, which exploits multiple hidden layers to learn hierarchical representations automatically from massive input data, presents a promising tool for characterizing fault conditions. This paper proposes a DL-based multi-signal fault diagnosis method that leverages the powerful feature learning ability of a convolutional neural network (CNN) in images. The proposed deep model is able to learn from multiple types of sensor signals simultaneously so that it can achieve robust performance and finally realize accurate induction motor fault recognition. First, the acquired sensor signals are converted to time–frequency distribution (TFD) by wavelet transform. Then, a deep CNN is applied to learning discriminative representations from the TFD images. Since then, a fully connected layer in deep architecture gives the prediction of induction motor condition based on learned features. In order to verify the effectiveness of the designed deep model, experiments are carried out on a machine fault simulator where both vibration and current signals are analyzed. Experimental results indicate that the proposed method outperforms traditional fault diagnosis methods, hence, demonstrating effectiveness in induction motor application. Compared with conventional methods that rely on delicate features extracted by experienced experts, the proposed deep model is able to automatically learn and select suitable features that contribute to accurate fault diagnosis. Compared with single-signal input, the multi-signal model has more accurate and stable performance and overcomes the overfitting problem to some degree. [ABSTRACT FROM AUTHOR]

Effective detection of multifaults in bearings and gears is a challenging issue in rotary machinery health monitoring. As such, a generalized Vold–Kalman filtering (GVKF)-based compound faults diagnosis method is presented in this paper. The technique includes four main steps: 1) a time–frequency ridge is separated from the time–frequency representation (TFR) of the vibration signal using a peak search method; 2) according to the time–frequency ridge, GVKF parameters corresponding to all the fault characteristic frequencies (FCFs) are estimated; 3) the fault feature components are obtained using the generalized demodulation transform (GDT) and the VKF with the GVKF parameters; and 4) the spectra obtained by the fast Fourier transform (FFT) are used to fault detection. The main contributions of the proposed method are as follows: 1) the influence of speed fluctuations and the unrelated harmonic components are removed through the integration of the GDT and the VKF and 2) the tachometerless GVKF parameters are defined and calculated to quantitatively detect different fault types, which avoids missed diagnosis and misdiagnosis. The proposed multifault diagnosis algorithm is verified by both simulation and experiment data. Comparison with other commonly used techniques has shown the advantage of the new method. [ABSTRACT FROM AUTHOR]

A small optical fiber temperature sensor employing the fluorescence lifetime of Mn4+ doped oxyfluoride germanate phosphor was presented and experimentally demonstrated, which has a fairly good temperature measurement precision. The performance of the fluorescent materials for temperature measurement was compared and analyzed. A small temperature sensing probe with a diameter of 1.8 mm was proposed. For the first time, the integrated tee-light-path structure was put forward, the transmission and separation of excitation light and fluorescence in only a single optical path was realized. Experimental results showed that standard deviation errors of 0.45 °C were obtained within the temperature range from 0 °C to 90 °C. [ABSTRACT FROM PUBLISHER]