Xuanquan Wang

Unlike uniform motion, when an object moves underwater with variable speed, it experiences additional resistance from the water, commonly referred to as added mass force. At present, several methods exist to solve this force, including theoretical, experimental, and simulation approaches. This paper addresses the challenge of determining the added mass force for irregularly shaped small objects undergoing variable speed motion underwater, proposing a method to obtain the added mass force through numerical simulation. It employs regression analysis and parameter separation analysis to solve the added mass force, added mass, viscous drag coefficient, and pressure drag coefficient. The results indicate that an added mass force exists during both the acceleration and deceleration of the object, with little difference between them. Under the same velocity conditions, significant differences exist in pressure drag forces, while differences in viscous drag forces are not significant. This suggests that the primary source of added mass force is pressure drag, with viscous drag having little effect on it. During acceleration, the surrounding fluid accelerates with the object, increasing the pressure drag with a high-pressure area concentrating at the object’s front, forming an added mass force that is directed backward. By contrast, during deceleration, the fluid at the object’s front tends to detach, and the fluid at the rear rushes forward, leading to a smaller high-pressure area at the front and a larger one at the rear, reducing the pressure drag and forming an added mass force that is directed forward. By comparing the added mass of a standard ellipsoid obtained from numerical simulation with theoretical values, the regression analysis method is proven to be highly accurate and entirely applicable for solving the added mass of underwater vehicles.

Convenient and high-fidelity 3D model reconstruction is crucial for industries like manufacturing, medicine and archaeology. Current scanning approaches struggle with high manual costs and the accumulation of errors in large-scale modeling. This paper is dedicated to achieving industrial-grade seamless and high-fidelity 3D reconstruction with minimal manual intervention. The innovative method proposed transforms the multi-frame registration into a graph optimization problem, addressing the issue of error accumulation encountered in frame-by-frame registration. Initially, a global consistency cost is established based on point cloud cross-multipath registration, followed by using the geometric and color differences of corresponding points as dynamic nonlinear weights. Finally, the iteratively reweighted least squares (IRLS) method is adopted to perform the bundle adjustment (BA) optimization of all poses. Significantly enhances registration accuracy and robustness under the premise of maintaining near real-time efficiency. Additionally, for generating watertight, seamless surface models, a local-to-global transitioning strategy for multiframe fusion is introduced. This method facilitates efficient correction of normal vector consistency, addressing mesh discontinuities in surface reconstruction resulting from normal flips. To validate our algorithm, we designed a 3D reconstruction platform enabling spatial viewpoint transformations. We collected extensive real and simulated model data. These datasets were rigorously evaluated against advanced methods, roving the effectiveness of our approach. Our data and implementation is made available on GitHub for community development.

Indirect time-of-flight (ToF) imaging systems enable a broad array of applications owing to their high frame rate, strong durability, and low cost. However, the wiggling-related error caused by the harmonics in the emitted signal significantly affects the range accuracy of indirect ToF imaging systems. In this paper, we establish a mathematical model of the wiggling-related error and propose a wiggling-related error correction method for indirect ToF imaging systems. This method adds a delay measurement and utilizes raw intensity measurements to evaluate the system state based on an adaptive Kalman filter (AKF), which is easy to implement in most indirect ToF imaging systems. Simulation and experimental results show that the proposed method performed well in reducing the wiggling-related error and had good robustness in different integration times. Compared with the existing methods, the proposed method not only has better performance but also is easier to implement. We believe that this study provides effective guidance for researchers understanding the wiggling-related error and a potential direction for the accuracy improvement of indirect ToF imaging systems.

The accuracy of target distance obtained by a frequency modulated continuous wave (FMCW) laser ranging system is often affected by factors such as white Gaussian noise (WGN), spectrum leakage, and the picket fence effect. There are some traditional spectrum correction algorithms to solve the problem above, but the results are unsatisfactory. In this article, a decomposition filtering-based dual-window correction (DFBDWC) algorithm is proposed to alleviate the problem caused by these factors. This algorithm reduces the influence of these factors by utilizing a decomposition filtering, dual-window in time domain and two phase values of spectral peak in the frequency domain, respectively. With the comparison of DFBDWC and these traditional algorithms in simulation and experiment on a built platform, the results show a superior performance of DFBDWC based on this platform. The maximum absolute error of target distance calculated by this algorithm is reduced from 0.7937 m of discrete Fourier transform (DFT) algorithm to 0.0407 m, which is the best among all mentioned spectrum correction algorithms. A high performance FMCW laser ranging system can be realized with the proposed algorithm, which has attractive potential in a wide scope of applications.

To solve the poor real-time performance of the existing fault diagnosis algorithms on transmission system rotating components, this paper proposes a novel high-dimensional OT-Caps (Optimal Transport–Capsule Network) model. Based on the traditional capsule network algorithm, an auxiliary loss is introduced during the offline training process to improve the network architecture. Simultaneously, an optimal transport theory and a generative adversarial network are introduced into the auxiliary loss, which accurately depicts the error distribution of the fault characteristic. The proposed model solves the low real-time performance of the capsule network algorithm due to complex architecture, long calculation time, and oversized hardware resource consumption. Meanwhile, it ensures the high precision, early prediction, and transfer aptitude of fault diagnosis. Finally, the model’s effectiveness is verified by the public data sets and the actual faults data of the transmission system, which provide technical support for the application.

As a typical application of indirect-time-of-flight (ToF) technology, photonic mixer device (PMD) solid-state array Lidar has gained rapid development in recent years. With the advantages of high resolution, frame rate and accuracy, the equipment is widely used in target recognition, simultaneous localization and mapping (SLAM), industrial inspection, etc. The PMD Lidar is vulnerable to several factors such as ambient light, temperature and the target feature. To eliminate the impact of such factors, a proper calibration is needed. However, the conventional calibration methods need to change several distances in large areas, which result in low efficiency and low accuracy. To address the problems, this paper presents an improved calibration method based on electrical analog delay. The method firstly eliminates the lens distortion using a self-adaptive interpolation algorithm, meanwhile it calibrates the grayscale image using an integral time simulating based method. Then, the grayscale image is used to estimate the parameters of ambient light compensation in depth calibration. Finally, by combining four types of compensation, the method effectively improves the performance of depth calibration. Through several experiments, the proposed method is more adaptive to multiscenes with targets of different reflectivities, which significantly improves the ranging accuracy and adaptability of PMD Lidar.