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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.
Yi Hao; Ping Song; Xuanquan Wang; Zhikang Pan. A Spectrum Correction Algorithm Based on Beat Signal of FMCW Laser Ranging System. Sensors 2021, 21, 5057 .
AMA StyleYi Hao, Ping Song, Xuanquan Wang, Zhikang Pan. A Spectrum Correction Algorithm Based on Beat Signal of FMCW Laser Ranging System. Sensors. 2021; 21 (15):5057.
Chicago/Turabian StyleYi Hao; Ping Song; Xuanquan Wang; Zhikang Pan. 2021. "A Spectrum Correction Algorithm Based on Beat Signal of FMCW Laser Ranging System." Sensors 21, no. 15: 5057.
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.
Xuanquan Wang; Xiongjun Liu; Ping Song; Yifan Li; Youtian Qie. A Novel Deep Learning Model for Mechanical Rotating Parts Fault Diagnosis Based on Optimal Transport and Generative Adversarial Networks. Actuators 2021, 10, 146 .
AMA StyleXuanquan Wang, Xiongjun Liu, Ping Song, Yifan Li, Youtian Qie. A Novel Deep Learning Model for Mechanical Rotating Parts Fault Diagnosis Based on Optimal Transport and Generative Adversarial Networks. Actuators. 2021; 10 (7):146.
Chicago/Turabian StyleXuanquan Wang; Xiongjun Liu; Ping Song; Yifan Li; Youtian Qie. 2021. "A Novel Deep Learning Model for Mechanical Rotating Parts Fault Diagnosis Based on Optimal Transport and Generative Adversarial Networks." Actuators 10, no. 7: 146.
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.
Xuanquan Wang; Ping Song; Wuyang Zhang. An Improved Calibration Method for Photonic Mixer Device Solid-State Array Lidars Based on Electrical Analog Delay. Sensors 2020, 20, 7329 .
AMA StyleXuanquan Wang, Ping Song, Wuyang Zhang. An Improved Calibration Method for Photonic Mixer Device Solid-State Array Lidars Based on Electrical Analog Delay. Sensors. 2020; 20 (24):7329.
Chicago/Turabian StyleXuanquan Wang; Ping Song; Wuyang Zhang. 2020. "An Improved Calibration Method for Photonic Mixer Device Solid-State Array Lidars Based on Electrical Analog Delay." Sensors 20, no. 24: 7329.
With the widespread application of wireless sensor networks, large-scale systems with high sampling rates are becoming more and more common. The amount of original data generated by the wireless sensor network is very large, and transmitting all the original data back to the host wastes network bandwidth and energy. This paper proposes a wireless transmission method for large data based on hierarchical compressed sensing and sparse decomposition. This method includes a hierarchical signal decomposition method based on the same sparse basis and different sparse basis hierarchical compressed sensing method with a mask. Compared with the traditional compressed sensing method, this method reduces the error of signal reconstruction, reduces the amount of calculation during signal reconstruction, and reduces the occupation of hardware resources. We designed comparison experiments between the traditional compressed sensing algorithm and the method proposed in this article. In addition, the experiments’ results prove that our proposed method reduces the execution time, as well as the reconstruction error, compared with the traditional compressed sensing algorithm, and it can achieve better reconstruction at a relatively low compression ratio.
Youtian Qie; Chuangbo Hao; Ping Song. Wireless Transmission Method for Large Data Based on Hierarchical Compressed Sensing and Sparse Decomposition. Sensors 2020, 20, 7146 .
AMA StyleYoutian Qie, Chuangbo Hao, Ping Song. Wireless Transmission Method for Large Data Based on Hierarchical Compressed Sensing and Sparse Decomposition. Sensors. 2020; 20 (24):7146.
Chicago/Turabian StyleYoutian Qie; Chuangbo Hao; Ping Song. 2020. "Wireless Transmission Method for Large Data Based on Hierarchical Compressed Sensing and Sparse Decomposition." Sensors 20, no. 24: 7146.
The photonic mixer device (PMD) solid-state array lidar, as a three-dimensional imaging technology, has attracted research attention in recent years because of its low cost, high frame rate, and high reliability. To address the disadvantages of traditional PMD solid-state array lidar calibration methods, including low calibration efficiency and accuracy, and serious human error factors, this paper first proposes a calibration method for an array complementary metal–oxide–semiconductor photodetector using a black-box calibration device and an electrical analog delay method; it then proposes a modular lens distortion correction method based on checkerboard calibration and pixel point adaptive interpolation optimization. Specifically, the ranging error source is analyzed based on the PMD solid-state array lidar imaging mechanism; the black-box calibration device is specifically designed for the calibration requirements of anti-ambient light and an echo reflection route; a dynamic distance simulation system integrating the laser emission unit, laser receiving unit, and delay control unit is designed to calibrate the photodetector echo demodulation; the checkerboard calibration method is used to correct external lens distortion in grayscale mode; and the pixel adaptive interpolation strategy is used to reduce distortion of distance images. Through analysis of the calibration process and results, the proposed method effectively reduces the calibration scene requirements and human factors, meets the needs of different users of the lens, and improves both calibration efficiency and measurement accuracy.
Yayu Zhai; Ping Song; Xiaoxiao Chen. A Fast Calibration Method for Photonic Mixer Device Solid-State Array Lidars. Sensors 2019, 19, 822 .
AMA StyleYayu Zhai, Ping Song, Xiaoxiao Chen. A Fast Calibration Method for Photonic Mixer Device Solid-State Array Lidars. Sensors. 2019; 19 (4):822.
Chicago/Turabian StyleYayu Zhai; Ping Song; Xiaoxiao Chen. 2019. "A Fast Calibration Method for Photonic Mixer Device Solid-State Array Lidars." Sensors 19, no. 4: 822.
The hydro-pneumatic spring, as an important element of the suspension system for heavy vehicles, has attracted the attention of researchers for a long time because it plays such an important role in the steering stability, driving comfort, and driving safety of these vehicles. In this paper, we aim to solve the maintenance problems caused by gas leakage and oil leakage faults in hydro-pneumatic springs. The causes of hydro-pneumatic spring faults and their modes are investigated first. Then, we propose a novel time domain fault feature, called degraded pressure under the same displacement, and a novel feature extraction method based on linear interpolation and redefined time intervals. This feature extraction method is then combined with a data-driven prognostic method that is based on support vector regression to predict the failure trends. When compared with traditional prognostic methods for suspension systems based on vibration signals and vehicle dynamics models, the proposed method can evaluate the real-time spring condition without use of additional sensors or an accurate dynamic model. Therefore, the computational cost of the proposed method is very low and is also suitable for use in vehicles that are equipped with low-cost microprocessors. In addition, hydro-pneumatic spring performance degradation experiments and simulations based on AMEsim software are designed. The experimental data, real vehicle historical data, and simulation data are used to verify the feasibility of the proposed method.
Cheng Yang; Ping Song; Xiongjun Liu. Failure prognostics of heavy vehicle hydro-pneumatic spring based on novel degradation feature and support vector regression. Neural Computing and Applications 2017, 31, 139 -156.
AMA StyleCheng Yang, Ping Song, Xiongjun Liu. Failure prognostics of heavy vehicle hydro-pneumatic spring based on novel degradation feature and support vector regression. Neural Computing and Applications. 2017; 31 (1):139-156.
Chicago/Turabian StyleCheng Yang; Ping Song; Xiongjun Liu. 2017. "Failure prognostics of heavy vehicle hydro-pneumatic spring based on novel degradation feature and support vector regression." Neural Computing and Applications 31, no. 1: 139-156.
Data acquisition is the foundation of soft sensor and data fusion. Distributed data acquisition and its synchronization are the important technologies to ensure the accuracy of soft sensors. As a research topic in bionic science, the firefly-inspired algorithm has attracted widespread attention as a new synchronization method. Aiming at reducing the design difficulty of firefly-inspired synchronization algorithms for Wireless Sensor Networks (WSNs) with complex topologies, this paper presents a firefly-inspired synchronization algorithm based on a multiscale discrete phase model that can optimize the performance tradeoff between the network scalability and synchronization capability in a complex wireless sensor network. The synchronization process can be regarded as a Markov state transition, which ensures the stability of this algorithm. Compared with the Miroll and Steven model and Reachback Firefly Algorithm, the proposed algorithm obtains better stability and performance. Finally, its practicality has been experimentally confirmed using 30 nodes in a real multi-hop topology with low quality links.
Chuangbo Hao; Ping Song; Cheng Yang; Xiongjun Liu. Testing a Firefly-Inspired Synchronization Algorithm in a Complex Wireless Sensor Network. Sensors 2017, 17, 544 .
AMA StyleChuangbo Hao, Ping Song, Cheng Yang, Xiongjun Liu. Testing a Firefly-Inspired Synchronization Algorithm in a Complex Wireless Sensor Network. Sensors. 2017; 17 (3):544.
Chicago/Turabian StyleChuangbo Hao; Ping Song; Cheng Yang; Xiongjun Liu. 2017. "Testing a Firefly-Inspired Synchronization Algorithm in a Complex Wireless Sensor Network." Sensors 17, no. 3: 544.
For the weakness of formation adjustment in formation control of mobile sensor network nodes and formation disorder caused by long distance travel, a dual formation constraint mechanism based on congestion will is proposed in this paper. The mobile nodes use both general constraint and local constraint to maintain a stable formation based on the analysis of the kinematics model of the mobile node. The general constraint makes the nodes coordinated and improves the efficiency. The local constraint ensures the balanced position of each node in the formation. Simulation results show that the mobile nodes can move to the target in a stable and regular formation utilizing this method.
Cheng Yang; Ping Song; Chuangbo Hao; Guang Wang; Lin Xie; Wenjuan Guo. A Dual Formation Constraint Mechanism of Mobile Sensor Network Based on Congestion Will. Mechanical Engineering and Materials 2014, 483 -490.
AMA StyleCheng Yang, Ping Song, Chuangbo Hao, Guang Wang, Lin Xie, Wenjuan Guo. A Dual Formation Constraint Mechanism of Mobile Sensor Network Based on Congestion Will. Mechanical Engineering and Materials. 2014; ():483-490.
Chicago/Turabian StyleCheng Yang; Ping Song; Chuangbo Hao; Guang Wang; Lin Xie; Wenjuan Guo. 2014. "A Dual Formation Constraint Mechanism of Mobile Sensor Network Based on Congestion Will." Mechanical Engineering and Materials , no. : 483-490.
There exist several difficulties in the design of monolithic high-shock three-axis accelerometer, such as high g overload, transverse overload and the cross coupling in three dimensions, etc. It is necessary to optimize the sensitivity to improve the performance of the accelerometer. For the monolithic high-shock three-axis accelerometer, the complexity of the sensitivity optimization is that it should consider not only the sensitivity difference between different axes but also the elimination of cross-coupling outputs, together with the natural frequency, structural integrity and high g overload. In this paper, the optimization process for decreasing the difference of the sensitivities between different axes of a monolithic high-shock three-axis piezoresistive accelerometer with single sensing element is established. The optimization is conducted in the condition of 100000 g acceleration by two methods-the method based on the optimization module of ANSYS and the ACO (ant colony optimization) method. The comparison between un-optimized and optimized models proves the efficiency of the optimization methods. In addition, the optimization results show that the ACO method combined with the FEA (finite element analysis) is much more efficient than the method based on the optimization module of ANSYS for the structural optimization problem. And the ACO method can be widely used in the optimization problem of the sensing elements with complicated structure.
Ping Song; Qingzhou Li; Kejie Li. Sensitivity optimization of a monolithic high-shock three-axis piezoresistive accelerometer with single sensing element. Science Bulletin 2009, 54, 3600 -3607.
AMA StylePing Song, Qingzhou Li, Kejie Li. Sensitivity optimization of a monolithic high-shock three-axis piezoresistive accelerometer with single sensing element. Science Bulletin. 2009; 54 (19):3600-3607.
Chicago/Turabian StylePing Song; Qingzhou Li; Kejie Li. 2009. "Sensitivity optimization of a monolithic high-shock three-axis piezoresistive accelerometer with single sensing element." Science Bulletin 54, no. 19: 3600-3607.