Brain-Like Navigation Scheme based on MEMS-INS and Place Recognition

High-G MEMS Accelerometer Calibration Denoising Method Based on EMD and Time-Frequency Peak Filtering

In order to remove noise generated during the accelerometer calibration process, an accelerometer denoising method based on empirical mode decomposition (EMD) and time-frequency peak filtering (TFPF) is proposed in this paper. Firstly, a new design of the accelerometer structure is introduced and analyzed by finite element analysis software. Then, an algorithm combining EMD and TFPF is proposed for the first time to deal with the noise of the accelerometer calibration process. Specific steps taken are to remove the intrinsic mode function (IMF) component of the high frequency band after the EMD decomposition, and then to use the TFPF algorithm to process the IMF component of the medium frequency band; meanwhile, the IMF component of the low frequency band is reserved, and finally the signal is reconstructed. The reconstruction results show that the algorithm can effectively suppress the random noise generated during the calibration process. The results of spectrum analysis show that EMD + TFPF can effectively protect the characteristics of the original signal and that the error can be controlled within 0.5%. Finally, Allan variance is used to analyze the results of the three methods to verify the filtering effect. The results show that the filtering effect of EMD + TFPF is the most obvious, being 97.4% higher than the original data.

Self-Localization of the Autonomous Robot for View-Based Navigation Using Street View Images

Navigation requires self-localization, which GPS typically performs outdoors. However, GPS frequently introduces significant errors in environments where radio reflection occurs, such as in urban areas, which makes precise self-localization occasionally impossible. Meanwhile, a human can use street view images to understand their surroundings and their current location. To implement this, we must solve image matching between the current scene and the images in a street view database. However, because of the wide variations in the field angle, time, and season between images, standard pattern matching by feature is difficult. DeepMatching can precisely match images with various lighting and field angles. Nevertheless, DeepMatching tends to misinterpret street images because it may find unnecessary feature points in the road and sky. This study proposes several methods to reduce misjudgment: (1) gaining image similarity with features such as buildings by excluding the road and sky, and (2) splitting the panoramic image into four directions and matching in each. This study shows the results of each method and summarizes their performance using various images and resolutions.

Activation of peroxymonosulfate by MgCoAl layered double hydroxide: Potential enhancement effects of catalyst morphology and coexisting anions

The morphology and specific surface area of layered double hydroxide (LDH) are of great significance for optimizing the application of LDH in sewage treatment. Herein, we present a study of the relation between the catalytic property and the morphology of LDH via activating peroxymonosulfate (PMS) for degradation of organic pollutants. The results demonstrated that LDH nanoscrolls possessed a superior performance for methylene blue (MB) degradation, which achieved almost 100% removal in 40 min and the calculated apparent rate constant was about 2.1, 4.5 and 11.5 times higher than that of LDH nanosheets, Co²⁺ and Co₃O₄, respectively. According to the results of X-ray photoelectrons spectroscopy (XPS) and electron paramagnetic resonance (EPR), ¹O₂ was confirmed to play a dominant role in the MB degradation, where the redox cycle of Co³⁺/Co²⁺ provided the impetus for the reaction. Moreover, the pH and ion tolerance abilities of LDH nanoscrolls in PMS activating process were determined as well. Remarkably, CO₃^2- and H₂PO₄^- could even promote the generation of •OH and ¹O₂ to facilitate the progress of reaction. Overall, these findings in the study may provide more opportunities in the preparation of high-efficiency catalysts and give insight into the accelerated degradation of refractory contaminants with surrounding anions.

Dextran mediated MnFe2O4/ZnS magnetic fluorescence nanocomposites for controlled self-heating properties

Dextran mediated MnFe2O4/ZnS opto-magnetic nanocomposites with different concentrations of ZnS were competently synthesized adopting the co-precipitation method. The structural, morphological, magnetic, and optical properties of the nanocomposites were exhaustively characterized by XRD, HRTEM, FTIR, VSM techniques, and PL spectroscopy. XRD spectra demonstrate the existence of the cubic spinel phase of MnFe2O4 and the cubic zinc blend phase of ZnS in the nanocomposites. HRTEM images show the average crystallite size ranges of 15-21 nm for MnFe2O4 and 14-45 nm for ZnS. Investigation of the FTIR spectra reveals the incorporation of ZnS nanoparticles on the surface of MnFe2O4 nanoparticles by dint of biocompatible surfactant dextran. The nanocomposites exhibit both magnetic and photoluminescence properties. Photoluminescence analysis confirmed the redshift of the emission peaks owing to the trap states in the ZnS nanocrystals. The room temperature VSM analysis shows that the saturation magnetization and coercivity of MnFe2O4 nanoparticles initially increase then decrease with the increasing concentration of ZnS in the nanocomposite. The induction heating analysis shows that the presence of dextran enhances the self heating properties of the MnFe2O4/ZnS nanocomposites which can also be controlled by tailoring the concentration of the ZnS nanoparticles. These suggest that MnFe2O4/Dex/ZnS is a decent candidate for hyperthermia applications.

Obtaining World Coordinate Information of UAV in GNSS Denied Environments

GNSS information is vulnerable to external interference and causes failure when unmanned aerial vehicles (UAVs) are in a fully autonomous flight in complex environments such as high-rise parks and dense forests. This paper presents a pan-tilt-based visual servoing (PBVS) method for obtaining world coordinate information. The system is equipped with an inertial measurement unit (IMU), an air pressure sensor, a magnetometer, and a pan-tilt-zoom (PTZ) camera. In this paper, we explain the physical model and the application method of the PBVS system, which can be briefly summarized as follows. We track the operation target with a UAV carrying a camera and output the information about the UAV’s position and the angle between the PTZ and the anchor point. In this way, we can obtain the current absolute position information of the UAV with its absolute altitude collected by the height sensing unit and absolute geographic coordinate information and altitude information of the tracked target. We set up an actual UAV experimental environment. To meet the calculation requirements, some sensor data will be sent to the cloud through the network. Through the field tests, it can be concluded that the systematic deviation of the overall solution is less than the error of GNSS sensor equipment, and it can provide navigation coordinate information for the UAV in complex environments. Compared with traditional visual navigation systems, our scheme has the advantage of obtaining absolute, continuous, accurate, and efficient navigation information at a short distance (within 15 m from the target). This system can be used in scenarios that require autonomous cruise, such as self-powered inspections of UAVs, patrols in parks, etc.

Hydrogen bonding derived self-healing polymer composites reinforced with amidation carbon fibers

Self-healing polymer materials (SHPM) have aroused great interests in recent years. Ideal SHPM should have not only simple operations, but also high elongations at break, tensile strain and self-healing properties at room temperature. Herein, the amidated carbon fibers (CFs) reinforced self-healing polymer composites were designed by hydrogen bonding interaction between functionalized CFs and hyperbranched polymers. The amidated CFs were prepared by transformation of hydroxyl to acylamino through a one-step amidation. By introducing amidated CFs, amidated CFs self-healing polymer composites (called AD-CF) exhibited many desirable characteristics compared to pure polymer composites, such as a better elasticity, lower healing temperatures, and higher self-healing efficiencies. The stress-strain test was selected to carefully study the self-healing property of the AD-CF. The observed same recovery condition, i.e. without any mechanical breakdown after the 10 sequential cycles of cutting and healing indicates no aging of the AD-CF. The ability of AD-CF to exhibit a soft state and rapid self-healing at room temperature makes it possible for much wider applications.

Porous Fe@C Composites Derived from Silkworm Excrement for Effective Separation of Anisole Compounds

Silkworm excrement is a very useful biomass waste, composed of layer-structured fats and proteins, which are great precursors for carbon composite materials. In this work, new porous composites derived from silkworm excrement were prepared for selective separation of flavor 4-methylanisole from the binary 4-methylanisole/4-anisaldehyde mixture. In particular, the silkworm excrement, possessing a unique nanosheet structure, is converted into a graphite-like carbon by a simple calcination strategy followed by a metal-ion-doping procedure. This Fe@C composite exhibits a special nano-spongy morphology, anchoring Fe₃C/Fe₅C₂ on the carbon nanosheets. Density functional theory simulations showed that 4-methylanisole presents a stronger π-π interaction and attraction forces with sp² carbon nanosheets in Fe@C composites than 4-anisaldehyde. The selective adsorption experiments further confirmed that the Fe@C composites exhibited a 4-methylanisole capacity of 7.3 mmol/g at 298 K and the highest selectivity of 17 for an equimolar 4-methylanisole/4-anisaldehyde mixture among the examined adsorbents including MOFs and commercial activated carbon materials, which demonstrates the potential of this low-cost and eco-friendly porous carbon material as a promising sustainable adsorbent.

Combination of iterated cubature Kalman filter and neural networks for GPS/INS during GPS outages

To improve the performance of the Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation system, current research studies merely combine neural networks with nonlinear filter methods. Few studies focus on how to optimize the parameters of the neural network and how to further improve the small error accumulated into the next filter step due to the imprecise design of the filter when setting the initial parameters in the GPS/INS integrated system. In this article, a dual optimization method consisting of an iterated cubature Kalman filter-Feedforward Neural Network (ICKF-FNN) and a radial basis function-cubature Kalman filter (RBF-CKF) is proposed to compensate the position and velocity errors of the integrated system during GPS outages. The prominent advantages of the proposed method include the following. (i) The ICKF is designed to optimize the parameters of the introduced FNN adaptively and obtain an appropriate internal structure when GPS is available, which improves the accuracy of the training model. (ii) The RBF establishes the relationship between filter parameters and the optimal estimation errors, reducing the errors caused by inaccurate predicted observation during GPS outages. (iii) The proposed dual optimization method takes advantages over other combination algorithms under different moving conditions or even during long period of GPS outages, which shows its great stability. Experimental results show that the root mean squared error of the east position is reduced by 85.79% to 3.2187 m using the proposed strategy during turning movement and the east velocity error accumulation rate decreases by 92.69% during the long straight movement of 250 s. These results are from offline processing.

Attitude measurement based on imaging ray tracking model and orthographic projection with iteration algorithm

In the field of vision-based attitude estimation, camera model and attitude solving algorithm are the key technologies, which determine the measurement accuracy, effectiveness and applicability. Aiming at this issue, in this paper we probe into the generic imaging model and then develop a corresponding generic camera calibration method using two auxiliary calibration planes. The camera model is named as imaging ray tracking model. Based on the imaging ray tracking camera model and with the knowledge of the calibration parameters, an advanced attitude solving algorithm, imaging ray tracking model and attitude from orthographic projection with iterations algorithm, is deeply investigated, which is inspired by the classical POSIT algorithm. The initial attitude value is provided by the orthographic projection of the object on the two calibration planes and then refined by iteration to approximate the true object attitude. Experimental platform is setup to conduct the imaging ray tracking camera calibration procedure and further evaluate our attitude estimation algorithm. We show the effectiveness and superiority of our proposed attitude estimation algorithm by thorough testing on real-data and by comparison with the POSIT algorithm.

Application of a Novel Adaptive Med Fault Diagnosis Method in Gearboxes

Minimum entropy deconvolution (MED) is not effective in extracting fault features in strong noise environments, which can easily lead to misdiagnosis. Moreover, the noise reduction effect of MED is affected by the size of the filter. In the face of different vibration signals, the size of the filter is not adaptive. In order to improve the efficiency of MED fault feature extraction, this paper proposes a firefly optimization algorithm (FA) to improve the MED fault diagnosis method. Firstly, the original vibration signal is stratified by white noise-assisted singular spectral decomposition (SSD), and the stratified signal components are divided into residual signal components and noisy signal components by a detrended fluctuation analysis (DFA) algorithm. Then, the noisy components are preprocessed by an autoregressive (AR) model. Secondly, the envelope spectral entropy is proposed as the fitness function of the FA algorithm, and the filter size of MED is optimized by the FA algorithm. Finally, the preprocessed signal is denoised and the pulse enhanced with the proposed adaptive MED. The new method is validated by simulation experiments and practical engineering cases. The application results show that this method improves the shortcomings of MED and can extract fault features more effectively than the traditional MED method.

Tungsten oxide nanostructures and nanocomposites for photoelectrochemical water splitting

Hydrogen production from photoelectrochemical (PEC) water splitting using semiconductor photocatalysts has attracted great attention to realize clean and renewable energy from solar energy. The visible light response of WO3 with a long hole diffusion length (∼150 nm) and good electron mobility (∼12 cm2 V-1 s-1) makes it suitable as the photoanode. However, WO3 suffers from issues including rapid recombination of photoexcited electron-hole pairs, photo-corrosion during the photocatalytic process due to the formation of peroxo-species, sluggish kinetics of photogenerated holes, and slow charge transfer at the semiconductor/electrolyte interface. This work highlights the approaches to overcome these drawbacks of WO3 photoanodes, including: (i) the manipulation of nanostructured WO3 photoanodes to decrease the nanoparticle size to promote hole migration to the WO3/electrolyte interface which benefits the charge separation; (ii) doping or introducing oxygen vacancies to improve electrical conductivity; exposing high energy crystal surfaces to promote the consumption of photogenerated holes on the high-active crystal face, thereby suppressing the recombination of photogenerated electrons and holes; (iii) decorating with co-catalysts to reduce the overpotential which inhibits the formation of peroxo-species; (iv) other methods such as coupling with narrow band semiconductors to accelerate the charge separation and controlling the crystal phase via annealing to reduce defects. These approaches are reviewed with detailed examples.

Attitude determination method integrating square-root cubature Kalman filter with expectation-maximization for inertial navigation system applied to underwater glider

The uncertainty, complexity, and variability of the marine environment inevitably lead to a change in the measurement error resulting in erroneous estimation of navigation information. To solve this problem, this paper proposes a novel method integrating the square-root cubature Kalman filter (SCKF) with the expectation-maximization (EM) algorithm. The proposed new SCKF (NSCKF) algorithm makes better use of the advantages of SCKF and the EM online algorithm. The performance of NSCKF is verified theoretically and evaluated by experiments. The results indicate that the proposed NSCKF algorithm can better estimate predicted error covariance and measurement noise than two other comparison methods owing to the online EM method so that the more accurate attitude estimation can be obtained by the NSCKF algorithm although the measurement error has a great variation. Moreover, the accuracy and efficiency can be guaranteed by employing the SCKF. Experimental results demonstrate that the NSCKF can provide a more stable attitude estimation in different cases of measurement errors. Therefore, the NSCKF is more suitable to be used in underwater navigation than other comparison methods because of higher accuracy, more efficiency, and better robustness.

An Improved Optical Flow Algorithm Based on Mask-R-CNN and K-Means for Velocity Calculation

Aiming at enhancing the accuracy and reliability of velocity calculation in vision navigation, an improved method is proposed in this paper. The method integrates Mask-R-CNN (Mask Region-based Convolutional Neural Network) and K-Means with the pyramid Lucas Kanade algorithm in order to reduce the harmful effect of moving objects on velocity calculation. Firstly, Mask-R-CNN is used to recognize the objects which have motions relative to the ground and covers them with masks to enhance the similarity between pixels and to reduce the impacts of the noisy moving pixels. Then, the pyramid Lucas Kanade algorithm is used to calculate the optical flow value. Finally, the value is clustered by the K-Means algorithm to abandon the outliers, and vehicle velocity is calculated by the processed optical flow. The prominent advantages of the proposed algorithm are (i) decreasing the bad impacts to velocity calculation, due to the objects which have relative motions; (ii) obtaining the correct optical flow sets and velocity calculation outputs with less fluctuation; and (iii) the applicability enhancement of the optical flow algorithm in complex navigation environment. The proposed algorithm is tested by actual experiments. Results with superior precision and reliability show the feasibility and effectiveness of the proposed method for vehicle velocity calculation in vision navigation system.