Spatial Position Information Research Articles

Robot dynamic trajectory tracking control algorithm based on steady-state closed-loop learning

In order to improve the stability control ability of flexible lower limb exoskeleton robot, a dynamic trajectory tracking control algorithm of flexible lower limb exoskeleton robot based on steady-state closed-loop learning is proposed. Gyroscope and rangefinder are used as information sensors of flexible lower limb exoskeleton robot to collect position information of flexible lower limb exoskeleton robot, fuse collected positioning information of flexible lower limb exoskeleton robot, fuse physical information and measure parameters of flexible lower limb exoskeleton robot by using dynamic information measurement method, and obtain mapping feature set of Cartesian space according to trajectory components of flexible lower limb exoskeleton robot. The pose information of the flexible lower limb exoskeleton robot is obtained through the forward kinematics, and the information is enhanced according to the spatial position information. The steady-state closed-loop learning method is adopted to realize the adaptive learning of the robot dynamic trajectory tracking control. The simulation results show that this method is adaptive to the dynamic trajectory tracking control of the robot, and the positioning control ability of the robot is strong.

The fully automated bat (FAB) flight room: A human-free environment for studying navigation in flying bats and its initial application to the retrosplenial cortex

BackgroundBats can offer important insight into the neural computations underlying complex forms of navigation. Up to now, this had been done with the confound of the human experimenter being present in the same environment the bat was navigating in. New methodWe, therefore, developed a novel behavioral setup, the fully automated bat (FAB) flight room, to obtain a detailed and quantitative understanding of bat navigation flight behavior while studying its relevant neural circuits, but importantly without human intervention. As a demonstration of the FAB flight room utility we trained bats on a four-target, visually-guided, foraging task and recorded neural activity from the retrosplenial cortex (RSC). ResultsWe find that bats can be efficiently trained and engaged in complex, multi-target, visuospatial behavior in the FAB flight room. Wireless neural recordings from the bat RSC during the task confirm the multiplexed characteristics of single RSC neurons encoding spatial positional information, target selection, reward obtainment and the intensity of visual cues used to guide navigation. Comparison with existing methodsIn contrast to the methods introduced in previous studies, we now can investigate spatial navigation in bats without potential experimental biases that can be easily introduced by active physical involvement and presence of experimenters in the room. ConclusionsCombined, we describe a novel experimental approach for studying spatial navigation in freely flying bats and provide support for the involvement of bat RSC in aerial visuospatial foraging behavior.

An Online Intelligent Method to Calibrate Radar and Camera Sensors for Data Fusing

This paper introduces an intelligent method to calibrate radar and camera sensors for data fusing. Camera can recognize pedestrians, non-motor vehicles, motor vehicles high accuracy by advanced deep learning neural networks. But it’s hard to acquire the target’s spatial position and speed information simply relying on image. For the radar sensor, it’s easy to acquire the target’s spatial position and speed information by itself, but it cannot recognize target. In other words, the advantage of radar is the disadvantage of camera, and the advantage of camera is the disadvantage of radar. So combining these two complementary sensors is valid and necessary for solving the problem: Who is doing what. Then the first and foremost thing is to calibrate the radar and camera sensors. Calibration includes time synchronization and space calibration, which will seriously affect the sensors data fusing performance. This paper emphasizes on spatial calibration. Traditional methods (based on the principle of geometric projection or four-point calibration method) cannot acquire enough high calibration accuracy in the general scenarios with low time cost or easy operation. So this paper introduces an online intelligent method to calibrate radar and camera sensor without human assistance operation in the general scenarios. Firstly, we introduce what is spatial calibration. Secondly, we introduce two conventional methods and their defects. And then we introduce the online intelligent calibration method. At last we compare three calibration methods with the actual data to further explain the advance of our new method.

Pulse Couple Neural Network Based on Visual Perception

The traditional pulse coupled neural network model only considers the spatial position information between neurons, but neglects sensitivity of human visual perception to luminance change, especially when the brightness is high or low, the visual sensitivity of the human eye is relatively low, and in a certain range of brightness, the visual sensitivity of the human eye is relatively high, so it is easy to have false segmentation. In order to solve this problem of the traditional pulse coupled neural network model, this paper redescribes the connection weights between neurons by using the human eye luminance weight factor, and obtains the new pulse coupled neural network model based on visual perception. Through the simulation experiment of life images, experimental results show that the proposed algorithm is better than the traditional pulse coupled neural network model.

Using stacked neural network to improve the auto-segmentation accuracy of Graves' ophthalmopathy target volumes for radiotherapy

Compared with the previous automatic segmentation neural network for the target area which considered the target area as an independent area, a stacked neural network which uses the position and shape information of the organs around the target area to regulate the shape and position of the target area through the superposition of multiple networks and fusion of spatial position information to improve the segmentation accuracy on medical images was proposed in this paper. Taking the Graves' ophthalmopathy disease as an example, the left and right radiotherapy target areas were segmented by the stacked neural network based on the fully convolutional neural network. The volume Dice similarity coefficient (DSC) and bidirectional Hausdorff distance (HD) were calculated based on the target area manually drawn by the doctor. Compared with the full convolutional neural network, the stacked neural network segmentation results can increase the volume DSC on the left and right sides by 1.7% and 3.4% respectively, while the two-way HD on the left and right sides decrease by 0.6. The results show that the stacked neural network improves the degree of coincidence between the automatic segmentation result and the doctor's delineation of the target area, while reducing the segmentation error of small areas. The stacked neural network can effectively improve the accuracy of the automatic delineation of the radiotherapy target area of Graves' ophthalmopathy.

Parameters optimization of light field micro-particle image velocimetry

The light field micro-particle image velocimetry (LF-μPIV) can record the instantaneous three-dimensional (3D) spatial position information of tracer particles in a single photographic exposure by a single camera, and thus achieve the instantaneous 3D velocity measurement of the micro-scale flow. The accuracy of the spatial positions of the reconstructed tracer particles is closely related to the spatial reconstruction resolution of the LF-μPIV system, and determines the measurement accuracy of the 3D velocity distribution. In this paper, the effects of the optical parameters of the LF-μPIV system and the spatial position of the tracer particle on the spatial resolution and reconstruction quality are numerically studied. The location of the measurement volume and the optical parameters of the LF-μPIV system are further optimized. Based on the optimized parameters, a LF-μPIV system is assembled. Finally, experiments are carried out to verify the feasibility of the optical parameters optimization from simulations and to evaluate the performance of the LF-μPIV system for three-dimensional velocity field in a micro-scale convergent channel. Results indicate that the lateral and axial reconstruction resolutions are higher in the outside and inside regions away from the focal plane, compared with those in the region near the focal plane of the objective lens, and is less affected by the microlens aperture, CCD sensor pixel size and objective lens magnification. The spatial reconstruction resolution of the LF-μPIV system at different depths is in good agreement with the simulation results, proving the effectiveness of the optical parameters optimization of the LF-μPIV system. The measurement results of 3D velocity field in a micro-scale convergent channel are consistent with the simulation results by ANSYS Fluent, further verifying the feasibility of the optimized LF-μPIV system for 3D velocity field.

A new accuracy evaluation method for water body extraction

ABSTRACT This paper employs the Normalized Difference Water Index (NDWI), Automated Water Extraction Index (AWEI), Modified Normalized Difference Water Index (MNDWI) and Near-Infrared (NIR) threshold methods to extract the boundaries of Hongjiannao Lake, Zasak reservoir and Changjiagou reservoir. The real boundary of the water bodies was obtained by visual interpretation from high-resolution imagery. The Digital Shoreline Analysis System (DSAS) is further used to calculate the net shoreline movement (NSM) between the real lake boundary and the lake boundary extracted by the NDWI, AWEI, MNDWI and NIR threshold methods. We quantitatively evaluated the accuracy of each water body extraction method by NSM, which was competed with kappa coefficient (κ) and edge detection. The results showed that the average of the |NSM| of the NDWI, MNDWI, AWEI and NIR threshold methods are 12.77 m, 16.76 m, 28.65 m and 31.43 m, respectively. The κ is 0.9869, 0.9855, 0.9747 and 0.9736, respectively and the correct extracted (T) ratios for the edge detection are 84.06%, 82.68%, 56.8% and 53.52%, respectively. The mean value of |NSM| for NDWI is the smallest, while κ and T are the highest. It indicates that the accuracy of NDWI is the highest in Hongjiannao lake. The smaller the |NSM| is, the larger κ and the higher T are. This shows that the |NSM| is consistent with the commonly used accuracy verification method such as κ and edge detection. The results of water extraction from the other two reservoirs also support this conclusion. Therefore, the accuracy verification of NSM can reflect the spatial position information and has reliability. It provides a new approach in verifying the accuracy of water body extraction methods. The optimum water extraction index for different study areas is different. The NIR band threshold method and AWEI can accurately extract water boundaries with a small amount of aquatic vegetation. However, NDWI and MNDWI are more suitable for extracting water bodies located in complex terrains.

First arrival picking of microseismic signals based on nested U-Net and Wasserstein Generative Adversarial Network

Picking the first arrival of microseismic signals, quickly and accurately, is the key for real-time data processing of microseismic monitoring. The traditional method cannot meet the high-accuracy and high-efficiency requirements for the firstarrival microseismic picking, in a low SNR environment. Concentrating on the problem of relatively low microseismic SNR, this paper proposes the Residual Link Nested U-Net Network (RLU-Net), which can not only retain the spatial position information of input signal and profile, but also realize the first arrival picking from end-to-end. The added number of layers with the residual block is important to extract more features and better distinguish the signal and noise in low SNR environments, which improves the accuracy and efficiency of first arrival picking. We also use the improved Wasserstein Generative Adversarial Network (WGAN) generated adversarial sample sets and conducted adversarial training on the network to enhance the generalization ability of the model. The resulting model then accurately and efficiently identified the first arrival of effective signals. The testing of forward modeling microseismic signals and measured microseismic data show that, compared with the Short Term Average/Long Term Average (STA/LTA) algorithm, the proposed algorithm not only has a high accuracy, but also can pick the first arrival time of microseismic signals in a low SNR environment.

Research of improving semantic image segmentation based on a feature fusion model

The context information of images had been lost due to the low resolution of features, and due to repeated combinations of max-pooling layer and down-sampling layer. When the feature extraction process had been performed using a convolutional network, the result of semantic image segmentation loses sensitivity to the location of the object. The semantic image segmentation based on a feature fusion model with context features layer-by-layer had been proposed. Firstly, the original images had been pre-processed by the Gaussian Kernel Function to generate a series of images with different resolutions to form an image pyramid. Secondly, inputting an image pyramid into the network structure in which the plurality of fully convolutional network was been combined in parallel to obtain a set of initial features with different granularities by expanding receptive fields using Atrous Convolutions, and the initialization of feature fusion with different layer-by-layer granularities in a top-down method. Finally, the score map of feature fusion model had been calculated and sent to the conditional random field, modeling the class correlations between image pixels of the original image by the fully connected conditional random field, and the spatial position information and color vector information of image pixels were jointed to optimize and obtain results. The experiments on the PASCAL VOC 2012 and PASCAL Context datasets had achieved better mean Intersection Over Union than the state-of-the-art works. The proposed method has about 6.3% improved to the conventional methods.

A Fault Diagnostic Scheme Based on Capsule Network for Rolling Bearing under Different Rotational Speeds.

Deep learning-based intelligent fault diagnosis methods have attracted increasing attention for their automatic feature extraction ability. However, existing works are usually under the assumption that the training and test dataset share similar distributions, which unfortunately always violates real practice due to the variety of working conditions. In this paper, an end-to-end scheme of joint use of two-direction signals and capsule network (CN) is proposed for fault diagnosis of rolling bearing. With the help of the superior ability of CN in capturing the spatial position information between features, more valuable information can be mined. Aiming to eliminate the influence of different rotational speeds, vertical and horizontal vibration signals are fused as the input to CN, so that invariant features can be extracted automatically from the raw signals. The effectiveness of the proposed method is verified by experimental data of rolling bearing under different rotational speeds and compared with a deep convolutional neural network (DCNN). The results demonstrate that the proposed scheme is able to recognize the fault types of rolling bearing under scenarios of different rotational speeds.

A New Method for Detecting Initial Position of IPMSM Rotor

Aiming at the speed sensorless control system of permanent magnet synchronous motor which embedded in rotor magnet steel, a new method for detecting the initial position of the rotor is proposed in this paper. This method is injecting a high-frequency rotating voltage to generate a high-frequency current in the stator windings that contains the spatial position information of the rotor. Compared with the traditional high-frequency injection method, this paper uses Discrete Fourier Transform (DFT) to demodulate high-frequency current. It avoids the signal phase delay caused by the use of a large number of filters when demodulating the traditional heterodyne method. At the same time, this method has nothing to do with the initial phase of the injected signal. Compared with the heterodyne method, the extracted rotor position angle is more accurate, and the structure is simple Number system. The validity and feasibility of the method are verified by simulation and experimental platform based on dSPACE.

Analysis and Design of a Spatial Planetary Noncircular Gear Train for Rice Seedling Transplanting Based on Three Given Positions

HighlightsA method for solving a spatial transplanting mechanism with noncircular gears is proposed.A new mechanism for transplanting rice pot seedlings is proposed.A trajectory with a small lateral displacement at the preparation phase is obtained.The working performance is validated by simulations and field tests.Abstract. This study proposes a method of solving the parameters of a spatial planetary gear train with noncircular gears to meet the requirements of wide-narrow row pot seedling transplanting (WPST). First, the planetary gear train was simplified to a spatial open-chain 2R mechanism (planetary carrier). A kinematic model of the 2R mechanism was derived from the three given homogeneous matrices describing the spatial position and attitude information of grasping, extracting, and planting seedlings. Second, the length of each link, attitude of each rotation axis, and relative initial angles were calculated. The model for solving the transmission ratio was deduced. A spatial planetary gear train configuration with a single planet carrier and two-stage driving was derived, in which the middle axis could be determined by combining the selected configuration, represented trajectory, and transmission ratio of each gear pair. Finally, a planetary gear train combining a noncircular gear pair and a helical gear pair was used in the WPST mechanism design. Simulations and tests conducted on a prototype confirmed the correctness of the theoretical model and the practicality of the design. Keywords: Noncircular gear, Planetary gear train, Spatial trajectory, Transmission ratio, Transplanting mechanism.

Study of Tunnel Surface Parameterization of 3-D Laser Point Cloud Based on Harmonic Map

In the maintenance work of tunnels, images are often used to detect diseases, but collections of tunnel images are limited by the tunnel environment and working time. Three-dimensional laser scanning technology can acquire high-precision tunnel information efficiently, and the main problem to be solved by using this technology to collect tunnel inner wall images is the dimensionality reduction of the laser tunnel point cloud data. This letter proposes a tunnel surface parameterization algorithm based on a harmonic map, where a 3-D tunnel point cloud is used as a data source to reconstruct a triangle mesh model of the tunnel and then generate a harmonic map depth map of the tunnel inner wall on the triangle mesh. We can obtain the spatial distribution and position information of the appendages and detect whether there are cracks, water leakage, falling pieces, and other diseases by the depth images. The results of this study indicate that the proposed algorithm is suitable for tunnels of various shapes and has low area distortion, which can better avoid the loss of information during dimensionality reduction. Compared with other existing methods, the algorithm has higher efficiency and applicability.

Unsupervised Classification of Multispectral Images Embedded With a Segmentation of Panchromatic Images Using Localized Clusters

There are many approaches to fuse panchromatic (PAN) and multispectral (MS) images for classification, mainly including sharpening-then-classification methods, classification-then-sharpening methods, and segmentation-then-classification methods. The generalized Chinese restaurant franchise (gCRF) is a segmentation-then-classification-like method to fuse very high resolution (VHR) PAN and MS images for classification, which has the limitation the same as that of the general segmentation-then-classification methods that segmentation errors will affect the subsequent classification. The problems of gCRF are that during the segmentation step, the spatial coherence in the image plane is deficient and the global clusters without spatial position information are used for segmentation, which may lead to undersegmented and disconnected regions in the segmentation results and decrease classification accuracy. In this paper, we propose an improved model, which overcomes the problems of the gCRF during the segmentation step, to increase the classification accuracy by the following two ways: 1) building the spatial coherence in the image plane by introducing neighborhood information of superpixels to construct the subimages and 2) using localized clusters with spatial location information instead of global clusters to measure the similarity between superpixels and segments. The experimental results show that the problems of undersegmentation and disconnected segments are both alleviated, resulting in better classification results in terms of the visual and quantitative aspects.

Research progress of microphone array based front-end speech enhancement technology for cochlear implant

Microphone array based methods are gradually applied in the front-end speech enhancement and speech recognition improvement for cochlear implant in recent years. By placing several microphones in different locations in space, this method can collect multi-channel signals containing a lot of spatial position and orientation information. Microphone array can also yield specific beamforming mode to enhance desired signal and suppress ambient noise, which is particularly suitable to be applied in face-to-face conversation for cochlear implant users. And its application value has attracted more and more attention from researchers. In this paper, we describe the principle of microphone array method, analyze the microphone array based speech enhancement technologies in present literature, and further present the technical difficulties and development trend.

Spatial pattern analysis of Haloxylon ammodendron using UAV imagery - A case study in the Gurbantunggut Desert

Spatial patterns are not only the foundation for the understanding of plant interactions, but also reflect the spatial processes among plant populations. The primary requirement of spatial pattern analysis is the collections of location information of individual plants. In this study, we used low-altitude Unmanned Aerial Vehicle (UAV) remote sensing technology to obtain regional high-precision remote sensing images for Haloxylon ammodendron (H. ammodendron) forest in the southwestern Gurbantunggut Desert, and extracted spatial position information to analyze the spatial patterns using Ripley's L(r) function. Applying and comparing seven spatial position extraction methods, this study showed that the index RGRI (Red-Green Ratio Index) made 83.46% accuracy in the spatial position extraction, and an accuracy of 79.48% was obtained using NGBDI (Normalized Green-Blue Difference Index), while other five location information extraction methods resulted relatively lower accuracy. Results from spatial pattern analysis indicated that the extraction by UAV remote sensing were consistent with those obtained by field measurements. The H. ammodendron population showed a random distribution within the scale of 0–15 m, which suggested that the dependence of mutual asylum between individuals was low and not important. This distribution may be caused by the intense competition of individual vegetation for soil moisture, nutrients and other resources in desert areas. This study with low-altitude UAV imagery index analysis provided an efficient approach to rapid monitoring of plant population distribution characteristics in desert areas.

Fiber diameter measuring method of textile materials based on phase information

For achieving rapid and accurate measurement of fiber diameter, so as to evaluate product performance and guide the improvement of manufacturing process, a method based on phase information for measuring fiber diameter of which the fiber images of textile materials is collected by scanning electron microscope was proposed. Firstly, the improved Kuwahara filter is used to smooth the fiber image and enhance the foreground target. Then the fiber edge was detected by phase consistency method and repaired. Then, the spatial position and direction information of each pixel on the axis of a single fiber were determined, and the boundary pixel points were found by scanning along the vertical direction of the axis. Finally, the fiber diameter is calculated according to the position of border pixel points obtained by scanning. The experimental results show that this method can accurately and effectively measure the fiber diameter. Compared with the standard manual measurement, the fiber with a relative error of less than 2% accounts for 95% of the total fiber.

Multi-Layer Convolutional Features Concatenation With Semantic Feature Selector for Vein Recognition

Pre-trained DCNN trained on a large-scale image database can be used as a universal feature representation for image classification, which has achieved significant progress in some image recognition tasks. Compared with other image recognition tasks, directly utilizing a single convolutional feature as feature representation for vein recognition task cannot achieve the impressive result due to the sparse distribution of vein information. Therefore, to obtain more representative and discriminative convolutional feature for vein recognition, a novel multi-layer convolutional features' concatenation with semantic feature selector is proposed in this paper. In the pre-trained DCNN, different convolutional layers can encode different-level feature information. High-level convolutional features with vein information cover more semantic information and low-level convolutional features with vein information cover more detail information. However, low-level convolutional features also contain some background information. Therefore, in order to remove the background information of low-level convolutional features, a novel semantic feature selector is presented. First, the proposed local max-pooling of preserving spatial position (LMP-PSP) information is applied on activation map obtained by adding up all feature maps of the high-level convolutional layer to generate the semantic weighting map, which reflects key vein information of high-level convolutional features. Then, semantic weighting map is regarded as a feature selector to discard the background information of the low-level convolutional features and preserve the detail information of low level convolutional features. Finally, low-level convolutional features with vein information are selectively linked to high-level convolutional features with vein information based on the proposed semantic feature selector. A series of rigorous experiments on two lab-made vein databases named CUMT-Hand-Dorsa Vein database and CUMT-Palm Vein database is conducted to verify the effectiveness and feasibility of the proposed model. Besides, additional experiments with PUT Palm Vein database and the subset of PolyU database illustrate its generalization ability and robustness.

High-precision three-dimensional atom localization in a three-level pump–probe atomic system

We propose a new scheme for three-dimensional (3D) atom localization controlled by incoherent pump in a three-level Λ-type atomic system. The spatial position information of an atom in 3D space can be achieved via measuring probe gain and absorption when the atom passes through three mutually perpendicular standing-wave fields. It is found that high-detecting-probability and high-precision 3D atom localization can be obtained via properly adjusting the relevant parameters in the presence of the combination of a traveling-wave field and three orthogonal standing-wave fields. Furthermore, it is also shown that the incoherent pumping field can switch the localization patterns from the probe-gain sphere to the probe-absorption sphere and enhance 3D atom-localization precision.

Object Recognition Based on Improved Context Model

An object recognition method is proposed in this paper by introducing the spatial location relationship of objects into the context model. The spatial-position information of the objects is first utilized to model the context model. The model parameters and dependency structure of objects can be learned by integrating the context information into the same probabilistic framework. The image recognition is accomplished by using the advantages of efficient inference of the tree structure model. The proposed method can greatly improve the object recognition rate and better keep the consistency of scenes. The effectiveness of the proposed algorithm is verified by testing and comparing with other existing algorithms in the actual dataset.