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The purpose of using electroencephalogram to explore the dynamic changes of brain functional connectivity during natural grasping tasks is to uncover the underlying mechanisms of information transmission between different brain regions during cognitive processing. This exploration aims to provide new insights for the development of brain-computer interface technology and contribute to the diagnosis and treatment of brain disorders. In this study, we used time-frequency cross mutual information to evaluate the brain functional connectivity during 3-class natural grasping tasks (palmar grasp, lateral grasp and rest state). Specifically, our analysis focused on the functional brain connectivity generated by the amplitude and phase of electroencephalogram signals within the alpha (8-13 Hz) and beta (20-30 Hz) frequency bands. To assess the differences in global coupling strength, we employed two-series correlation coefficients, between different motor periods and between different brain regions for the three motor tasks. Furthermore, it was compared that the differences in the global coupling strength between different motor periods in the same motor task. Finally, the analysis of topologic characteristics in brain functional connectivity networks between the three tasks was investigated. The findings of our study indicate that functional reorganization of frontal region closely related to external visual stimuli occurs during the motor preparation period. The onset of movement leads to a lateralized reorganization of brain functional connectivity, which is associated with the right or left of the executive hand. Both the central and parietal regions contribute prominently to motor execution, and the parietal region in particular plays an important role in the execution of fine motor movements. Further analysis revealed that it is the brain’s dynamic regulation of functional connectivity across frequency bands, amplitudes and phases, enabling it to perform multiple tasks with limited energy resources.

Dysarthria, a speech disorder often caused by neurological damage, compromises the control of vocal muscles in patients, making their speech unclear and communication troublesome. Recently, voice-driven methods have been proposed to improve the speech intelligibility of patients with dysarthria. However, most methods require a significant representation of both the patient’s and target speaker’s corpus, which is problematic. This study aims to propose a data augmentation-based voice conversion (VC) system to reduce the recording burden on the speaker. We propose dysarthria voice conversion 3.1 (DVC 3.1) based on a data augmentation approach, including text-to-speech and StarGAN-VC architecture, to synthesize a large target and patient-like corpus to lower the burden of recording. An objective evaluation metric of the Google automatic speech recognition (Google ASR) system and a listening test were used to demonstrate the speech intelligibility benefits of DVC 3.1 under free-talk conditions. The DVC system without data augmentation (DVC 3.0) was used for comparison. Subjective and objective evaluation based on the experimental results indicated that the proposed DVC 3.1 system enhanced the Google ASR of two dysarthria patients by approximately [62.4%, 43.3%] and [55.9%, 57.3%] compared to unprocessed dysarthria speech and the DVC 3.0 system, respectively. Further, the proposed DVC 3.1 increased the speech intelligibility of two dysarthria patients by approximately [54.2%, 22.3%] and [63.4%, 70.1%] compared to unprocessed dysarthria speech and the DVC 3.0 system, respectively. The proposed DVC 3.1 system offers significant potential to improve the speech intelligibility performance of patients with dysarthria and enhance verbal communication quality.

Automatic road extraction from remote sensing images plays an important role for navigation, intelligent transportation, and road network update, etc. Convolutional neural network (CNN)-based methods have presented many achievements for road extraction from remote sensing images. CNN-based methods require a large dataset with high quality labels for model training. However, there is still few standard and large dataset, which is specially designed for road extraction from optical remote sensing images. Besides, the existing end-to-end CNN models for road extraction from remote sensing images are usually with symmetric structure, studying on asymmetric structure between encoding and decoding is rare. To address the above problems, this article first provides a publicly available dataset LRSNY for road extraction from optical remote sensing images with manually labelled labels. Second, we propose a reconstruction bias U-Net for road extraction from remote sensing images. In our model, we increase the decoding branches to obtain multiple semantic information from different upsamplings. Experimental results show that our method achieves better performance compared with other six state-of-the-art segmentation models when testing on our LRSNY dataset. We also test on Massachusetts and Shaoshan datasets. The good performances on the two datasets further prove the effectiveness of our method.

This paper describes a perforating gun detonation monitoring system developed to meet the need for real-time monitoring of perforation effect in the petroleum industry. A high-sensitivity acceleration sensor, serving as the response module of the physical characteristics of the tubing vibration, acquires a set of vibration signals of the whole physical process during the impact after the detonation of the shaped charge. The jump point in the monitoring process is extracted by comparing the signal's short-time energy. The amplitude, energy, and envelope characteristics of the signal are then evaluated. From the time-frequency characteristics of the tubing vibration upon the detonation of the perforating gun, a judgment is made as to whether the perforating gun has detonated. Tubing vibration experiments were conducted involving different perforating guns. The results suggested that the proposed monitoring method is able to monitor perforating guns of different shaped charges with a perforating depth between 500m-3500m and in oil wells with an inclination of 0° - 90°. As a means of vibration detection, this method also works well in other types of vibration monitoring.

Source location based on polarization angle information is a research hot spot in the field of shallow distributed source localization. The near-field P-wave and S-wave aliasing are severe and the polarization characteristics of the wave group are poor, making it difficult to extract the polarization angle, an important source localization parameter. To address the above difficulty, this paper proposes a method that is based on high-resolution parabolic Radon transform (HRP-Radon) and incorporates adaptive covariance matrix (ACM) to extract the polarization angle information of the wavefront. First, a data set is constructed from the data acquired by the sensor array, and this data set is morphologically corrected against the first break time information. Second, the time domain data set is converted into the Radon domain by HRP-Radon, and the direct P-wave is extracted from the aliasing information by use of the focusing characteristics of the P-wave and S-wave and the phase characteristics of the far-field P-wave. Third, a model is built using the ACM algorithm to extract the direct P-wave angle information and the angle extraction is assessed using a metric-polarization method. The numerical simulation and experimental results show that the proposed method is able to extract the wavefront angle information of the blasting near-field aliasing signal and determine the source location from the polarization angle information. This method is of practical value to the field of underground space.

This study aims to analyze the cascading failure process of traffic network in tourist attraction under unexpected emergency events, and discuss the measures to improve traffic network invulnerability. For that purpose, taking tourist attraction as the research object, the topology model of traffic network in tourist attraction is established based on Space L method. After different types of emergency events are simulated as different attack strategies, we discuss the spatial and temporal distribution characteristics and evolution process of tourism emergency events. The cascading failure model of traffic network based on load-capacity is constructed, then the main factors affecting the scale of dynamic cascading failures are given and their sensitivity are analyzed. Taking the Summer Palace in Beijing as an example, the invulnerability analysis of the traffic network is carried out and the node protection strategies with different network load are proposed. The study reveals that (a) the traffic network of the Summer Palace has a typical network characteristics of small world; (b) network load, node capacity, node attack strategy, adjacent nodes relationship, load distribution rule significantly affect the scale of cascading failure of the traffic network in tourist attraction, (c) when the network load coefficient δ

Shallow underground acoustic source localization is a component of near-field source localization, which is involved in numerous application fields. The positioning accuracy is mainly limited by the accuracy of time of arrival (TOA)/time difference of arrival (TDOA) extraction and velocity extraction from noisy data. The steered response power with phase transform (SRP-PHAT) is one of the most robustness and high-precision acoustic source localization approaches, which avoids extracting the TDOA in advance. But SRP-PHAT is constrained for only using under known velocity. Furthermore, it is barely possible for shallow underground sound source localization to easily obtain high-quality velocity models. This paper proposes an improved SRP-PHAT with unknown velocity (SRP-PHAT-UNVEL), which avoids extracting the TDOA and velocity in advance. SRP-PHAT-UNVEL matches the calculated time delay curve with the actual time delay curve by scanning of the candidate spatial position and the candidate velocity simultaneously, so as to maximize the output energy to fulfill the positioning. However, SRP-PHAT-UNVEL has larger computational complexity as it proceeds with the optimization of space and velocity. Since the spatial position and velocity affect the shape and the curvature of the calculated delay curve respectively, these are two relatively independent processes. Therefore, the simultaneous optimization of space and velocity can be replaced by a two-stage optimization to improve the efficiency and accuracy of SRP-PHAT-UNVEL. Spatial optimization is equivalent to the optimization of SRP-PHAT, and the spatial optimization of the bat algorithm has faster convergence rate and higher location precision than traditional methods. Velocity optimization can be achieved by the common linear search technique since the function of velocity and energy is an ideal convex function. Simulation experiment results show that the proposed method is insensitive to noise, which can achieve high accuracy of the acoustic source position and velocity simultaneously. With grouping the measured data, the proposed method can further improve the robustness and accuracy by fusing the grouping location results with principal component analysis.

In X-ray imaging, the image quality is related to the ray energy, imaging detector, object construction, etc. For complex workpieces, if all imaging conditions are identical, not all of the construction projection information can be captured with a fixed energy and limited dynamic range. This will result in over- and underexposed regions, which will affect projection quality. Thus, this paper presents an approach to extend the dynamic range of X-ray imaging systems based on linear constraints with variable energy. First, we study the relationship of grey variation between the adjacent energy images and present the grey gain distribution and linear dispersion. Then, based on a piecewise curve linear approximation, auto-self-adaption variation of ray energy is used to capture the image sequences under the linear constraint of the grey gain distribution. Also, based on the linear assumption, we obtain the weighting fusion coefficient to fuse the image sequences to extend the dynamic range. Finally, experiments demonstrate that this method has an advantage for ray energy auto-control and fusion coefficient calculation, which can realize optimal matching between the X-ray imaging system and the testing object to extend the dynamic range of the imaging system.

Radar detection of high-speed targets suffers from range walks during the integration time. Methods in current use for mitigating range walks are beset by high computational complexity therein that hinders practical real-time processing. In this context, we exploit the sparsity of the target echo in the transform domain and propose an efficient range walk mitigation algorithm based on sparse Fourier transform (SFT). Concretely, the input long echo sequence is first divided into short overlapped segments with an SFT bucket structure. Then, speed compensation is performed to the short segments, which involves less complex multiplications. Subsequently, SFT is employed which efficiently obtains the Fourier transform of the long sequence such that the range alignment of the multi-pulse echo is accomplished. As such, the proposed SFT-based algorithm significantly reduces the amount of complex multiplications required in speed compensation and long sequence transform, and thus substantially improves the computational efficiency. In this paper, the selection of the window function and the length of segments are examined for their influence on the detection performance with different signal-to-noise ratios. The superiorities of the proposed algorithm in both detection performance and computational efficiency are demonstrated by numerical experiments. The proposed algorithm can potentially find applications in other radar systems such as synthetic aperture radars, inverse synthetic aperture radars, and so on where echo range walk is also encountered.

The recent advancement in intelligent transportation system makes it crucial to equip vehicles with intelligent onboard system (IOS). With the help of IOSs, vehicles can obtain varieties of convenient services from both remote service centers and other vehicles. However, the deficiency of the existing IOS makes the upgrade process complex and costly for the reason of its closed architecture. In order to solve this problem, network function virtualization (NFV) is introduced to make the IOS open by virtualizing services as software applications that can be executed on standard IT platforms. Furthermore, the NFV-based IOS can build an ordered service chain, consisting of virtualized network functions (VNFs), among different IOSs, and support VNF reuse. Thus, it is imperative to optimize the chaining scheme to obtain a shorter average service time (AST). In this paper, we propose a clustered VNF chaining scheme, which deploys VNFs in clusters according to the cluster head of each vehicle clusters, and derive the expression of the AST in a relative static scenario. Based on this scheme, the dynamic AST of a given cluster in a moving scenario is also analyzed on the grounds of both macroscopic mobility model and microscopic mobility model. The analyses show that both of the vehicle density and distance-headway variations have influence on the AST of one given cluster, which is verified by numerical results. In addition, the experiment results also show that the proposed scheme has a better performance compared with other two schemes, which reduces the AST 61% and 43%, respectively.

During recent years, distributed manufacturing optimization problems have been researched and applied in many fields, such as steelmaking system and textile production process. To solve the multi-objective distributed flexible job shop scheduling problem, a hybrid Pareto-based tabu search algorithm (HPTSA) is investigated to minimize four objectives simultaneously, i.e., the makespan, the maximal workload, the total workload, and the earliness/tardiness (E/T) criteria. In the proposed algorithm, several approaches considering both the problem characteristics and the objective features are used to initialize the group of solutions. Then, five types of neighborhood structures that consider both problem structures are developed to enhance the exploitation and exploration capabilities. In addition, a well-designed backward method is proposed to optimize the E/T criteria. Based on the realistic production data in the steelmaking system, several instances with different problem scales are randomly generated. Four efficient multi-objective optimization algorithms are selected to make detailed comparisons with the proposed HPTSA algorithm. After detailed tests on the realistic instances, the experimental comparison results show that the proposed algorithm shows competitive performance compared with the selected efficient algorithms.

A magnetic induction-based angular rate measurement method is proposed to address the need for internal measurement of high-speed angular rate of carriers. This method relies on the rotational periodicity fed back by the magnetic field. Three coils aligned orthogonal one to another are employed as the feedback module that supplies the rotational periodicity information, the physical characteristic that contains the periodic signals, which in turn reflect the highly dynamic rotation of the carrier in its accelerating process. The periodic, instantaneous start point is extracted from the magnetic signal using zero-phase threshold technique, whereby the signal periodicity is established, enabling experimental measurement of the rotational angular rate of a high-speed rotation object. A semi-physical simulation was conducted at different speeds using a low maximum speed high-precision rotary table, and the results demonstrate that this measurement method is capable of measuring angular rate varying dynamically between 1 and 100 r/s, with a measurement error within 20/00 of the full scale. This method also provides a means for evaluating angular rate of other types of rotational movement.