In the application of scheduling strategies for power dispatching robots, traditional particle swarm optimization algorithms often become trapped in local optima during the optimization process, preventing the attainment of globally optimal results. Therefore, this paper proposes multi-objective optimization scheduling strategies for power dispatch robots based on an improved particle swarm optimization algorithm framework. When constructing the multi-objective optimization model for power dispatch robots, the particle swarm optimization algorithm is applied, and an adaptive inertia weight adjustment mechanism is used to improve search efficiency, ultimately achieving the optimal solution for multi-objective scheduling. The experimental results show that using the improved particle swarm optimization algorithm to optimize the multi-objective scheduling task of the power dispatching robot significantly reduces the total operating cost of the power system by approximately 19,326.8 US dollars, while keeping monthly pollutant emissions below 100 tons and effectively reducing system network loss. This method not only improves power scheduling efficiency but also promotes the development of green energy, providing strong support for the sustainable development of the power system.

Positron emission particle tracking (PEPT) enables non-intrusive Lagrangian trajectory measurements in opaque flow systems, yet accurate estimation of turbulent flow fields from PEPT data remains challenging due to uncertainty propagation inherent in numerical differentiation. Statistical characterization of a stationary tracer reveals that PEPT measurement uncertainties are well approximated by Gaussian distributions with pronounced spatial anisotropy. Based on this finding, a Gaussian-guided adaptive wavelet denoising framework is proposed to improve PEPT data quality. In this framework, the optimal denoising parameters are iteratively determined by verifying that the denoising residuals conform to Gaussian statistics, rather than relying on empirical selection. From the wavelet-denoised PEPT trajectories, instantaneous tracer kinematic quantities (location, velocity and acceleration), and 3D turbulent flow fields (mean velocity, mean acceleration and turbulent kinetic energy) are subsequently derived. Validation through a rotating motion benchmark confirms the effectiveness of the proposed denoising framework, achieving a reduction in the RMSE of location, velocity, and acceleration by 51%, 66.7%, and 97.2%, respectively. Further validation on a turbulent mixing flow confirms accurate estimation of 3D turbulent flow fields, showing excellent agreement between wavelet-denoised PEPT measurements and high-fidelity PIV measurements. The proposed denoising framework establishes PEPT as a reliable alternative tool for quantitative turbulence analysis, particularly in opaque fluids that optical techniques are infeasible.

This study proposes a method for generating inspection instructions for an intelligent power dispatching robot based on a directed graph model. This method involves constructing a three-dimensional spatial model, establishing line of sight and viewpoint constraints, and subsequently employing an improved chaotic genetic algorithm to identify inspection points. Following this, a programmable logic controller (PLC) is used to receive control data from the inspection point, and the PLC ladder diagram is abstracted into a directed graph model. In conjunction with a binary tree structure to represent logical relationships, a patrol instruction list is generated. Experiment results demonstrate that this method can efficiently select detection points and generate inspection instructions, exhibiting significant application efficacy and innovation.

Underwater terrain-aided navigation system can effectively correct the drift error of inertial navigation system that gradually increases with the accumulation of time, among which, underwater terrain suitability analysis is an important part of underwater terrain-aided navigation system. Traditional approaches view terrain matching and terrain suitability analysis as separate processes, which leaves terrain suitability analysis devoid of the knowledge used through navigation and terrain matching in particular. Whereas terrain matching can often provide useful information for TSA. This study attempts to use a deep learning model to deal with the terrain matching problem and terrain suitability analysis problem in the dimension of abstract features using a unified feature encoder. We propose a framework for sharing knowledge during the terrain analysis and matching phases, and construct a fitted probabilistic nonlinear classification network for matching. We validate the effectiveness of the method by performing suitability analysis in terrain regions with different geomorphic differences. After that, the good performance of the method is further verified by the navigation results in regions with different suitability. Finally, the feasibility and effectiveness of the terrain-assisted navigation system proposed in this paper are verified and analyzed as a whole in the form of semi-physical simulation.

Underwater navigation reliability heavily depends on accurate terrain evaluation. While conventional approaches treat terrain matching and suitability assessment as separate processes, this separation often leads to inconsistent and imprecise terrain evaluations. To address these limitations, we propose an integrated framework that employs a unified terrain map encoder to simultaneously handle both matching and suitability analysis tasks. At the core of our framework lies the SO(2) Elevation Embedding Transformer (SEET), which combines rotation-equivariant CNN with elevation embeddings. The SEET encoder is pre-trained through self-supervised contrastive learning on underwater elevation data, eliminating the need for manual labeling. Our extensive experimental validation demonstrates the framework’s effectiveness, showing superior matching accuracy with minimal navigation deviation. The distinct performance gap observed between suitable and unsuitable regions further validates the effectiveness of our suitability analysis approach.

Spectral CT can be used to perform material decomposition from polychromatic attenuation data, generate virtual monochromatic or virtual narrow-energy-width images in which beam hardening artifacts are suppressed, and provide detailed energy attenuation coefficients for material characterization. We propose an energy-coded spectral CT imaging method that is based on projection mix separation, which enables simultaneous energy decoding and image reconstruction. An X-ray energy-coded forward model is then constructed. Leveraging the Poisson statistical properties of the measurement data, we formulate a constrained optimization problem for both the energy-coded coefficient matrix and the material decomposition coefficient matrix, which is solved using a block coordinate descent algorithm. Simulations and experimental results demonstrate that the decoded energy spectrum distribution and virtual narrow-energy-width CT images are accurate and effective. The proposed method suppresses beam hardening artifacts and enhances the material identification capabilities of traditional CT.

When undertaking optical sparse projection reconstruction, the reconstruction of the tested field often requires the utilization of a priori knowledge to compensate for the lack of information due to the sparse projection angle. In order to reconstruct the radiation field of unknown materials or in situations where a priori knowledge cannot be obtained, this paper proposes an extremely sparse tomography multispectral temperature field reconstruction algorithm that analyzes the similarity (the similarity here compares and calculates the Euclidean distance of the spectral emissivity values at various wavelengths between different spectral curves) of radiation characteristics of materials under the same pressure and concentration but different temperature, describes the similarity between the radiation information of the tested field using the dynamic time warping (DTW) algorithm, and uses the similarity sum of the radiation information among the subregions of the temperature field as the optimization objective. This is combined with the equation-constrained optimization algorithm and multispectral thermometry to establish the statistical law between the missing information and finally realize the reconstruction of the temperature field. Simulation experiments show that, without any a priori knowledge, the method in this paper can realize reconstruction of the temperature field with an accuracy of 1.53–12.05% under two projection angles and has fewer projection angles and stronger robustness than other methods.

Multispectral thermometry is based on the law of blackbody radiation and is widely used in engineering practice today. Temperature values can be inferred from radiation intensity and multiple sets of wavelengths. Multispectral thermometry eliminates the requirements for single-spectral and spectral similarity, which are associated with two-colour thermometry. In the process of multispectral temperature inversion, the solution of spectral emissivity and multispectral data processing can be seen as the keys to accurate thermometry. At present, spectral emissivity is most commonly estimated using assumption models. When an assumption model closely matches an actual situation, the inversion of the temperature and the accuracy of spectral emissivity are both very high; however, when the two are not closely matched, the inversion result is very different from the actual situation. Assumption models of spectral emissivity exhibit drawbacks when used for thermometry of a complex material, or any material whose properties dynamically change during a combustion process. To address the above problems, in the present study, we developed a multispectral thermometry method based on optimisation ideas. This method involves analysing connections between measured temperatures of each channel in a multispectral temperature inversion process; it also makes use of correlations between multispectral signals at different temperatures. In short, we established a multivariate temperature difference correlation function based on the principles of multispectral radiometric thermometry, using information correlations between data for each channel in a temperature inversion process. We then established a high-precision thermometry model by optimising the correlation function and correcting any measurement errors. This method simplifies the modelling process so that it becomes an optimisation problem of the temperature difference function. This also removes the need to assume the relationships between spectral emissivity and other physical quantities, simplifying the process of multispectral thermometry. Finally, this involves correction of the spectral data so that any impact of measurement error on the thermometry is reduced. In order to verify the feasibility and reliability of the method, a simple eight-channel multispectral thermometry device was used for experimental validation, in which the temperature emitted from a blackbody furnace was identified as the standard value. In addition, spectral data from the 468-603 nm band were calibrated within a temperature range of 1923.15-2273.15 K, resulting in multispectral thermometry based on optimisation principles with an error rate of around 0.3% and a temperature calculation time of less than 3 s. The achieved level of inversion accuracy was better than that obtained using either a secondary measurement method (SMM) or a neural network method, and the calculation speed achieved was considerably faster than that obtained using the SMM method.

In optical sparse projection reconstruction, the reconstruction of the tested field often requires the utilization of a priori knowledge to compensate for the lack of information due to the sparse projection angle. For situations where the radiation field of unknown materials is reconstructed or prior knowledge cannot be obtained, this paper proposes a multi-spectral temperature field reconstruction technology under a sparse projection. This technology utilizes the principles of multi-spectral temperature measurement technology, takes the correlation of radiation information between sub-regions of the temperature field as the optimization objective, and establishes statistical rules between the missing information by combining the equation constraint optimization algorithm and multi-spectral temperature measurement technology. Finally, the temperature field to be measured is reconstructed. The simulation and experimental tests show that, without any prior knowledge, the proposed method can reconstruct the temperature field under two projection angles, with an accuracy of 1.64~12.25%. Moreover, the projection angle is lower, and the robustness is stronger than that of the other methods.

X-ray images of complex workpieces generally exhibit low contrast using a highly dynamic X-ray imaging system, which reduces the detection sensitivity for minor defects. In previous work, the combination of local variance and variational methods was used for image enhancement, but failed to achieve a good compromise between image enhancement and noise. In this work, an X-ray image enhancement framework based on an improved local adaptive contrast field is proposed to improve the visual quality of X-ray images. The improved contrast field is implemented by constructing a local adaptive gain function, where an improved local variance is used to quantify the fluctuation degree of local information and to reduce the noise in an image. Specifically, the improved local variance is computed with the mean squared error between the real surface and a smooth reference plane determined by Taylor’s theorem. Furthermore, an objective function between the improved contrast field and the objective image is solved using a variational approach to obtain a higher-quality image with tiny details highlighted. Experiments with three typical complex workpieces were performed, and results verified the effectiveness of the proposed approach for image enhancement and minor defect detection.

Terrain matching is a core component of underwater terrain-aided navigation system, which determines the accuracy of the underwater vehicle's localization. Traditional terrain matching methods are lacking in improving the matching performance by extracting effective terrain features, and the effects of sample noise and rotational changes in the measurement data on the matching precision and matching accuracy are not well addressed. This paper proposes a deep learning method for extracting global style and local detail terrain features, which yields terrain feature mappings with enhanced representational capabilities. Meanwhile, without using any sample labels and negative samples, we propose an end-to-end learning framework that combines self-distillation and contrastive learning to achieve characterization learning for two augmented samples of the same terrain data. To improve the model's ability to resist sample noise and rotational variations, we implement data augmentation by simulating measurement noise and rotational variations in underwater topographic measurements. In addition, we achieved high-precision terrain matching by comparing the abstract representations between samples rather than the terrain elevation values themselves. We have done a large number of comparison experiments with other methods, and the results show that our method has gained superior matching performance. The proposed method also enables high-precision matching of terrain data with varying sizes and resolutions, addressing the issue of limited high-resolution terrain data for training in real-world applications. Simulation test results show that our proposed method can obtain good localization accuracy in both rough and flat areas with good robustness.

针对线膛炮使用过程对身管内表面磨损检测的需求,建立线膛炮内表面磨损磁散射模型,研究一种线膛炮内表面磨损检测方法.在地磁环境中,基于磁偶极子模型分析阴线双侧膛线壁的二维磁偶极子模型,推导内膛、导转侧、烧蚀沟、镀层等磨损的散射磁场分布模型,通过仿真得到身管内表面磨损的磁场分布变化规律,对膛线磨损检测进行了半实物模拟试验.实验结果表明:当阳线无磨损时,阴线与阳线磁场强度差值为17.6 A/m;当阳线磨损为56.15％时,磁场强度差值减小到1.78 A/m;该方法为身管出厂和使用提供一种膛线磨损检查理论与方法.

针对地下浅层地层结构复杂、地面获取的爆炸波振动信号波形混叠、频散严重,采用逆时偏移方法震源成像模糊、震源定位精度低的问题,将图像融合引入震源定位中,提出了一种基于3D-UNet的多谱图像融合定位方法.首先,对传感器采集到的信号通过变分模态分解(VMD)进行多频主成分分解,通过逆时聚焦成像方法形成多谱能量场;之后,将多谱能量场作为3D-UNet的输入,并结合注意力机制进行多谱能量场对应系数的自适应调整,通过梯度下降法使网络输出最大值位置逼近真实震源位置,生成融合网络模型.实验验证表明,本文方法相比于基于VMD多谱图像融合定位方法定位精度更高,且均方根误差在0.5 m以内.

Multispectral thermometry is based on Blackbody radiationlaw, and the temperature value can be calculated based on the radiation intensity and multiple sets of wavelengths. This method has become widely used in engineering practice, as it overcomes the constraints of the single spectrum and similar colorimetric spectrum requirements for colorimetric temperature measurement. In multispectral temperature inversion, the solution of spectral emissivity and multispectral data processing are the keys to accurate temperature measurement. At present, the solution of spectral emissivity is mostly based on the assumption model of spectral emissivity. When the hypothetical model is close to reality, the accuracy of the inverted temperature and spectral emissivity is very high; otherwise, the inversion result deviates significantly. For the temperature measurement of complex materials and the dynamic changes of material properties during the combustion process, the method of assuming the model of spectral emissivity is groundless; In recent years, the deep learning method based on the neural network has been applied to multispectral temperature measurement, which avoids the assumption model of spectral emissivity, and can establish the nonlinear statistical relationship between temperature and multi spectrum, but it requires massive data and supercomputing power support, and the modeling process is complicated. In order to solve the above problems, this paper proposes a multispectral temperature measurement method named multi-element extreme value optimization (MEVO) measurement method. This method utilizes the correlation between multispectral signals at different temperatures, and by analyses the relationship between the measured temperatures of each channel in the process of multispectral temperature inversion, based on the principle of multispectral radiation temperature measurement and the information correlation between the data of each channel in the process of temperature inversion, establish a multivariate temperature difference correlation function, and establish a highprecision temperature measurement model through the optimization of the correlation function. This method simplifies the modeling process to the optimization problem of multivariate temperature difference function, avoids the assumption of the relationship between spectral emissivity and other physical quantities, reduces the requirement of data sample size for deep learning methods, and simplifies the process of multispectral temperature measurement. A simple 8 -channel temperature measuring device was used for experimental verification. In the experiment, we determined that the temperature emitted by the Blackbody furnace was the standard value. The spectral data of the 468(sic)603 nm band in the 1 923. 15(sic)2 273. 15 K temperature zone was calibrated, and the multispectral thermometry based on the optimization of multiple extreme values was realized. The temperature measurement accuracy is about 0. 5%, and the temperature inversion time is within 2. 5 s. Compared with the second measurement method ( SMM) and the neural network method, the inversion accuracy is substantially improved. Moreover, the inversion speed is significantly faster than the SMM method.

Individuals suffering from motor dysfunction due to various diseases often face challenges in performing essential activities such as grasping objects using their upper limbs, eating, writing, and more. This limitation significantly impacts their ability to live independently. Brain–computer interfaces offer a promising solution, enabling them to interact with the external environment in a meaningful way. This exploration focused on decoding the electroencephalography of natural grasp tasks across three dimensions: movement-related cortical potentials, event-related desynchronization/synchronization, and brain functional connectivity, aiming to provide assistance for the development of intelligent assistive devices controlled by electroencephalography signals generated during natural movements. Furthermore, electrode selection was conducted using global coupling strength, and a random forest classification model was employed to decode three types of natural grasp tasks (palmar grasp, lateral grasp, and rest state). The results indicated that a noteworthy lateralization phenomenon in brain activity emerged, which is closely associated with the right or left of the executive hand. The reorganization of the frontal region is closely associated with external visual stimuli and the central and parietal regions play a crucial role in the process of motor execution. An overall average classification accuracy of 80.3% was achieved in a natural grasp task involving eight subjects.

For X-ray CT imaging of complex objects, the tube voltage is an important parameter. At fixed voltage, the complete projection cannot be obtained because the voltage does not match the equivalent attenuation thickness of the object at different CT scanning angles under the limitation of the dynamic range of the imaging system. Multivoltage exposure imaging is an effective solution. However, due to the limitation of high-voltage-regulation ripple, the imaging efficiency is very low if the voltage is adjusted frequently. Actually, for a ray detector with a wide dynamic range, the small difference in equivalent thickness between adjacent scanning angles cannot affect the imaging quality for complex objects. Therefore, this article proposes a novel autoexposure imaging method with stepped voltage scanning. First, according to the known imaging system, the voltage regulation method of stepped voltage adjustment is researched by evaluating the image quality of different thicknesses. Then, considering that stepped voltage adjustment cannot obtain the complete projection at the thinner thickness, the projections of negative logarithmic transformation (NLT), with stepped voltage and low voltage, are fused to obtain the high dynamic range (HDR) projection. Finally, the method is validated on both a customized titanium specimen and an actual engine blade. The results show that the proposed method can achieve HDR CT imaging of complex objects with higher quality and clearer edge details. Additionally, this new scanning mode of stepped voltage adjustment can reduce the frequency of voltage adjustment and improve imaging efficiency.

Among various temperature measurement technologies, Multispectral thermometry is a better method to measure temperature under complex conditions. To solve the problems in the multi-spectral temperature measurement technology, which included the detector’s non-linear sensitivity characteristics, consistency calibration, data processing, etc., this paper, based on the previous research and development of the multi-spectral dynamic temperature measurement system, realized data acquisition of the dynamic temperature in 20us sample rates within 1550~2000 Celsius, and then the dynamic measurement data is processed by the probabilistic neural network (PNN). Finally, temperature measurement system realized the accuracy of the measurement error less than 1.1%. The results show that the PNN neural network can fit the temperature distribution curve better and faster, and can be better applied to the miniaturization of the temperature measurement system.

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.

Audio signals play a crucial role in our perception of our surroundings. People rely on sound to assess motion, distance, direction, and environmental conditions, aiding in danger avoidance and decision making. However, in real-world environments, during the acquisition and transmission of audio signals, we often encounter various types of noises that interfere with the intended signals. As a result, the essential features of audio signals become significantly obscured. Under the interference of strong noise, identifying noise segments or sound segments, and distinguishing audio types becomes pivotal for detecting specific events and sound patterns or isolating abnormal sounds. This study analyzes the characteristics of Mel’s acoustic spectrogram, explores the application of the deep learning ECAPA-TDNN method for audio type recognition, and substantiates its effectiveness through experiments. Ultimately, the experimental results demonstrate that the deep learning ECAPA-TDNN method for audio type recognition, utilizing Mel’s acoustic spectrogram as features, achieves a notably high recognition accuracy.

Terrain matching is a core component of underwater terrain-aided navigation system, which is the key to whether underwater vehicle can realize accurate positioning. This paper proposes a deep learning method based on the combination of self-distillation and contrastive learning ideas, which realizes end-to-end learning of terrain features without using any sample labels and negative samples in a more abstract feature dimension to achieve higher accuracy terrain matching. We achieved data augmentation by simulating measurement errors and rotational variations in underwater terrain measurements, which improved the model's ability to resist sample errors and rotational variations; obtained terrain feature mapping with stronger characterization capabilities by constructing a network that fuses local details with global styles; achieved comparative learning of two augmented samples of the same topographic data by means of self-distillation; and achieved high-accuracy terrain matching by comparing the abstract representations between samples rather than the terrain elevation values themselves. We have done a large number of comparison experiments with other methods, and the results show that our method has better matching performance. We have also realized high-precision matching of terrain data of different sizes and resolutions, which solves the problem of insufficient number of high-resolution terrain data training in practical applications.