Search Results (15)

To address the concerns of contrast deterioration, detail loss, and color distortion in images produced under haze conditions in scenarios such as intelligent driving and remote sensing detection, an algorithm for image defogging that combines Retinex theory and the dark channel prior is proposed in this paper. The method involves building a two-stage optimization framework: in the first stage, global contrast enhancement is achieved by Retinex preprocessing, which effectively improves the detail information regarding the dark area and the accuracy of the transmittance map and atmospheric light intensity estimation; in the second stage, an a priori compensation model for the dark channel is constructed, and a depth-map-guided transmittance correction mechanism is introduced to obtain a refined transmittance map. At the same time, the atmospheric light intensity is accurately calculated by the Otsu algorithm and edge constraints, which effectively suppresses the halo artifacts and color deviation of the sky region in the dark channel a priori defogging algorithm. The experiments based on self-collected data and public datasets show that the algorithm in this paper presents better detail preservation ability (the visible edge ratio is minimally improved by 0.1305) and color reproduction (the saturated pixel ratio is reduced to about 0) in the subjective evaluation, and the average gradient ratio of the objective indexes reaches a maximum value of 3.8009, which is improved by 36–56% compared with the classical DCP and Tarel algorithms. The method provides a robust image defogging solution for computer vision systems under complex meteorological conditions. Full article

The pump heating effect of DFB fiber laser is normally ignored due to the short length of the laser cavity. However, by fabricating a phase-shifted FBG on high concentration Er-Yb codoped fiber to obtain a 16 mm long DFB fiber laser, the gradient surface temperature distributions along the active grating with different pump powers were observed. The average surface temperature rose by 16.82 K with a variation of less than 1.11 K, and the position with the highest temperature moved towards the center of the grating by 5.5 mm, when the pump power was increased from 0 mW to 191.6 mW. The transmission spectrum of the active phase-shifted FBG at different pump powers were measured, and an additional drift of the transmission peak in the stopband was testified. It was identified as an equivalent phase shift up to −0.1 π, which was induced by the gradient longitudinal temperature distribution. Considering that the initial phase shift of the grating was about 1.15 π, the increasing chirp of the active grating due to the pump heating could compensate the phase shift deviation from π surprisingly. The experimental results coincided with the simulation results by using the transmission matrix method under the assumption of piecewise-uniform structure for the chirped phase-shifted grating. The modified model of the active phase-shifted FBG reveals the difference between the cool cavity and the hot cavity at different pump powers, which may be used as a self-optimization mechanism for DFB fiber laser operation. Full article

Navigating crowded environments poses significant challenges for mobile robots, particularly as traditional Simultaneous Localization and Mapping (SLAM)-based methods often struggle with dynamic and unpredictable settings. This paper proposes a visual target-driven navigation method using self-attention enhanced deep reinforcement learning (DRL) to overcome these limitations. The navigation policy is developed based on the Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm, enabling efficient obstacle avoidance and target pursuit. We utilize a single RGB-D camera with a limited field of view (FOV) for target detection and surrounding sensing, where environmental features are extracted from depth data via a convolutional neural network (CNN). A self-attention network (SAN) is employed to compensate for the limited FOV, enhancing the robot’s capability of searching for the target when it is temporarily lost. Experimental results show that our method achieves a higher success rate and shorter average target-reaching time in dynamic environments, while offering hardware simplicity, cost-effectiveness, and ease of deployment in real-world applications. Full article

Super-resolution (SR) of remote sensing images is essential to compensate for missing information in the original high-resolution (HR) images. Single-image super-resolution (SISR) technique aims to recover high-resolution images from low-resolution (LR) images. However, traditional SISR methods often result in blurred and unclear images due to the loss of high-frequency details in LR images at high magnifications. In this paper, a super-segmental reconstruction model STGAN for remote sensing images is proposed, which fuses the Generative Adversarial Networks (GANs) and self-attention mechanism based on the Reference Super Resolution method (RefSR). The core module of the model consists of multiple CNN-Swin Transformer blocks (MCST), each of which consists of a CNN layer and a specific modified Swin Transformer, constituting the feature extraction channel. In image hypersegmentation reconstruction, the optimized and improved correlation attention block (RAM-V) uses feature maps and gradient maps to improve the robustness of the model under different scenarios (such as land cover change). The experimental results show that the STGAN model proposed in this paper exhibits the best image data perception quality results with the best performance of LPIPS and PI metrics in the test set under RRSSRD public datasets. In the experimental test set, the PSNR reaches 31.4151, the SSIM is 0.8408, and the performance on the RMSE and SAM metrics is excellent, which demonstrate the model’s superior image reconstruction details in super-resolution reconstruction and highlighting the great potential of RefSR’s application to the task of super-scalar processing of remotely sensed images. Full article

The aircraft anti-skid braking system (AABS) plays an important role in aircraft taking off, taxiing, and safe landing. In addition to the disturbances from the complex runway environment, potential component faults, such as actuators faults, can also reduce the safety and reliability of AABS. To meet the increasing performance requirements of AABS under fault and disturbance conditions, a novel reconfiguration controller based on linear active disturbance rejection control combined with deep reinforcement learning was proposed in this paper. The proposed controller treated component faults, external perturbations, and measurement noise as the total disturbances. The twin delayed deep deterministic policy gradient algorithm (TD3) was introduced to realize the parameter self-adjustments of both the extended state observer and the state error feedback law. The action space, state space, reward function, and network structure for the algorithm training were properly designed, so that the total disturbances could be estimated and compensated for more accurately. The simulation results validated the environmental adaptability and robustness of the proposed reconfiguration controller. Full article

Holographic optical tweezers have unique non-physical contact and can manipulate and control single or multiple cells in a non-invasive way. In this paper, the dynamics model of the cells captured by the optical trap is analyzed, and a control system based on a novel self-organizing fuzzy cerebellar model neural network (NSOFCMNN) is proposed and applied to the cell manipulation control of holographic optical tweezers. This control system consists of a main controller using the NSOFCMNN with a new self-organization mechanism, a robust compensation controller, and a higher order sliding mode. It can accurately move the captured cells to the expected position through the optical trap generated by the holographic optical tweezers system. Both the layers and blocks of the proposed NSOFCMNN can be adjusted online according to the new self-organization mechanism. The compensation controller is used to eliminate the approximation errors. The higher order sliding surface can enhance the performance of controllers. The distances between cells are considered in order to further realize multi-cell cooperative control. In addition, the stability and convergence of the proposed NSOFCMNN are proved by the Lyapunov function, and the learning law is updated online by the gradient descent method. The simulation results show that the control system based on the proposed NSOFCMNN can effectively complete the cell manipulation task of optical tweezers and has better control performance than other neural network controllers. Full article

Liquid crystal elastomer (LCE) fiber with a fixed end in an inhomogeneous temperature field is capable of self-oscillating because of coupling between heat transfer and deformation, and the dynamics of a translating LCE fiber in an inhomogeneous temperature field are worth investigating to widen its applications. In this paper, we propose a theoretic constitutive model and the asymptotic relationship of a LCE fiber translating in a linear temperature field and investigate the dynamical behaviors of a corresponding fiber-mass system. In the three cases of the frame at rest, uniform, and accelerating translation, the fiber-mass system can still self-oscillate, which is determined by the combination of the heat-transfer characteristic time, the temperature gradient, and the thermal expansion coefficient. The self-oscillation is maintained by the energy input from the ambient linear temperature field to compensate for damping dissipation. Meanwhile, the amplitude and frequency of the self-oscillation are not affected by the translating frame for the three cases. Compared with the cases of the frame at rest, the translating frame can change the equilibrium position of the self-oscillation. The results are expected to provide some useful recommendations for the design and motion control in the fields of micro-robots, energy harvesters, and clinical surgical scenarios. Full article

In common irrigation systems, sprinklers are mounted with circular nozzles, but innovative noncircular nozzles can save water and energy by improving fragmentation in a low–intermediate pressure irrigation system. In order to investigate the effects of nozzle orifice shapes (circular, square, and equilateral triangular) on droplet characteristics, experiments using high-speed photography and water droplet spectrum measurement were performed. Using ImageJ to observe with the overlapped droplets and using the self-compiled programs of MATLAB to observe the morphology of droplets, we extracted the outlines of droplets. In addition, several empirical formulas for the prediction of droplets were obtained by way of a regression analysis of the experimental data. In particular, the shape coefficient of the nozzle orifice and the operating pressure of the nozzle were added to these formulas as variable factors to make them applicable to a variety of nozzles and working conditions. The results show that with the increase in shape coefficient, the jet atomization intensifies, and the droplets breaking from the jet will be dense and uniform. The velocity distribution of the droplets conforms to exponential functions (R² > 0.7). The prediction formulas of diameter and kinetic energy were established with coefficients of determination exceeding 0.95. In low pressure conditions, the specific power multiplies at the end of spraying, and the maximum is proportional to the nozzle orifice coefficient. The impact-driven arm compensates for the disadvantage of the noncircular nozzles with the high irrigation-specific power, by producing a wider diameter gradient of droplets. Therefore, innovative sprinklers based on noncircular nozzles can be applied in a low–intermediate pressure system to increase water use efficiency, reduce energy consumption, and reduce costs. Full article

Background: Solid waste workers are exposed to a plethora of occupational hazards and may also experience work-related stress. Our study had three specific hypotheses: (1) waste workers experience effort–reward imbalance (ERI) with high self-reported effort but low reward, (2) unionized workers experience greater ERI, and (3) workers with higher income have lower ERI. Methods: Waste workers from three solid waste sites in Michigan participated in this cross-sectional study. We characterized perceived work stress using the short-version ERI questionnaire. Descriptive statistics and linear tests for trend were assessed for each scale. Linear regression models were constructed to examine the relationship between structural factors of work stress and ERI. Gradient-boosted regression trees evaluated which factors of effort or reward best characterize workers’ stress. Results: Among 68 participants, 37% of workers reported high effort and low reward from work (ERI > 1). Constant pressure due to heavy workload was most indicative of ERI among the solid waste workers. Union workers experienced 79% times higher ERI than non-unionized workers, while no significant differences were observed by income, after adjusting for confounders. Conclusions: Organizational-level interventions, such as changes related to workload, consideration of fair compensation, and increased support from supervisors, can decrease work stress. Full article

The output model of a rotating accelerometer gravity gradiometer (RAGG) established by the inertial dynamics method cannot reflect the change of signal frequency, and calibration sensitivity and self-gradient compensation effect for the RAGG is a very important stage in the development process that cannot be omitted. In this study, a model based on the outputs of accelerometers on the disc of RGAA is established to calculate the gravity gradient corresponding to the distance, through the study of the RAGG output influenced by a surrounding mass in the frequency domain. Taking particle, sphere, and cuboid as examples, the input-output models of gravity gradiometer are established based on the center gradient and four accelerometers, respectively. Simulation results show that, if the scale factors of the four accelerometers on the disk are the same, the output signal of the RAGG only contains $(4k+2)\omega$ ($\omega$ is the spin frequency of disc for RAGG) harmonic components, and its amplitude is related to the orientation of the surrounding mass. Based on the results of numerical simulation of the three models, if the surrounding mass is close to the RAGG, the input-output models of gravity gradiometer are more accurate based on the four accelerometers. Finally, some advantages and disadvantages of cuboid and sphere are compared and some suggestions related to calibration and self-gradient compensation are given. Full article

We report on the production of a ${}^{41}$ K- ${}^{87}$ Rb dual-species Bose–Einstein condensate with tunable interspecies interaction and we study the mixture in the attractive regime; i.e., for negative values of the interspecies scattering length $a_{12}$ . The binary condensate is prepared in the ground state and confined in a pure optical trap. We exploit Feshbach resonances for tuning the value of $a_{12}$ . After compensating the gravitational sag between the two species with a magnetic field gradient, we drive the mixture into the attractive regime. We let the system evolve both in free space and in an optical waveguide. In both geometries, for strong attractive interactions, we observe the formation of self-bound states, recognizable as quantum droplets. Our findings prove that robust, long-lived droplet states can be realized in attractive two-species mixtures, despite the two atomic components possibly experiencing different potentials. Full article

In this manuscript, the synchronization of four-dimensional (4D) chaotic systems with uncertain parameters using a self-evolving recurrent interval type-2 Petri cerebellar model articulation controller is studied. The design of the synchronization control system is comprised of a recurrent interval type-2 Petri cerebellar model articulation controller and a fuzzy compensation controller. The proposed network structure can automatically generate new rules or delete unnecessary rules based on the self-evolving algorithm. Furthermore, the gradient-descent method is applied to adjust the proposed network parameters. Through Lyapunov stability analysis, bounded system stability is guaranteed. Finally, the effectiveness of the proposed controller is illustrated using numerical simulations of 4D chaotic systems. Full article

In the process of airborne gravity gradiometry for the full-tensor airborne gravity gradiometer (FTAGG), the attitude of the carrier and the fuel mass will seriously affect the accuracy of gravity gradiometry. A self-gradient is the gravity gradient produced by the surrounding masses, and the surrounding masses include distribution mass for the carrier mass and fuel mass. In this paper, in order to improve the accuracy of airborne gravity gradiometry, a self-gradient compensation model is proposed for FTAGG. The self-gradient compensation model is a fuction of attitude for carrier and time, and it includes parameters ralated to the distribution mass for the carrier. The influence of carrier attitude and fuel mass on the self-gradient are simulated and analyzed. Simulation shows that the self-gradient tensor element ${\mathsf{\Gamma }}_{xx},{\mathsf{\Gamma }}_{xy},{\mathsf{\Gamma }}_{xz},{\mathsf{\Gamma }}_{yz}$ and ${\mathsf{\Gamma }}_{zz}$ are greatly affected by the middle part of the carrier, and the self-gradient tensor element ${\mathsf{\Gamma }}_{yz}$ is affected by the carrier’s fuel mass in three attitudes. Further simulation experiments show that the presented self-gradient compensation method is valid, and the error of the self-gradient compensation is within 0.1 Eu. Furthermore, this method can provide an important reference for improving the accuracy of aviation gravity gradiometry. Full article

The measurement accuracy of the intelligent flexible morphological sensor based on fiber Bragg grating (FBG) structure was limited in the application of geotechnical engineering and other fields. In order to improve the precision of intellisense for displacement, an FBG implantable flexible morphological sensor was designed in this study, and the classification morphological correction method based on conjugate gradient method and extreme learning machine (ELM) algorithm was proposed. This study utilized finite element simulations and experiments, in order to analyze the feasibility of the proposed method. Then, following the corrections, the results indicated that the maximum relative error percentages of the displacements at measuring points in different bending shapes were determined to be 6.39% (Type 1), 7.04% (Type 2), and 7.02% (Type 3), respectively. Therefore, it was confirmed that the proposed correction method was feasible, and could effectively improve the abilities of sensors for displacement intellisense. In this paper, the designed intelligent sensor was characterized by temperature self-compensation, bending shape self-classification, and displacement error self-correction, which could be used for real-time monitoring of deformation field in rock, subgrade, bridge, and other geotechnical engineering, presenting the vital significance and application promotion value. Full article

Ionic gradients play a crucial role in the physiology of the human body, ranging from metabolism in cells to muscle contractions or brain activities. To monitor these ions, inexpensive, label-free chemical sensing devices are needed. Field-effect transistors (FETs) based on silicon (Si) nanowires or nanoribbons (NRs) have a great potential as future biochemical sensors as they allow for the integration in microscopic devices at low production costs. Integrating NRs in dense arrays on a single chip expands the field of applications to implantable electrodes or multifunctional chemical sensing platforms. Ideally, such a platform is capable of detecting numerous species in a complex analyte. Here, we demonstrate the basis for simultaneous sodium and fluoride ion detection with a single sensor chip consisting of arrays of gold-coated SiNR FETs. A microfluidic system with individual channels allows modifying the NR surfaces with self-assembled monolayers of two types of ion receptors sensitive to sodium and fluoride ions. The functionalization procedure results in a differential setup having active fluoride- and sodium-sensitive NRs together with bare gold control NRs on the same chip. Comparing functionalized NRs with control NRs allows the compensation of non-specific contributions from changes in the background electrolyte concentration and reveals the response to the targeted species. Full article