Search Results (10)

This work investigates the design of liquid-cooled heat sinks for IGBT modules via optimizing the Triple Periodic Minimal Surface (TPMS) structure. The performance of heat sinks with different porosity TPMS structures was compared through the finite element simulation software Fluent. The results indicate that smaller porosity is conducive to improving the heat dissipation efficiency, but the difference in pressure between the entrance and exit increases. The field-driven design method is further adopted to adjust the porosity according to the temperature field distribution, and the TPMS channel structures were optimized by nTopology software. The results show that the optimized Schwarz P, Gyroid, and Diamond structures have a comparable effect on reducing the maximum surface temperature as that of TPMS structures with uniform porosity; however, the differential pressure at the inlet and outlet decreased remarkably by 94.8%, 90.8%, and 88.9%, respectively, compared to the structure with a uniform porosity of 0.32. The Nusselt numbers of the optimized Gyroid and Diamond structures increased by 19.2% and 12.3%, respectively, compared to their structures with a uniform porosity of 0.84. This study illustrates the advantages of the field-driven design in enhancing the heat dissipation and reducing pressure loss, which provides an effective design solution for the heat dissipation of IGBT modules. Full article

This paper presents an improved approach for scene-aware camera relocalization using RGB images and poses. Building upon the ACE network, we proposed a refined head structure that integrates skip and dense connections alongside channel attention mechanisms. Additionally, we introduced modifications to the loss function and pose solver, leveraging SQPnP and iterative optimization. These enhancements led to significant improvements in the localization accuracy and speed, as evidenced by our experiments on the 7scenes, 12scenes, and wayspots datasets. Here, we show that the average localization errors were reduced by up to 30% and the computational times were cut by approximately 10% compared to the original ACE network, demonstrating the practicality and robustness of our approach. Full article

Aiming at the control challenges faced by unmanned surface vessels (USVs) in complex environments, such as nonlinearities, parameter uncertainties, and environmental perturbations, we propose a non-singular terminal integral sliding mode control strategy based on an extended state observer (ESO). The strategy first employs a third-order linear extended state observer to estimate the total disturbances of the USV system, encompassing both external disturbances and internal nonlinearities. Subsequently, a backstepping sliding mode controller based on the Lyapunov theory is designed to generate the steering torque control commands for the USV. To further enhance the tracking performance of the system, we introduce a non-singular terminal integral sliding mode surface with a double power convergence law and redesign the backstepping sliding mode controller for the USV heading control. Meanwhile, to circumvent the differential explosion issue in traditional backstepping control, we simplify the controller design by utilizing a second-order sliding mode filter to accurately estimate the differential signals of the virtual control quantities. Theoretical analysis and simulation results demonstrate that the proposed control algorithm improves the convergence speed, adaptive ability, and anti-interference ability in complex environments compared to traditional linear backstepping sliding mode control, thereby enhancing its engineering practicability. This research offers a more efficient and reliable method for precise heading control and path tracking of USVs in complex and dynamic environments. Full article

This paper examines the control challenges faced by underactuated Autonomous Underwater Vehicles (AUVs) under ocean current disturbances. It proposes a Backstepping Integral Sliding Mode Control (BISMC) strategy to enhance their adaptability and robustness. The BISMC strategy integrates the system decomposition capability of the backstepping control method with the rapid response and robustness advantages of the Sliding Mode Control method, enabling the design of a heading controller and a double closed-loop depth controller. By introducing an integral component, the strategy eliminates steady-state errors caused by ocean currents, accelerating system convergence and improving accuracy. Furthermore, a saturation function is employed to mitigate output chattering issues. Simulation results demonstrate that the BISMC controller significantly enhances the control precision and anti-disturbance capabilities of AUVs under low-frequency ocean current disturbances, showcasing exceptional adaptive and self-disturbance rejection performance. Full article

Sign language is the main communication way for deaf and hard-of-hearing (i.e., DHH) people, which is unfamiliar to most non-deaf and hard-of-hearing (non-DHH) people. To break down the communication barriers between DHH and non-DHH people and to better promote communication among DHH individuals, we have summarized the research progress on sign language translation. We provide the necessary background on sign language translation and introduce its four subtasks (i.e., sign2gloss2text, sign2text, sign2(gloss+text), and gloss2text). We distill the basic mode of sign language translation (SLT) and introduce the transformer-based framework of SLT. We analyze the main challenges of SLT and propose possible directions for its development. Full article

The unmanned aerial vehicle (UAV) has drawn attention from the military and researchers worldwide, which has advantages such as robust survivability and execution ability. Mobility models are usually used to describe the movement of nodes in drone networks. Different mobility models have been proposed for different application scenarios; currently, there is no unified mobility model that can be adapted to all scenarios. The mobility of nodes is an essential characteristic of mobile ad hoc networks (MANETs), and the motion state of nodes significantly impacts the network’s performance. Currently, most related studies focus on the establishment of mathematical models that describe the motion and connectivity characteristics of the mobility models with limited universality. In this study, we use a backpropagation neural network (BPNN) to explore the relationship between the motion characteristics of mobile nodes and the performance of routing protocols. The neural network is trained by extracting five indicators that describe the relationship between nodes and the global features of nodes. Our model shows good performance and accuracy of classification on new datasets with different motion features, verifying the correctness of the proposed idea, which can help the selection of mobility models and routing protocols in different application scenarios having the ability to avoid repeated experiments to obtain relevant network performance. This will help in the selection of mobility models for drone networks and the setting and optimization of routing protocols in future practical application scenarios. Full article

High-level applications of geo-processing services generally lack accurate temporal and spatial information. BDS-3 provides high precision temporal and spatial reference for geoprocessing services, but their signal is prone to cycle slips in a severe multipath environment. Aiming at the problem of the reliable detection and repair of cycle slips in BDS-3 (B1c + B2a) dual-frequency positioning in a severe multipath environment, an AR (autoregressive) model-assisted MW + GF BDS dual-frequency combined detection method (AMG method) is proposed in this research. A sliding-window autoregressive prediction strategy is introduced to correct the pseudorange observations interfered by a multipath, then an AR + MW + GF cycle slips detection model is constructed, and a cycle slips statistical completeness test index is established to verify the effectiveness of the algorithm. Six groups of cycle slips are artificially added into the different constellations and dual-frequency point phase observations of BDS-3 (B1c and B2a) in a multipath environment to demonstrate the cycle slips’ detection performance. The experimental results show that the traditional MW + GF method fails, but the proposed AMG method still maintains accurate cycle slip detection and repair capabilities. The detection success rate and repair success rate obtained by using the new method are significantly improved by 63.4%, and the cycle slips’ false detection rate and missed detection rate are reduced by 64.5% and 42.0%, respectively, even in harsh environments. Full article

In this paper, we propose a partial ambiguity method of global navigation satellite system (GNSS) reliable positioning based on a robust estimation model to address the problems of the low reliability and availability of GNSS positioning in urban complex environments. First, the high-precision observations selected on the basis of the signal-to-noise ratio (SNR) were used to solve ambiguities. Then, the fixed ambiguities were used as constraints to solve the ambiguities of low-quality observations. The robust estimation method was used to reduce the impact of outliers for the ambiguity solutions. The robust estimation was also used to solve the position parameters to reduce the influence of the residual errors and uncorrected ambiguities for GNSS high-accuracy positioning. Static and dynamic data were used to evaluate the proposed algorithm. These experiments show that the proposed algorithm with the robust estimation can reduce the fixed time of ambiguity initialization, compared with the conventional algorithm without the robust estimation. The positioning accuracy and solution rate are similar regardless of whether the robust estimation is used in the GNSS unblocked environment. In blocked environments, the solution rate improves to more than 99%, and the three-dimensional (3D) position accuracy improves by more than 70% when the robust estimation is used. When the observation number of simulated small gross error accounts for 40.91% of total observations, the centimeter-level positioning accuracy can still be obtained via several robust estimation models. In the urban blocked environment, the IGG (Institute of Geodesy and Geophysics) III scheme has a better performance than other robust schemes discussed in this paper with regard to the positioning performance and computational efficiency. Full article

Indoor WLAN fingerprint localization systems have been widely applied due to the simplicity of implementation on various mobile devices, including smartphones. However, collecting received signal strength indication (RSSI) samples for the fingerprint database, named a radio map, is significantly labor-intensive and time-consuming. To solve the problem, this paper proposes a semi-supervised self-adaptive local linear embedding algorithm to build the radio map. First, this method uses the self-adaptive local linear embedding (SLLE) algorithm based on manifold learning to reduce the dimension of the high-dimensional RSSI samples and to extract a neighbor weight matrix. Secondly, a graph-based label propagation (GLP) algorithm is employed to build the radio map by semi-supervised learning from a large number of unlabeled RSSI samples to a few labeled RSSI samples. Finally, we propose a k self-adaptive neighbor weight (kSNW) algorithm, used for radio map construction in this paper, to realize online localization. The results of the experiments conducted in a real indoor environment show that the proposed method reduces the demand for large quantities of labeled samples and achieves good positioning accuracy. With only 25% labeled RSSI samples, our system can obtain positioning accuracy of more than 88%, within 3 m of localization errors. Full article

SiBCN ceramics were introduced into porous Si₃N₄ ceramics via a low-pressure chemical vapor deposition and infiltration (LPCVD/CVI) technique, and then the composite ceramics were heat-treated from 1400 °C to 1700 °C in a N₂ atmosphere. The effects of annealing temperatures on microstructure, phase evolution, dielectric properties of SiBCN ceramics were investigated. The results revealed that α-Si₃N₄ and free carbon were separated below 1700 °C, and then SiC grains formed in the SiBCN ceramic matrix after annealing at 1700 °C through a phase-reaction between free carbon and α-Si₃N₄. The average dielectric loss of composites increased from 0 to 0.03 due to the formation of dispersive SiC grains and the increase of grain boundaries. Full article