Over the last decade, a 3D reconstruction technique has been developed to present the latest as-is information for various objects and build the city information models. Meanwhile, deep learning based approaches are employed to add semantic information to the models. Studies have proved that the accuracy of the model could be improved by combining multiple data channels (e.g., XYZ, Intensity, D, and RGB). Nevertheless, the redundant data channels in large-scale datasets may cause high computation cost and time during data processing. Few researchers have addressed the question of which combination of channels is optimal in terms of overall accuracy (OA) and mean intersection over union (mIoU). Therefore, a framework is proposed to explore an efficient data fusion approach for semantic segmentation by selecting an optimal combination of data channels. In the framework, a total of 13 channel combinations are investigated to pre-process data and the encoder-to-decoder structure is utilized for network permutations. A case study is carried out to investigate the efficiency of the proposed approach by adopting a city-level benchmark dataset and applying nine networks. It is found that the combination of IRGB channels provide the best OA performance, while IRGBD channels provide the best mIoU performance.
The absence of tactile perception limits the dexterity of a prosthetic hand and its acceptance by amputees. Recreating the sensing properties of the skin using a flexible tactile sensor could have profound implications for prosthetics, whereas existing tactile sensors often have limited functionality with cross‐interference. In this study, we propose a machine‐learning‐assisted multifunctional tactile sensor for smart prosthetics, providing a human‐like tactile sensing approach for amputations. This flexible sensor is based on a poly(3,4‐ethylenedioxythiophene): poly(styrene sulfonate) (PEDOT:PSS)–melamine sponge, which enables the detection of force and temperature with low cross‐coupling owing to two separate sensing mechanisms: the open‐circuit voltage of the sensor as a force‐insensitive intrinsic variable to measure the absolute temperature and the resistance as a temperature‐insensitive extrinsic variable to measure force. Furthermore, by analyzing the unsteady heat conduction and characterizing it using real‐time thermal imaging, we demonstrated that the process of open‐circuit voltage variation resulting from the unsteady heat conduction is closely correlated with the heat‐conducting capabilities of materials, which can be utilized to discriminate between substances. Assisted by the decision tree algorithm, the device is endowed with thermal conductivity sensing ability, which allows it to identify 10 types of substances with an accuracy of 94.7%. Furthermore, an individual wearing an advanced myoelectric prosthesis equipped with the above sensor can sense pressure, temperature, and recognize different materials. We demonstrated that our multifunctional tactile sensor provides a new strategy to help amputees feel force, temperature and identify the material of objects without the aid of vision.
Double-motor drive tracked vehicles (2MDTV) are widely used in the tracked vehicle industry due to the development of electric vehicle drive systems. The aim of this paper is to solve the problem of insufficient propulsion motor torque in low-speed, small-radius steering and insufficient power in high-speed large-radius steering. In order to do this a new type of steering system with a coupling device is designed and a closed-loop control strategy based on speed is adopted to improve the lateral stability of the vehicle. The work done entails modeling and simulating the 2MDTV and the proposed control strategy in RecurDyn and Matlab/Simulink. The simulation results show that the 2MDTV with the coupling device outputs more torque and power in both steering cases compared to the 2MDTV without the coupling device, and the steering stability of the vehicle is improved by using the strategy based on speed.
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