An automatic docking method for large-scale sections based on real-time pose measuring and assembly deviation control

Aiming at the problem of poor accuracy consistency of large sections’ docking assembly, an automatic docking method using multiple laser trackers to measure the position and posture of the docking sections in real time was proposed. In the solution of the pose of the docking section, real-time pose measurement of the docking section was realized by establishing a global coordinate system and a coordinate fusion method of three or more laser trackers. In the automatic control of the docking process, the real-time communication protocol and the circular negative feedback control strategy of measurement-adjustment-re-measurement are adopted, and the fully-automated docking of large sections is realized. Finally, an experimental verification system was set up, and the docking of the large-scale section reduction models was realized under the requirements of docking accuracy, and the effectiveness of the automatic docking scheme was successfully verified.     

Lightweight deep learning methods for panoramic dental X-ray image segmentation

Neural Computing and Applications - Dental X-ray image segmentation is helpful for assisting clinicians to examine tooth conditions and identify dental diseases. Fast and lightweight segmentation...     

Thermally Responsive Fibers for Versatile Thermoactivated Protective Fabrics

Smart textiles with good mechanical adaptability play an important role in personal protection, health monitoring, and aerospace applications. However, most of the reported thermally responsive polymers has long response time and poor processability, comfort, and wearability. Skin-core structures of thermally responsive fibers with multiple commercial fiber cores and temperature-responsive hydrogel skins are designed and fabricated, which exhibit rapid mechanical adaptability, good thermohardening, and thermal insulation. This universal method enables tight bonding between various commercial fiber cores and hydrogel skins via specific covalently anchored networks. At room temperature, prepared fibers show softness, flexibility, and skin compatibility similar to those of ordinary fibers. As temperature rises, smart fibers become hard, rigid, and self-supporting. The modulus of hydrogel skin increases from 304% to 30883%, showing good mechanoadaptability and impact resistance owing to the synergy between hydrophobic interactions and ionic bonding. Moreover, this synergistic effect leads to an increase in heat absorption, and fibers exhibit good thermal insulation, which reduces the contact temperature of the body surface by ≈25 °C under the external temperature of 95 °C, effectively preventing thermal burns. Notably, the active mechanoadaptability of these smart fibers using conductive fibers as cores is demonstrated. This study provides feasibility for fabricating environmentally adaptive intelligent textiles.     

2D Bi2O2Te Semiconductor with Single-Crystal Native Oxide Layer

Following logic in the silicon semiconductor industry, the existence of native oxide and suitable fabrication technology is essential for 2D semiconductors in planar integronics, which are surface-sensitive to typical coating technologies. To date, very few types of integronics are found to possess this feature. Herein, the 2D Bi₂O₂Te developed recently is reported to possess large-area synthesis and controllable thermal oxidation behavior toward single-crystal native oxides. This shows that surface-adsorbed oxygen atoms are inclined to penetrate across [Bi₂O₂]_n²ⁿ⁺ layers and bond with the underlying [Te]_n²ⁿ⁻ at elevated temperatures, transforming directly into [TeO₄]_n²ⁿ⁻ with the basic architecture remaining stable. The oxide can be adjusted to form in an accurate layer-by-layer manner with a low-stress sharp interface. The native oxide Bi₂TeO₆ layer (bandgap of ≈2.9 eV) exhibits visible-light transparency and is compatible with wet-chemical selective etching technology. These advances demonstrate the potential of Bi₂O₂Te in planar-integrated functional nanoelectronics such as tunnel junction devices, field-effect transistors, and memristors.     

Study on Recognition Method of Similar Weather Scenes in Terminal Area

Weather is a key factor affecting the control of air traffic. Accurate recognition and classification of similar weather scenes in the terminal area is helpful for rapid decision-making in air traffic flow management. Current researches mostly use traditional machine learning methods to extract features of weather scenes, and clustering algorithms to divide similar scenes. Inspired by the excellent performance of deep learning in image recognition, this paper proposes a terminal area similar weather scene classification method based on improved deep convolution embedded clustering (IDCEC), which uses the combination of the encoding layer and the decoding layer to reduce the dimensionality of the weather image, retaining useful information to the greatest extent, and then uses the combination of the pre-trained encoding layer and the clustering layer to train the clustering model of the similar scenes in the terminal area. Finally, terminal area of Guangzhou Airport is selected as the research object, the method proposed in this article is used to classify historical weather data in similar scenes, and the performance is compared with other state-of-the-art methods. The experimental results show that the proposed IDCEC method can identify similar scenes more accurately based on the spatial distribution characteristics and severity of weather; at the same time, compared with the actual flight volume in the Guangzhou terminal area, IDCEC's recognition results of similar weather scenes are consistent with the recognition of experts in the field.     

SVPWM教学研究之重点解析

SVPWM是电力电子技术教学中的重要内容,本文对空间矢量调制的教学重点做了一个较为全面系统的梳理。重点阐述SVPWM的核心思想,特别是三相正弦交流电SVPWM的基本原理,严格证明了三相电压的空间矢量相等与线电压相等等价。同时,证明在SVPWM下,逆变器输出的最大圆为基本空间矢量构成的正六边形的内接圆、直流母线电压利用率提高了约15.4%。     

Editorial: Machine Learning and Intelligent Communications (MLICOM 2018)

Mobile Networks and Applications -     

Confidence regions of two-stage stochastic linear complementarity problems

Two-stage stochastic linear complementarity problems (TSLCP) model a large class of equilibrium problems subject to data uncertainty, and are closely related to two-stage stochastic optimization problems. The sample average approximation (SAA) method is one of the basic approaches for solving TSLCP and the consistency of the SAA solutions has been well studied. This paper focuses on building confidence regions of the solution to TSLCP when SAA is implemented. We first establish the error-bound condition of TSLCP and then build the asymptotic and nonasymptotic confidence regions of the solutions to TSLCP by error-bound approach, which is to combine the error-bound condition with central limit theory, empirical likelihood theory, and large deviation theory.     

A novel approach of data race detection based on CNN-BiLSTM hybrid neural network

Neural Computing and Applications - Existing data race detection approaches based on deep learning are suffering from the problems of unique feature extraction and low accuracy. To this end, this...     

Heterogeneous feature ensemble modeling with stochastic configuration networks for predicting furnace temperature of a municipal solid waste incineration process

Neural Computing and Applications - Considering the accuracy, generalization ability, stability, and training efficiency of a furnace temperature model in the process of municipal solid waste...