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Rapid Response and Recovery Performance of NO2 Gas Sensor Based on Gan Hexagonal Pits at Room Temperature

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SSRN 2022年

作者： Han, Xiaomei Han, Dan Liu, Xiaoru Chen, Yi Liu, Lulu Liu, Zhihua Li, Donghui Sang, Shengbo Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System The Ministry of Education Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Interface Science and Engineering in Advanced Materials Taiyuan University of Technology Ministry of Education Taiyuan030024 China

Although fantastic milestones of Gallium nitride (GaN)-based materials in optoelectronic devices had been reached, the focus on the optimization of their geometrical structure for gas-sensing applications was relatively scarce. In this work, we demonstrated NO2 gas sensor based on p-type GaN hexagonal pits (GaN-HP) with high surface-to-volume ratio by wet etching method. The fabricated NO2 gas sensor had high sensitivity, high selectivity, fast response/recovery times. The response of p-type GaN-HP with etching time for 10 min to 2 ppm NO2 gas was 6.2%, which also have quick response and recovery time at room temperature. UV light drastically enhanced the response of p-type GaN-HP sensor by 1.3 times to 100 ppm NO2 . The gas sensing mechanism of p-type GaN-HP gas sensor was explored, which provided a theoretical and experimental basis for further research and development of high-performance gas sensors based on GaN material. © 2022, The Authors. All rights reserved.

关键词： Gallium nitride

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Ma, Jiajun Liang, Guoyuan Liang, Yu Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665436786

3D facial similarity measure is a critical research direction in fields such as 3D face recognition, craniofacial reconstruction, and heritability estimation, etc. and represents a challenging and meaningful task. In this paper, we propose a novel method based on deformation field to establish the similarity relationship between 3D faces. The main idea is divided into two steps as follows: (1) The landmarks are semi-automatically marked on all faces to build correspondences between landmarks (2) According to the distance field and the dense corresponding relationship of faces, a deformation field is generated between 3D faces that deform the source model to the target model. The path of deformation is computed to measure the similarity between two 3D faces. Comprehensive experiments conducted on the BU3DFE database illustrate that the similarity measure method is consistent with the subjective evaluation and provides high discriminability for different individuals. © 2021 IEEE.

关键词： Deformation

Black Phosphorus-Organic Framework-Derived Pei/Peg Nanohybrid Prepared by a One-Pot Method for a Wide Detection Range Co2 Gas Sensor

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SSRN 2022年

作者： Liu, Lulu Sang, Shengbo Han, Dan Liu, Zhihua Han, Xiaomei Li, Donghui Chen, Yi Liu, Dongming Liu, Xiaoru Zhuo, Kai Cheng, Yongqiang Yang, Kun Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Interface Science and Engineering in Advanced Materials Taiyuan University of Technology Ministry of Education Taiyuan030024 China

In this paper, we report a hybrid PEI/PEG-BP sensor (composite material of black phosphorus-organic framework-derived PEI/PEG nanohybrid) prepared by a one-pot method, in which the existence of PEI/PEG onto BP was confirmed by SEM, EDS, XPS and UPS. The gas sensing properties of the PEI/PEG-BP sensor, including the sensor response, response/recovery times, repeatability, selectivity, and effects of humidity on the sensor, were studied by exposure to several CO 2 gas concentrations ranging from 200 ppm to 300,000 ppm at room temperature (25 ℃). The results show that the PEI/PEG-BP sensor had an extremely wide detection range, with lower and upper detection limits of 200 ppm and 160,000 ppm, respectively. The response of the PEI/PEG-BP sensor to 200 ppm, and 160,000 ppm CO 2 gas at room temperature ( RT ) was 4.48%, and 254.02%, respectively, showing fast response/recovery time, excellent repeatability and high selectivity. The gas-sensing properties of the PEI/PEG-BP sensor are better than BP, PEI, PEG, PEI-BP and PEG-BP sensors. The superior properties of the PEI/PEG-BP sensor can be attributed to the BP framework, porous structure, electron doping, and surface reactive groups such as -NH 2 and -OH. The PEI/PEG-BP sensor, with its excellent gas-sensing performance will help us to further explore the changes of CO 2 concentration in microbial growth systems, natural environments, industrial production and so on. © 2022, The Authors. All rights reserved.

关键词： Carbon dioxide

Gan/Moo3 N-N Heterojunction Based Anti-Humidity Gas Sensor for No2 Real-Time Monitoring

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SSRN 2024年

作者： Hong, Yutao Han, Dan Duan, Qi Li, Donghui He, Xiuli Li, Bing Liang, Hua Li, Hongwei Sang, Shengbo Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Integrated Circuits Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of Technology Taiyuan030024 China State Key Laboratory of Transducer Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials Shanxi Medical University School and Hospital of Stomatology Shanxi Taiyuan030001 China Shanxi Province Cancer Hospital Shanxi Hospital Affiliated to Cancer Hospital Chinese Academy of Medical Sciences Cancer Hospital Affiliated to Shanxi Medical University Taiyuan030000 China University of Chicago United States

Effective and accurate detection of NO2 gas is crucial for protecting the environment and public health. In this work, balsam pear-like GaN/MoO3 porous nanosheets (GaN/MoO3) composites were synthesized by in situ assembling, the hydrothermal method, and the nitridation process to detect NO2. A series of characterization methods were used to manifest the successful synthesis and to analyze the morphology and elemental composition features of GaN/MoO3 composites. The results of the gas-sensing experiments show that, at the operating temperature of 225°C, the gas sensor based on the optimal ratio of GaN/MoO3 composites exhibits a 25-fold lower limit of detection for NO2 than that of MoO3, with a reduction of baseline resistance drift. Further, the response value of the sensor based on GaN/MoO3 composites to 200 ppm NO2 (44.74) is 4.36 times higher than that of the sensor based on pure GaN, with an improvement in stability. Meanwhile, the sensor possessed excellent repeatability, selectivity, stability, and splendid anti-humidity ability at 15-70% RH. The improved gas-sensing performance is ascribed to the unique microstructure of both GaN and MoO3 and the heterojunction effect between them. In addition, we constructed a real-time NO2 monitoring device based on the gas-sensing characteristic of GaN/MoO3, which realizes the alarm of excessive concentrations of NO2. Overall, this work provides a way to improve the ability of NO2 monitoring for the sensors of MoO3-based composites. © 2024, The Authors. All rights reserved.

关键词： Gallium nitride

Gan/Moo3 N-N Heterojunction Based Anti-Humidity Gas Sensor for No2 Real-Time Monitoring

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SSRN 2024年

作者： Hong, Yutao Han, Dan Duan, Qi Li, Donghui He, Xiuli Li, Bing Liang, Hua Li, Hongwei Sang, Shengbo Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Integrated Circuits Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China State Key Laboratory of Transducer Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials Shanxi Medical University School Hospital of Stomatology Shanxi Taiyuan030001 China Shanxi Province Cancer Hospital Shanxi Hospital Affiliated to Cancer Hospital Chinese Academy of Medical Sciences Cancer Hospital Affiliated to Shanxi Medical University Taiyuan030000 China University of Chicago United States

Effective and accurate detection of NO2 gas is crucial for protecting the environment and public health. In this work, balsam pear-like GaN/MoO3 porous nanosheets (GaN/MoO3) composites were synthesized by in situ assembling, the hydrothermal method, and the nitridation process to detect NO2. A series of characterization methods were used to manifest the successful synthesis and to analyze the morphology and elemental composition features of GaN/MoO3 composites. The results of the gas-sensing experiments show that, at the operating temperature of 225℃, the gas sensor based on the optimal ratio of GaN/MoO3 composites exhibits a 25-fold lower limit of detection for NO2 than that of MoO3, with a reduction of baseline resistance drift. Further, the response value of the sensor based on GaN/MoO3 composites to 200 ppm NO2 (44.74) is 4.36 times higher than that of the sensor based on pure GaN, with an improvement in stability. Meanwhile, the sensor possessed excellent repeatability, selectivity, stability, and splendid anti-humidity ability at 15-70% RH. The improved gas-sensing performance is ascribed to the unique microstructure of both GaN and MoO3 and the heterojunction effect between them. In addition, we constructed a real-time NO2 monitoring device based on the gas-sensing characteristic of GaN/MoO3, which realizes the alarm of excessive concentrations of NO2. Overall, this work provides a way to improve the ability of NO2 monitoring for the sensors of MoO3-based composites. © 2024, The Authors. All rights reserved.

关键词： III-V semiconductors

MOSFET/TFET Mixed Power Clamp Circuit For ESD and Surge Protection

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MOSFET/TFET Mixed Power Clamp Circuit For ESD and Surge Prot...

Design and system (IEDS), International EOS/ESD Symposium on

作者： Zhaonian Yang Yaping Yue Shi Pu Xiaohan Yang Yue Zhang Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Shandong Inspur Intelligence Research Institute Co. Ltd. Xi'an China Xi'an Xiangteng Microelectronics Co. Ltd. Xi'an China

ISBN: (数字)9781585373383

ISBN: (纸本)9781665469814

In this paper, a high sensitive voltage detector is proposed and its electrical characteristics are investigated using technology computer-aided design (TCAD) simulation. Germanium (Ge) source and line configuration TFET is adopted to obtain an ultra sensitive detection capability. Corresponding operating principle is explained and the impact of the key parameters on the voltage detector is also discussed. The simulation results show that the new proposed Ge source line TFET based voltage detector has a low leakage current and high detection sensitivity under the electrostatic discharge (ESD) and surge events compared to traditional detectors.

关键词： TFETs Sensitivity Simulation Surge protection Detectors Voltage Germanium

UIILD: A Unified Interpretable Intelligent Learning Diagnosis Framework for Intelligent Tutoring systems

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arXiv

arXiv 2022年

作者： Wang, Zhifeng Yan, Wenxing Zeng, Chunyan Tian, Yuan Dong, Shi School of Educational Information Technology Faculty of Artificial Intelligence in Education Central China Normal University Wuhan430079 China Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of Technology Wuhan430068 China

Intelligent learning diagnosis is a critical engine of intelligent tutoring systems, which aims to estimate learners' current knowledge mastery status and predict their future learning performance. The significant challenge with traditional learning diagnosis methods is the inability to balance diagnostic accuracy and interpretability. Although the existing psychometric-based learning diagnosis methods provide some domain interpretation through cognitive parameters, they have insufficient modeling capability with a shallow structure for large-scale learning data. While the deep learning-based learning diagnosis methods have improved the accuracy of learning performance prediction, their inherent black-box properties lead to a lack of interpretability, making their results untrustworthy for educational applications. To settle the above problem, the proposed unified interpretable intelligent learning diagnosis (UIILD) framework, which benefits from the powerful representation learning ability of deep learning and the interpretability of psychometrics, achieves a better performance of learning prediction and provides interpretability from three aspects: cognitive parameters, learner-resource response network, and weights of self-attention mechanism. Within the proposed framework, this paper presents a two-channel learning diagnosis mechanism LDM-ID as well as a three-channel learning diagnosis mechanism LDM-HMI. Experiments on two real-world datasets and a simulation dataset show that our method has higher accuracy in predicting learners' performances compared with the state-of-the-art models, and can provide valuable educational interpretability for applications such as precise learning resource recommendation and personalized learning tutoring in intelligent tutoring systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Data mining

S$^{\text{4}}$TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles

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IEEE Transactions on Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles 2023年第2期9卷 3220-3231页

作者： Xiao Wang Ke Tang Xingyuan Dai Jintao Xu Quancheng Du Rui Ai Yuxiao Wang Weihao Gu Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei China Haomo Technology Company Ltd. Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning (S $^{\text{4}}$ TP) framework. Specifically, S $^{\text{4}}$ TP integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV's planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. S $^{\text{4}}$ TP employs an optimization-based approach for motion planning, utilizing the predicted HDVs' trajectories as input. With the integration of SADRF, S $^{\text{4}}$ TP executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed S $^{\text{4}}$ TP in terms of safety and rationality. S $^{\text{4}}$ TP achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-Decision of 94.75%.

关键词： Trajectory Safety Vehicles Trajectory planning Autonomous vehicles Risk management Behavioral sciences

Simultaneously Calibration of Multi Hand-Eye Robot system Based on Graph

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arXiv

arXiv 2023年

作者： Zhou, Zishun Ma, Liping Liu, Xilong Cao, Zhiqiang Yu, Junzhi The Institute of Automation Chinese Academy of Sciences Beijing100190 China The College of Electrical Engineering and Computer Science Australian National University Canberra2601 Australia The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The State Key Laboratory for Turbulence and Complex System Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Peking University Beijing100871 China

Precise calibration is the basis for the vision-guided robot system to achieve high-precision operations. systems with multiple eyes (cameras) and multiple hands (robots) are particularly sensitive to calibration errors, such as micro-assembly systems. Most existing methods focus on the calibration of a single unit of the whole system, such as poses between hand and eye, or between two hands. These methods can be used to determine the relative pose between each unit, but the serialized incremental calibration strategy cannot avoid the problem of error accumulation in a large-scale system. Instead of focusing on a single unit, this paper models the multi-eye and multi-hand system calibration problem as a graph and proposes a method based on the minimum spanning tree and graph optimization. This method can automatically plan the serialized optimal calibration strategy in accordance with the system settings to get coarse calibration results initially. Then, with these initial values, the closed-loop constraints are introduced to carry out global optimization. Simulation experiments demonstrate the performance of the proposed algorithm under different noises and various hand-eye configurations. In addition, experiments on real robot systems are presented to further verify the proposed method. © 2023, CC BY-SA.

关键词： Multipurpose robots