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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Ru Li Aimin Li Rong Fei Xie Guo Fang-Xiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions.

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LHMap-loc: Cross-Modal Monocular Localization Using LiDAR Point Cloud Heat Map

LHMap-loc: Cross-Modal Monocular Localization Using LiDAR Po...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Xinrui Wu Jianbo Xu Puyuan Hu Guangming Wang Hesheng Wang Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China Department of Engineering University of Cambridge Cambridge U.K

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Localization using a monocular camera in the pre-built LiDAR point cloud map has drawn increasing attention in the field of autonomous driving and mobile robotics. However, there are still many challenges (e.g. difficulties of map storage, poor localization robustness in large scenes) in accurately and efficiently implementing cross-modal localization. To solve these problems, a novel pipeline termed LHMap-loc is proposed, which achieves accurate and efficient monocular localization in LiDAR maps. Firstly, feature encoding is carried out on the original LiDAR point cloud map by generating offline heat point clouds, by which the size of the original LiDAR map is compressed. Then, an end-to-end online pose regression network is designed based on optical flow estimation and spatial attention to achieve real-time monocular visual localization in a pre-built map. In addition, a series of experiments have been conducted to prove the effectiveness of the proposed method. Our code is available at: https://***/IRMVLab/LHMap-loc.

关键词： Location awareness Point cloud compression Visualization Laser radar Accuracy Robot vision systems Real-time systems

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Effect of Annealing Process Parameters on N-GaAs Ohmic Contacts

SSRN

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SSRN 2022年

作者： Lin, Tao Xie, Jianan Ning, Shaohuan Ma, Zekun Mu, Yan Sun, WanJun Yang, Sha Department of Electronic Engineering Xi'an University of Technology Xi'An710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Shaanxi Xi’an710048 China

Ni/Ge/Ni/Au multi-layer metal electrodes were prepared on N-GaAs substrate by electron-beam evaporation to investigate the effect of annealing process parameters on N-GaAs ohmic contacts. In the annealing temperature range of 370 °C to 430 °C, the metal-semiconductor contact was found changing from Schottky contact to ohmic contact as the annealing temperature was above 380 °C. Specific contact resistivity of n-GaAs samples firstly decreased and then increased with the increasing of annealing temperature, and the minimum specific contact resistivity of 3.3×10-5 Ω·cm2 was formed at 420 °C annealing for 80 s. Although the specific contact resistivity of the metal-semiconductor contact performed satisfactorily, the surface morphology of the n-GaAs metal electrode was found having metal shrinkage after annealing. The formation mechanism of ohmic contact was qualitatively explained by the elements diffusion under different annealing parameters, which were confirmed by the comparative analysis of SEM and EDX results. © 2022, The Authors. All rights reserved.

关键词： Gallium arsenide

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MFGB: Molecular Properties Prediction leveraging Self-supervised Morgan Fingerprint Representation Learning

MFGB: Molecular Properties Prediction leveraging Self-superv...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Fang, Shuyan Liu, Yu Liu, Song Wuhan University of Science and Technology School of Computer Science and Technology Wuhan China Wuhan University of Science and Technology Hubei Prov. Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Wuhan University of Science and Technology Hubei Provincial Key Laboratory of Occupational Hazard Identification and Control Hubei Province Wuhan China

ISBN: (纸本)9798350337488

Simple molecular graphs or molecular line notations are insufficient for molecular representation learning models that automatically learn molecule representations to acquire deep semantic features about chemistry. Therefore, we proposed the Morgan Fingerprint Graph, a novel molecular graph model constructed with the nodes of the Morgan Fingerprint Identifier, whose nodes incorporate various properties of the atom neighborhood. Based on our Morgan Fingerprint Graph, we trained a BERT-based molecular representation model MFGB which employs a local rather than global attention module to adapt the model to the Morgan Fingerprint Graph. The attention module of MFGB regulates the exchange of information in the attention layer based on the connection information of the atoms in the molecules. To verify the effectiveness of MFGB, we selected several mainstream datasets for evaluation. The results demonstrated that our method improve the performance of molecular properties prediction. In addition, we analyzed MFGB from both the over-smoothing and the attention perspectives. The analysis indicates that the node vector representations learned from the Morgan Fingerprint Graph are generally less smoothing, and the attention distributions of MFGB match the partial charge density distribution of the molecules. © 2023 IEEE.

关键词： Molecular property prediction Molecular representation learning Morgan Fingerprint Self-supervised learning

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Bionic Geomagnetic Navigation for Autonomous Underwater Vehicle with Temporal Attention-based Data-Driven Dead Reckoning

TechRxiv

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TechRxiv 2023年

作者： Yang, Songnan Zhang, Xiaohui Liu, Ding Bai, Wenqi Liu, Fan The School of Automation and Information Engineering Xi’an University of Technology Shaanxi 710048 China The Institute of Advanced Navigation and Electromagnetics Xi’an University of Technology Shaanxi 710048 China The State Key Laboratory of Eco-Hydraulics in the Northwest Arid Region of China Xi’an University of Technology Shaanxi 710048 China The Shaanxi Key Laboratory of Complex System Control with the Intelligent Information Processing Xi’an University of Technology Shaanxi 710048 China

Numerous studies have demonstrated that numerous animal species are capable of goal-directed navigation using environmental information for dead reckoning. The stable magnetic field of the earth provides important information for the migration of animals over long distances. Inspired by the goal-directed navigation of animals, a novel Geomagnetic Navigation with Temporal Attention-based Data-Driven Dead Reckoning (Attention-DR) method for Autonomous Underwater Vehicles (AUV) is presented in this article, which only utilizes the inclination angle (I) and the declination angle (D) of the Geomagnetic Field (GF) for underwater navigation without any prior knowledge of the geographical location or geographic map. This article proposes a Temporal Attention-based Long Short-Term Memory (TA-LSTM) neural network by combining history, GF information, and location in time series to achieve the optimization heading angle of underwater dead reckoning. Due to its ability to adjust the utilization weights of local and global temporal information in the path, TA-LSTM possesses the capability to continually update neural network model parameters through a data-driven process during the navigation process. This enables the Attention-DR to achieve efficient navigation in regions without prior magnetic maps and overcome navigation failures caused by magnetic field anomalies. The simulation outcomes confirm the efficiency, precision, and practicability of the proposed algorithm. © 2023, CC BY.

关键词： Navigation

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Edge-Assisted Flexible Platooning of Connected and Automated Vehicles Under Traffic Anomaly

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IEEE Transactions on Vehicular Technology 2025年

作者： Gao, Fengkun Yang, Bo Chen, Cailian Guan, Xinping Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai200240 China

The platooning of connected and automated vehicles (CAVs) has the great potential to significantly improve travel experience in terms of safety, comfortableness, and energy efficiency. However, constrained by sensing range and prediction accuracy, the performance of CAV platoon can not be guaranteed in the scenario of mixed traffic consisting of CAVs and human-driven vehicles (HDVs). This is not only due to the uncertainty of HDVs but also related to the occurrence of various traffic anomalies, such as emergency braking and accidents. In this paper, we aim to investigate an efficient control strategy to guarantee the operation of the platoon in mixed traffic, especially in the case of traffic anomalies. To tackle the problem, an edge-assisted platoon control strategy, called E-CACC, is proposed. First, an error-aware flexible tracking policy is designed to adapt to HDV uncertainties and speed prediction errors. Based on the tracking policy, a zone model predictive control (ZMPC) method is then designed to guarantee travel safety and avoid undesired acceleration. Further, a coordination strategy of CAV platoon and roadside unit (RSU) is proposed to prevent the platoon from potential collision in the case of traffic anomalies, where an event-based communication trigger is designed to determine whether and when the platoon establishes communication links with RSU to acquire exogenous sensing data of anomaly segment. Finally, the evaluation results illustrate the effectiveness of the proposed method in improving travel safety and comfortableness. © 1967-2012 IEEE.

关键词： Braking performance

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Capture the Flag Based Assignment Algorithm for Tracking Task of Multi-UAVs 5

Capture the Flag Based Assignment Algorithm for Tracking Tas...

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5th International Symposium on Autonomous systems, ISAS 2022

作者： Dalei, Song Aoliang, Dai Xiaoqian, Wang Hongjun, Ma Donghui, Wei Shenyang University of Technology Shagnhai China School of Information Northeastern University Shagnhai China Beijing Research Institute of Mechanical Electrical Technology Key Laboratory of Complex System Control and Intelligent Collaboration Technology Beijing China

ISBN: (纸本)9781665487085

In order to solve the problem of real-time target allocation in the multi-UAVs task for cooperative tracking the moving targets on ground, based on the behavior model of capture the flag, this paper proposes a dynamic assignment algorithm, which has fast convergence performance, for cluster UAVs. Based on satisfaction and total flight distance, the proposed algorithm constructs the assignment target function, and the optimal value of the target function is achieved quickly and the uniform saturation tracking is realized for the target group through the behavior model of grabbing flags. Finally, compared with traditional genetic algorithm, the simulation results show that the algorithm has a significant improvement in convergence speed and single assignment imply operation speed with the increase of the number of assigned targets and cluster UAVs © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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End-To-End Autonomous Exploration for Mobile Robots in Unknown Environments through Deep Reinforcement Learning

End-To-End Autonomous Exploration for Mobile Robots in Unkno...

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2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022

作者： Li, Zhi Xin, Jinghao Li, Ning Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Autonomous exploration in unknown environments is a significant capability for mobile robots. In this paper, we present an end-To-end autonomous exploration model based on deep reinforcement learning (DRL), which takes the sensor data and a novel exploration map as inputs, and directly outputs the motion control commands of the robot. In contrast to the existing DRL-based exploration methods, the proposed model has no requirements to be combined with the traditional exploration or navigation algorithms, resulting in lower computational complexity. We directly transfer the DRL-based model trained in the training map to four test maps with different sizes and layouts, and the results show that the robot can rapidly adapt to unknown scenes. Besides, a comparison study with RRT-exploration algorithm indicates that the proposed model can reach a higher map exploration rate within less distance and time. Furthermore, we also conduct experiments on the real physical robot to demonstrate the transferability of learned policy from simulation to reality. A video of our experiments in the Gazebo simulator and real world can be found here1 © 2022 IEEE.

关键词： Reinforcement learning

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A Dual-Optimization Fault Diagnosis Method For Rolling Bearing Based On Hierarchical Slope Entropy And SVM Synergized with Shark Optimization Algorithm

SSRN

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SSRN 2022年

作者： Li, Yuxing Tang, Bingzhao Huang, Bo Jiao, Shangbin Xue, Xiaohui School of Automation and Information Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China

Slope entropy (SlopEn) has been widely applied in fault diagnosis and has performed excellent performance, while SlopEn suffers from the problem of thresholds selection. Aim to further enhance the identifying capability of SlopEn in fault diagnosis, on basis of the SlopEn, the concept of hierarchy is combined and proposed new complexity feature, namely hierarchical slope entropy (HSlopEn). Meanwhile, the white shark optimizer (WSO) is applied to optimize the both HSlopEn and support vector machine (SVM), and WSO-HSlopEn and WSO-SVM are proposed respectively. Then, a dual-optimization fault diagnosis method for rolling bearing based on WSO-HSlopEn and WSO-SVM is put forward. Firstly, different types of bearing signals are input and decomposed into several nodes by hierarchical decomposition. Then taking the recognition rate as the fitness function, the parameters of HSlopEn and SVM are determined adaptively by WSO. After that, the WSO-HSlopEn of the nodes are extracted and single-feature and multi-features extraction are performed. Finally, the diagnosis results are output by WSO-SVM. Experimental results demonstrated that whether under single-feature or multi-features, based on WSO-HSlopEn and WSO-SVM fault diagnosis method has the highest recognition rate compared to other hierarchical entropies;moreover, under multi-features, the recognition rates are all higher than 97.5%, and the more features we select, the better the recognition effect will be. when five nodes are selected, the highest recognition rate reaches 100%. © 2022, The Authors. All rights reserved.

关键词： Support vector machines

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Capacity Optimization of a Hybrid Energy Storage system Considering Wind-Solar Reliability Evaluation Based on a Novel Multi-Strategy Snake Optimization Algorithm

SSRN

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SSRN 2022年

作者： Wang, Chen Jiao, Shangbin Li, Yujun Zhang, Qing School of Automation and Information Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China

When the capacity configuration of a hybrid energy storage system (HESS) is optimized considering the reliability of a wind turbine and photovoltaic generator (PVG), the sequential Monte Carlo method is typically adopted to simulate the normal operation and fault probability of wind turbines and PVG units. The capacity optimization configuration model of the HESS is constructed with the life cost cycle (LCC) as an objective and the loss of power supply probability (LPSP) as constraints. The variational mode decomposition is used to decompose the unbalanced power in the wind-PV microgrid. The maximum power of the optimized the battery bank and the supercapacitor bank are used to determine the frequency division point, and the high-frequency power and low-frequency power are respectively allocated to the supercapacitor bank and the battery bank. When the snake optimizer (SO) is used to solve the nonconvex capacity optimization configuration model of the HESS, poor accuracy and stability are significant problems. The opposition-based learning, adaptive weight factor, and Lévy flight strategy are introduced in the algorithm framework. Twenty-three test functions show that multi strategy snake optimizer (MSO) significantly improves the accuracy and convergence speed compared with SO. Nine optimization algorithms including MSO are used to solve the capacity optimization problem of the HESS. The results show that MSO is superior to other algorithms in performance and numerical accuracy. It can reduce the LCC 4.5% and ensure that the capacity of the HESS meets the load demand when the wind turbine and PVGs are in a fault state. © 2022, The Authors. All rights reserved.

关键词： Variational mode decomposition

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