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arXiv 2024年

作者： Jiang, Changjian Gao, Ruilan Shao, Kele Wang, Yue Xiong, Rong Zhang, Yu State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Key Laboratory of Collaborative sensing and autonomous unmanned systems of Zhejiang Province Hangzhou China

Large-scale 3D reconstruction is critical in the field of robotics, and the potential of 3D Gaussian Splatting (3DGS) for achieving accurate object-level reconstruction has been demonstrated. However, ensuring geometric accuracy in outdoor and unbounded scenes remains a significant challenge. This study introduces LI-GS, a reconstruction system that incorporates LiDAR and Gaussian Splatting to enhance geometric accuracy in large-scale scenes. 2D Gaussain surfels are employed as the map representation to enhance surface alignment. Additionally, a novel modeling method is proposed to convert LiDAR point clouds to plane-constrained multimodal Gaussian Mixture Models (GMMs). The GMMs are utilized during both initialization and optimization stages to ensure sufficient and continuous supervision over the entire scene while mitigating the risk of over-fitting. Furthermore, GMMs are employed in mesh extraction to eliminate artifacts and improve the overall geometric quality. Experiments demonstrate that our method outperforms state-of-the-art methods in large-scale 3D reconstruction, achieving higher accuracy compared to both LiDAR-based methods and Gaussian-based methods with improvements of 52.6% and 68.7%, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical radar

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Efficient learning of robust quadruped bounding usingpretrained neural networks

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IET Cyber-Systems and Robotics 2022年第4期4卷 331-338页

作者： Zhicheng Wang Anqiao Li Yixiao Zheng Anhuan Xie Zhibin Li Jun Wu Qiuguo Zhu Institute of Cyber-Systems and Control Zhejiang UniversityZhejiangChina Intelligent Robot Research Center Zhejiang LabZhejiangChina Department of Computer Science University College LondonLondonUK State Key Laboratory of Industrial Control Technology Zhejiang UniversityZhejiangChina

Bounding is one of the important gaits in quadrupedal locomotion for negotiating *** authors proposed an effective approach that can learn robust bounding gaits more efficiently despite its large variation in dynamic body *** authors first pretrained the neural network(NN)based on data from a robot operated by conventional model-based controllers,and then further optimised the pretrained NN via deep reinforcement learning(DRL).In particular,the authors designed a reward function considering contact points and phases to enforce the gait symmetry and periodicity,which improved the bounding *** NN-based feedback controller was learned in the simulation and directly deployed on the real quadruped robot Jueying Mini successfully.A variety of environments are presented both indoors and outdoors with the authors’*** authors’approach shows efficient computing and good locomotion results by the Jueying Mini quadrupedal robot bounding over uneven *** cover image is based on the Research Article Efficient learning of robust quadruped bounding using pretrained neural networks by Zhicheng Wang et al.,https://***/10.1049/csy2.12062.

关键词： legged locomotion reinforcement learning robot learning

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Constructing a Raman and surface-enhanced Raman scattering spectral reference library for fine-particle analysis

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Journal of Environmental sciences 2022年第8期34卷 1-13页

作者： Hui Chen Fengkui Duan Kebin He Jingjing Du Zhenli Sun Suhua Wang Key Laboratory of Resources and Environmental System Optimization of Ministry of Education College of Environmental Science and EngineeringNorth China Electric Power UniversityBeijing 102206China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China State Key Laboratory of Environmental Chemistry and Ecotoxicology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China Guangdong Provincial Key Laboratory of Petrochemcial Pollution Processes and Control School of Environmental Science and EngineeringGuangdong University of Petrochemical TechnologyMaoming 525000 China

Fine particles associated with haze pollution threaten the health of over 400 million people in China. Owing to excellent non-destructive fingerprint recognition characteristics, Raman and surface-enhanced Raman scattering(SERS) are often used to analyze the composition of fine particles to determine their physical and chemical properties as well as reaction mechanisms. However, there is no comprehensive Raman spectral library of fine particles. Furthermore, various studies that used SERS for fine-particle composition analysis showed that the uniqueness of the SERS substrates and different excitation wavelengths can produce a different spectrum for the same fine-particle component. To overcome this limitation, we conducted SERS experiments with a portable Raman spectrometer using two common SERS substrates(silver(Ag) foil and gold nanoparticles(Au NPs)) and a 785 nm laser. Herein, we introduced three main particle component types(sulfate-nitrate-ammonium(SNA), organic material, and soot) with a total of 39 chemical substances. We scanned the solid Raman, liquid Raman, and SERS spectra of these substances and constructed a fine-particle reference library containing 105 spectra. Spectral results indicated that for soot and SNA, the differences in characteristic peaks mainly originated from the solid-liquid phase transition;Ag foil had little effect on this difference, while the Au NPs caused a significant red shift in the peak positions of polycyclic aromatic hydrocarbons. Moreover, with various characteristic peak positions in the three types of spectra, we could quickly and correctly distinguish substances. We hope that this spectral library will aid in the future identification of fine particles.

关键词： Raman Surface-enhanced Raman scattering(SERS) Spectral library Fine-particle

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A Distributed Algorithm for Large-Scale Multi-Agent MINLPs

A Distributed Algorithm for Large-Scale Multi-Agent MINLPs

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IEEE Conference on Decision and control

作者： Sheng Dong Weiguo Xia Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education and School of Control Science and Engineering Dalian University of Technology China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we focus on the optimization of large-scale multi-agent systems, where agents collaboratively optimize the sum of local objective functions through their own continuous and/or discrete decision variables, subject to global coupling constraints and local constraints. The resulting Mixed-Integer Nonlinear Programmings (MINLPs) are NP-hard, non-convex, and large-scale. Therefore, this paper aims to design distributed algorithms to find feasible suboptimal solutions with a guaranteed bound. To this end, considering dual decomposition as an effective method to decompose large-scale constraint-coupled optimization problems, we first show, based on the convexification effects of large-scale MINLPs, that the primal solutions from the dual are near-optimal under certain conditions. This expands recent results in Mixed-Integer Linear Programmings (MILPs) to the nonlinear case but requires additional efforts on the proof. Utilizing this result to tighten the coupling constraints, we develop a fully distributed algorithm for the tightened problem, based on dual decomposition and consensus protocols. The algorithm is guaranteed to provide feasible solutions for the original MINLP. Moreover, asymptotic suboptimality bounds are established for the obtained solution. Finally, the efficacy of the method is verified through numerical simulations.

关键词： Couplings Programming Numerical simulation Linear programming Mixed integer linear programming Consensus protocol Distributed algorithms Optimization Standards Multi-agent systems

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Principal Trotter Observation Error with Truncated Commutators

arXiv

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arXiv 2024年

作者： Li, Langyu Institute of Cyber-system and Control College of Control Science and Engineering Zhejiang University Hangzhou310027 China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China

Hamiltonian simulation is one of the most promising applications of quantum computers, and the product formula is one of the most important methods for this purpose. Previous related work has mainly focused on the worst-case or average-case scenarios. In this work, we consider the simulation error under a fixed observable. Under a fixed observable, errors that commute with this observable become less important. To illustrate this point, we define the observation error as the expectation under the observable and provide a commutativity-based upper bound using the Baker–Campbell–Hausdorff formula. For highly commuting observables, the simulation error indicated by this upper bound can be significantly compressed. In the experiment with the Heisenberg model, the observation bound compresses the Trotter number by nearly half compared to recent commutator bounds. Additionally, we found that the evolution order significantly affects the observation error. By utilizing a simulated annealing algorithm, we designed an evolution order optimization algorithm, achieving further compression of the Trotter number. The experiment on the hydrogen molecule Hamiltonian demonstrates that optimizing the order can lead to nearly half the reduction in the Trotter number. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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Synthesis,Crystal Structure,Fungicidal Activities and Molecular Docking of Acyl Urea Derivatives Containing 2-Chloronicotine Motif

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Chinese Journal of Structural Chemistry 2022年第2期41卷 114-122,I0010页

作者： SUN Guo-Xiang WANG Qiao MIN Li-Jing HAN Liang LIU Xing-Hai School of Chemistry and Chemical Engineering Yancheng Institute of TechnologyJiangsu 224051China College of Chemical Engineering Zhejiang University of TechnologyHuzhou 313000China College of Life Science Key Laboratory of Vector Biology and Pathogen Control of Zhejiang ProvinceHuzhou UniversityHangzhou 310014China

Eighteen new acyl urea derivatives containing 2-chloronicotine moiety were synthesized using2-chloronicotinic acid as starting material via 4 steps *** 2-chloronicotine acyl urea structures were confirmed by ^(1)H NMR,^(13)C NMR and *** 4r was further confirmed by X-ray *** crystallizes in the orthorhombic system,space group Pbca with a=7.2960(3),b=14.8546(6),c=25.2840(11)?,V=2740.3(2)?^(3),Z=8,the final R=0.0442 and w R=0.1033 for 4028 observed reflections with I>2σ(I).The antifungal activity results demonstrate that some of these compounds possessed good activity against ***,***,***,and *** at 50 *** molecular docking results indicated that the key group is urea bridge and pyridine ring.

关键词： acyl urea compounds crystal structure synthesis antifungal activity docking

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VHF:A Time Series Hybrid Model Based on Variational Decomposition and Prior Knowledge Reconstruction for the Bio-Pharmaceutical Industry

SSRN

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SSRN 2024年

作者： Song, Yichen Qiao, Simengxu He, Qunshan Tian, Zijian Li, Changdi Liu, Xinggao State Key Laboratory of Industry Control Technology College of Control Science & Engineering Zhejiang University Hangzhou310027 China

In the bio-pharmaceutical industry, normalizing key indicators is crucial for enhancing efficiency. Thus, precise predictions of these indicators can significantly boost operational effectiveness. This task has a high industrial value, but also poses great challenges. The nonlinearity, time-varying characteristics, high trend, weak seasonality, and coupled noise effects among indicators in the bio-pharmaceutical require unconventional methods of series analysis. Here, we design a novel hybrid model that can excellently fulfill the task of predict key indicators. The model transcends traditional decomposition and reconstruction approaches by employing an adaptive parameter series decomposition method that leverages the Marginal Spectral Sparsity Index. Additionally, it utilizes a reconstruction method based on deterministic regression and prior knowledge, effectively addressing issues such as high trend, weak seasonality, and the coupled noise effects. At the same time, a hybrid of the improved Transformer model introducing the pharmaceutical mechanism is used to solve the nonlinear and time-varying problems. Benchmarking against prevailing models from the past triennium, our hybrid model demonstrates state-of-the-art performance in prediction key indicators: the volumetric dissolved oxygen coefficient, oxygen uptake rate, and carbon dioxide evolution rate. These findings not only attest to the model’s precision but also underscore its practical applicability in the bio-pharmaceutical industry. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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A Novel Intelligent Online Fire Monitoring Approach for Underground Pipe Galleries

A Novel Intelligent Online Fire Monitoring Approach for Unde...

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Data Driven control and Learning Systems (DDCLS)

作者： Yongjing Wang Chenying Wen Zheren Zhu Xinming Zhang Zhihuan Song Zugang Zhu Xueer Fan The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou Hangzhou Underground Pipeline Development Co. Ltd Hangzhou

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Underground pipe galleries are constructed beneath urban areas, providing pathways for the installation of various municipal pipelines. These galleries help protect pipelines and efficiently utilize underground space, serving as a significant facilitator for the construction of smart cities. Due to the confined space inside the galleries and the simultaneous presence of multiple pipelines, there is a potential safety hazard of fire incidents. To ensure the safe operation of pipelines, online monitoring and timely warning of fire incidents are valuable tasks. Current traditional computer vision and machine learning approaches for object detection often experience slow response time. Therefore the You Only Look Once (YOLO) series due to its fast response time, has become a popular method in real-time detection. Nonetheless, YOLOv8 still suffer from information fusion problems. In response to this issue, this paper proposed YOLOH algorithm, which improves YOLOv8 by employing the Gather-and-Distribute (GD) mechanism for intelligent online monitoring of fire incidents in underground pipe galleries. Finally, the effectiveness of YOLOH was validated using a combination of publicly available fire datasets and simulated fire datasets in underground pipe galleries. The YOLOH attains a 63.2 mAP, surpassing the YOLOv8-N baseline by +2.15 mAP.

关键词： YOLO Machine learning algorithms Smart cities Pipelines Propagation losses Real-time systems Neck

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Cross-Modality Comprehensive Feature Aggregation Network for Road Scene Segmentation 8

Cross-Modality Comprehensive Feature Aggregation Network for...

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8th International Symposium on Computer science and Intelligent control, ISCSIC 2024

作者： Yang, Yale Zhang, Zihao Hou, Huifang Zhang, Wenwen Zhuang, Chunsheng Henan University of Technology College of Information Science and Engineering Zhengzhou China Henan University of Technology Key Laboratory of Grain Information Processing and Control Ministry of Education Henan Key Laboratory of Grain Photoelectric Detection and Control School of Artificial Intelligence and Big Data Zhengzhou China Data Henan University of Technology School of Artificial Intelligence and Big Zhengzhou China Henan Academy of Sciences Zhengzhou China

ISBN: (纸本)9798350380286

RGB-thermal semantic segmentation is a potential solution for reliable semantic scene understanding under adverse weather and complex lighting conditions. However, past studies have primarily focused on the design of fusion paradigms. Unfortunately, these methods have not fully considered the modality differences caused by different imaging mechanisms, thereby reducing the performance of pixel-level semantic analysis. In response to this issue, this paper proposes an innovative Cross-Modality Comprehensive Feature Aggregation Network (CFANet). This method first designs a Cross-Modality Feature Rectification Module (CFRM) to align modality differences;then, it constructs a Global-Local Fusion Module (GLF) to sharpen object edges and refine the segmentation map. Results on two urban scene understanding datasets demonstrate that CFANet can effectively aggregate multi-layer deep features and outperform existing advanced deep learning methods in scene understanding performance. © 2024 IEEE.

关键词： Semantic Segmentation

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Diagnosis and Research for Losing One RCP Loop During Power Conditions in a GEN-III Nuclear Power Plant 6th

Diagnosis and Research for Losing One RCP Loop During Power ...

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6th International Symposium on Software Reliability, industrial Safety, Cyber Security and Physical Protection of Nuclear Power Plant, ISNPP 2021

作者： Song, Fei Luan, Zhen-Hua Liu, Peng Liu, Hang Qiao, Jian-Feng State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment Guangdong Shenzhen518172 China College of Mechanical and Electrical Control Shenzhen University Guangdong Shenzhen518000 China State Key Lab of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9789811911804

There are four reactor coolant loops in reactor coolant system in a GEN-III nuclear power plant and each loop has a reactor coolant pump in nuclear island. The four reactor coolant pumps are in operation during power conditions. In order to improve plant availability and reduce the impact to electric grid, the plant allows losing one reactor coolant loop during power conditions and continue to generate power to the grid. Losing one reactor coolant loop function is a new design which has not been verified by practice, so the Losing one reactor coolant loop test have very high systemic risks and may even cause equipment *** the defect diagnosis for Losing one reactor coolant loop function should be carried out in advance before doing the test. By diagnosis we find eight important flaws and improve the design logic to ensure that Losing one reactor coolant loop tests are successful at once. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Nuclear power plants

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