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Road sub-surface defect detection based on gprMax forward simulation-sample generation and Swin Transformer-YOLOX

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Frontiers of Structural and Civil Engineering 2024年第3期18卷 334-349页

作者： Longjian LI Li YANG Zhongyu HAO Xiaoli SUN Gongfa CHEN School of Civil and Transportation Engineering Guangdong University of TechnologyGuangzhou 510006China JSTI Group Guizhou Engineering Survey and Design co. Ltd.Guangzhou 510800China guangzhou Municipal Engineering Testing co. Ltd.Guangzhou 510520China

Training samples for deep learning networks are typically obtained through various field experiments,which require significant manpower,resource and time ***,it is possible to utilize simulated data to augment the training *** this paper,by co.paring the actual experimental model with the simulated model generated by the gprMax[1]forward simulation method,the feasibility of obtaining simulated samples through gprMax simulation is ***,the samples generated by gprMax forward simulation are used for training the network to detect objects in existing real *** the same time,aiming at the detection and intelligent reco.nition of road sub-surface defects,the Swin-YOLOX algorithm is introduced,and the excellence of the detection network,which is improved by augmenting the simulated samples with real samples,is further *** co.paring the prediction performance of the object detection models,it is observed that the model trained with mixed samples achieved a recall of 94.74%and a mean average precision(maP)of 97.71%,surpassing the model trained only on real samples by 12.95%and 15.64%,*** feasibility and excellence of training the model with mixed samples are *** potential of using a fusion of simulated and existing real samples instead of repeatedly acquiring new real samples by field experiment is demonstrated by this study,thereby improving detection efficiency,saving resources,and providing a new approach to the problem of multiple interpretations in ground penetrating radar(GPR)data.

关键词： ground penetrating radar gprMax forward modeling sample generation Swin-YOLOX object detection

Feature Selection Using Tree Model and Classification Through co.volutional Neural Network for Structural Damage Detection

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Acta Mechanica Solida Sinica 2024年第3期37卷 498-518页

作者： Zihan Jin Jiqiao Zhang Qianpeng He Silang Zhu Tianlong Ouyang Gongfa Chen School of Civil and Transportation Engineering Guangdong University of TechnologyGuangzhou510006China Guangdong Tianxin Electric Power Engineering Testing co. LtdGuangzhou510663China

Structural damage detection(SDD)remains highly challenging,due to the difficulty in selecting the optimal damage features from a vast amount of *** this study,a tree model-based method using decision tree and random forest was employed for feature selection of vibration response signals in *** datasets were obtained by numerical experiments and vibration experiments,*** features extracted using this method were input into a co.volutional neural network to determine the location of structural *** indicated a 5%to 10%improvement in detection accuracy co.pared to using original datasets without feature selection,demonstrating the feasibility of this *** proposed method,based on tree model and classification,addresses the issue of extracting effective information from numerous vibration response signals in structural health monitoring.

关键词： Feature selection Structural damage detection Decision tree Random forest co.volutional neural network

Predictive value of T cell receptor repertoire profiling for immunosuppressive therapy in severe aplastic anemia

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Genes & Diseases 2024年第1期11卷 95-98页

作者： Cunte Chen Yuling Zhang Dongpei Lu Zelong Zhang Jun Yang Xiaowei Chen Ming Zhou Wenjian Mo Caixia Wang Qinghua Cai Yumiao Li Ruiqing Zhou Shilin Xu Wei Zhou Tingfen Deng Shiyi Pan Yanli Xu Shunqing Wang Yuping Zhang Department of Hematology Guangzhou First People's HospitalSouth China University of TechnologyGuangzhouGuangdong 510180China guangzhou Junruikang biotechnology co. LtdGuangzhouGuangdong 510700China

Increasing evidence supports the hypothesis of autologous immune attack in severe aplastic anemia(SAA):the predominant role of activated cytotoxic T cells(CTL)expressing-interferon in inhibiting the growth of bone marrow(BM)cells,putative autoantigens,and oligoclonal expansion of CD8+T cells.1 For SAA patients,the definitive therapies are immunosuppressive therapy(IST)or hematopoietic stem transplantation(HSCT).

关键词： anemia aplastic interferon

High-frequency Resonance Analysis and Impedance Reshaping co.trol of MMC-HVDC System Based on Frequency co.pling Impedance Model

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 646-657页

作者： Juanjuan Wang Wei Chen Yuekun Liu Chuang Fu Yunming Ye Junjie Feng School of Electric Power South China University of TechnologyGuangzhouChina Guangdong Power Grid co. Ltd.GuangzhouChina Electric Power Research Institute China Southern Power GridGuangzhouChina

In recent years, high-frequency resonance (HFR) events occurred in several modular multilevel co.verter based high-voltage direct current (MMC-HVDC) projects. The time delay of an MMC-HVDC system is the critical factor that induces HFR. The frequency co.pling affects the impedance characteristics of an MMC and further deteriorates system stability. Therefore, in this paper, a multi-input multi-output admittance model of an MMC-HVDC system is developed to analyze its frequency characteristics. The effects of current loop, power loop, phase-locked loop, and operating point on the MMC frequency co.pling degree are analyzed in detail. Meanwhile, to further suppress HFR in the MMC-HVDC system, an enhanced impedance reshaping co.trol strategy based on the equivalent single-input single-output impedance model is proposed. Finally, the accuracy of the enhanced impedance model and the effectiveness of the impedance reshaping co.trol are verified by electromagnetic transient simulations in PSCAD.

关键词： Modular multilevel co.verter based high-voltage direct current(MMC-HVDC) high-frequency resonance frequency co.pling impedance model impedance reshaping co.trol

Enhanced wear prediction of tunnel boring machine disc cutters for accurate remaining useful life estimation using a hybrid model

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Frontiers of Structural and Civil Engineering 2024年第4期18卷 642-662页

作者： Xinghai ZHOU Yakun ZHANG Guofang GONG Huayong YANG Qiaosong CHEN Yuxi CHEN Zhixue SU The State Key Laboratory of Fluid Power&Mechatronic Systems Zhejiang UniversityHangzhou 310027China guangzhou Rail Transit co.struction Engineering co.sulting co. Ltd.Guangzhou 510000China China Railway Engineering Equipment Group co. Ltd.Zhengzhou 450016China

In tunnel co.struction with tunnel boring machines(TBMs),accurate prediction of the remaining useful life(RUL)of disc cutters is critical for timely maintenance and replacement to avoid delays and co.t *** paper introduces a novel hybrid model,integrating fundamental and data-driven approaches,to enhance wear prediction of TBM disc cutters and enable accurate RUL *** fundamental model is improved by inco.porating co.posite wear mechanisms and load estimation techniques,showcasing superior prediction accuracy co.pared to single-mechanism ***,the hybrid model innovatively inco.porates a data-driven supplementary residual term into the improved fundamental model,leading to a high-performance wear prediction *** actual field data from a highway tunnel project in Shenzhen,the performance of the hybrid model is rigorously tested and co.pared with pure fundamental and data-driven *** hybrid model outperforms the other models,achieving the highest accuracy in predicting TBM disc cutter wear(mean absolute error(MAE)=0.53,root mean square error(RMSE)=0.64).Furthermore,this study thoroughly analyzes the hybrid model’s generalization capability,revealing significant impacts of geological co.ditions on prediction *** model’s generalization capability is also improved by expanding and updating the data *** RUL estimation results provided by the hybrid model are straightforward and effective,making it a valuable tool by which co.struction staff can monitor TBM disc cutters.

关键词： tunnel boring machine disc cutter wear prediction remaining useful life field data hybrid model

RL and AHP-Based Multi-Timescale Multi-Clock Source Time Synchronization for Distribution Power Internet of Things

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co.puters, Materials & co.tinua 2024年第3期78卷 4453-4469页

作者： Jiangang Lu Ruifeng Zhao Zhiwen Yu Yue Dai Kaiwen Zeng Electric Power Dispatching and co.trol Center Guangdong Power Grid Co.Ltd.Guangzhou510600China

Time synchronization(TS)is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things(IoT).Multi-clock source time synchronization(MTS)has significant advantages of high reliability and accuracy but still faces challenges such as optimization of the multi-clock source selection and the clock source weight calculation at different timescales,and the co.pling of synchronization latency jitter and pulse phase *** this paper,the multi-timescale MTS model is co.ducted,and the reinforcement learning(RL)and analytic hierarchy process(AHP)-based multi-timescale MTS algorithm is designed to improve the weighted summation of synchronization latency jitter standard deviation and average pulse phase ***,the multi-clock source selection is optimized based on Softmax in the large timescale,and the clock source weight calculation is optimized based on lower co.fidence bound-assisted AHP in the small *** shows that the proposed algorithm can effectively reduce time synchronization delay standard deviation and average pulse phase difference.

关键词： Multi-clock source time synchronization(TS) power Internet of Things reinforcement learning analytic hierarchy process

An optimization-oriented modeling approach using input co.vex neural networks and its application on optimal chiller loading

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Building Simulation 2024年第4期17卷 639-655页

作者： Shanshuo Xing Jili Zhang Song Mu Dalian University of Technology Dalian116024China Guangdong Airport Baiyun Information Technology co. Ltd.Guangzhou510000China

Optimization for the multi-chiller system is an indispensable approach for the operation of highly efficient chiller *** optima obtained by model-based optimization algorithms are dependent on precise and solvable objective *** classical neural networks cannot provide co.vex input-output mappings despite capturing impressive nonlinear fitting capabilities,resulting in a reduction in the robustness of model-based *** this paper,we leverage the input co.vex neural networks(ICNN)to identify the chiller model to co.struct a co.vex mapping between co.trol variables and the objective function,which enables the NN-based OCL as a co.vex optimization problem and apply it to multi-chiller optimization for optimal chiller loading(OCL).Approximation performances are evaluated through a four-model co.parison based on an experimental data set,and the statistical results show that,on the premise of retaining prior co.vexities,the proposed model depicts excellent approximation power for the data set,especially the unseen ***,the ICNN model is applied to a typical OCL problem for a multi-chiller system and co.bined with three types of optimization *** with co.ventional and meta-heuristic methods,the numerical results suggest that the gradient-based BFGS algorithm provides better energy-saving ratios facing co.secutive co.ling load inputs and an impressive co.vergence speed.

关键词： chiller plant input co.vex neural network optimal load distribution co.vex optimization

Cloud-Edge co.laborative Federated GAN Based Data Processing for IoT-Empowered Multi-Flow Integrated Energy Aggregation Dispatch

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co.puters, Materials & co.tinua 2024年第7期80卷 973-994页

作者： Zhan Shi Electric Power Dispatching co.trol Center Guangdong Power Grid Co.Ltd.Guangzhou510030China

The co.vergence of Internet of Things(IoT),5G,and cloud co.laboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data *** generative adversarial networks(GANs)are instrumental in resource scheduling,their application in this domain is impeded by challenges such as co.vergence speed,inferior optimality searching capability,and the inability to learn from failed decision making ***,a cloud-edge co.laborative federated GAN-based co.munication and co.puting resource scheduling algorithm with long-term co.straint violation sensitiveness is proposed to address these *** proposed algorithm facilitates real-time,energy-efficient data processing by optimizing transmission power co.trol,data migration,and co.puting resource *** employs federated learning for global parameter aggregation to enhance GAN parameter updating and dynamically adjusts GAN learning rates and global aggregation weights based on energy co.sumption co.straint *** results indicate that the proposed algorithm effectively reduces data processing latency,energy co.sumption,and co.vergence time.

关键词： IoT federated learning generative adversarial network data processing multi-flowintegration energy aggregation dispatch

Fabrication of UV-Curing Linalool-Polysiloxane Hybrid Films with High Refractive Index and Transparency

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Journal of Renewable Materials 2024年第3期12卷 569-583页

作者： Wenqing Xiao Lewen Tan Xunjun Chen Qiaoguang Li Yunqing Ruan co.lege of Chemistry and Chemical Engineering Zhongkai University of Agriculture and EngineeringGuangzhou510225China R&D Department Guangzhou Silicon Core Material Technology Co.Ltd.Guangzhou510220China

In this article,a series of high refractive indices(1.50-1.53)thiol phenyl polysiloxane(TPS)were synthesized via hydrolytic sol-gel *** Fourier transform infrared spectra(FT-IR)and nuclear magnetic resonance spectra(NMR)results showed that TPS co.formed to the predicted *** terpene linalool was exploited as photocrosslinker to fabricate UV-curing linalool-polysiloxane hybrid films(LPH)with TPS via photoinitiated thiol-ene *** rapidly cured under UV irradiation at the intensity of 80 mW/cm^(2) in 30 s,exhibiting good UV-curing *** optical transmittance of LPH in the wavelength of 300-800 nm was over 90%,exhibiting good optical *** water co.tact angle and water vapor permeability results showed that the introduction of phenyl groups enhance the hydrophobicity and water vapor barrier properties of *** results indicated the potential of LPHs in the applications of optical functional co.tings.

关键词： UV-curing polysiloxane hybrid films high refractive index transparency