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| 1 | Optimal bulk-heterojunction morphology enabled by fibril network strategy for high-performance organic solar cells显示文摘A bicontinuous network formed spontaneously upon film preparation is highly desirable for bulk-heterojunction(BHJ) organic solar cells(OSCs). Many donor-acceptor(D-A) type conjugated polymers can self-assemble into polymer fibrils in the solid state and such fibril-assembly can construct the morphological framework by forming a network structure, inducing the formation of ideal BHJ morphology. Our recent works have revealed that the fibril network strategy(FNS) can control the blend morphology in fullerene, non-fullerene and ternary OSCs. It has been shown that the formation of fibril network can optimize phase separation scale and ensure efficient exciton dissociation and charge carriers transport, thus leading to impressive power conversion efficiencies(PCEs) and high fill factor(FF) values. We believe that FNS will provide a promising approach for the optimization of active layer morphology and the improvement of photovoltaic performance, and further promote the commercialization of OSCs. | Tian Xia Yunhao Cai Huiting Fu Yanming Sun | 2019 | Science China Chemistry2019，62，6： | 4 |
| 2 | The construction of personalized virtual landslide disaster environments based on knowledge graphs and deep neural networks显示文摘Virtual Landslide Disaster environments are important for multilevel simulation,analysis and decision-making about Landslide Disasters.However,in the existing related studies,complex disaster scene objects and relationships are not deeply analyzed,and the scene contents are fixed,which is not conducive to meeting multilevel visualization task requirements for diverse users.To resolve the above issues,a construction method for Personalized Virtual Landslide Disaster Environments Based on Knowledge Graphs and Deep Neural networks is proposed in this paper.The characteristics of relationships among users,scenes and data were first discussed in detail;then,a knowledge graph of virtual Landslide Disaster environments was established to clarify the complex relationships among disaster scene objects,and a Deep Neural network was introduced to mine the user history information and the relationships among object entities in the knowledge graph.Therefore,a personalized Landslide Disaster scene data recommendation mechanism was proposed.Finally,a prototype system was developed,and an experimental analysis was conducted.The experimental results show that the method can be used to recommend intelligently appropriate disaster information and scene data to diverse users.The recommendation accuracy stabilizes above 80%–a level able to effectively support The Construction of Personalized Virtual Landslide Disaster environments. | Yunhao Zhang Jun Zhu Qing Zhu Yakun Xie Weilian Li Lin Fu Junxiao Zhang Jianmei Tan | 2020 | International Journal of Digital Earth2020，13，12： | 3 |
| 3 | Tunnel vision optimization method for VR flood scenes based on Gaussian blur显示文摘The visualization of flood disasters in virtual reality(VR)scenes is useful for the representation and sharing of disaster knowledge and can effectively improve users’cognitive efficiency in comprehending disaster information.However,the existing VR methods of visualizing flood disaster scenes have some shortcomings,such as low rendering efficiency and poor user experience.In this paper,a tunnel vision optimization method for VR flood scenes based on Gaussian blur is proposed.The key techniques are studied,such as region of interest(ROI)calculation and tunnel vision optimization considering the characteristics of the human visual system.A prototype system has been developed and used to carry out an experimental case analysis.The experimental results show that the number of triangles drawn in a flood VR scene is reduced by approximately 30%–40%using this method and that the average frame rate is stable at approximately 90 frames per second(fps),significantly improving the efficiency of scene rendering and reducing motion sickness. | Lin Fu Jun Zhu Weilian Li Qing Zhu Bingli Xu Yakun Xie Yunhao Zhang Ya Hu Jingtao Lu Pei Dang Jigang You | 2021 | International Journal of Digital Earth2021，14，7： | 1 |
| 4 | The design and simulation of new downhole vibration device about acoustic oil recovery technology显示文摘More and more oilfields are using acoustic technology to enhance oil recovery.In order to know the mechanism of acoustic oil recovery technology,the sound radiator of a new downhole vibration device is modeled and analyzed.Based on the theoretical background,this paper firstly analyzes the acoustic mechanism for the oil reservoir and then makes a acoustic response analysis on the sound radiator model for frequency and time-domain investigation by using professional acoustic simulation softwareeLMS Virtual.lab Acoustics,finally calculates the acoustic transmission loss in the downhole oil reservoir.The research reveals that firstly,acoustic waves have influences on the oil&water fluidity in the oil reservoir,the oil pressure gradient and the interfacial tension of capillary;secondly,the acoustic radiation power and sound pressure of field point attain a peak on the natural frequency of the sound radiator;thirdly,with the acoustic impact,the sound pressure of oil reservoir would fluctuate so as to improve the oil recovery ratio;the last but not the least one is both the sound pressure of oil reservoir point and the transmission loss of rock have a positive correlation with the vibration frequency.Therefore,it is of great importance for the research of vibration frequency and structure optimization of sound radiator. | Yongjun Hou Ran Zhou Xiaokang Long Peng Liu Yunhao Fu | 2015 | Petroleum2015，1，3： | 1 |
| 5 | Toward autonomous mining:design and development of an unmanned electric shovel via point cloud-based optimal trajectory planning显示文摘With the proposal of intelligent mines,unmanned mining has become a research hotspot in recent years.In the field of autonomous excavation,environmental perception and excavation trajectory planning are two key issues because they have considerable influences on operation performance.In this study,an unmanned electric shovel(UES)is developed,and key robotization processes consisting of environment modeling and optimal excavation trajectory planning are presented.Initially,the point cloud of the material surface is collected and reconstructed by polynomial response surface(PRS)method.Then,by establishing the dynamical model of the UES,a point to point(PTP)excavation trajectory planning method is developed to improve both the mining efficiency and fill factor and to reduce the energy consumption.Based on optimal trajectory command,the UES performs autonomous excavation.The experimental results show that the proposed surface reconstruction method can accurately represent the material surface.On the basis of reconstructed surface,the PTP trajectory planning method rapidly obtains a reasonable mining trajectory with high fill factor and mining efficiency.Compared with the common excavation trajectory planning approaches,the proposed method tends to be more capable in terms of mining time and energy consumption,ensuring high-performance excavation of the UES in practical mining environment. | Tianci ZHANG Tao FU Yunhao CUI Xueguan SONG | 2022 | Frontiers of Mechanical Engineering2022，17，3： | 0 |
| 6 | An insider user authentication method based on improved temporal convolutional network显示文摘With the rapid development of information technology,information system security and insider threat detection have become important topics for organizational management.In the current network environment,user behavioral bio-data presents the characteristics of nonlinearity and temporal sequence.Most of the existing research on authentication based on user behavioral biometrics adopts the method of manual feature extraction.They do not adequately capture the nonlinear and time-sequential dependencies of behavioral bio-data,and also do not adequately reflect the personalized usage characteristics of users,leading to bottlenecks in the performance of the authentication algorithm.In order to solve the above problems,this paper proposes a Temporal Convolutional Network method based on an Efficient Channel Attention mechanism(ECA-TCN)to extract user mouse dynamics features and constructs an one-class Support Vector Machine(OCSVM)for each user for authentication.Experimental results show that compared with four existing deep learning algorithms,the method retains more adequate key information and improves the classification performance of the neural network.In the final authentication,the Area Under the Curve(AUC)can reach 96%. | Xiaoling Tao Yuelin Yu Lianyou Fu Jianxiang Liu Yunhao Zhang | 2023 | High-Confidence Computing2023，3，4： | 0 |
| 7 | Novel Hybrid Physics‑Informed Deep Neural Network for Dynamic Load Prediction of Electric Cable Shovel显示文摘Electric cable shovel(ECS)is a complex production equipment,which is widely utilized in open-pit mines.Rational valuations of load is the foundation for the development of intelligent or unmanned ECS,since it directly influences the planning of digging trajectories and energy consumption.Load prediction of ECS mainly consists of two types of methods:physics-based modeling and data-driven methods.The former approach is based on known physical laws,usually,it is necessarily approximations of reality due to incomplete knowledge of certain processes,which introduces bias.The latter captures features/patterns from data in an end-to-end manner without dwelling on domain expertise but requires a large amount of accurately labeled data to achieve generalization,which introduces variance.In addition,some parts of load are non-observable and latent,which cannot be measured from actual system sensing,so they can’t be predicted by data-driven methods.Herein,an innovative hybrid physics-informed deep neural network(HPINN)architecture,which combines physics-based models and data-driven methods to predict dynamic load of ECS,is presented.In the proposed framework,some parts of the theoretical model are incorporated,while capturing the difficult-to-model part by training a highly expressive approximator with data.Prior physics knowledge,such as Lagrangian mechanics and the conservation of energy,is considered extra constraints,and embedded in the overall loss function to enforce model training in a feasible solution space.The satisfactory performance of the proposed framework is verified through both synthetic and actual measurement dataset. | Tao Fu Tianci Zhang Yunhao Cui Xueguan Song | 2022 | Chinese Journal of Mechanical Engineering2022，35，6： | 0 |
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