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| 1 | A parameterized geometry design method for inward turning inlet compatible waverider显示文摘Intensive studies have been carried out on generations of waverider geometry and hypersonic inlet geometry.However,integration efforts of waverider and related air-intake system are restricted majorly around the X43A-like or conical flow feld induced confguration,which adopts mainly the two-dimensional air-breathing technology and limits the judicious visions of developing new aerodynamic profles for hypersonic designers.A novel design approach for integrating the inward turning inlet with the traditional parameterized waverider is proposed.The proposed method is an alternative means to produce a compatible confguration by linking the off-the-shelf results on both traditional waverider techniques and inward turning inlet techniques.A series of geometry generations and optimization solutions is proposed to enhance the lift-to-drag ratio.A quantitative but effcient aerodynamic performance evaluation approach(the hypersonic flow panel method) with lower computational cost is employed to play the role of objective function for optimization purpose.The produced geometry compatibility with a computational fluid dynamics(CFD) solver is also verifed for detailed flow feld investigation.Optimization results and other numerical validations are obtained for the feasibility demonstration of the proposed method. | Tian Chao Li Ni Gong Guanghong Su Zeya | 2013 | Chinese Journal of Aeronautics2013，26，5： | 4 |
| 2 | Theory and techniques of data mining in CGF behavior modeling显示文摘For the large quantity of data,rules and models generated in the course of computer generated forces (CGFs) behavior modeling,the common analytical methods are statistical methods based on the tactical rules,tactical doctrine and empirical knowledge.However,from the viewpoint of data mining,we can find many of these analytical methods are also each-and-every different data mining methods.In this paper,we survey the data mining theory and techniques that have appeared in the course of CGF behavior modeling from the viewpoint of data mining.Further,we redefine and reclassify these theories and techniques according to the research requirements of data mining (the knowledge structure of data mining).We aim to help the CGF investigators to learn the key points and the research methods of the data intelligent processing in the CGF behavior modeling,and thus we can provide the references for the colleagues who are engaged in the research and practice of the CGF behavior modeling by this article.We also introduce our ongoing work on the CGF behavior modeling based on data mining. | YIN YunFei GONG GuangHong HAN Liang | 2011 | Science China(Information Sciences)2011，54，4： | 4 |
| 3 | A fast, accurate and dense feature matching algorithm for aerial images显示文摘Three-dimensional(3D)reconstruction based on aerial images has broad prospects,and feature matching is an important step of it.However,for high-resolution aerial images,there are usually problems such as long time,mismatching and sparse feature pairs using traditional algorithms.Therefore,an algorithm is proposed to realize fast,accurate and dense feature matching.The algorithm consists of four steps.Firstly,we achieve a balance between the feature matching time and the number of matching pairs by appropriately reducing the image resolution.Secondly,to realize further screening of the mismatches,a feature screening algorithm based on similarity judgment or local optimization is proposed.Thirdly,to make the algorithm more widely applicable,we combine the results of different algorithms to get dense results.Finally,all matching feature pairs in the low-resolution images are restored to the original images.Comparisons between the original algorithms and our algorithm show that the proposed algorithm can effectively reduce the matching time,screen out the mismatches,and improve the number of matches. | LI Ying GONG Guanghong SUN Lin | 2020 | Journal of Systems Engineering and Electronics2020，31，6： | 2 |
| 4 | Han Liang School of Auto-mation Science and Electrical Engineering Beijing University ofAeronautics and Astronautics Beijing显示文摘 | Huang Zhanpeng Gong Guanghong | 2011 | NetGL:A 3D GraphicsFramework for Next Generation Web2011，11，： | 1 |
| 5 | Air-combat behavior data mining based on truncation method显示文摘This paper considers the problem of applying data mining techniques to aeronautical field.The truncation method,which is one of the techniques in the aeronautical data mining,can be used to efficiently handle the air-combat behavior data.The technique of air-combat behavior data mining based on the truncation method is proposed to discover the air-combat rules or patterns.The simulation platform of the air-combat behavior data mining that supports two fighters is implemented.The simulation experimental results show that the proposed air-combat behavior data mining technique based on the truncation method is feasible whether in efficiency or in effectiveness. | Yunfei Yin Guanghong Gong Liang Han | 2010 | Journal of Systems Engineering and Electronics2010，21，5： | 1 |
| 6 | Multi-agent based modeling and simulation of microscopic traffic in virtual reality system显示文摘 | Yue Yu Abdelkader El Kamel Guanghong Gong Fengxia Li | 2014 | Simulation Modelling Practice and Theory2014，，： | 1 |
| 7 | Digital product data exchange in semantic service-oriented architecture显示文摘 | SONG Xiao ZHANG Lin GONG Guanghong | 2009 | COMPEL:The Internationl Journal for Computation and Mathematics in Electrical and Electronic Engineering2009，28，6： | 1 |
| 8 | Hierarchical fuzzy ART for Q-learning and its application in air combat simulation显示文摘Value function approximation plays an important role in reinforcement learning(RL)with continuous state space,which is widely used to build decision models in practice.Many traditional approaches require experienced designers to manually specify the formulization of the approximating function,leading to the rigid,non-adaptive representation of the value function.To address this problem,a novel Q-value function approximation method named‘Hierarchical fuzzy Adaptive Resonance Theory’(HiART)is proposed in this paper.HiART is based on the Fuzzy ART method and is an adaptive classification network that learns to segment the state space by classifying the training input automatically.HiART begins with a highly generalized structure where the number of the category nodes is limited,which is beneficial to speed up the learning process at the early stage.Then,the network is refined gradually by creating the attached subnetworks,and a layered network structure is formed during this process.Based on this adaptive structure,HiART alleviates the dependence on expert experience to design the network parameter.The effectiveness and adaptivity of HiART are demonstrated in the Mountain Car benchmark problem with both fast learning speed and low computation time.Finally,a simulation application example of the one versus one air combat decision problem illustrates the applicability of HiART. | Yanan Zhou Yaofei Ma Xiao Song Guanghong Gong | 2017 | International Journal of Modeling, Simulation, and Scientific Computing2017，8，4： | 1 |
| 9 | Controlapproach to rough set reduction 显示文摘 | Yin Yunfei Gong Guanghong Han Liang | 2009 | Computers &Mathematics with Applications2009，57，1： | 1 |
| 10 | Adaptive Boundary and Semantic Composite Segmentation Method for Individual Objects in Aerial Images显示文摘There are two types of methods for image segmentation.One is traditional image processing methods,which are sensitive to details and boundaries,yet fail to recognize semantic information.The other is deep learning methods,which can locate and identify different objects,but boundary identifications are not accurate enough.Both of them cannot generate entire segmentation information.In order to obtain accurate edge detection and semantic information,an Adaptive Boundary and Semantic Composite Segmentation method(ABSCS)is proposed.This method can precisely semantic segment individual objects in large-size aerial images with limited GPU performances.It includes adaptively dividing and modifying the aerial images with the proposed principles and methods,using the deep learning method to semantic segment and preprocess the small divided pieces,using three traditional methods to segment and preprocess original-size aerial images,adaptively selecting traditional results tomodify the boundaries of individual objects in deep learning results,and combining the results of different objects.Individual object semantic segmentation experiments are conducted by using the AeroScapes dataset,and their results are analyzed qualitatively and quantitatively.The experimental results demonstrate that the proposed method can achieve more promising object boundaries than the original deep learning method.This work also demonstrates the advantages of the proposed method in applications of point cloud semantic segmentation and image inpainting. | Ying Li Guanghong Gong Dan Wang Ni Li | 2023 | Computer Modeling in Engineering & Sciences2023，，9： | 0 |
| 11 | Multimodal fusion of EEG and fMRI for epilepsy detection显示文摘Technology of brain–computer interface(BCI)provides a new way of communication and control without language or physical action.Brain signal tracking and positioning is the basis of BCI research,while brain modeling affects the treatment analysis of(EEG)and functional magnetic resonance imaging(fMRI)directly.This paper proposes human ellipsoid brain modeling method.Then,we use non-parametric spectral estimation method of time–frequency analysis to deal with simulation and real EEG of epilepsy patients,which utilizes both the high spatial and the high time resolution to improve the doctor’s diagnostic efficiency. | Xiashuang Wang Guanghong Gong Ni Li | 2018 | International Journal of Modeling, Simulation, and Scientific Computing2018，9，2： | 0 |
| 12 | Study of rapid face modeling technology based on Kinect显示文摘This paper improves the algorithm of point cloud filtering and registration in 3D modeling,aiming for smaller sampling error and shorter processing time of point cloud data.Based on collaborative sampling among several Kinect devices,we analyze the deficiency of current filtering algorithm,and use a novel method of point cloud filtering.Meanwhile,we use Fast Point Feature Histogram(FPFH)algorithm for feature extraction and point cloud registration.Compared with the aligning process using Point Feature Histograms(PFH),it only takes 9min when the number of points is about 500,000,shortening the aligning time by 47.1%.To measure the accuracy of the registration,we propose an algorithm which calculates the average distance of the corresponding coincident parts of two point clouds,and we improve the accuracy to an average distance of 0.7mm.In the surface reconstruction section,we adopt Ball Pivoting algorithm for surface reconstruction,obtaining image with higher accuracy in a shorter time. | Shan Liu Guanghong Gong Luhao Xiao Mengyuan Sun Zhengliang Zhu | 2018 | International Journal of Modeling, Simulation, and Scientific Computing2018，9，1： | 0 |
| 13 | Learning robust features by extended generative stochastic networks显示文摘Deep neural networks have achieved state-of-the-art performance on many object recognition tasks,but they are vulnerable to small adversarial perturbations.In this paper,several extensions of generative stochastic networks(GSNs)are proposed to improve the robustness of neural networks to random noise and adversarial perturbations.Experimental results show that compared to normal GSN method,the extensions using adversarial examples,lateral connections and feedforward networks can improve the performance of GSNs by making the models more resistant to overfitting and noise. | Da Teng Xiao Song Guanghong Gong Junhua Zhou | 2018 | International Journal of Modeling, Simulation, and Scientific Computing2018，9，1： | 0 |
| 14 | Use of multimodal physiological signals to explore pilots’cognitive behaviour during flight strike task performance显示文摘This study explored the use of multi-physiological signals and simultaneously recorded high-density electroencephalography(EEG),electrocardiogram(ECG),and eye movements to better understand pilots’cognitive behaviour during flight simulator manoeuvres.Multimodal physiological signals were collected from 12 experienced pilots with international aviation qualifications under the wide-angle and impressive vision simulation.The data collection spanned two flight strike missions,each with three mission intensities,resulting in a data set of EEG,ECG,and eye movement signals from six subtasks.The multimodal data were analysed using signal processing methods.The results indicated that,when the flight missions were performed,the pilots’physiological characteristics exhibited rhythmic changes in the power spectrum ofθwaves in the EEG,r-MSSD in the ECG,and average gaze duration.Furthermore,the pilots’physiological signals were more sensitive during the target mission than during the empty target mission.The results also showed correlations between different physiological characteristics.We showed that specific multimodal features are useful for advancing neuroscience research into pilots’cognitive behaviour and processes related to brain activity,psychological rhythms,and eye movement. | Xiashuang Wang Guanghong Gong Ni Li Li Ding | 2020 | Medicine in Novel Technology and Devices2020，，1： | 0 |
| 15 | A model of organizational performance based on the big-five factor theory and ABMS method显示文摘How to predict and change organizational performance has been a focus problem drawing economists and managers’attention for a long time.The Big-five Factor theory is very popular among psychologists and ABMS(Agent-Based Modeling and Simulation)method is widely used in Systems Science.By integrating Big-five Factor and ABMS,we proposed a model to predict organizational performance.Fuzzy rules were built up to describe one-on-one cooperation in the five dimensions of personalities and an unsymmetrical network model was established to depict cooperation relationships between team members.What is more,a series of cases was studied and the result was proved to be rational. | Mo Hao Gong Guanghong Li Ni Kong Haipeng | 2016 | International Journal of Modeling, Simulation, and Scientific Computing2016，7，2： | 0 |
| 16 | Boltzmann machines with clusters of stochastic binary units显示文摘The original restricted Boltzmann machines(RBMs)are extended by replacing the binary visible and hidden variables with clusters of binary units,and a new learning algorithm for training deep Boltzmann machine of this new variant is proposed.The sum of binary units of each cluster is approximated by a Gaussian distribution.Experiments demonstrate that the proposed Boltzmann machines can achieve good performance in the MNIST handwritten digital recognition task. | Da Teng Zhang Li Guanghong Gong Liang Han | 2016 | International Journal of Modeling, Simulation, and Scientific Computing2016，7，2： | 0 |
| 17 | Decoding pilot behavior consciousness of EEG, ECG, eye movements via an SVM machine learning model显示文摘To decode the pilot’s behavioral awareness,an experiment is designed to use an aircraft simulator obtaining the pilot’s physiological behavior data.Existing pilot behavior studies such as behavior modeling methods based on domain experts and behavior modeling methods based on knowledge discovery do not proceed from the characteristics of the pilots themselves.The experiment starts directly from the multimodal physiological characteristics to explore pilots’behavior.Electroencephalography,electrocardiogram,and eye movement were recorded simultaneously.Extracted multimodal features of ground missions,air missions,and cruise mission were trained to generate support vector machine behavior model based on supervised learning.The results showed that different behaviors affects different multiple rhythm features,which are power spectra of theθwaves of EEG,standard deviation of normal to normal,root mean square of standard deviation and average gaze duration.The different physiological characteristics of the pilots could also be distinguished using an SVM model.Therefore,the multimodal physiological data can contribute to future research on the behavior activities of pilots.The result can be used to design and improve pilot training programs and automation interfaces. | Xiashuang Wang Guanghong Gong Ni Li Li Ding Yaofei Ma | 2020 | International Journal of Modeling, Simulation, and Scientific Computing2020，11，4： | 0 |
| 18 | A comprehensive performance evaluation framework of complex products based on a fuzzy AHP and DS theory显示文摘With the development of computer technique,performance evaluation of complex products is playing an increasingly critical role in ensuring product quality and improving development process.An extensible comprehensive performance evaluation framework with the integration of effective group decision-making algorithms could be a supporting tool to achieve an efficient evaluation process and reduce comprehensive evaluation dif-ficulty.This paper aims to provide a evaluation framework with friendly interactive operation and extensive expansibility,which adopts a multi-expert evaluation approach based on fuzzy,analytical hierarchy process(FAHP)and Dempstere–Shafer(DS)theory(FADS)in order to consider experts’relative importance degree.In addition,an extensible evaluation process and related auxiliary functions are implemented in the framework,including the establishment of an assessment index system,integration and calls of multiple types of testing data preprocessing methods and index assessment methods suitable for small sample data,graphical result display and data analysis,etc.Finally,performance evaluation cases of two models of airborne radar anti-jamming are presented to verify the feasibility and expansibility of our assessment framework.The group decision-making method shows its effectiveness compared with the experimental evaluation results by the FAHP researched method. | Yuhong Li Guanghong Gong Ni Li | 2016 | International Journal of Modeling, Simulation, and Scientific Computing2016，7，3： | 0 |
| 19 | 3D reconstruction of human head based on consumer RGB-D sensors显示文摘Three-dimensional(3D)reconstruction of a human head with high precision has promising applications in scientific research,product design and other fields.However,it still faces resistance from two factors.One is inaccurate registration caused by symmetrical distribution of head feature points,and the other is economic burden due to highaccuracy sensors.Research on 3D reconstruction with portable consumer RGB-D sensors such as the Microsoft Kinect has been highlighted in recent years.Based on our multi-Kinect system,a precise and low-cost three-dimensional modeling method and its system implementation are introduced in this paper.A registration method for multisource point clouds is provided,which can reduce the fusion differences and reconstruct the head model accurately.In addition,a template-based texture generation algorithm is presented to generate a fine texture.The comparison and analysis of our experiments show that our method can reconstruct a head model in an acceptable time with less memory and better effect. | Zihan Liu Guanghong Gong Ni Li Zihao Yu | 2020 | International Journal of Modeling, Simulation, and Scientific Computing2020，11，6： | 0 |
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