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Deep 3D reconstruction: methods, data, and challenges Review Article
Caixia Liu, Dehui Kong, Shaofan Wang, Zhiyong Wang, Jinghua Li, Baocai Yin,lcxxib@emails.bjut.edu.cn,wangshaofan@bjut.edu.cn
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5, Pages 615-766 doi: 10.1631/FITEE.2000068
Keywords： 深度学习模型；三维重建；循环神经网络；深度自编码器；生成对抗网络；卷积神经网络
Research on Tracing Evaluation System in Virtual Enterprise Based on Neural Network
Wang Shuo,Tang Xiaowo
Strategic Study of CAE 2003, Volume 5, Issue 4, Pages 65-69
The paper designed tracing evaluation index system in virtual enterprise and established neural network trace evaluation model. As a result, it was simple and nicety than traditional method, so it had wider application foreground.
Keywords： virtual enterprise neural network trace evaluation system
Pressure in Gas-assisted Injection Molding
Ou Changjin
Strategic Study of CAE 2007, Volume 9, Issue 5, Pages 27-32
In this study, an effective control method and strategy based on fuzzy neural network has been developed for gas injection pressure accurate control in gas-assisted injection. A fuzzy neural network controller with five layers and its control algorithm are established. The learning ability of neural network is used to optimize the rules of the fuzzy logic so as to improve the adaptability of system. The simulation of the system capability and three segmental injected pressure control are carried out under the environment of MATLAB and the results show that this theoretic model is feasible, and the control system has good characteristics and control action.
Keywords： gas-assisted injection molding fuzzy neural network gas-injection pressure control
Research on fuzzy neural network control method for high-frequency vacuum drying of wood
Jiang Bin,Sun Liping,Cao Jun and Zhou Zheng
Strategic Study of CAE 2014, Volume 16, Issue 4, Pages 17-20
High- frequency vacuum combined wood drying is a kind of fast drying speed， low energy consumption，little environmental pollution of new drying technology. On the basis of theoretical analysis with high frequency in wood vacuum drying process，the fuzzy controller and fuzzy neural network controller of wood drying are designed in view of the neural network method to establish model of wood drying. The simulation experiment results show that fuzzy neural network control is better，such as the temperature rising fast，high control precision，good stability. The method to realize the automatic control of timber drying process has important research significance.
Keywords： high-frequency vacuum wood drying fuzzy neural network
Simulation Algorithm of Flightdeck Airflow Based on Neural Network
Xun Wensheng,Lin Ming
Strategic Study of CAE 2003, Volume 5, Issue 5, Pages 76-79
The airflow on the flightdeck is an important factor which influences helicopter flight safety. The airflow velocity distribution characteristics directly influences simulation accuracy of helicopter flight dynamics. Based on the Navier-Stokes equations, the method to determine the airflow velocity in real-time is discussed using BP neural network. This method can be used for flightdeck airflow real-time simulation, and it can improve helicopter flight simulation accuracy.
Keywords： flow finite element neural network
Yang Maosheng,Chen Yueliang,Yu Dazhao
Strategic Study of CAE 2008, Volume 10, Issue 5, Pages 46-50
A prediction model for residual strength of stiffened panels with multiple site damage based on artificial neural network (ANN) is developed, and the results obtained from the trained BP model are compared to the analytical and experimental data available in the literature. The results obtained indicate that the neural network model predictions are in the best agreement with the experimental data than any other methods, and the modified linkup models predict better than the linkup model proposed by Swift. In the end several simulations are carried out to predict the trends with varying input parameters. The results show that the residual strength decreases linearly as the half-crack length of lead crack increases and increases linearly as the ligament length increases for both kinds of stiffened panels, but the one-bay stiffened panels are more sensitive to the change than the two-bay stiffened panels.
Keywords： neural network multiple site damage stiffened panel residual strength
An Improving Method of BP Neural Network and Its Application
Li Honggang,Lü Hui,Li Gang
Strategic Study of CAE 2005, Volume 7, Issue 5, Pages 63-65
Seeing on that in BPNN the small learning gene will make the long training time, but the large learning gene will make the BPNN surging, this paper brings forward a way to modify the learning gene, that is, adding a proportion gene before the learning gene, The proportion gene will change when the weight of the BPNN needs to be modified. This can shorten the training time and make convergence better as well. The simulating results show that the new algorithm is much better than the old one during BPNN scouting the missile command.
Keywords： BPNN improved algorithm simulation
Study of Forecast of Building Cost Based on Neural Network
Nie Guihua,Liu Pingfeng,He Liu
Strategic Study of CAE 2005, Volume 7, Issue 10, Pages 56-59
In the constantly changing marketing economy, it has become an urgent task for construction industry to find a rapid, simple and practical way to organize construction project budget. To solve this problem, this paper adopts the model of the back-propagation neural network, takes the features of construction as input variables, trains the network using actual data as samples and optimizes the network structure by contribution analysis. It shows the validity of the model in the forecast of construction project budget.
Keywords： BP neural network building budget forecast
Diffractive Deep Neural Networks at Visible Wavelengths Article
Hang Chen, Jianan Feng, Minwei Jiang, Yiqun Wang, Jie Lin, Jiubin Tan, Peng Jin
Engineering 2021, Volume 7, Issue 10, Pages 1485-1493 doi: 10.1016/j.eng.2020.07.032
Optical deep learning based on diffractive optical elements offers unique advantages for parallel processing, computational speed, and power efficiency. One landmark method is the diffractive deep neural network (D2NN) based on three-dimensional printing technology operated in the terahertz spectral range. Since the terahertz bandwidth involves limited interparticle coupling and material losses, this paper
extends D2NN to visible wavelengths. A general theory including a revised formula is proposed to solve any contradictions between wavelength, neuron size, and fabrication limitations. A novel visible light D2NN classifier is used to recognize unchanged targets (handwritten digits ranging from 0 to 9) and targets that have been changed (i.e., targets that have been covered or altered) at a visible wavelength of 632.8 nm. The obtained experimental classification accuracy (84%) and numerical classification accuracy (91.57%) quantify the match between the theoretical design and fabricated system performance. The presented framework can be used to apply a D2NN to various practical applications and design other new applications.
Keywords： Optical computation Optical neural networks Deep learning Optical machine learning Diffractive deep neural networks
Application of Artificial Neural Network to Engineering Project Management
Wang Yingluo,Yang Yaohong
Strategic Study of CAE 2004, Volume 6, Issue 7, Pages 26-33
Applications of ANN to engineering project management were summarized, including prediction and evaluation of risk, cost estimation, performance prediction, organization effectivity, engineering accident diagnoses, claim and litigation analysis, enter bidding decision, schedule/cost optimation and resource leveling. Problems existing in application were summarized and analyzed, some suggestions on how to develop application of ANN to engineering project management in China were submitted.
Keywords： engineering project management ANN prediction optimization DS
Hydrogeological Parameter Identification Based on the Radial Basis Function Neural Networks
Zhang Junyan,Wei Lianwei,Han Weixiu,Shao Jingli,Cui Yali,Zhang Jianli
Strategic Study of CAE 2004, Volume 6, Issue 8, Pages 74-78
The problem of hydrogeological parameter identification is actually a complex one. With the limit of identifying the parameter by traditional methods, the radial basis function neural networks (RBF) is applied into this area. Not only the parameter identification is automatically realized, but also th.e problem of local optimization is solved. The feasibility and effectiveness have been proved by the examples.
Keywords： groundwater hydrogeological parameter radial basis function (RBF) neural networks BP neural networks
A Forecasting Method for Tunnel Surrounding Rock Deformation Using RBF Neural Networks
Zhang Junyan,Feng Shouzhong,Liu Donghai
Strategic Study of CAE 2005, Volume 7, Issue 10, Pages 87-90
Owing to the difficulty of traditional multi-variable regression methods to represent the surrounding rock deformation curve with inflexion points, a method for forecasting tunnel surrounding rock deformation using radial basis function neural networks is presented. This method not only can be utilized to approximate the complex deformation curves, but also has higher convergence speed and better globally-searching ability than those using BP neural networks. An example is given to show the effectiveness and practicability of this method.
Keywords： RBF neural networks tunnel construction surrounding rock deformation forecasting
Fang Zhiqing,Wang Xueqing,Li Baolong
Strategic Study of CAE 2011, Volume 13, Issue 9, Pages 105-108
Combining with the characters of the practicing qualification personnel in construction market, evaluation method based on the self-organizing nerural network is brought out to analyze the credit classification of the practicing qualification personnel. And the impact factors on the credit classification of the practicing qualification personnel, such as the number of neurons, the training steps, the dimension of neurons and the field of winning neurons are studied. Then a self-organizing competitive neural network is built. At last, a case study is conducted by taking practicing qualification personnel as an example. The research result reveals that the method can efficiently evaluate the credit of the practicing qualification personnel; thus,it could provide scientific advice to the construction enterprise to prevent relevant discreditable behaviors of practicing qualification personnel.
Keywords： practicing qualification personnel credit cluster analysis self-organizing neural network
Shi Shiliang,Wu Aiyou
Strategic Study of CAE 2009, Volume 11, Issue 9, Pages 91-96
The coal and gas outburst is a dynamic pheaomenon in the underground exploitation of coal mine,and the strong dynamic effect can result in damage of belongs and death of workers of coal mine. Therefore,it is very important to advance coal industry healthy and continual in forecast the area of coal and gas outburst reasonablely.This paper aimed at the defect that neural network is easy to fall into some extremely local smallness and cause the unreasonable distribution of the weight value of the forecast indexes,ade the area forecast model of the coal and gas outburst was established based on coupling of the neural network and the genetic algorithm according to the natural conditions and the characteristics of the geologic structure. The coupling forecast model was validated with the practical example.The study results has proved the validity of the model, and laid the foundation of the area forecast of the coal and gas outburst based on coupling of the neural network and genetic algorithm.
Keywords： coal and gas outburst area forecast neural network genetic algorithm isoneph of outburst
A Method of Constructing Fuzzy Neural Network Based on Rough Set Theory
Huang Xianming,Yi Jikai
Strategic Study of CAE 2004, Volume 6, Issue 4, Pages 44-50
A new method of constructing fuzzy neural network is presented and Rough set theory is applied to this method. Since Rough set theory has strong numeric analyzing ability and fuzzy neural network has exact function approaching ability, their combination can produce a neural network model with good intelligibility and fast convergence. First, some rules are acquired from given data set by rough set theory. Then, these rules are applied to constructing neural cell numbers and relative parameters in fuzzy neural network. Finally the initial network is trained by BP arithmetic and the whole network design is finished. Also in this paper, an example of nonlinear function approaching is discussed and the feasibility of this method is proved.
Keywords： fuzzy neural network rough set acquire rule function approaching
Title Author Date Type Operation
Deep 3D reconstruction: methods, data, and challenges
Caixia Liu, Dehui Kong, Shaofan Wang, Zhiyong Wang, Jinghua Li, Baocai Yin,lcxxib@emails.bjut.edu.cn,wangshaofan@bjut.edu.cn
Journal Article
Research on Tracing Evaluation System in Virtual Enterprise Based on Neural Network
Wang Shuo,Tang Xiaowo
Journal Article
Research on fuzzy neural network control method for high-frequency vacuum drying of wood
Jiang Bin,Sun Liping,Cao Jun and Zhou Zheng
Journal Article
Simulation Algorithm of Flightdeck Airflow Based on Neural Network
Xun Wensheng,Lin Ming
Journal Article
Prediction model for residual strength of stiffened panels with multiple site damage based on artificial neural network
Yang Maosheng,Chen Yueliang,Yu Dazhao
Journal Article
An Improving Method of BP Neural Network and Its Application
Li Honggang,Lü Hui,Li Gang
Journal Article
Study of Forecast of Building Cost Based on Neural Network
Nie Guihua,Liu Pingfeng,He Liu
Journal Article
Diffractive Deep Neural Networks at Visible Wavelengths
Hang Chen, Jianan Feng, Minwei Jiang, Yiqun Wang, Jie Lin, Jiubin Tan, Peng Jin
Journal Article
Application of Artificial Neural Network to Engineering Project Management
Wang Yingluo,Yang Yaohong
Journal Article
Hydrogeological Parameter Identification Based on the Radial Basis Function Neural Networks
Zhang Junyan,Wei Lianwei,Han Weixiu,Shao Jingli,Cui Yali,Zhang Jianli
Journal Article
A Forecasting Method for Tunnel Surrounding Rock Deformation Using RBF Neural Networks
Zhang Junyan,Feng Shouzhong,Liu Donghai
Journal Article
Research on the credit classification of practicing qualification personnel in construction market based on self-organizing neural network
Fang Zhiqing,Wang Xueqing,Li Baolong
Journal Article
Study on area forecast of coal and gas outburst based on coupling of neural network and genetic algorithm
Shi Shiliang,Wu Aiyou
Journal Article
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