|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks显示文摘In this paper, we propose a novel approach to achieve spectrum prediction, parameter fitting, inverse design, and performance optimization for the plasmonic waveguide-coupled with cavities structure(PWCCS) based on artificial neural networks(ANNs). The Fano resonance and plasmon-induced transparency effect originated from the PWCCS have been selected as illustrations to verify the effectiveness of ANNs. We use the genetic algorithm to design the network architecture and select the hyperparameters for ANNs. Once ANNs are trained by using a small sampling of the data generated by the Monte Carlo method, the transmission spectra predicted by the ANNs are quite approximate to the simulated results. The physical mechanisms behind the phenomena are discussed theoretically, and the uncertain parameters in the theoretical models are fitted by utilizing the trained ANNs.More importantly, our results demonstrate that this model-driven method not only realizes the inverse design of the PWCCS with high precision but also optimizes some critical performance metrics for the transmission spectrum. Compared with previous works, we construct a novel model-driven analysis method for the PWCCS that is expected to have significant applications in the device design, performance optimization, variability analysis,defect detection, theoretical modeling, optical interconnects, and so on. | TIAN ZHANG JIA WANG QI LIU JINZAN ZHOU JIAN DAI XU HAN YUE ZHOU KUN XU | 2019 | Photonics Research2019，7，3： | 13 |
| 2 | Study on correlations of modal frequencies and environmental factors for a suspension bridge based on improved neural networks显示文摘By using of long-term monitoring data of Runyang Suspension Bridge,the improved back-propagation neural networks (BPNNs) are formulated for modeling the correlations between modal frequencies and environmental conditions including wind,temperature and vehicle load.Then,with the correlation models the environmental effects on modal frequencies are quantified and the abnormal changes of measured frequencies are detected by means of the hypothesis tests.Analysis results reveal that BPNN-based correlation models improved by both early stopping and Bayesian regularization techniques exhibit excellent generalization capability.And the developed correlation models can effectively reduce the environmental variability in modal frequencies.The t-test method provides a good capability to detect the damage-induced 0.16% and 0.12% abnormal changes of the 5th and 6th modal frequencies,respectively.Hence,the proposed method is suitable for real-time monitoring of suspension bridge conditions. | DING YouLiang,DENG Yang & LI AiQun Key Laboratory of Concrete and Prestressed Concrete Structure of Ministry of Education,Southeast University,Nanjing 210096,China | 2010 | Science China(Technological Sciences)2010，53，9： | 9 |
| 3 | Artificial neural networks enabled by nanophotonics显示文摘The growing demands of brain science and artificial intelligence create an urgent need for the development of artificial neural networks(ANNs)that can mimic the structural,functional and biological features of human neural networks.Nanophotonics,which is the study of the behaviour of light and the light–matter interaction at the nanometre scale,has unveiled new phenomena and led to new applications beyond the diffraction limit of light.These emerging nanophotonic devices have enabled scientists to develop paradigm shifts of research into ANNs.In the present review,we summarise the recent progress in nanophotonics for emulating the structural,functional and biological features of ANNs,directly or indirectly. | Qiming Zhang Haoyi Yu Martina Barbiero Baokai Wang Min Gu | 2019 | Light(Science & Applications)2019，8，1： | 7 |
| 4 | Fuzzy Entropy Based Combined Learning Algorithm for Neural Networks显示文摘FuzzyEntropyBasedCombinedLearningAlgorithmforNeuralNetworks￥MinYao(Dept.ofComputerScience,HangzhouUniversity,Hangzhou310028,P... | Min Yao (Dept. of Computer Science, Hangzhou University, Hangzhou 310028,P. R. China ) | 1996 | Journal of Systems Engineering and Electronics1996，7，1： | 3 |
| 5 | Application of Neural Network in Fault Location of Optical Transport Network显示文摘Due to the increasing variety of information and services carried by optical networks, the survivability of network becomes an important problem in current research. The fault location of OTN is of great significance for studying the survivability of optical networks. Firstly, a three-channel network model is established and analyzing common alarm data, the fault monitoring points and common fault points are carried out. The artificial neural network is introduced into the fault location field of OTN and it is used to judge whether the possible fault point exists or not. But one of the obvious limitations of general neural networks is that they receive a fixedsize vector as input and produce a fixed-size vector as the output. Not only that, these models is even fixed for mapping operations (for example, the number of layers in the model). The difference between the recurrent neural network and general neural networks is that it can operate on the sequence. In spite of the fact that the gradient disappears and the gradient explodes still exist in the neural network, the method of gradient shearing or weight regularization is adopted to solve this problem, and choose the LSTM (long-short term memory networks) to locate the fault. The output uses the concept of membership degree of fuzzy theory to express the possible fault point with the probability from 0 to 1. Priority is given to the treatment of fault points with high probability. The concept of F-Measure is also introduced, and the positioning effect is measured by using location time, MSE and F-Measure. The experiment shows that both LSTM and BP neural network can locate the fault of optical transport network well, but the overall effect of LSTM is better. The localization time of LSTM is shorter than that of BP neural network, and the F1-score of LSTM can reach 0.961566888396156 after 45 iterations, which meets the accuracy and real-time requirements of fault location. Therefore, it has good application prospect and practical value to introduce neural network into the fault location field of optical transport network. | Tianyang Liu Haoyuan Mei Qiang Sun Huachun Zhou | 2019 | China Communications2019，16，10： | 3 |
| 6 | Design optimization of multilayer perceptron neural network by ant colony optimization applied to engine emissions data显示文摘A multilayer perceptron(MLP) artificial neural network(ANN) model has been optimized by the multi-objective ant colony optimization(MOACO) algorithm, which uses three objective functions. A sensitivity analysis to choose MOACO parameter values is carried out by calculating hypervolume metric, and the proposed approach adopts the Vlsekriterijumska Optimizacija I Kompromisno Resenje(VIKOR) decision method to choose final compromised solution on the Pareto front obtained from MOACO. As a result, we used the MLP-MOACO developed model to estimate the value of engine emissions of NOxin a four stroke, spark ignition(SI) gasoline engine and observed acceptable correlation coefficient(R^2) of 0.99978. | MARTINEZ-MORALES Jose QUEJ-COSGAYA Hector LAGUNAS-JIMENEZ Jose PALACIOS-HERNANDEZ Elvia MORALES-SALDANA Jorge | 2019 | Science China(Technological Sciences)2019，62，6： | 3 |
| 7 | Architectures and Algorithms of Generalized Congruence Neural Networks显示文摘茫铮睿纾颍酰澹睿悖? Neural NetworksTX@靳蕃IntroductionItiswelknownthattheerorbackpropagation(BP)algorithmhasbenawidespreadlearningalgorithm?.. | 靳蕃 | 1998 | Journal of Modern Transportation1998，15，2： | 2 |
| 8 | Groundwater Level Predictions Using Artificial Neural Networks显示文摘The prediction of groundwater level is important for the use and management of groundwater resources. In this paper, the artificial neural networks (ANN) were used to predict groundwater level in the Dawu Aquifer of Zibo in Eastern China. The first step was an auto-correlation analysis of the groundwater level which showed that the monthly groundwater level was time dependent. An auto-regression type ANN (ARANN) model and a regression-auto-regression type ANN (RARANN) model using back-propagation algorithm were then used to predict the groundwater level. Monthly data from June 1988 to May 1998 was used for the network training and testing. The results show that the RARANN model is more reliable than the ARANN model, especially in the testing period, which indicates that the RARANN model can describe the relationship between the groundwater fluctuation and main factors that currently influence the groundwater level. The results suggest that the model is suitable for predicting groundwater level fluctuations in this area for similar conditions in the future. | 毛晓敏 尚松浩 刘翔 | 2002 | Tsinghua Science and Technology2002，7，6： | 2 |
| 9 | Neural Network Based Model for Predicting Housing Market Performance显示文摘The United States real estate market is currently facing its worst hit in two decades due to the slowdown of housing sales. The most affected by this decline are real estate investors and home developers who are currently struggling to break-even financially on their investments. For these investors, it is of utmost importance to evaluate the current status of the market and predict its performance over the shortterm in order to make appropriate financial decisions. This paper presents the development of artificial neural network based models to support real estate investors and home developers in this critical task. The paper describes the decision variables, design methodology, and the implementation of these models. The models utilize historical market performance data sets to train the artificial neural networks in order to predict unforeseen future performances. An application example is analyzed to demonstrate the model capabili-ties in analyzing and predicting the market performance. The model testing and validation showed that the error in prediction is in the range between -2% and +2%. | Ahmed Khalafallah | 2008 | Tsinghua Science and Technology2008，13，S1： | 2 |
| 10 | Interfacing photonics with artificial intelligence:an innovative design strategy for photonic structures and devices based on artificial neural networks显示文摘Over the past decades,photonics has transformed many areas in both fundamental research and practical applications.In particular,we can manipulate light in a desired and prescribed manner by rationally designed subwavelength structures.However,constructing complex photonic structures and devices is still a time-consuming process,even for experienced researchers.As a subset of artificial intelligence,artificial neural networks serve as one potential solution to bypass the complicated design process,enabling us to directly predict the optical responses of photonic structures or perform the inverse design with high efficiency and accuracy.In this review,we will introduce several commonly used neural networks and highlight their applications in the design process of various optical structures and devices,particularly those in recent experimental works.We will also comment on the future directions to inspire researchers from different disciplines to collectively advance this emerging research field. | Yihao Xu Xianzhe Zhang Yun Fu Yongmin Liu | 2021 | Photonics Research2021，9，4： | 1 |
| 11 | Artificial neural networks in steel-mushy aluminum pressing bonding显示文摘Artificial neural networks were successfully used to research the modeling of aluminum solid fraction, preheat temperature of steel plate, preheat temperature of dies, free diffusing time before pressing and the interfacial shear strength in steel mushy aluminum pressing bonding. Further more, the optimum bonding parameters for the largest interfacial shear strength were also optimized with a genetic algorithm. | 张鹏 刘汉武 任学平 巴立民 | 2000 | 中国有色金属学会会刊:英文版2000，10，2： | 1 |
| 12 | Artificial senses for characterization of food quality显示文摘Food quality is of primary concern in the food industry and to the consumer. Systems that mimic human senses have been developed and applied to the characterization of food quality. The five primary senses are: vision, hearing, smell, taste and touch. In the characterization of food quality, people assess the samples sensorially and differentiate “good”from “bad”on a continuum. However, the human sensory system is subjective, with mental and physical inconsistencies, and needs time to work. Artificial senses such as machine vision, the electronic ear, electronic nose, electronic tongue, artificial mouth and even artificial the head have been developed that mimic the human senses. These artificial senses are coordinated individually or collectively by a pat- tern recognition technique, typically artificial neural networks, which have been developed based on studies of the mechanism of the human brain. Such a structure has been used to formulate methods for rapid characterization of food quality. This research presents and discusses individual artificial sensing systems. With the concept of multi-sensor data fusion these sensor systems can work collectively in some way. Two such fused systems, artificial mouth and artificial head, are described and discussed. It indicates that each of the individual systems has their own artificially sensing ability to differentiate food samples. It further indicates that with a more complete mimic of human intelligence the fused systems are more powerful than the individual sys- tems in differentiation of food samples. | R.E. Lacey | 2004 | Journal of Bionic Engineering2004，1，3： | 1 |
| 13 | Colorant Formulation Using 3 - layer Artificial Neural Networks显示文摘The colorant formulation using artificial neural networks (ANN) was investigated in this study. A simple 3 -layer, input - hidden - output system was constructed for the recipe formulation of one - , two - , and three -dye mixtures. Comprehensive tests were carried out to explore the properties of a 3 - layer simple ANN systematically . These properties include number of neurons in the hidden layer, learning rate of the network, momentum factor of the network, as well as the number of epochs for the learning process. The tests show accurate results for one - and two - dye mixtures while less accurate but comparable results to conventional colorant formulation systems for three - dye mixtures. It is also found that the optimum values of the neural network parameters are important towards the accuracy of the colorant formulation. | 忻浩忠 | 1999 | Journal of China Textile University(English Edition)1999，16，1： | 1 |
| 14 | Optimization and modeling of coagulation-flocculation to remove algae and organic matter from surface water by response surface methodology显示文摘Seasonal algal blooms of Lake Yangcheng highlight the necessity to develop an effective and optimal water treatment process to enhance the removal of algae and dissolved organic matter (DOM). In the present study, the coagulation performance for the removal of algae, turbidity, dissolved organic carbon (DOC) and ultraviolet absorbance at 254 nm (UV254) was investigated systematically by central composite design (CCD) using response surface methodology (RSM). The regression models were developed to illustrate the relationships between coagulation performance and experimental variables. Analysis of variance (ANOVA) was performed to test the significance of the response surface models. It can be concluded that the major mechanisms of coagulation to remove algae and DOM were charge neutralization and sweep flocculation at a pH range of 4.66–6.34. The optimal coagulation conditions with coagulant dosage of 7.57 mg Al/L, pH of 5.42 and initial algal cell density of 3.83 × 106 cell/mL led to removal of 96.76%, 97.64%, 40.23% and 30.12% in term of cell density, turbidity, DOC and UV254 absorbance, respectively, which were in good agreement with the validation experimental results. A comparison between the modeling results derived through both ANOVA and artificial neural networks (ANN) based on experimental data showed a high correlation coefficient, which indicated that the models were significant and fitted well with experimental results. The results proposed a valuable reference for the treatment of algae-laden surface water in practical application by the optimal coagulation-flocculation process. | Ziming Zhao Wenjun Sun Madhumita BRay Ajay K Ray Tianyin Huang Jiabin Chen | 2019 | Frontiers of Environmental Science & Engineering2019，13，5： | 1 |
| 15 | Day-ahead industrial load forecasting for electric RTG cranes显示文摘Given the increase in international trading and the significant energy and environmental challenges in ports around the world, there is a need for a greater understanding of the energy demand behaviour at ports.The move towards electrified rubber-tyred gantry(RTG)cranes is expected to reduce gas emissions and increase energy savings compared to diesel RTG cranes but it will increase electrical energy demand. Electrical load forecasting is a key tool for understanding the energy demand which is usually applied to data with strong regularities and seasonal patterns. However, the highly volatile and stochastic behaviour of the RTG crane demand creates a substantial prediction challenge. This paper is one of the first extensive investigations into short term load forecastsfor electrified RTG crane demand. Options for model inputs are investigated depending on extensive data and correlation analysis. The effect of estimation accuracy of exogenous variables on the forecast accuracy is investigated as well. The models are tested on two different RTG crane data sets that were collected from the Port of Felixstowe in the UK. The results reveal the effectiveness of the forecast models when the estimation of the number of crane moves and container gross weight are accurate. | Feras ALASALI Stephen HABEN Victor BECERRA William HOLDERBAUM | 2018 | Journal of Modern Power Systems and Clean Energy2018，6，2： | 1 |
| 16 | Parameter selecting and quality predicting of spot welding based on artificial neural networks显示文摘This paper proposes a procedure for using artificial neural networks (ANN) in spot welding, and estabishesspot wilding parameter selecting ANN systems and sopt welding joint quality predicting ANN systems.It has been provedthat the ANN systems have high prediction precision ,providing a new way of parameter selecting and quality predicting in spot welding. | 赵熹华 王宸煜 张若冰 | 1998 | China Welding1998，7，2： | 1 |
| 17 | Modeling and Optimizing of Deformed Steel Bars Hot Rolling显示文摘Deformedsteelbarsarewidelyusedinconstructionengineering.Thetensilestfengthisthemainparameterthatdetermineswhetherdeformedsteelbarsaregoodornot.Intheprocessofhotrolling,therearemanyfactorsinfluencingthetensilestrengthofdeformedsteelbars,suchasMnandStc... | Peng Zhang Yunhui Du Xueping Ren(Material Science and Engineering School, University’ of Science and Technology’ Beijing, Beijing 100083) | 1999 | International Journal of Minerals,Metallurgy and Materials1999，13，4： | 1 |
| 18 | Prediction of the effectiveness of rolling dynamic compaction using artificial neural networks and cone penetration test data显示文摘Rolling Dynamic Compaction(RDC),which is a ground improvement technique involving non-circular modules drawn behind a tractor,has provided the construction industry with an improved ground compaction capability,especially with respect to a greater influence depth and a higher speed of compaction,resulting in increased productivity. However,to date,there is no reliable method to predict the effectiveness of RDC in a range of ground conditions. This paper presents a new and unique predictive tool developed by means of artificial neural networks(ANNs) that permits a priori prediction of density improvement resulting from a range of ground improvement projects that employed 4-sided RDC modules;commercially known as'impact rollers'. The strong coefficient of correlation(i.e. R>0.86) and the parametric behavior achieved in this study indicate that the model is successful in providing reliable predictions of the effectiveness of RDC in various ground conditions. | R.A.T.M.Ranasinghe M.B.Jaksa F.Pooya Nejad Y.L.Kuo | 2019 | 岩石力学与工程学报2019，38，1： | 1 |
| 19 | PARALLEL SELF-ORGANIZING MAP显示文摘1INTRODUCTION“Oncesaw,neverforgoten”isasentencewhichusedtodescribeahumansenseandlearningsequence.Forexample,aboyglancedatalo... | Li Weigang Department of Computer Science CIC, University of Brasilia UnB, C. P. 4466, CEP: 70919-970, Brasilia DF, Brazil, E mail: Weigang@cic.unb.br | 1999 | 中国有色金属学会会刊:英文版1999，9，1： | 1 |
| 20 | CAM-BRAIN'ATR's ARTIFICIAL BRAIN PROJECT A Progress Report显示文摘"CAM-BRAIN"ATR'sARTIFICIALBRAINPROJECTAProgressReport￥HugodeGaris(BrainBuilderGroup,EvoluhonarySystemsDepartment,ATRHumanInfo... | Hugo de Garis(Brain Builder Group, Evoluhonary Systems Department,ATR Human Information Processing Research Laboratories,2-2 Maridai, Seika-cho, Soraku-gun, Kansai Sclience City, Kyoto, 619-02, Japan.tel. + 81 7749 5 1079, fax. + 81 7749 5 1008 degaris@hi | 1996 | Wuhan University Journal of Natural Sciences1996，1，Z1： | 1 |
您所在的机构暂未开通此部分的功能，请联系管理员