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Application of Fuzzy Pattern Recognition in the Measurement of Slurry Concentration
Li Dejun,Lv Runhua,Wang Runtian
Strategic Study of CAE 2007, Volume 9, Issue 5, Pages 81-84
Slurry is widely used in construction projects, and it is important to control the slurry's physical characteristic properly. The acoustic method is used, which can effectively monitor the physical parameters of slurry, such as concentration. Data processing affects directly the precision in the measurement of slurry concentration by the sound attenuation and velocity. Based on the fuzzy pattern recognition, data are sorted and further classified, with cooperative clustering algorithm.
Keywords： fuzzy pattern recognition nearest neighbor(NN) cooperative clustering algorithm(CCA) slurry concentration
Pattern Recognition With Fuzzy Central Clustering Algorithms
Zen Huanglin,Yuan Hui,Liu Xiaofang
Strategic Study of CAE 2004, Volume 6, Issue 11, Pages 33-37
Based on optimization of constrained nonlinear programming, an approach of clustering center and a fuzzy membership function of pattern classification are derived from an objective function of the constrained nonlinear programming. An unsupervised algorithm with recursive expression and a fuzzy central cluster neural network are suggested in this paper. The fuzzy central cluster neural network proposed here can realize crisp decision or fuzzy decision in pattern classification.
Keywords： fuzzy sets central cluster pattern recognition neural network
Fault Pattern Recognition of Rolling Bearing Based on Radial Basis Function Neural Networks
Lu Shuang,Zhang Zida,Li Meng
Strategic Study of CAE 2004, Volume 6, Issue 2, Pages 56-60
Radial basis function neural network is a type of three — layer feedforward network. It has many good properties, such as powerful ability for function approximation, classification and learning rapidly. In this paper, in the light of the merit of radial basis function neural network and on the basis of the feature analysis of vibration signal of rolling bearing, AR model is presented by using time series method. Radial basis function neural networks is established based on AR model parameters. In the light of the theory of radial basis function neural networks, fault pattern of rolling bearing is recognized correspondingly. Theory and experiment show that the recognition of fault pattern of rolling bearing based on radial basis function neural networks theory is available and its precision is high.
Keywords： rolling bearing vibration signal AR model RBF neural networks pattern recognition
Chen Shouyu
Strategic Study of CAE 2001, Volume 3, Issue 2, Pages 33-38
Based on analysis of the relationship between sustainable development and bearing capacity of regional water resources, this paper presents a fuzzy pattern recognition model and a method for qualitative assessment of sustainable development of regional water resources, which are applied to assess the sustainable development of water resources of Hanzhong Basin. The case study proves that the model and method are reliable and that results are reasonable and practicable. They can also be applied to the assessment of the sustainable development of social economies.
Keywords： water resources sustainable development assessment pattern recognition fuzzy
Learning and Applications of Procedure Neural Networks
He Xingui,Liang Jiuzhen,Xu Shaohua
Strategic Study of CAE 2001, Volume 3, Issue 4, Pages 31-35
This paper deals with learning algorithms for procedure neural networks (PNN) and its applications in aggregation chemical reaction and seepage test in oil geology. Weight bases selection rules and pattern curve standard problems are also discussed. These examples show that PNN have extensive applications.
Keywords： procedure neural networks learning algorithm pattern recognition chemical reaction seepage
Qian Xuesen and Noetic Science
Lu Mingsen
Strategic Study of CAE 2002, Volume 4, Issue 2, Pages 8-15
This article analyzes the background and the necessity of the establishment of noetic science which was initiated by Qian Xuesen. The article also presents a whole string of Qian’s views on the object of study, basic approach and ideological root of noetic science. The article describes in particular that noetic science is one of the eleven department of the contemporary system of science and technology. It contains three hierarchies, as fundamental science, technical science and engineering. The preliminary achievements in each hierarchy in the recent two decades are presented.
Keywords： cognitive science noetic science abstractive thinking figurative thinking creative thinking pattern recognition
A Study on the Essence of Optimal Statistical Uncorrelated Discriminant Vectors
Wu Xiaojun,Yang Jingyu,Wang Shitong,Liu Tongming,Josef Kittler
Strategic Study of CAE 2004, Volume 6, Issue 2, Pages 44-47
A study has been made on the essence of optimal set of uncorrelated discriminant vectors in this paper. A whitening transform has been constructed on the basis of the eigen decomposition of population scatter matrix, which makes the population scatter matrix an identity matrix in the transformed sample space. Thus, the optimal discriminant vectors solved by conventional LDA methods are statistical uncorrelated. The research indicates that the essence of the statistical uncorrelated discriminant transform is the whitening transform plus conventional linear discriminant transform. The distinguished characteristic of the proposed method is that the obtained optimal discriminant vectors are orthogonal and statistical uncorrelated. The proposed method suits for all the problems of algebraic feature extraction. The numerical experiments on facial database of ORL show the effectiveness of the proposed method.
Keywords： pattern recognition feature extraction disciminant analysis generalized optimal set of discriminant vectors face recognition
A partition approach for robust gait recognition based on gait template fusion Research Articles
Kejun Wang, Liangliang Liu, Xinnan Ding, Kaiqiang Yu, Gang Hu,heukejun@126.com,liuliangliang@hrbeu.edu.cn,dingxinnan@hrbeu.edu.cn,yukaiqiang@hrbeu.edu.cn,hugang@hrbeu.edu.cn
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5, Pages 615-766 doi: 10.1631/FITEE.2000377
Dynamic time prediction for electric vehicle charging based on charging pattern recognition Research Article
Chunxi LI, Yingying FU, Xiangke CUI, Quanbo GE
Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 2, Pages 299-313 doi: 10.1631/FITEE.2200212
Keywords： Charging mode Charging time Random forest Long short-term memory (LSTM) Simplified particle swarm optimization (SPSO)
Parallel Authentication Modes Based on Double Blocks or Key Counter
Huang Yuhua,Huai Aiqun,Song Yubo
Strategic Study of CAE 2004, Volume 6, Issue 7, Pages 70-74
The CBC - MAC mode is not a parallel one. A parallel authentication mode (PKCB) based on double blocks was put forward in this paper. The PKCB mode had a marked improvement on security & speed over parallel authentication mode, PMAC. And it may be combined with the CTR (counter) encryption mode to form a full block cipher mode. On this ground, another parallel authentication mode (KCTR - MAC) based on key counter was advanced. As compared with the PMAC mode, the KCTR - MAC mode had a marked improvement on security, while its speed did not become lower. The KCTR - MAC authentication mode may be combined with the CTR (counter) encryption mode to form a full block cipher mode (2CTR)，too. The 2CTR mode had a performance advantage over the standard mode, CCM (CTR with CBC - MAC). And it was a fast, practicable mode with security.
Keywords： authentication mode CBC - MAC mode PMAC mode CTR mode CCM mode
一种易用的实体识别消歧系统评测框架 Article
辉 陈,宝刚 魏,一鸣 李,Yong-huai LIU,文浩 朱
Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 2, Pages 195-205 doi: 10.1631/FITEE.1500473
Keywords： 实体识别消歧；评测框架；信息抽取
Surface Ship Target Recognition Research Based on SGA
Jiang Dingding,Xu Zhaolin,Li Kairui
Strategic Study of CAE 2004, Volume 6, Issue 8, Pages 79-81
Surface ship recognition is an important contents of navy's aviation probe. This text discussed the principle, characteristics and calculating step of SGA, and applied this calculating way to surface ship recognition. The experiment results proved the scientificalness and the practical applicability of that methoded.
Keywords： SGA target recognition surface ship
Application of RFID in the Visual Logistics System
Wang Aiming,Mu Xiaoxi,Li Aihua
Strategic Study of CAE 2006, Volume 8, Issue 8, Pages 65-68
The radio frequency identification (RFID) is a kind of new automatic identification technology. It has many characteristics such as high reliability, privacy , non-contact, convenient and swift. Applying RFID to visual logistics system, can gain actual requirement of guaranteed object and information about the type, amount and way of materials to supply and ensure the supply in the whole time, orientation and course. The paper introduces the RFID system and its principle, and brings about an application of RFID in visual logistics system, which is realized by the radio frequency labels stuck to the containers and equipments.
Keywords： visual logistics radio frequency identification visual system for the carrying materials
Binary neural networks for speech recognition Regular Papers
Yan-min QIAN, Xu XIANG
Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 5, Pages 701-715 doi: 10.1631/FITEE.1800469
Recently, deep neural networks (DNNs) significantly outperform Gaussian mixture models in acoustic modeling for speech recognition. However, the substantial increase in computational load during the inference stage makes deep models difficult to directly deploy on low-power embedded devices. To alleviate this issue, structure sparseness and low precision fixed-point quantization have been applied widely. In this work, binary neural networks for speech recognition are developed to reduce the computational cost during the inference stage. A fast implementation of binary matrix multiplication is introduced. On modern central processing unit (CPU) and graphics processing unit (GPU) architectures, a 5–7 times speedup compared with full precision floatingpoint matrix multiplication can be achieved in real applications. Several kinds of binary neural networks and related model optimization algorithms are developed for large vocabulary continuous speech recognition acoustic modeling. In addition, to improve the accuracy of binary models, knowledge distillation from the normal full precision floating-point model to the compressed binary model is explored. Experiments on the standard Switchboard speech recognition task show that the proposed binary neural networks can deliver 3–4 times speedup over the normal full precision deep models. With the knowledge distillation from the normal floating-point models, the binary DNNs or binary convolutional neural networks (CNNs) can restrict the word error rate (WER) degradation to within 15.0%, compared to the normal full precision floating-point DNNs or CNNs, respectively. Particularly for the binary CNN with binarization only on the convolutional layers, the WER degradation is very small and is almost negligible with the proposed approach.
Keywords： Speech recognition Binary neural networks Binary matrix multiplication Knowledge distillation Population count
Analysis of Disruptive Technology Identification Methods in Foreign Countries
Wang An,Sun Zongtan,Shen Yanbo and Xu Yuan
Strategic Study of CAE 2017, Volume 19, Issue 5, Pages 79-84 doi: 10.15302/J-SSCAE-2017.05.014
Disruptive technologies, which have groundbreaking effects on existing traditional or mainstream technologies, have great potential applications, and are actively recognized and nurtured by all countries. This article summarizes the standard reports on technology identification research released by foreign government agencies, think tanks, intelligence agencies, consulting firms, universities, and patent analysis institutions. It then analyzes and evaluates the disruptive technology identification methods in order to provide reference for the corresponding disruptive technology identification methods in China.
Keywords： disruptive technologies identification methods
Title Author Date Type Operation
Application of Fuzzy Pattern Recognition in the Measurement of Slurry Concentration
Li Dejun,Lv Runhua,Wang Runtian
Journal Article
Pattern Recognition With Fuzzy Central Clustering Algorithms
Zen Huanglin,Yuan Hui,Liu Xiaofang
Journal Article
Fault Pattern Recognition of Rolling Bearing Based on Radial Basis Function Neural Networks
Lu Shuang,Zhang Zida,Li Meng
Journal Article
Theory Model and a Method for Qualitative Assessment of Sustainable Development of Regional Water Resources
Chen Shouyu
Journal Article
Learning and Applications of Procedure Neural Networks
He Xingui,Liang Jiuzhen,Xu Shaohua
Journal Article
A Study on the Essence of Optimal Statistical Uncorrelated Discriminant Vectors
Wu Xiaojun,Yang Jingyu,Wang Shitong,Liu Tongming,Josef Kittler
Journal Article
A partition approach for robust gait recognition based on gait template fusion
Kejun Wang, Liangliang Liu, Xinnan Ding, Kaiqiang Yu, Gang Hu,heukejun@126.com,liuliangliang@hrbeu.edu.cn,dingxinnan@hrbeu.edu.cn,yukaiqiang@hrbeu.edu.cn,hugang@hrbeu.edu.cn
Journal Article
Dynamic time prediction for electric vehicle charging based on charging pattern recognition
Chunxi LI, Yingying FU, Xiangke CUI, Quanbo GE
Journal Article
Parallel Authentication Modes Based on Double Blocks or Key Counter
Huang Yuhua,Huai Aiqun,Song Yubo
Journal Article
Surface Ship Target Recognition Research Based on SGA
Jiang Dingding,Xu Zhaolin,Li Kairui
Journal Article
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