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Dependable Deep Computation Model for Feature Learning on Big Data in Cyber-Physical Systems

ACM TRANSACTIONS ON CYBER-PHYSICAL SYSTEMS 2019年第1期3卷 11-1-11-17页

作者： Zhang, Qingchen Yang, Laurence T. Chen, Zhikui Li, Peng Univ Elect Sci & Technol China Sch Informat & Commun Engn Beijing Peoples R China St Francis Xavier Univ Dept Comp Sci 18 Hillcrest St Antigonish NS B2G 1Z3 Canada St Francis Xavier Univ Dept Comp Sci 2323 Notre Dame Ave Antigonish NS B2G 2W5 Canada Dalian Univ Technol Sch Software Technol 321 Tuqing St Dalian 116620 Liaoning Peoples R China

With the ongoing development of sensor devices and network techniques, big data are being generated from the cyber-physical systems. Because of sensor equipment occasional failure and network transmission un-reliability, a large number of low-quality data, such as noisy data and incomplete data, is collected from the cyber-physical systems. Low-quality data pose a remarkable challenge on deep learning models for big data feature learning. As a novel deep learning model, the deep computation model achieves superior performance for big data feature learning. However, it is difficult for the deep computation model to learn dependable features for low-quality data, since it uses the nonlinear function as the encoder. In this article, a dependable deep computation model is proposed for feature learning on low-quality big data in cyber-physical systems. Specially, a regularity is added into the objective function of the deep computation model to obtain reliable features in the intermediate-level representation space. Furthermore, a learning algorithm based on the back-propagation strategy is devised to train the parameters of the proposed model. Finally, experiments are conducted on three representative datasets and a real dataset to evaluate the effectiveness of the dependable deep computation model for low-quality big data feature learning. Results show that the proposed model achieves a remarkable result for the tasks of classification, restoration, and prediction, proving the potential of this work for practical applications in cyber-physical systems.

关键词： Cyber-physical systems big data dependable deep computation model feature learning back-propagation algorithm

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Application of 3-algorithm ANN programming to predict the strength performance of hydrated-lime activated rice husk ash treated soil

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2021年第4期4卷 259-274页

作者： Onyelowe, Kennedy C. Iqbal, Mudassir Jalal, Fazal E. Onyia, Michael E. Onuoha, Ifeanyichukwu C. Michael Okpara Univ Agr Dept Civil Engn Umudike Nigeria Kampala Int Univ Dept Civil & Mech Engn Kampala Uganda Shangai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai Key Lab Digital Maintenance Bldg & Infra Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Civil Engn Shanghai Peoples R China Univ Nigeria Fac Engn Dept Civil Engn Nsukka Nigeria Fed Univ Technol Owerri Dept Environm Technol Owerri Nigeria

Artificial neural network (ANN) method has been applied in the present work to predict the California bearing ratio (CBR), unconfined compressive strength (UCS), and resistance value (R) of expansive soil treated with recycled and activated composites of rice husk ash. Pavement foundations suffer from poor design and construction, poor material handling and utilization and management lapses. The evolutions of soft computing techniques have produced various algorithms developed to overcome certain lapses in performance. Three of such algorithms from ANN are Levenberg-Muarquardt backpropagation (LMBP), Bayesian Programming (BP), and Conjugate Gradient (CG) algorithms. In this work, the expansive soil classified as A-7-6 group soil was treated with hydrated-lime activated rice husk ash (HARHA) in varying proportions between 0.1 and 12% by weight of soil at the rate of 0.1% to produce 121 datasets. These were used to predict the behavior of the soil's strength parameters (CBR, UCS and R) utilizing the evolutionary hybrid algorithms of ANN. The predictor parameters were HARHA, liquid limit (w(L)), (plastic limit (w(P)), plasticity index (I-P), optimum moisture content (w(OMC)), clay activity (A(C)), and (maximum dry density (delta(max)). A multiple linear regression (MLR) was also conducted on the datasets in addition to ANN to serve as a check and linear validation mechanism. MLR and ANN methods agreed in terms of performance and fit at the end of computing and iteration. However, the response validation on the predicted models showed a good correlation above 0.9 and a great performance index. Comparatively, the LMBP algorithm yielded an accurate estimation of the results in lesser iterations than the Bayesian and the CG algorithms, while the Bayesian technique produced the best result with the required number of iterations to minimize the error. And finally, the LMBP algorithm outclassed the other two algorithms in terms of the predicted models' accuracy.

关键词： Soft computing Artificial intelligence Artificial neural network (ANN) Machine learning in geotechnics back-propagation algorithm Levenberg– muarquardt algorithm Bayesian algorithm Conjugate gradient algorithm Sustainable construction materials

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Convergence of BP algorithm for product unit neural networks with exponential weights

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NEUROCOMPUTING 2008年第1-3期72卷 513-520页

作者： Zhang, C. Wu, W. Chen, X. H. Xiong, Y. Dalian Univ Technol Dept Appl Math Dalian 116024 Peoples R China

Product unit neural networks with exponential weights (PUNNs) can provide more powerful internal representation capability than traditional feed-forward neural networks. In this paper, a convergence result of the back-propagation (BP) algorithm for training PUNNs is presented. The monotonicity of the error function in the training iteration process is also guaranteed. A numerical example is given to support the theoretical findings. (c) 2008 Elsevier B.V. All rights reserved.

关键词： Neural network Product unit Exponential weights back-propagation algorithm Convergence

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Neural Network and Classification Approach in Identifying Customer Behavior in the Banking Sector: A Case Study of an International Bank

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HUMAN FACTORS AND ERGONOMICS IN MANUFACTURING & SERVICE INDUSTRIES 2015年第1期25卷 28-42页

作者： Ogwueleka, Francisca Nonyelum Misra, Sanjay Colomo-Palacios, Ricardo Fernandez, Luis Fed Univ Technol Dept Comp Sci Minna Niger State Nigeria Atilim Univ Dept Comp Engn Ankara Turkey Univ Carlos III Madrid E-28903 Getafe Spain Univ Alcala de Henares Dept Ciencias Comp Alcala De Henares 28400 Spain

The customer relationship focus for banks is in development of main competencies and strategies of building strong profitable customer relationships through considering and managing the customer impression, influence on the culture of the bank, satisfactory treatment, and assessment of valued relationship building. Artificial neural networks (ANNs) are used after data segmentation and classification, where the designed model register records into two class sets, that is, the training and testing sets. ANN predicts new customer behavior from previously observed customer behavior after executing the process of learning from existing data. This article proposes an ANN model, which is developed using a six-step procedure. The back-propagation algorithm is used to train the ANN by adjusting its weights to minimize the difference between the current ANN output and the desired output. An evaluation process is conducted to determine whether the ANN has learned how to perform. The training process is halted periodically, and its performance is tested until an acceptable result is obtained. The principles underlying detection software are grounded in classical statistical decision theory.

关键词： Customer relationship management (CRM) Artificial neural network (ANN) Classification Banks back-propagation algorithm

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Neural networks with hybrid morphological/rank/linear nodes: a unifying framework with applications to handwritten character recognition

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PATTERN RECOGNITION 2000年第6期33卷 945-960页

作者： Pessoa, LFC Maragos, P Motorola Inc Austin TX 78721 USA Natl Tech Univ Athens Dept Elect & Comp Engn GR-15773 Zografos Athens Greece

In this paper, the general class of morphological/rank/linear (MRL) multilayer feed-forward neural networks (NNs) is presented as a unifying signal processing tool that incorporates the properties of multilayer perceptrons (MLPs) and morphological/rank neural networks (MRNNs). The fundamental processing unit of MRL-NNs is the MRL-filter, where the combination of inputs in every node is formed by hybrid linear and nonlinear (of the morphological/rank type) operations. For its design we formulate a methodology using ideas from the back-propagation algorithm and robust techniques to circumvent the non-differentiability of rank functions. Extensive experimental results are presented from the problem of handwritten character recognition, which suggest that MRL-NNs not only provide better or similar performance when compared to MLPs but also can be trained faster. The MRL-NNs are a broad interesting class of nonlinear systems with many promising applications in pattern recognition and signal/image processing. (C) 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

关键词： morphological systems MRL-filters neural networks back-propagation algorithm handwritten character recognition

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Hybrid Fuzzy Neural Search Retrieval System

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INTERNATIONAL JOURNAL OF ENTERPRISE INFORMATION SYSTEMS 2016年第3期12卷 78-93页

作者： Ghnemat, Rawan Shaout, Adnan Princess Sumaya Univ Technol King Hussein Fac Comp Sci Dept Comp Sci Amman Jordan Univ Michigan Dept Elect & Comp Engn Dearborn MI 48128 USA

Search engines are crucial for information gathering systems (IGS). New challenges face search engines concerning automatic learning from user requests. In this paper, a new hybrid intelligent system is proposed to enhance the search process. Based on a Multilayer Fuzzy Inference System (MFIS), the first step is to implement a scalable system to relay logical rules in order to produce three classifications for search behavior, user profiles, and query characteristics from analysis of navigation log files. These three outputs from the MFIS are used as inputs for the second step, an Adaptive Neuro-Fuzzy Inference System (ANFIS). The training process of the ANFIS replaced the rules by adjusting the weights in order to find the most relevant result for the search query. This proposed system, called MFIS-ANFIS, is implemented as an experimental system. The system performance is evaluated using quantitative and comparative analysis. MFIS-ANFIS aimed to be the core of intelligent and reliable search process.

关键词： back-propagation algorithm Hybrid Intelligent System Information Retrieval Multilayer Fuzzy Inference Systems Neuro-Fuzzy System Search Engines

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Parallel medical image analysis for diabetic diagnosis

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2005年第1期22卷 34-41页

作者： Huang, Yueh-Min Cheng, Shu-Chen Natl Cheng Kung Univ Dept Engn Sci Tainan Taiwan Southern Taiwan Univ Technol Dept Comp Sci & Informat Engn 1 Nan Tai St Tainan Taiwan

This paper aims to investigate the characteristics of medical images. A novel diagnostic scheme to develop quantitative indexes of diabetes is introduced in this paper. To achieve a better image-processing result, an appropriate image-processing algorithm is adopted in this work. The computation time increases as the image size grows. Fortunately, the computation can be partitioned and performed in parallel in a high performance system and a grid computing system can be a good infrastructure for it. An important fractal feature introduced in this paper is the measure of lacunarity, which describes the characteristics of fractals that have the same fractal dimension but different appearances. In this study, the measure of lacunarity and the moment of inertia have shown their significance in the classification of diabetes and are adequate for use as quantitative indexes.

关键词： back-propagation algorithm diagnosis system image-processing lacunarity moment of inertia parallel processing grid computing

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Adaptive-Slope Squashing-Function-Based ANN for CSI Estimation and Symbol Detection in SFBC-OFDM System

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第10期46卷 9451-9464页

作者： Kapoor, Divneet Singh Kohli, Amit Kumar Thapar Inst Engn & Technol Dept Elect & Commun Engn Patiala 147004 Punjab India

This paper presents an adaptive-slope squashing-function (ASF)-based artificial neural network (ANN) for efficient estimation of smoothly time-varying multipath fading channels, in a 4x1 space-frequency-block-coded orthogonal-frequency-division-multiplexing (SFBC-OFDM) system using 64 subcarriers. The channel-state-information (CSI) estimated at first stage is further used for OFDM information symbol detection (through minimum mean square error criterion-based detection) at second stage. To combat the impact of smoothly time-varying environment, we emphasize on the utilization of ASF-ANN using backpropagation (BP) algorithm for the estimation of channel tap coefficients in frequency domain. The underlying ANN is modeled as feedforward multi-layered perceptron that updates the network weights. The major focus is on the gradient-descent algorithm-based adaptation of the slope of squashing-function (SF) along with other ANN parameters, which enhances the training efficiency of ASF-ANN in terms of the lower mean-squared channel estimation error in comparison with the traditional fixed-slope squashing-function (FSF) ANN technique. Simulation results corresponding to the underlying 4x1SFBC-OFDM system are presented to depict that ASF-ANN-based approach outperforms the FSF-ANN technique by providing lower bit-error-rate (BER) due to the usage of well-estimated CSI. At 15 dB SNR and fade rate = 0.001, the average BER reduces to 2.85x10(-4) for the ASF-ANN based approach, due to improved CSI estimation, which accounts for approximately 5% improvement in the detection success rate as compared to the FSF-ANN-based approach.

关键词： SFBC-OFDM Artificial neural networks (ANN) Adaptive-slope squashing-function Channel estimation Multi-layered perceptron back-propagation algorithm

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Application of convolution neural network in medical image processing

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TECHNOLOGY AND HEALTH CARE 2021年第2期29卷 407-417页

作者： Liu, Jie Zhao, Hongbo Xian Med Univ Sch Basic Med Sci Xian Shaanxi Peoples R China Xian Med Univ Sch Med Technol Xian 710002 Shaanxi Peoples R China

backGROUND: Convolution neural network is often superior to other similar algorithms in image classification. Convolution layer and sub-sampling layer have the function of extracting sample features, and the feature of sharing weights greatly reduces the training parameters of the network. OBJECTIVE: This paper describes the improved convolution neural network structure, including convolution layer, sub-sampling layer and full connection layer. This paper also introduces five kinds of diseases and normal eye images reflected by the blood filament of the eyeball "***" data set, convenient to use MATLAB software for calculation. METHODSL: In this paper, we improve the structure of the classical LeNet-5 convolutional neural network, and design a network structure with different convolution kernels, different sub-sampling methods and different classifiers, and use this structure to solve the problem of ocular bloodstream disease recognition. RESULTS: The experimental results show that the improved convolutional neural network structure is ideal for the recognition of eye blood silk data set, which shows that the convolution neural network has the characteristics of strong classification and strong robustness. The improved structure can classify the diseases reflected by eyeball bloodstain well.

关键词： Convolution neural network image processing self-learning back-propagation algorithm

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DeltaFrame-BP: An algorithm Using Frame Difference for Deep Convolutional Neural Networks Training and Inference on Video Data

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IEEE TRANSACTIONS ON MULTI-SCALE COMPUTING SYSTEMS 2018年第4期4卷 624-634页

作者： Han, Bing Roy, Kaushik Purdue Univ Dept Elect & Comp Engn W Lafayette IN 47906 USA

Inspired by the success of deep convolutional neural networks (CNNs) with back-propagation (BP) training on large-scale image recognition tasks, recent research efforts concentrated on expending deep CNNs toward more challenging automatized video analysis, such as video classification, object tracking, action recognition and optical flow detection. Video comprises a sequence of images (frames) captured over time in which image data is a function of space and time. Extracting three-dimensional spatial-temporal features from multiple frames becomes a key ingredient for capturing and incorporating appearance and dynamic representations using deep CNNs. Hence, training deep CNNs on video involves significant computational resources and energy consumption due to extended number of frames across the time line of video length. We propose DeltaFrame-BP, a deep learning algorithm, which significantly reduces computational cost and energy consumption without accuracy degradation by streaming frame differences for deep CNNs training and inference. The inherent similarity between video frames due to high fps (frames per second) in video recording helps achieving high-sparsity and low-dynamic range data streaming using frame differences in comparison with raw video frames. According to our simulation, nearly 25 percent energy reduction was achieved in training using the proposed accuracy-lossless DeltaFrame-BP algorithm in comparison with the standard back-propagation algorithm.

关键词： Computer vision deep neural network back-propagation algorithm convolutional neural network energy efficiency

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