scholarly journals A Customized Deep Neural Network Approach to Investigate Travel Mode Choice with Interpretable Utility Information

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhengchao Zhang ◽  
Congyuan Ji ◽  
Yineng Wang ◽  
Yanni Yang
Keyword(s):  
Neural Network ◽  
Learning Community ◽  
Mode Choice ◽  
Feature Representation ◽  
Classification Problems ◽  
Neural Network Approach ◽  
Nonlinear Fitting ◽  
Linear Utility ◽  
Utility Models ◽  
Fully Connected

Discrete choice modeling of travel modes is an essential part of traffic planning and management. Thus far, this field has been dominated by multinomial logit (MNL) models with a linear utility specification. However, deep neural networks (DNNs), owing to their powerful capacity of nonlinear fitting, are now rapidly replacing these models. This is because, by using DNNs, mode choice can be assimilated with the classification problems within the machine learning community. This article proposes a newly designed DNN framework for traffic mode choice in the style of two hidden layers. First, a local-connected layer automatically extracts an effective utility specification from the available data, and then, a fully connected layer augments the feature representation. Validated by a practical city-wide multimodal traffic dataset in Beijing, our model significantly outperforms the random utility models and simple fully connected neural network in terms of the prediction accuracy. Besides the comparison of the predictive power, we also present the interpretability of the proposed model.

Damage Localization in Pressure Vessel by Guided Waves Based on Convolution Neural Network Approach

2020 ◽  
Vol 142 (6) ◽  
Author(s):  
Chaojie Hu ◽  
Bin Yang ◽  
Jianjun Yan ◽  
Yanxun Xiang ◽  
Shaoping Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Pressure Vessel ◽  
Guided Waves ◽  
Pressure Vessels ◽  
Guided Wave ◽  
Damage Localization ◽  
Accuracy Rate ◽  
Neural Network Approach ◽  
Monitoring Process ◽  
Fully Connected

Abstract This paper investigates the damage localization in a pressure vessel using guided wave-based structural health monitoring (SHM) technology. An online SHM system was developed to automatically select the guided wave propagating path and collect the generated signals during the monitoring process. Deep learning approach was employed to train the convolutional neural network (CNN) model by the guided wave datasets. Two piezo-electric ceramic transducers (PZT) arrays were designed to verify the anti-interference ability and robustness of the CNN model. Results indicate that the CNN model with seven convolution layers, three pooling layers, one fully connected layer, and one Softmax layer could locate the damage with 100% accuracy rate without overfitting. This method has good anti-interference ability in vibration or PZTs failure condition, and the anti-interference ability increases with increasing of PZT numbers. The trained CNN model can locate damage with high accuracy, and it has great potential to be applied in damage localization of pressure vessels.

scholarly journals FnnmOS-ELM: A Flexible Neural Network Mixed Online Sequential Elm

2019 ◽  
Vol 9 (18) ◽  
pp. 3772
Author(s):  
Xiali Li ◽  
Shuai He ◽  
Junzhi Yu ◽  
Licheng Wu ◽  
Zhao Yue
Keyword(s):  
Neural Network ◽  
Network Performance ◽  
Classification Performance ◽  
Feature Representation ◽  
Mixed Structure ◽  
Training Time ◽  
Learning Speed ◽  
Learning Machine ◽  
Fully Connected ◽  
The Relationship

The learning speed of online sequential extreme learning machine (OS-ELM) algorithms is much higher than that of convolutional neural networks (CNNs) or recurrent neural network (RNNs) on regression and simple classification datasets. However, the general feature extraction of OS-ELM makes it difficult to conveniently and effectively perform classification on some large and complex datasets, e.g., CIFAR. In this paper, we propose a flexible OS-ELM-mixed neural network, termed as fnnmOS-ELM. In this mixed structure, the OS-ELM can replace a part of fully connected layers in CNNs or RNNs. Our framework not only exploits the strong feature representation of CNNs or RNNs, but also performs at a fast speed in terms of classification. Additionally, it avoids the problem of long training time and large parameter size of CNNs or RNNs to some extent. Further, we propose a method for optimizing network performance by splicing OS-ELM after CNN or RNN structures. Iris, IMDb, CIFAR-10, and CIFAR-100 datasets are employed to verify the performance of the fnnmOS-ELM. The relationship between hyper-parameters and the performance of the fnnmOS-ELM is explored, which sheds light on the optimization of network performance. Finally, the experimental results demonstrate that the fnnmOS-ELM has a stronger feature representation and higher classification performance than contemporary methods.

scholarly journals AN INTELLIGENT DENGUE HEMORRHAGIC FEVER SEVERITY LEVEL DETECTION BASED ON DEEP NEURAL NETWORK APPROACH

2019 ◽  
Vol 12 (2) ◽  
pp. 57
Author(s):  
Dian Pratiwi ◽  
Gatot Budi Santoso ◽  
Leni Muslimah ◽  
Raden Davin Rizki
Keyword(s):  
Neural Network ◽  
Dengue Hemorrhagic Fever ◽  
Deep Neural Network ◽  
Hemorrhagic Fever ◽  
Training Data ◽  
Network Approach ◽  
Neural Network Approach ◽  
Severity Level ◽  
Fully Connected ◽  
Learning Data

Dengue hemorrhagic fever is one of the most dangerous diseases which often leads to death for the sufferer due to delays or improper handling of the severity that has occurred. In determining that severity level, a specialist analyzes it from the symptoms and blood testing results. This research was developed to produce a system by applying Deep Neural Network approach that is able to give the same analytical ability as a doctor, so that it can give fast and precise decision of dengue handling. The research stages consisted of normalizing data to 0 – 1 intervals by Min-Max method, training data into multilayer networks with fully connected and partially connected schemes to produce the best weights, validating data and final testing. From the use of network parameters as much as 10 input units, 1 bias, 2 hidden layers, 2 output units, learning rate of 0.3, epoch 1000, tolerance rate 0.02, threshold 0.5, the system succeeded in generating a maximum accuracy of 95% in data learning (60 data), 87.5% on data learning and non-learning (40 data), 85% on non-learning data (20 data).

scholarly journals A new approach to multiple class pattern classification with random matrices

2005 ◽  
Vol 2005 (3) ◽  
pp. 165-175 ◽  
Author(s):  
Dong Q. Wang ◽  
Mengjie Zhang
Keyword(s):  
Neural Network ◽  
Maximum Likelihood ◽  
Pattern Classification ◽  
Likelihood Estimation ◽  
High Dimensional ◽  
Classification Problems ◽  
Feed Forward Neural Network ◽  
Neural Network Approach ◽  
New Approach ◽  
Multiple Class

We describe a new approach to multiple class pattern classification problems with noise and high dimensional feature space. The approach uses a random matrix X which has a specified distribution with mean M and covariance matrix rij(Σs+Σε) between any two columns of X. When Σε is known, the maximum likelihood estimators of the expectation M, correlation Γ, and covariance Σs can be obtained. The patterns with high dimensional features and noise are then classified by a modified discriminant function according to the maximum likelihood estimation results. This new method is compared with a multilayer feed forward neural network approach on nine digit recognition tasks of increasing difficulty. Both methods achieved good results for those classification tasks, but the new approach was more effective and more efficient than the neural network method for difficult problems.

scholarly journals Using DEA-neural network approach to solve binary classification problems

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Farhad Hosseinzadeh Lotfi ◽  
Gholam Reza Jahanshahloo ◽  
Shadi Givehchi ◽  
Mohsen Vaez-Ghasemi
Keyword(s):  
Neural Network ◽  
Binary Classification ◽  
Network Approach ◽  
Classification Problems ◽  
Neural Network Approach

A deep convolutional neural network approach with information fusion for bearing fault diagnosis under different working conditions

2020 ◽  
pp. 095440622090218
Author(s):  
Tang Tang ◽  
Tianhao Hu ◽  
Ming Chen ◽  
Ronglai Lin ◽  
Guorui Chen
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Information Fusion ◽  
Vibration Signal ◽  
Deep Convolutional Neural Network ◽  
Feature Representation ◽  
Training Data ◽  
Data Sets ◽  
Neural Network Approach

In recent years, deep learning-based fault diagnosis methods have drawn lots of attention. However, for most cases, the success of machine learning-based models relies on the circumstance that training data and testing data are under the same working condition, which is too strict for real implementation cases. Combined with the features of robustness of deep convolutional neural network and vibration signal characteristics, information fusion technology is introduced in this study to enhance the feature representation capability as well as the transferability of diagnosis models. With the basis of multi-sensors and narrow-band decomposition techniques, a convolutional architecture named fusion unit is proposed to extract multi-scale features from different sensors. The proposed method is tested on two data sets and has achieved relatively higher generalization ability when compared with several existing works, which demonstrates the effectiveness of our proposed fusion unit for feature extraction on both source task and target task.

scholarly journals Neural Network for Intelligent and Efficient Volleyball Passing Training

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Bo Liu ◽  
Ning Yang ◽  
Xiangwei Han ◽  
Chen Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Level ◽  
Gaussian Mixture ◽  
Generalization Performance ◽  
Softmax Classifier ◽  
Nonlinear Fitting ◽  
Redundant Feature ◽  
Fully Connected

Passing is a relatively basic technique in volleyball. In volleyball passing teaching, training the correct passing technique plays a very important role. The correct pass can not only accurately grasp the direction of the ball point and the drop point but also effectively connect the defense and the offense. In order to improve the efficiency and quality of volleyball passing training, improve the precise extraction of sport targets, reduce redundant feature information, and improve the generalization performance and nonlinear fitting capabilities of the algorithm, this paper studies volleyball based on the nested convolutional neural network model and passing training wrong movement detection method. The structure of the convolutional neural network is improved by nesting mlpconv layers, and the Gaussian mixture model is used to effectively and accurately extract the foreground objects in the video. The nested multilayer mlpconv layer automatically learns the deep-level features of the foreground target, and the generated feature map is vectorized and input to the Softmax classifier connected to the fully connected layer for passing wrong behavior detection in volleyball training. Based on the detection of nearly 1,000 athletes’ action datasets, the simulation experiment results show that the algorithm reduces the acquisition of redundant information and shortens the calculation time and learning time of the algorithm, and the improved convolutional neural network has generalization performance and nonlinearity. The fitting ability has been improved, and the detection of abnormal volleyball passing behaviors has achieved a higher accuracy rate.

scholarly journals Methodology for Knowledge Extraction from Trained Artificial Neural Networks

2018 ◽  
Vol 21 ◽  
pp. 6-14
Author(s):  
Andrey Bondarenko ◽  
Ludmila Aleksejeva
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Binary Classification ◽  
Knowledge Extraction ◽  
Classification Problems ◽  
Critical Areas ◽  
Artificial Neural ◽  
Fully Connected

Artificial neural networks are widely spread models that outperform more basic, but explainable machine learning models like classification decision tree. Although their lack of explainability severely limits their area of application. All mission critical areas or law regulated areas (like European GDPR) require model to be explained. Explainability allows model validation for correctness and lack of bias. Thus methods for knowledge extraction from artificial neural networks have gained attention and development efforts. Current paper addresses this problem and describes knowledge extraction methodology which can be applied to classification problems. It is based on previous research and allows knowledge to be extracted from trained fully connected feed-forward artificial neural network, from radial basis function neural network and from hyper-polytope based classifier in the form of binary classification decision tree, elliptical rules and If-Then rules.

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