One-dimensional Deep Convolutional Neural Network Research Articles

Analyzing the opening and closing of windows in residential for predicting the energy consumption using optimized multi-scale convolution networks

PurposeThis proposal aims to forecast energy consumption in residential buildings based on the effect of opening and closing windows by the deep architecture approach. In this task, the developed model has three stages: (1) collection of data, (2) feature extraction and (3) prediction. Initially, the data for the closing and opening frequency of the window are taken from the manually collected datasets. After that, the weighted feature extraction is performed in the collected data. The attained weighted feature is fed to predict energy consumption. The prediction uses the efficient hybrid multi-scale convolution networks (EHMSCN), where two deep structured architectures like a deep temporal context network and one-dimensional deep convolutional neural network. Here, the parameter optimization takes place with the hybrid algorithm named jumping rate-based grasshopper lemur optimization (JR-GLO). The core aim of this energy consumption model is to predict the consumption of energy accurately based on the effect of opening and closing windows. Therefore, the offered energy consumption prediction approach is analyzed over various measures and attains an accurate performance rate than the conventional techniques.Design/methodology/approachAn EHMSCN-aided energy consumption prediction model is developed to forecast the amount of energy usage during the opening and closing of windows accurately. The emission of CO2 in indoor spaces is highly reduced.FindingsThe MASE measure of the proposed model was 52.55, 43.83, 42.01 and 36.81% higher than ANN, CNN, DTCN and 1DCNN.Originality/valueThe findings of the suggested model in residences were attained high-quality measures with high accuracy, precision and variance.

Vision transformer-based electronic nose for enhanced mixed gases classification

The classification of mixed gases is one of the major functions of the electronic nose. To address the challenges associated with complex feature construction and inadequate feature extraction in gas classification, we propose a classification model for gas mixtures based on the vision transformer (ViT). The whole-process signals of the sensor array are taken as input signals in the proposed classification model, and self-attention mechanism is employed for the fusion of global information and adaptive feature extraction to make full use of the dependence of responses at different stages of the whole-process signals to improve the model’s classification accuracy. Our model exhibited a remarkable accuracy (96.66%) using a dataset containing acetone, methanol, ammonia, and their binary mixtures. In comparison, experiments conducted by support vector machine and a one-dimensional deep convolutional neural network model demonstrated classification accuracy of 90.56% and 92.75%, respectively. Experimental results indicate that the ViT gas classification model can be effectively combined with multi-channel time series data from the sensor array using the self-attention mechanism, thereby improving the accuracy of mixed gases classification. This advancement can be expected to become a standard method for classifying mixed gases.

Deep Learning for Gas Sensing via Infrared Spectroscopy.

Deep learning methods, a powerful form of artificial intelligence, have been applied in a number of spectroscopy and gas sensing applications. However, the speciation of multi-component gas mixtures from infrared (IR) absorption spectra using deep learning remains to be explored. Here, we propose a one-dimensional deep convolutional neural network gas classification model for the identification of small molecules of interest based on IR absorption spectra in flexible user-defined frequency ranges. The molecules considered include ten that are of interest in the atmosphere or in industrial and environmental processes: water vapor, carbon dioxide, ozone, nitrous oxide, carbon monoxide, methane, nitric oxide, sulfur dioxide, nitrogen dioxide, and ammonia. A simulated dataset of IR absorption spectra for mixtures of these molecules diluted in air was generated and used to train a deep learning model. The model was tested against simulated spectra containing noise and was found to provide speciation predictions with accuracy from 82 to 97%. The internal operation of the model was investigated using class activation maps that illustrate how the model prioritizes spectral information for classification. Finally, the model was demonstrated for the prediction of speciation for two synthetic experimental mixture spectra. The proposed model and the dataset generation strategies are generalized and can be implemented for other gases, different frequency ranges, and spectroscopy types. The multi-component speciation method developed herein is the first application of a convolutional neural network model, trained on HITRAN-based simulations, for spectral identification.

Single-Cycle Pulse Signal Recognition Based on One-Dimensional Deep Convolutional Neural Network

Pulse signals carry comprehensive information regarding human cardiovascular physiology and pathology, providing a noninvasive and continuous method to assess cardiovascular health status in blood pressure monitoring. The blood pressure measurement method based on the pulse signal needs to extract the features of the single-cycle pulse signal, while the pulse signal pertains to the weak physiological signal of body surface. The acquisition process is susceptible to various factors leading to abnormal cycles, especially adjacent channel interference, affecting the subsequent feature extraction. To address this problem, this paper conducts an analysis of the formation mechanism of adjacent channel interference and proposes a single-cycle pulse signal recognition algorithm based on a one-dimensional deep convolutional neural network (1D-CNN) model. Radial pulse signals were collected from 150 subjects by pulse bracelet, and a dataset comprising 3446 single-cycle signals was extracted in total after denoising, single-cycle segmentation, and standardized preprocessing. The 1D-CNN model is trained to classify input signals into three categories: effective pulse signals, distortion, and interference signals. This classification is achieved by evaluating the waveform morphology of the signals within a single cycle. The results show that the overall classification accuracy of the algorithm on the test set is 98.26%, in which the classification accuracy of pulse waves is 99.8%, indicating that it can effectively recognize single-cycle pulse waves, which lays the foundation for subsequent continuous blood pressure measurement.

Improving Rainfall Forecasting via Radial Basis Function and Deep Convolutional Neural Networks Integration

The foremost challenge of rainfall forecasting is the intensity of rainfall in some particular stations. The unpredictable rainfall volume owing to the climate transformation can root cause for either overflow or dryness in the reservoir. In this article, we coin a novel model to predict the monthly rainfall by using an Ensemble Radial basis function Network and a One-Dimensional Deep Convolutional Neural Network algorithm. In the first step, nine climatological parameters, which are highly related to monthly rainfall disparity, are given as input for an ensemble model. In the second step, a hybrid approach is proposed and compared with Bayesian Linear Regression (BLR) and Decision Forest Regression (DFR). Experimental results show that the ensemble approach yields good results in seizing the multifaceted association among causal variables and also it extracted the most relevant hidden features of hydro meteorological rainfall system.

Fault Diagnosis Method for Rotating Machinery Based on Multi-scale Features

The vibration signals of rotating machinery usually contain various natural oscillation modes, exhibiting multi-scale features. This paper proposes a Multi-Branch one-dimensional deep Convolutional Neural Network model (MBCNN) that can extract multi-scale features from raw data hierarchically, thereby improving the diagnostic accuracy of gearbox faults in noisy environments. Meanwhile, the algorithms for multi-branch generation and algorithms of the convolution and pooling for each branch are deducted. The MBCNN integrates multiple branches with interrelated convolution kernels of different widths, and each branch can extract the high-level features of the signal. The network parameters of each branch are adjusted by the loss function, which makes the features of the branches complementary. Through the design of MBCNN, the local, global, deep layer and comprehensive information can be obtained from the raw data. On the widely used Case Western Reserve University Bearing Dataset, this paper conducted a performance comparison between the proposed MBCNN and other baselines including the shallow learning methods, 1D-CNN, and multi-scale feature learning methods. Moreover, our gearbox dataset was conducted on a fault diagnosis platform, and a series of experiments were conducted to verify the effectiveness and superiority of the MBCNN. The results indicate that the MBCNN can identify the faults in the gearbox with an accuracy of higher than 92%, and the average validation time per sample is less than 3.2 ms. In a noisy environment, the diagnostic accuracy can reach 90%. The proposed MBCNN provides an effective and intelligent detection method to identify the faults of rotating machinery in the manufacturing processes.

A deep convolutional neural network for vibration-based health-monitoring of rotating machinery

The gearbox is a critical component in the mechanical system, requiring vigilant monitoring to prevent adverse consequences on safety and quality due to malfunction. Therefore, early fault diagnosis of the gearbox before the fatal breakdown of the entire mechanical system is of imperative importance. This study proposes a one-dimensional deep convolutional neural network (1D-DCNN) to learn features directly from the vibrational signals and identify the gear fault under different health conditions. The performance is compared with the decision tree, random forest, and support vector machine to validate the superiority of the 1D-DCNN. Experimental results showed that the proposed scheme outperforms other comparative methods, with a diagnostic accuracy of 97.11 %, thus confirming its effectiveness.

Speech Emotion Recognition Based on Multiple Acoustic Features and Deep Convolutional Neural Network

Speech emotion recognition (SER) plays a vital role in human–machine interaction. A large number of SER schemes have been anticipated over the last decade. However, the performance of the SER systems is challenging due to the high complexity of the systems, poor feature distinctiveness, and noise. This paper presents the acoustic feature set based on Mel frequency cepstral coefficients (MFCC), linear prediction cepstral coefficients (LPCC), wavelet packet transform (WPT), zero crossing rate (ZCR), spectrum centroid, spectral roll-off, spectral kurtosis, root mean square (RMS), pitch, jitter, and shimmer to improve the feature distinctiveness. Further, a lightweight compact one-dimensional deep convolutional neural network (1-D DCNN) is used to minimize the computational complexity and to represent the long-term dependencies of the speech emotion signal. The overall effectiveness of the proposed SER systems’ performance is evaluated on the Berlin Database of Emotional Speech (EMODB) and the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) datasets. The proposed system gives an overall accuracy of 93.31% and 94.18% for the EMODB and RAVDESS datasets, respectively. The proposed MFCC and 1-D DCNN provide greater accuracy and outpace the traditional SER techniques.

Multi-Morphological Pulse Signal Feature Point Recognition Based on One-Dimensional Deep Convolutional Neural Network

Radial pulse signals are produced by the periodic ejection of blood from the heart, and physiological and pathological information of the human body can be analyzed by extracting the time-domain characteristics of pulse waves. However, since pulse signals are weak physiological signals on the body surface and complex, the acquisition of pulse characteristics using the traditional curvature method will produce a large error, which cannot meet the needs of pulse wave analysis in current clinical practice. To solve this problem, a multi-morphological pulse signal feature recognition algorithm based on the one-dimensional deep convolutional neural network (1D-DCNN) model is proposed. We used the multi-channel pulse diagnosis instrument independently developed by the team to collect radial pulse signals under continuous pressure of the test subjects and collected 115 subjects and extracted a total of 1300 single-cycle pulse signals and then divided these pulse signals into 6 different forms. Five types of pulse signal time-domain feature points were labeled, and five independent feature point datasets were labeled and formed five customized neural network models that were generated to train and identify the pulse feature point datasets independently. The results show that the correction coefficient (Radjusted2) of the multi-class pulse signal processing algorithm proposed in this paper for each type of feature point recognition reaches more than 0.92. The performance is significantly better than that of the traditional curvature method, which shows the accuracy and superiority of the proposed method. Therefore, the multi-class pulse signal characteristic parameter recognition model based on the 1D-DCNN model proposed in this paper can efficiently and accurately identify pulse time-domain characteristic parameters, which can be applied to discriminate time-domain pulse information in clinical practice and assist doctors in diagnosis.

Deep multi feature dynamic adversarial diagnosis approach of rotating machinery

Recent works show that knowledge transfer is an effective strategy to solve cross-domain diagnosis problems. The existing domain adaptation methods considering both global and local distribution between domains do not make the most of the knowledge learned by deep neural network, resulting in low diagnosis accuracy. To solve this problem, a deep multi feature dynamic adversarial diagnosis (DMDAD) method for transfer diagnosis of rotating machinery is presented in this paper. Firstly, the one-dimensional deep convolutional neural network is utilized as the feature encoder to learn the characteristics of vibration signals in different working conditions. The class prediction vector and feature vector are fused by the multilinear mapping. The fused features are conducted for the dynamic discrimination network for adversarial training. At the same time, considering the statistical alignment and adversarial alignment, the domain adaptation is finally realized. The experimental study demonstrates the effectiveness of the DMDAD.

Deep meta-learning and variational autoencoder for coupling fault diagnosis of rolling bearing under variable working conditions

Considering the characteristics of rolling bearing such as variable working conditions, unbalanced fault sample size, and multiple coupling fault types, it is a great challenge to achieve general accurate fault diagnosis model. In this paper, deep meta-learning and variational autoencoder (DML-VAE) is applied for coupling fault diagnosis of rolling bearing under variable working conditions. The collected vibration signals of rolling bearing are divided into long time series samples, including normal samples, single fault samples, and coupling fault samples. Then, variational autoencoder (VAE) is utilized for data augmentation of time series samples, and the generated samples are brought into one-dimensional deep convolutional neural network (1-DCNN) for further classification of multiple coupling faults. Subsequently, the trained 1-DCNN is regarded as embedding model. Training samples and other working condition samples are defined as support set and query set. Based on metric-based meta-learning method, sample pairs composed of support set and query set are constructed and brought into the embedding model to get the category with shortest metric distance as the classification result. In addition, the embedding model can be optimized by minimizing the contrastive loss among these sample pairs. The case study shows that the DML-VAE can achieve accurate classification results under the coupling of two faults and three faults, and maintain high diagnostic accuracy under variable working conditions. Compared with other models, the proposed model can also get the most accurate fault diagnosis results for all categories under unbalanced samples.

RobustSTL and Machine-Learning Hybrid to Improve Time Series Prediction of Base Station Traffic

Green networking is currently becoming an urgent compulsion applied for cellular network architecture. One of the treatments that can be undertaken to fulfill such an objective is a traffic-aware scheme of a base station. This scheme can control the power consumption of the cellular network based on the number of demands. Then, it requires an understanding of estimated traffic in future demands. Various studies have undertaken experiments to obtain a network traffic prediction with good accuracy. However, dynamic patterns, burstiness, and various noises hamper the prediction model from learning the data traffic comprehensively. Furthermore, this paper proposes a prediction model using deep learning of one-dimensional deep convolutional neural network (1DCNN) and gated recurrent unit (GRU). Initially, this study decomposes the network traffic data by RobustSTL, instead of standard STL, to obtain the trend, seasonal, and residual components. Then, these components are fed into the 1DCNN-GRU as input data. Through the decomposition method using RobustSTL, the hybrid model of 1DCNN-GRU can completely capture the pattern and relationship of the traffic data. Based on the experimental results, the proposed model overall outperforms the counterpart models in MAPE, RMSE, and MAE metrics. The predicted data of the proposed model can follow the patterns of actual network traffic data.

Atrial Fibrillation Classification with Smart Wearables Using Short-Term Heart Rate Variability and Deep Convolutional Neural Networks.

Atrial fibrillation (AF) is a type of cardiac arrhythmia affecting millions of people every year. This disease increases the likelihood of strokes, heart failure, and even death. While dedicated medical-grade electrocardiogram (ECG) devices can enable gold-standard analysis, these devices are expensive and require clinical settings. Recent advances in the capabilities of general-purpose smartphones and wearable technology equipped with photoplethysmography (PPG) sensors increase diagnostic accessibility for most populations. This work aims to develop a single model that can generalize AF classification across the modalities of ECG and PPG with a unified knowledge representation. This is enabled by approximating the transformation of signals obtained from low-cost wearable PPG sensors in terms of Pulse Rate Variability (PRV) to temporal Heart Rate Variability (HRV) features extracted from medical-grade ECG. This paper proposes a one-dimensional deep convolutional neural network that uses HRV-derived features for classifying 30-s heart rhythms as normal sinus rhythm or atrial fibrillation from both ECG and PPG-based sensors. The model is trained with three MIT-BIH ECG databases and is assessed on a dataset of unseen PPG signals acquired from wrist-worn wearable devices through transfer learning. The model achieved the aggregate binary classification performance measures of accuracy: 95.50%, sensitivity: 94.50%, and specificity: 96.00% across a five-fold cross-validation strategy on the ECG datasets. It also achieved 95.10% accuracy, 94.60% sensitivity, 95.20% specificity on an unseen PPG dataset. The results show considerable promise towards seamless adaptation of gold-standard ECG trained models for non-ambulatory AF detection with consumer wearable devices through HRV-based knowledge transfer.

One-Dimensional Deep Convolutional Neural Network for Mineral Classification from Raman Spectroscopy

One-Dimensional Deep Convolutional Neural Network for Mineral Classification from Raman Spectroscopy

Rice Leaf Blast Classification Method Based on Fused Features and One-Dimensional Deep Convolutional Neural Network

Rice leaf blast, which is seriously affecting the yield and quality of rice around the world, is a fungal disease that easily develops under high temperature and humidity conditions. Therefore, the use of accurate and non-destructive diagnostic methods is important for rice production management. Hyperspectral imaging technology is a type of crop disease identification method with great potential. However, a large amount of redundant information mixed in hyperspectral data makes it more difficult to establish an efficient disease classification model. At the same time, the difficulty and small scale of agricultural hyperspectral imaging data acquisition has resulted in unrepresentative features being acquired. Therefore, the focus of this study was to determine the best classification features and classification models for the five disease classes of leaf blast in order to improve the accuracy of grading the disease. First, the hyperspectral imaging data were pre-processed in order to extract rice leaf samples of five disease classes, and the number of samples was increased by data augmentation methods. Secondly, spectral feature wavelengths, vegetation indices and texture features were obtained based on the amplified sample data. Thirdly, seven one-dimensional deep convolutional neural networks (DCNN) models were constructed based on spectral feature wavelengths, vegetation indices, texture features and their fusion features. Finally, the model in this paper was compared and analyzed with the Inception V3, ZF-Net, TextCNN and bidirectional gated recurrent unit (BiGRU); support vector machine (SVM); and extreme learning machine (ELM) models in order to determine the best classification features and classification models for different disease classes of leaf blast. The results showed that the classification model constructed using fused features was significantly better than the model constructed with a single feature in terms of accuracy in grading the degree of leaf blast disease. The best performance was achieved with the combination of the successive projections algorithm (SPA) selected feature wavelengths and texture features (TFs). The modeling results also show that the DCNN model provides better classification capability for disease classification than the Inception V3, ZF-Net, TextCNN, BiGRU, SVM and ELM classification models. The SPA + TFs-DCNN achieved the best classification accuracy with an overall accuracy (OA) and Kappa of 98.58% and 98.22%, respectively. In terms of the classification of the specific different disease classes, the F1-scores for diseases of classes 0, 1 and 2 were all 100%, while the F1-scores for diseases of classes 4 and 5 were 96.48% and 96.68%, respectively. This study provides a new method for the identification and classification of rice leaf blast and a research basis for assessing the extent of the disease in the field.

A Gas Classification Algorithm of Electronic Noses Based on Convolutional Spiking Neural Network

The electronic nose is a gas detection instrument using the bionic olfactory mechanism, which usually consists of the gas sensor array and the gas classification algorithm. Furthermore, the capability of the gas classification algorithm is critical to the reliable accuracy of gas recognition. The process of gas classification usually involves pattern recognition of multiple time-related gas sensor response curves. Traditional gas classification algorithms are mainly machine learning methods, such as PCA, LDA, ICA, SVM, KNN, etc. These algorithms are relatively cumbersome because we need to extract handcrafted features before using them. Deep learning also has applications in electronic nose gas classification algorithms[1-4] which improve the accuracy of classification result, but most of deep neural networks have complex structures and consume huge computing resources. The spiking neural network is the third generation of artificial neural network, and its spiking neuron model which is more bionic than previous artificial neurons can process spike sequence signals[5]. The spiking neural network model has a simple structure with higher computational efficiency, and takes up less computational resources. Moreover, its time attribute makes it more suitable for processing information about time series. In order to simultaneously take advantage of the efficient feature extraction of the convolutional neural network and the high computational efficiency of the spiking neural network, our team converted the convolutional neural network into a convolutional spiking neural network(CSNN) and applied it to gas classification. The activation function layer in the traditional convolutional layer was replaced with the spiking neuron layer which used the IF or LIF spiking neuron model to transform the continuous values passed by the convolutional later into discrete values so as to achieve the transmission of spikes between layers. The first convolutional spiking layer was used as a spiking encoder, so the spiking encoding method such as Gaussian encoding was not used. The spike-firing-frequency output by the last layer of neurons was calculated to obtain the classification result. The probability that a gas sample belonged to a certain class was proportional to the spike-firing-frequency of the corresponding neuron of the class. Our team built a convolutional spiking neural network model with 9 layers of convolutional spiking layer and 2 layers of fully connected spiking layer, and used the food spoilage gas dataset collected by us and open source gas mixtures dataset[6] to evaluate the capability of our model. With regard to the gas mixtures dataset, ethylene, methane, CO and their mixed state need to be classified. After training, CSNN achieved the test accuracy of 92.6%, and the other algorithms’ test accuracy were 92.9% of ResNet-18, 91.2% of one-dimensional deep convolutional neural network(1D-DCNN) and 88.5% of SVM. As for the food spoilage gas dataset, 30 types of spoiled meat, vegetables, fruits and their mixed state samples were measured. The first task was to classify the major categories of spoiled food, furtuer, the 30 types of spoiled food odor samples were going to be divided into 4 categories: fresh food, spoiled meat, spoiled vegetables and spoiled fruits. After training, CSNN achieved the test accuracy of 81.4% which had a certain accuracy improvement comparing with 80.6% of ResNet-18, 80.1% of 1D-DCNN and 77.3% of SVM. The second task was to classify the subcategories of spoiled fruits, that is, 8 classes of spoiled fruit odor samples should be classified. After training, CSNN could achieve high test accuracy of 90.7%, and the accuracy of other algorithms was 88.8% of ResNet-18, 87.1% of 1D-DCNN and 77.9% of PCA+ANN. The CSNN output of a spoiled watermelon sample is shown in Figure 1.In conclusion, CSNN had similar odor classification performance to ResNet-18, but the computing resources occupied by CSNN was only 1/5 of ResNet-18. This research shows that the spiking neural network has the advantages of high odor classification accuracy, great calculation efficiency and occupying few computing resources. It is suitable as a gas classification algorithm of electronic nose and for further development.

A Novel One-Dimensional CNN with Exponential Adaptive Gradients for Air Pollution Index Prediction

Air pollution is one of the world’s most significant challenges. Predicting air pollution is critical for air quality research, as it affects public health. The Air Pollution Index (API) is a convenient tool to describe air quality. Air pollution predictions can provide accurate information on the future pollution situation, effectively controlling air pollution. Governments have expressed growing concern about air pollution due to its global effect on human health and sustainable growth. This paper proposes a novel forecasting model using One-Dimensional Deep Convolutional Neural Network (1D-CNN) and Exponential Adaptive Gradients (EAG) optimization to predict API for a selected location, Klang, a city in Malaysia. The proposed 1D-CNN–EAG exponentially accumulates past model gradients to adaptively tune the learning rate and converge in both convex and non-convex areas. We use hourly air pollution data over three years (January 2012 to December 2014) for training. Parameter optimization and model evaluation was accomplished by a grid-search with k-folds cross-validation. Results have confirmed that the proposed approach achieves better prediction accuracy than the benchmark models in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and the Correlation Coefficient (R-Squared) with values of 2.036, 2.354, 4.214 and 0.966, respectively, and time complexity.

Improved Deep Learning Technique to Detect Freezing of Gait in Parkinson’s Disease Based on Wearable Sensors

Freezing of gait (FOG) is a paroxysmal dyskinesia, which is common in patients with advanced Parkinson’s disease (PD). It is an important cause of falls in PD patients and is associated with serious disability. In this study, we implemented a novel FOG detection system using deep learning technology. The system takes multi-channel acceleration signals as input, uses one-dimensional deep convolutional neural network to automatically learn feature representations, and uses recurrent neural network to model the temporal dependencies between feature activations. In order to improve the detection performance, we introduced squeeze-and-excitation blocks and attention mechanism into the system, and used data augmentation to eliminate the impact of imbalanced datasets on model training. Experimental results show that, compared with the previous best results, the sensitivity and specificity obtained in 10-fold cross-validation evaluation were increased by 0.017 and 0.045, respectively, and the equal error rate obtained in leave-one-subject-out cross-validation evaluation was decreased by 1.9%. The time for detection of a 256 data segment is only 0.52 ms. These results indicate that the proposed system has high operating efficiency and excellent detection performance, and is expected to be applied to FOG detection to improve the automation of Parkinson’s disease diagnosis and treatment.

Towards an Interpretable Classifier for Characterization of Endoscopic Mayo Scores in Ulcerative Colitis Using Raman Spectroscopy.

Ulcerative colitis (UC) is one of the main types of chronic inflammatory diseases that affect the bowel, but its pathogenesis is yet to be completely defined. Assessing the disease activity of UC is vital for developing a personalized treatment. Conventionally, the assessment of UC is performed by colonoscopy and histopathology. However, conventional methods fail to retain biomolecular information associated to the severity of UC and are solely based on morphological characteristics of the inflamed colon. Furthermore, assessing endoscopic disease severity is limited by the requirement for experienced human reviewers. Therefore, this work presents a nondestructive biospectroscopic technique, for example, Raman spectroscopy, for assessing endoscopic disease severity according to the four-level Mayo subscore. This contribution utilizes multidimensional Raman spectroscopic data to generate a predictive model for identifying colonic inflammation. The predictive modeling of the Raman spectroscopic data is performed using a one-dimensional deep convolutional neural network (1D-CNN). The classification results of 1D-CNN achieved a mean sensitivity of 78% and a mean specificity of 93% for the four Mayo endoscopic scores. Furthermore, the results of the 1D-CNN are interpreted by a first-order Taylor expansion, which extracts the Raman bands important for classification. Additionally, a regression model of the 1D-CNN model is constructed to study the extent of misclassification and border-line patients. The overall results of Raman spectroscopy with 1D-CNN as a classification and regression model show a good performance, and such a method can serve as a complementary method for UC analysis.

Application of a new one-dimensional deep convolutional neural network for intelligent fault diagnosis of rolling bearings.

As one of the key parts of rotary machine, the fault diagnosis and running condition monitoring of rolling bearings are of great importance for normal working and safe production of rotary machine. However, the traditional diagnosis approaches merely count on artificial feature extraction and domain expertise. Meanwhile, the existing convolutional neural networks (CNNs) have the problem of low fault recognition rates. This paper proposes a novel convolutional neural network with one-dimensional structure (ODCNN) for the automatical fault diagnosis of rolling bearings, which adopts six sets of convolutional and max-pooling layers to extract signal features and applies a flattening convolutional layer followed by two fully-connected layers for feature classification. The architectures of one-dimensional LeNet-5, AlexNet, and the proposed ODCNN are illustrated in detail, followed by the obtaining of training and testing samples, which is pre-processed by overlapping the vibration signals of rolling bearings. Finally, the classification experiment is carried out. The experimental results show that the ODCNN has higher fault diagnosis rates and can achieve high accuracy with load variant. Additionally, the extracted features of three CNNs are visualized, which illustrate that the new CNN has a better classification capacity.