SDTM: A Novel Topic Model Framework for Syndrome Differentiation in Traditional Chinese Medicine

Syndrome differentiation is the most basic diagnostic method in traditional Chinese medicine (TCM). The process of syndrome differentiation is difficult and challenging due to its complexity, diversity, and vagueness. Recently, artificial intelligent methods have been introduced to discover the regularities of syndrome differentiation from TCM medical records, but the existing DM algorithms failed to consider how a syndrome is generated according to TCM theories. In this paper, we propose a novel topic model framework named syndrome differentiation topic model (SDTM) to dynamically characterize the process of syndrome differentiation. The SDTM framework utilizes latent Dirichlet allocation (LDA) to discover the latent semantic relationship between symptoms and syndromes in mass of Chinese medical records. We also use similarity measurement method to make the uninterpretable topics correspond with the labeled syndromes. Finally, Bayesian method is used in the final differentiated syndromes. Experimental results show the superiority of SDTM over existing topic models for the task of syndrome differentiation.

CNN-Based Fault Detection for Smart Manufacturing

A smart factory is a highly digitized and networked production facility based on smart manufacturing. A smart manufacturing plant is the result of intelligent systems deployed in the factory. Smart factories have higher production volumes and are prone to machine failures when operating in almost all applications on a daily basis. With the growing concept of smart manufacturing required for Industry 4.0, intelligent methods for detecting and classifying bearing faults have become a subject of scientific research and interest. In this paper, a deep learning-based 1-D convolutional neural network is proposed using the time-sequence bearing data from the Case Western Reserve University (CWRU) bearing database. Four different sets of data are used. The proposed method achieves state-of-the-art accuracy even with a small amount of training data. For the sensitivity analysis of the proposed method, metrics such as precision, recall, and f-measure are determined. Next, we compare the proposed method with a 2-D CNN that uses two-dimensional image illustrations of raw data as input. This method shows the effectiveness of using 1-D CNNs over 2-D CNNs for time-sequence data. The proposed method is computationally inexpensive and outperforms the most complex and computationally intensive algorithms used for bearing fault detection and diagnosis.

Cataract Disease Detection by Using Transfer Learning-Based Intelligent Methods

One of the most common visual disorders is cataracts, which people suffer from as they get older. The creation of a cloud on the lens of our eyes is known as a cataract. Blurred vision, faded colors, and difficulty seeing in strong light are the main symptoms of this condition. These symptoms frequently result in difficulty doing a variety of tasks. As a result, preliminary cataract detection and prevention may help to minimize the rate of blindness. This paper is aimed at classifying cataract disease using convolutional neural networks based on a publicly available image dataset. In this observation, four different convolutional neural network (CNN) meta-architectures, including InceptionV3, InceptionResnetV2, Xception, and DenseNet121, were applied by using the TensorFlow object detection framework. By using InceptionResnetV2, we were able to attain the avant-garde in cataract disease detection. This model predicted cataract disease with a training loss of 1.09%, a training accuracy of 99.54%, a validation loss of 6.22%, and a validation accuracy of 98.17% on the dataset. This model also has a sensitivity of 96.55% and a specificity of 100%. In addition, the model greatly minimizes training loss while boosting accuracy.

Locomotive asynchronous traction motor rolling bearing fault detection based on current intelligent methods

The paper is devoted to current issues of locomotive asynchronous traction motor (ATEM) fault detection using neural networks. Developed sophisticated intelligent methods for monitoring and inspecting the technical condition of ATE bearings. Current absorption spectra are analysed to assess the technical condition of the induction bearing units. The mechanical vibration frequencies of a squirrel cage induction motor are presented. The method of artificial neural networks which are universal approximators and can effectively and efficiently solve problems of monitoring and diagnostics of technical condition of locomotive induction traction motors is applied. A neural network model and framework for monitoring the technical condition of ATED bearings has been developed. They are based on rules and user-provided facts to recognise the situation, make a diagnosis, formulate a solution or make a recommendation. The main failures of the bearing units of squirrel cage ATED are analysed. A methodology has been developed to build a neural network model of the ATED. The structure and architecture of the artificial neural network is defined. An experimental research has been conducted. The results enable the determination of bearing faults in asynchronous traction motors with squirrel cage rotor.

Intelligent Approaches for Enhancing Networked Routing Protocol

The rapid evolution of wireless networking technologies opens the door to the evolution of the Wireless Sensor Networks (WSNs) and their applications in different fields. The WSN consists of small energy sensor nodes used in a harsh environment. The energy needed to communicate between the sensors networks can be identified as one of the major challenges. It is essential to avoid massive loss, or loss of packets, as well as rapid energy depletion and grid injustice, which lead to lower node efficiency and higher packet delivery delays. For this purpose, it was very important to track the usage of energy by nodes in order to improve general network efficiency by the use of intelligent methods to reduce the energy used to extend the life of the WSN and take successful routing decisions. For these reasons, designing an energy-efficient system that utilizes intelligent approaches is considered as the most powerful way to prolong the lifetime of the WSN. The proposed system is divided into four phases (sensor deployment phase, clustering phase, intra-cluster phase, and inter-cluster phase). Each of these phases uses a different intelligent algorithm with some enhancements. The performance of the proposed system was analyzed and evaluations were elaborated with well-known existing routing protocols. To assess the proficiency of the proposed system and evaluate the endurance of the network, efficiency parameters such as network lifetime, energy consumption, and packet delivery to the Sink (Base station) were exploited. The experimental outcomes justify that the proposed system surpasses the existing mechanisms by 50%.

Artificial intelligent techniques based on direct torque control of induction machines

The direct torque control (DTC) system, which is based on induction machine drive is a developed and simple control method. It allows high dynamic performance with very simple hysteresis control scheme; However, its disadvantages are high current, torque, and flux ripple. In this paper, DTC of induction machine drive has been improved by using the applications of artificial intelligence (AI) approaches to reduce the current, torque, and flux ripples and also get better performance of the machines. At the conclusion of this study, the outcomes of traditional DTC and artificial intelligent methods are compared. By the program MATLAB/SIMULINK, the modeling and simulation results of the DTC system for induction machine (IM) have been proposed.

A Fault Diagnosis Method Based on EEMD and Statistical Distance Analysis

Serious vibration or wear with large friction usually appear when faults occur, which leads to more serious faults such as the destruction of the oil film, bringing great damages to both the society and economic sector. Therefore, the accurate diagnosis of a fault in the early stage is important for the safety operation of machinery. To effectively extract the fault features for diagnosis, EMD-based methods are widely used. However, these methods spend lots of efforts diagnosing faults and require plenty of professional knowledge of diagnosis. Although many intelligent classifiers can be used to automatically diagnose faults such as wear, a broken tooth and imbalance, the combing EMD-based method, the scarcity of samplings with labels hinder the application of these methods to engineering. It is because the model of the intelligent classifier must be constructed based on sufficient samplings with a label. To solve this problem, we propose a novel fault diagnosis method, which is performed based on the EEMD and statistical distance analysis. In this method, the EEMD is used to decompose one original signal into several IMFs and then the probability density distribution of each IMF is calculated. To diagnose the fault of the machinery, the Euclidean distance between the signal acquired under an unknown fault with the other referenced signals acquired previously under various fault types is calculated. At last, the fault of the signal is the same with the referenced signal when the distance is the smallest. To verify the effectiveness of our proposed method, a dataset of bearings with different faults, and a dataset of 2009 Prognostics and Health Management (PHM) data challenge, including gear, bearing and shaft faults are used. The result shows that the proposed method can not only automatically diagnose faults effectively, but also fewer samplings with a label are used compared with the intelligent methods.

Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models

Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the brain, the function of some brain regions is out of balance, leading to the lack of coordination between thoughts, actions, and emotions. This study provides various intelligent deep learning (DL)-based methods for automated SZ diagnosis via electroencephalography (EEG) signals. The obtained results are compared with those of conventional intelligent methods. To implement the proposed methods, the dataset of the Institute of Psychiatry and Neurology in Warsaw, Poland, has been used. First, EEG signals were divided into 25 s time frames and then were normalized by z-score or norm L2. In the classification step, two different approaches were considered for SZ diagnosis via EEG signals. In this step, the classification of EEG signals was first carried out by conventional machine learning methods, e.g., support vector machine, k-nearest neighbors, decision tree, naïve Bayes, random forest, extremely randomized trees, and bagging. Various proposed DL models, namely, long short-term memories (LSTMs), one-dimensional convolutional networks (1D-CNNs), and 1D-CNN-LSTMs, were used in the following. In this step, the DL models were implemented and compared with different activation functions. Among the proposed DL models, the CNN-LSTM architecture has had the best performance. In this architecture, the ReLU activation function with the z-score and L2-combined normalization was used. The proposed CNN-LSTM model has achieved an accuracy percentage of 99.25%, better than the results of most former studies in this field. It is worth mentioning that to perform all simulations, the k-fold cross-validation method with k = 5 has been used.