Refined composite multivariate multiscale symbolic dynamic entropy and its application to fault diagnosis of rotating machine
آنتروپی پویای نمادین چند متغیره کامپوزیت تصفیه شده و کاربرد آن در تشخیص خطای ماشین چرخشی-2020
Accurate and efficient identification of various fault categories, especially for the big data and multisensory system, is a challenge in rotating machinery fault diagnosis. For the diagnosis problems with massive multivariate data, extracting discriminative and stable features with high efficiency is the significant step. This paper proposes a novel feature extraction method, called Refined Composite multivariate Multiscale Symbolic Dynamic Entropy (RCmvMSDE), based on the refined composite analysis and multivariate multiscale symbolic dynamic entropy. Specifically, multivariate multiscale symbolic dynamic entropy can capture more identification information from multiple sensors with superior computational efficiency, while refine composite analysis guarantees its stability. The abilities of the proposed method to measure the complexity of multivariate time series and identify the signals with different components are discussed based on adequate simulation analysis. Further, to verify the effectiveness of the proposed method on fault diagnosis tasks, a centrifugal pump dataset under constant speed condition and a ball bearing dataset under time-varying speed condition are applied. Compared with the existing methods, the proposed method improves the classification accuracy and F-score to 99.81% and 0.9981, respectively. Meanwhile, the proposed method saves at least half of the computational time. The result shows that the proposed method is effective to improve the efficiency and classification accuracy dealing with the massive multivariate signals.
Keywords: Multivariate multiscale symbolic dynamic | entropy | Random forest | Time-varying speed conditions | Fault diagnosis
Can the development of a patient’s condition be predicted through intelligent inquiry under the e-health business mode? Sequential feature map-based disease risk prediction upon features selected from cognitive diagnosis big dat
آیا می توان از طریق استعلام هوشمند تحت شرایط تجارت الکترونیکی ، وضعیت یک بیمار را پیش بینی کرد؟ پیش بینی خطر ابتلا به بیماری مبتنی بر ویژگی های توالی بر ویژگی های انتخاب شده از تشخیص شناختی داده های بزرگ-2020
The data-driven mode has promoted the researches of preventive medicine. In prediction of disease risks, physicians’ clinical cognitive diagnosis data can be used for early prevention of diseases and, therefore, to reduce medical cost, to improve accessibility of medical services and to lower medical risk. However, researches involved no physicians’ cognition of patients’ conditions in intelligent inquiry under e-health business mode, offered no diagnosis big data, neglected the values of the fused text information generated by joint activities of online and offline medical data, and failed to thoroughly analyze the phenomenon of redundancy-complementarity dispersion caused by high-order information shortage from the online inquiry data-driven perspective. Besides, the risk prediction simply based on offline clinical cognitive diagnosis data undoubtedly reduces prediction precision. Importantly, relevant researches rarely considered temporal relationships of different medical events, did not conduct detailed analysis on practical problems of pattern explosion, did not offer a thought of intelligent portrayal map, and did not conduct relevant risk prediction based on the sub-maps obtained from the map. In consequence, the paper presents a disease risk prediction method with the model for redundancy-complementarity dispersion-based feature selection from physicians’ online cognitive diagnosis big data to realize features selection from the cognitive diagnosis big data of online intelligent inquiry; the obtained features were ranked intelligently for subsequent high-dimensional information shortage compensation; the compensated key feature information of the cognitive diagnosis big data was fused with offline electronic medical record (EMR) to form the virtual electronic medical record (VEMR). The formed VEMR was combined with the method of the sequential feature map for modelling, and a sequential feature map-based model for disease risk prediction was presented to obtain online users’ medical conditions. A neighborhood-based collaborative prediction model was presented for prediction of an online intelligent medical inquiry user’s possible diseases in the future and to intelligently rank the risk probabilities of the diseases. In the experiments, the online intelligent medical inquiry users’ VEMRs were used as the foundation of the simulation experiments to predict disease risks in chronic obstructive pulmonary disease (OCPD) population and rheumatic heart disease (RHD) population. The experiments demonstrated that the presented method showed relatively good metric performances in the VEMR and improved disease risk prediction.
Keywords: Cognitive diagnosis big data | Online intelligent inquiry | Sequential feature map | Disease risk prediction | Redundancy and complementarity dispersion
Detection and classification of bruises of pears based on thermal images
تشخیص و طبقه بندی کبودی گلابی بر اساس تصاویر حرارتی-2020
The detection and classification of bruises of pears based on thermal images have been investigated. A simple thermal imaging system in the long-wavelength ranges (8–14 μm) assembledμwith hot air equipment was constructed to capture cleaner images. Higher velocity and temperature of the air reduced the time required to obtain a clean image, but the images were not sufficient able to discriminate the slight and invisible variation of bruises over consecutive days. The grey-level co-occurrence matrix of the thermal images were analysed, and the slight differences in the pears over consecutive days were presented in the form of a line chart. A traditional deep learning algorithm commonly used in classification of big data sets was modified to one suitable for classification of a small sample data set of thermasl images (3246 samples were used as the training data set and 1125 were used as a test data set) collected from 300 pears over 10 days. The best test prediction accuracy obtained was 99.25%.
Keywords: Detection and classification | Thermal images | Grey-level co-occurrence matrix | Deep learning
Highway crash detection and risk estimation using deep learning
تشخیص تصادف بزرگراه و تخمین ریسک با استفاده از یادگیری عمیق-2020
Crash Detection is essential in providing timely information to traffic management centers and the public to reduce its adverse effects. Prediction of crash risk is vital for avoiding secondary crashes and safeguarding highway traffic. For many years, researchers have explored several techniques for early and precise detection of crashes to aid in traffic incident management. With recent advancements in data collection techniques, abundant real-time traffic data is available for use. Big data infrastructure and machine learning algorithms can utilize this data to provide suitable solutions for the highway traffic safety system. This paper explores the feasibility of using deep learning models to detect crash occurrence and predict crash risk. Volume, Speed and Sensor Occupancy data collected from roadside radar sensors along Interstate 235 in Des Moines, IA is used for this study. This real-world traffic data is used to design feature set for the deep learning models for crash detection and crash risk prediction. The results show that a deep model has better crash detection performance and similar crash prediction performance than state of the art shallow models. Additionally, a sensitivity analysis was conducted for crash risk prediction using data 1-minute, 5-minutes and 10-minutes prior to crash occurrence. It was observed that is hard to predict the crash risk of a traffic condition, 10 min prior to a crash.
Keywords: Crash detection | Crash prediction | Deep learning
Automatic bad channel detection in implantable brain-computer interfaces using multimodal features based on local field potentials and spike signals
تشخیص خودکار کانال بد در رابط های قابل کاشت مغز با کامپیوتر با استفاده از ویژگی های چند حالته بر اساس پتانسیل های محلی و سیگنال های لبه-2020
“Bad channels” in implantable multi-channel recordings bring troubles into the precise quantitative description and analysis of neural signals, especially in the current “big data” era. In this paper, we combine multimodal features based on local field potentials (LFPs) and spike signals to detect bad channels automatically using machine learning. On the basis of 2632 pairs of LFPs and spike recordings acquired from five pigeons, 12 multimodal features are used to quantify each channel’s temporal, frequency, phase and firing-rate properties. We implement seven classifiers in the detection tasks, in which the synthetic minority oversampling technique (SMOTE) system and Fisher weighted Euclidean distance sorting (FWEDS) are used to cope with the class imbalance problem. The results of the two-dimensional scatterplots and classifications demonstrate that correlation coefficient, phase locking value, and coherence have good discriminability. For the multimodal features, almost all the classifiers can obtain high accuracy and bad channel detection rate after the SMOTE operation, in which the Random Forests classifier shows relatively better comprehensive performance (accuracy: 0.9092 � 0.0081, precision: 0.9123 � 0.0100, and recall: 0.9057 � 0.0121). The proposed approach can automatically detect bad channels based on multimodal features, and the results provide valuable references for larger datasets.
Keywords: Bad channel | Multimodal feature | LFP | Spike | Machine learning
Data-driven software defined network attack detection : State-of-the-art and perspectives
تشخیص حمله به شبکه تعریف شده نرم افزار داده محور: حالت پیشرفته و چشم انداز-2020
SDN (Software Defined Network) has emerged as a revolutionary technology in network, a substantial amount of researches have been dedicated to security of SDNs to support their various applications. The paper firstly analyzes State-of-the-Art of SDN security from data perspectives. Then some typical network attack detection (NAD) methods are surveyed, in- cluding machine learning based methods and statistical methods. After that, a novel tensor based network attack detection method named tensor principal component analysis (TPCA) is proposed to detect attacks. After surveying the last data-driven SDN frameworks, a ten- sor based big data-driven SDN attack detection framework is proposed for SDN security. In the end, a case study is illustrated to verify the effectiveness of the proposed framework.
Keywords: Network attack detection | Data-driven | Tensor | Network security | Software defined network (SDN)
A fully scalable big data framework for Botnet detection based on network traffic analysis
چارچوب داده های بزرگ کاملاً مقیاس پذیر برای تشخیص Botnet مبتنی بر آنالیز ترافیک شبکه-2020
Many traditional Botnet detection methods have trouble scaling up to meet the needs of multi-Gbps networks. This scalability challenge is not just limited to bottlenecks in the detection process, but across all individual components of the Botnet detection system in- cluding data gathering, storage, feature extraction, and analysis. In this paper, we propose a fully scalable big data framework that enables scaling for each individual component of Botnet detection. Our framework can be used with any Botnet detection method - includ- ing statistical methods, machine learning methods, and graph-based methods. Our experi- mental results show that the proposed framework successfully scales in live tests on a real network with 5Gbps of traffic throughput and 50 millions IP addresses visits. In addition, our run time scales logarithmically with respect to the volume of the input for example, when the scale of the input data multiplies by 4 ×, the total run time increases by only 31%. This is significant improvement compared to schemes such as Botcluster in which run time increases by 86% under similar scale condition.
Keywords: Botnet detection | Big data | Hadoop | Spark | Machine learning | Scalability
A hybrid deep learning model for efficient intrusion detection in big data environment
یک مدل یادگیری عمیق ترکیبی برای تشخیص نفوذ موثر در محیط داده های بزرگ-2020
The volume of network and Internet traffic is expanding daily, with data being created at the zettabyte to petabyte scale at an exceptionally high rate. These can be character- ized as big data, because they are large in volume, variety, velocity, and veracity. Security threats to networks, the Internet, websites, and organizations are growing alongside this growth in usage. Detecting intrusions in such a big data environment is difficult. Various intrusion-detection systems (IDSs) using artificial intelligence or machine learning have been proposed for different types of network attacks, but most of these systems either cannot recognize unknown attacks or cannot respond to such attacks in real time. Deep learning models, recently applied to large-scale big data analysis, have shown remarkable performance in general but have not been examined for detection of intrusions in a big data environment. This paper proposes a hybrid deep learning model to efficiently detect network intrusions based on a convolutional neural network (CNN) and a weight-dropped, long short-term memory (WDLSTM) network. We use the deep CNN to extract mean- ingful features from IDS big data and WDLSTM to retain long-term dependencies among extracted features to prevent overfitting on recurrent connections. The proposed hybrid method was compared with traditional approaches in terms of performance on a publicly available dataset, demonstrating its satisfactory performance.
Keywords: Big data | Intrusion | detection Deep learning | Convolution neural network | Weight-dropped long short-term memory | network
A new pyramidal opponent color-shape model based video shot boundary detection
A new pyramidal opponent color-shape model based video shot boundary detection-2020
Video shot boundary detection (VSBD) is one of the most essential criteria for many intelligent video analysis-related applications, such as video retrieval, indexing, browsing, categorization and summarization. VSBD aims to segment big video data into meaningful fragments known as shots. This paper put forwards a new pyramidal opponent colour-shape (POCS) model which can detect abrupt transition (AT) and gradual transition (GT) simultaneously, even in the presence of illumination changes, huge object movement between frames, and fast camera motion. First, the content of frames in the video subjected to VSBD is represented by the proposed POCS model. Consequently, the temporal nature of the POCS model is subjected to a suitable segment (SS) selection procedure in order to minimize the complexity of VSBD method. The SS from the video frames is examined for transitions within it using a bagged-trees classifier (BTC) learned on a balanced training set via parallel processing. To prove the superiority of the proposed VSBD algorithm, it is evaluated on the TRECVID 2001, TRECVID2007 and VIDEOSEG2004 data sets for classifying the basic units of video according to no transition (NT), AT and GT. The experimental evaluation results in an F1-score of 95.13%, 98.13% and 97.11% on the TRECVID 2001, TRECVID2007 and VIDEOSEG2004 data sets, respectively.
Keywords: Shot Boundary Detection | Abrupt Transition | Gradual Transition | Opponent Color space | Ensemble Algorithm
Multi-model ensemble with rich spatial information for object detection
اثر گروهی چند مدلی با اطلاعات مکانی غنی برای ردیابی شی-2020
Due to the development of deep learning networks and big data dimensionality, research on ensemble deep learning is receiving an increasing amount of attention. This paper takes the object detection task as the research domain and proposes an object detection framework based on ensemble deep learning. To guarantee the accuracy as well as real-time detection, the detector uses a Single Shot MultiBox Detector (SSD) as the backbone and combines ensemble learning with context modeling and multi-scale feature representation. Two modes were designed in order to achieve ensemble learning: NMS Ensembling and Feature Ensembling. In addition, to obtain contextual information, we used dilated convolution to ex- pand the receptive field of the network. Compared with state-of-the-art detectors, our detector achieves superior performance on the PASCAL VOC set and the MS COCO set.
Keywords: Ensemble learning | Object detection | Dilated convolution | Feature fusion