Multi-domain Information Research Articles

Bearing Fault Diagnosis Based on Multiple Transformation Domain Fusion and Improved Residual Dense Networks

Automatic feature extraction is one of the most advantageous merits of deep neural network (DNN), meanwhile, it is an important part for intelligent bearing fault diagnosis. However, most of fault diagnosis methods based on DNN usually excavate the complex relations from original time sequence signals which only present the fault information in time domain. Convolutional Neural Network (CNN) has demonstrated powerful feature learning capabilities in bearing fault diagnosis and the deeper the diagnosis model is, the better the recognition performance is, which resulted in some problems. In order to enrich the fault information from different views and enhance the discrimination for features learned from diagnosis network, this paper proposed a bearing fault diagnosis method based on multi-domain information fusion and improved residual dense network. The original signal and its transformed signals composed the multi-channel input, which contained more comprehensive information and will benefit the deep learning. Then it designed a residual dense network and introduced the convolution attention mechanism which can discriminate the importance of features further improve the feature extraction capability and efficiency of diagnosis network. Finally, it achieved the fault classification, analyzed the effects of key parameters and compared with other diagnosis to verify the effectiveness by lots of experimental results.

Multilevel Feature Learning Method for Accurate Interictal Epileptiform Spike Detection.

Interictal epileptiform spike (referred to as spike) detected from electroencephalograms lasting only 20- to 200-ms can provide a reliable evidence-based indicator for clinical seizure type diagnosis. Recent feature representation approaches focus either on the concrete-level or abstract-level information mining of the spike, thus demonstrating suboptimal detection performance. Additionally, existing abstract-level information mining methods of the spike based deep learning networks have not realized the effective feature representation of long-term dependent distinguished information within similar waveform cycles caused by morphological heterogeneity, which affects detection performance. Thus, a multilevel feature learning method for accurate spike detection was proposed in this study. Specifically, the spatio-temporal-frequency multidomain information in concrete-level first are inferred the common mimetic properties of the spike using the multidomain feature extractors. Then, the effective feature representation of long-term dependent distinguished information within similar waveform cycles caused by morphological heterogeneity is suitably captured using the temporal convolutional network. Finally, the spatio-temporal-frequency multidomain long-term dependent feature representation of spike is calculated using the element-wise manner to fuse the feature representation in concrete- and abstract-levels. The experimental results indicate that the proposed method can achieve an accuracy of 90.62±1.38%, sensitivity of 90.38±1.52%, specificity of 91.00±1.60%, precision of 90.33±4.71%, and the false detection rate per minute is [Formula: see text], which are higher than when using the feature representation in the concrete-or abstract-level alone. Additionally, the detection results indicate that the proposed method avoids the subjectivity and inefficiency of visual inspection, and it enables a highly accurate detection of the spike.

Bearing Fault Diagnosis Using Multidomain Fusion-Based Vibration Imaging and Multitask Learning.

Statistical features extraction from bearing fault signals requires a substantial level of knowledge and domain expertise. Furthermore, existing feature extraction techniques are mostly confined to selective feature extraction methods namely, time-domain, frequency-domain, or time-frequency domain statistical parameters. Vibration signals of bearing fault are highly non-linear and non-stationary making it cumbersome to extract relevant information for existing methodologies. This process even became more complicated when the bearing operates at variable speeds and load conditions. To address these challenges, this study develops an autonomous diagnostic system that combines signal-to-image transformation techniques for multi-domain information with convolutional neural network (CNN)-aided multitask learning (MTL). To address variable operating conditions, a composite color image is created by fusing information from multi-domains, such as the raw time-domain signal, the spectrum of the time-domain signal, and the envelope spectrum of the time-frequency analysis. This 2-D composite image, named multi-domain fusion-based vibration imaging (MDFVI), is highly effective in generating a unique pattern even with variable speeds and loads. Following that, these MDFVI images are fed to the proposed MTL-based CNN architecture to identify faults in variable speed and health conditions concurrently. The proposed method is tested on two benchmark datasets from the bearing experiment. The experimental results suggested that the proposed method outperformed state-of-the-arts in both datasets.

A massively multi-scale approach to characterizing tissue architecture by synchrotron micro-CT applied to the human placenta.

Multi-scale structural assessment of biological soft tissue is challenging but essential to gain insight into structure–function relationships of tissue/organ. Using the human placenta as an example, this study brings together sophisticated sample preparation protocols, advanced imaging and robust, validated machine-learning segmentation techniques to provide the first massively multi-scale and multi-domain information that enables detailed morphological and functional analyses of both maternal and fetal placental domains. Finally, we quantify the scale-dependent error in morphological metrics of heterogeneous placental tissue, estimating the minimal tissue scale needed in extracting meaningful biological data. The developed protocol is beneficial for high-throughput investigation of structure–function relationships in both normal and diseased placentas, allowing us to optimize therapeutic approaches for pathological pregnancies. In addition, the methodology presented is applicable in the characterization of tissue architecture and physiological behaviours of other complex organs with similarity to the placenta, where an exchange barrier possesses circulating vascular and avascular fluid spaces.

DTCNNMI: A deep twin convolutional neural networks with multi-domain inputs for strongly noisy diesel engine misfire detection

Although machine learning-based intelligent detection methods have made many achievements for diesel engine misfire diagnosis, they suffer from a certain degree of performance degradation due to the strong environmental noise and the change of working conditions in the actual application scenarios. To tackle this issue, this study presents a deep twin convolutional neural networks with multi-domain inputs (DTCNNMI) for diesel engine misfire diagnosis under strong environmental noise and different working conditions. Vibration signals of engine cylinder heads at different speeds are collected experimentally and fed into input layers of the proposed model. It constructs three input layers to combine automatically extracted time-domain, time-frequency-domain and hand-craft time-domain statistical features, improving the model performance. Twin convolutional neural networks with large first-layer kernels are presented for extracting multi-domain information of vibration signals and resist influence of environmental noise and the change of operating conditions on the final diagnosis results. The effectiveness of the proposed approach is validated on the datasets collected experimentally and by making comparison with the existing representative algorithms. The results demonstrate that the accuracy and anti-noise performance of the proposed DTCNNMI outperforms the existing algorithms. On the constructed four datasets under different working conditions, the proposed method achieved at least 97.019% accuracy even when signal-to-noise ratio was −4 dB, which is much higher than those of the other methods. Moreover, the accuracy of the proposed DTCNNMI on datasets A, B, C and D is at least 61.177%, 33.334%, 17.646% and 29.961% higher than other methods, respectively.

A proposed combined Cornell-Sokolow modeling approach to determine the left ventricular hypertrophy accurately based on multi-domain information processing

Left Ventricular Hypertrophy (LVH) is associated with cardiomyopathy and many other heart diseases. In this paper, the authors have proposed a combined Cornell-Sokolow (CCS) methodology to achieve significant improvement in detecting LVH by ECG/EKG with multi-domain analysis. Considering two famous voltage criteria simultaneously helps prevent the concealing of LVH in the patient’s data. The advancement of multi-domain analysis gives access to reach the algorithm from any common and cost-effective platform. This multi-domain analysis consists of both image and signal processing of the ECG data. In a MATLAB environment, these processes are quite accurate and optimized. With the help of the CCS model, the outcome of this research is satisfactory, both in terms of feature detection and LVH observation.

A CNN-Based Fast Inter Coding Method for VVC

The Versatile Video Coding (VVC) achieves superior coding efficiency as compared with the High Efficiency Video Coding (HEVC), while its excellent coding performance is at the cost of several high computational complexity coding tools, such as Quad-Tree plus Multi-type Tree (QTMT)-based Coding Units (CUs) and multiple inter prediction modes. To reduce the computational complexity of VVC, a CNN-based fast inter coding method is proposed in this paper. First, a multi-information fusion CNN (MF-CNN) model is proposed to early terminate the QTMT-based CU partition process by jointly using the multi-domain information. Then, a content complexity-based early Merge mode decision is proposed to skip the time-consuming inter prediction modes by considering the CU prediction residuals and the confidence of MF-CNN. Experimental results show that the proposed method reduces an average of 30.63% VVC encoding time, and the Bjøontegaard Delta Bit Rate (BDBR) increases about 3%.

Multidomain Features Fusion for Zero-Shot Learning

Given a novel class instance, the purpose of zero-shot learning (ZSL) is to learn a model to classify the instance by seen samples and semantic information transcending class boundaries. The difficulty lies in how to find a suitable space for zero-shot recognition. The previous approaches use semantic space or visual space as classification space. These methods, which typically learn visual-semantic or semantic-visual mapping and directly exploit the output of the mapping function to measure similarity to classify new categories, do not adequately consider the complementarity and distribution gap of multiple domain information. In this paper, we propose to learn a multidomain information fusion space by a joint learning framework. Specifically, we consider the fusion space as a shared space in which different domain features can be recovered by simple linear transformation. By learning a n-way classifier of fusion space from the seen class samples, we also obtain the discriminative information of the similarity space to make the fusion representation more separable. Extensive experiments on popular benchmark datasets manifest that our approach achieves state-of-the-art performances in both supervised and unsupervised ZSL tasks.

Classification of Mild Cognitive Impairment from multi-domain features of resting-state EEG.

In recent years, electroencephalography (EEG) has emerged as a low-cost, accessible and objective tools for the early diagnosis of Alzheimer's disease (AD). AD is preceded by Mild Cognitive Impairment (MCI), typically refers to early-stage AD disease. The purpose of this study is to classify MCI patients from the multi-domain features of their electroencephalography (EEG). Firstly, we extracted the multi-domain (time, frequency and information theory) features from resting-state EEG signals before and after a cognitive task from 15 MCI groups and 15 age-matched healthy controls. Then, principal component analysis (PCA) was used to perform feature selection. After that, we compared the performance between SVM and KNN on our EEG dataset. The good performance was observed both from SVM and KNN, which demonstrates the effectiveness of multi-domain features. Furthermore, KNN performs better than SVM and the EEG signals after the cognitive task works better than those before the task.

Optimization of hierarchical reinforcement learning relationship extraction model

PurposeEntity relation extraction is an important research direction to obtain structured information. However, most of the current methods are to determine the relations between entities in a given sentence based on a stepwise method, seldom considering entities and relations into a unified framework. The joint learning method is an optimal solution that combines relations and entities. This paper aims to optimize hierarchical reinforcement learning framework and provide an efficient model to extract entity relation.Design/methodology/approachThis paper is based on the hierarchical reinforcement learning framework of joint learning and combines the model with BERT, the best language representation model, to optimize the word embedding and encoding process. Besides, this paper adjusts some punctuation marks to make the data set more standardized, and introduces positional information to improve the performance of the model.FindingsExperiments show that the model proposed in this paper outperforms the baseline model with a 13% improvement, and achieve 0.742 in F1 score in NYT10 data set. This model can effectively extract entities and relations in large-scale unstructured text and can be applied to the fields of multi-domain information retrieval, intelligent understanding and intelligent interaction.Originality/valueThe research provides an efficient solution for researchers in a different domain to make use of artificial intelligence (AI) technologies to process their unstructured text more accurately.

RMMDI: A Novel Framework for Role Mining Based on the Multi-Domain Information

Role-based access control (RBAC) is widely adopted in network security management, and role mining technology has been extensively used to automatically generate user roles from datasets in a bottom-up way. However, almost all role mining methods discover the user roles from existing user-permission assignments, which neglect the dependency relationships between user permissions. To extend the ability of role mining technology, this paper proposes a novel role mining framework based on multi-domain information. The framework estimates the similarity between different permissions based on the fundamental information in the physical, network, and digital domains and attaches interdependent permissions to the same role. Three simulated network scenarios with different multi-domain configurations are used to validate the effectiveness of our method. The experimental results show that the method can not only capture the interdependent relationships between permissions, but also detect user roles and permissions more reasonably.

Collective spatial keyword search on activity trajectories

Collective spatial keyword query (CSKQ) is one of the most useful spatial queries in location-based service systems. Although the availability of large-scale activity trajectories has given us useful knowledge of users’ behavior, existing activity trajectory search methods are unable to support CSKQ queries reasonably. This paper studies effective and efficient CSKQ processing on activity trajectories to cover the gap. Specifically, we first formalize the problem by a trajectory based model that considers the spatial, activity and popularity issues, enabling more rational CSKQ results to be returned. To avoid high I/O cost, a novel hybrid index structure is further proposed to seamlessly integrate multi-domain information, so that inferior trajectories can be pruned during query processing. A novel candidate sub-trajectory search algorithm is also presented to reduce computation overhead by a linear scan on the trajectory. The experimental results on real check-in datasets demonstrate the efficiency and scalability of our proposed solution.

Security Awareness in Software-Defined Multi-Domain 5G Networks

Fifth generation (5G) technologies will boost the capacity and ease the management of mobile networks. Emerging virtualization and softwarization technologies enable more flexible customization of network services and facilitate cooperation between different actors. However, solutions are needed to enable users, operators, and service providers to gain an up-to-date awareness of the security and trustworthiness of 5G systems. We describe a novel framework and enablers for security monitoring, inferencing, and trust measuring. The framework leverages software-defined networking and big data technologies to customize monitoring for different applications. We present an approach for sharing security measurements across administrative domains. We describe scenarios where the correlation of multi-domain information improves the accuracy of security measures with respect to two threats: end-user location tracking and Internet of things (IoT) authentication storms. We explore the security characteristics of data flows in software networks dedicated to different applications with a mobile network testbed.

A Novel Clutter Cancellation Method Utilizing Joint Multi-Domain Information for Passive Radar

A Novel Clutter Cancellation Method Utilizing Joint Multi-Domain Information for Passive Radar

WizBee: Wise ZigBee Coexistence via Interference Cancellation with Single Antenna

Coexistence of Wi-Fi and ZigBee in 2.4 GHz ISM band is a long standing and challenging problem. Previous solutions either require modifications of current ZigBee protocols or Wi-Fi re-configurations, which is not feasible in large-scale wireless sensor networks. In this paper, we present WizBee, a coexistence system using single-antenna sink without changing current Wi-Fi and ZigBee design. WizBee is based on an observation that Wi-Fi signal is about 5 to 20 dB stronger than ZigBee signal in symmetric area, which leaves much room for applying interference cancelation technique to mitigate Wi-Fi interference, and extract ZigBee signals. However, we need to cancel the Wi-Fi interference perfectly for residual ZigBee signal decoding, which needs more accurate channel coefficient across data transmissions in spite of cross technology interference. For robust and accurate Wi-Fi decoding, we use soft Viterbi decoding with weighted confidence value over interfered subcarriers. Consequently, our solution uses decoded data for channel coefficient estimation instead of conventional training symbol based methods. The key insight is that, the signal recovery opportunity for cross technology coexistence, lies in multi-domain information, such as power, frequency and coding discrepancies. Using these information properly will improve the coexistence network throughput effectively. We implemented WizBee in USRP/GNURadio software radio platform, and studied the decoding performance of interference cancelation technique. Our extensive evaluations under real wireless conditions show that WizBee improves ZigBee throughput up to $1.9\times$ , with median throughput gain of $1.2\times$ .

Privacy-Preserving Multi-Keyword Search in Information Networks

In emerging information networks, it is crucially important to provide efficient search on distributed documents while preserving their owners’ privacy, for which privacy preserving indexes or PPI presents a possible solution. An understudied problem for the PPI techniques is how to provide differentiated privacy preservation in the presence of multi-keyword document search. The differentiation is necessary as terms and phrases bear innate differences in their semantic meanings. In this paper, we present $\epsilon$ -mPPI , the first work to provide the distributed document search with quantitatively differentiated privacy preservation. In the design of $\epsilon$ -mPPI , we identified a suite of challenging problems and proposed novel solutions. For one, we formulated the quantitative privacy computation as an optimization problem that strikes a balance between privacy preservation and search efficiency. We also addressed the challenging problem of secure $\epsilon$ -mPPI construction in the multi-domain information network which lacks mutual trusts between domains. Towards a secure $\epsilon$ -mPPI construction with practically acceptable performance, we proposed to optimize the performance of secure multi-party computations by making a novel use of secret sharing. We implemented the $\epsilon$ -mPPI construction protocol with a functioning prototype. We conducted extensive experiments to evaluate the prototype’s effectiveness and efficiency based on a real-world dataset.

A standardized SOA for clinical data interchange in a cardiac telemonitoring environment.

Care of chronic cardiac patients requires information interchange between patients' homes, clinical environments, and the electronic health record. Standards are emerging to support clinical information collection, exchange and management and to overcome information fragmentation and actors delocalization. Heterogeneity of information sources at patients' homes calls for open solutions to collect and accommodate multidomain information, including environmental data. Based on the experience gained in a European Research Program, this paper presents an integrated and open approach for clinical data interchange in cardiac telemonitoring applications. This interchange is supported by the use of standards following the indications provided by the national authorities of the countries involved. Taking into account the requirements provided by the medical staff involved in the project, the authors designed and implemented a prototypal middleware, based on a service-oriented architecture approach, to give a structured and robust tool to congestive heart failure patients for their personalized telemonitoring. The middleware is represented by a health record management service, whose interface is compliant to the healthcare services specification project Retrieve, Locate and Update Service standard (Level 0), which allows communication between the agents involved through the exchange of Clinical Document Architecture Release 2 documents. Three performance tests were carried out and showed that the prototype completely fulfilled all requirements indicated by the medical staff; however, certain aspects, such as authentication, security and scalability, should be deeply analyzed within a future engineering phase.

Multi-domain Information Exchange in a Bandwidth on Demand Tool

In this paper we describe the multi-domain information exchange aspects of the AutoBAHN tool, which is used for implementing a Bandwidth on Demand service as part of the GEANT project. This project was driven by the increasing demand for reliable and guaranteed data transportation for scientific purposes. In order to implement the process of information exchange in a multi-domain heterogeneous environment several design decisions have to take place, in order to balance and satisfy by the needs of information security, fast information dissemination, efficient request processing, reliability and robustness. The paper describes how AutoBAHN implements a flexible AAI in a distributed environment, how it exchanges and updates information and how it handles incoming requests in an efficient and timely manner. The multi-domain aspect of this effort stems from the fact that the tool is used in the production environment of several cooperating National Research and Educational Networks (NRENs) across Europe. Each NREN may have different underlying technologies, different policies and different use cases, but they all have to coordinate through usage of the AutoBAHN tool in order to produce a multi-domain service of circuit reservation

Radio environment map as enabler for practical cognitive radio networks

Latest regulations on TV white space communications and emerging trends of spectrum access through geolocation databases relax the regulatory constraints on Cognitive Radios (CRs). Geolocation databases are designed to store information related to incumbents, and CRs are envisioned to consult this database before spectrum access. Spectrum occupancy and related environment information can be constructed using these geolocation databases. In that regard, Radio Environment Map (REM) is a promising tool that provides a practical means for the realization of cognitive radio networks (CRNs). It constructs a comprehensive map of the CRN by utilizing multi-domain information from geolocation databases, characteristics of spectrum use, geographical terrain models, propagation environment, and regulations. REMs contribute to cognition engines by building long-term knowledge via processing spectrum measurements collected from sensors to estimate the state of locations where there is no measurement data. In addition, REM utilizes feedback from the CRs and can apply various learning tools. The vision is to design CRNs such that CRs, though being simple devices without advanced cognitive functionalities, can become cognitive via REMs and operate in an efficient manner. In this paper, an overview of the REM concept is presented in various dimensions ranging from its architecture and stored information to REM construction techniques as well as REM quality metrics.

Location estimation-based radio environment map construction in fading channels

Latest regulations on TV white space communications and trend toward spectrum access through geolocation databases relax the regulatory constraints on cognitive radios. Radio environment map REM is a kind of improved geolocation database and an emerging topic with the latest regulations on TV white space communications. It constructs a comprehensive temperature map of the cognitive radio network operation area by utilizing multi-domain information from geolocation databases, characteristics of spectrum use, geographical terrain models, propagation environment, and regulations. REMs act as cognition engines by building long-term knowledge via processing spectrum measurements collected from sensors to estimate the state of locations without any measurement data. Active transmitter LocatIon Estimation based REM construction technique is proposed and compared with the well-known REM construction techniques such as Kriging and inverse distance weighted interpolation in shadow and multipath fading channels. The simulation results suggest that the LocatIon Estimation based REM construction outperforms the compared methods in terms of RMSE and correct detection zone ratio by utilizing additional information about channel parameters that can be estimated by classical least squares method easily.Copyright © 2013 John Wiley & Sons, Ltd.