Multi-type Data Research Articles

Performance Analysis Internet of Things Based on Sensor and Data Analytics

The Internet of Things (IoT) is a transformative development. Sensors are known in scientific science as a prospective field. IoT sensors are used successfully to develop an intelligent world in different IoT implementations. To make a familiar operating image, ubiquitous sensing abilities provide shared knowledge. IoT systems can fail if one of the sensors stops working due to any failure, which causes serious living problems to some families. Thus, the problem consists of predicting when the subsequent loss will occur. A two-phase approach is proposed. First, an exploration of the provided data will take place. The purpose of such analysis is threefold: (1) to get familiar with the data, (2) to assess the quality of data and decide which methodology to employ accordingly, (3) to determine the features of interest. This research will serve as the basis for further theoretical work in the same field in the prospect. Analyzing these sensor-derived data is an important task that can find valuable latent information in addition to the data itself. Since the Internet of Things includes some sensors, the measurement data obtained by these sensors are multi-type data, often including information from the time series. Depending on the configuration flow rate, vibration, impeller speed, and even temperature, the sensors with solid correlation can be closely correlated with some delay involved. Data ranges investigation was done before correlation check. Results indicate that our procedure is accurate for the machine's regular, damaged, and recovering state.

A Cyber-Physical Data Collection System Integrating Remote Sensing and Wireless Sensor Networks for Coffee Leaf Rust Diagnosis

Coffee Leaf Rust (CLR) is a fungal epidemic disease that has been affecting coffee trees around the world since the 1980s. The early diagnosis of CLR would contribute strategically to minimize the impact on the crops and, therefore, protect the farmers’ profitability. In this research, a cyber-physical data-collection system was developed, by integrating Remote Sensing and Wireless Sensor Networks, to gather data, during the development of the CLR, on a test bench coffee-crop. The system is capable of automatically collecting, structuring, and locally and remotely storing reliable multi-type data from different field sensors, Red-Green-Blue (RGB) and multi-spectral cameras (RE and RGN). In addition, a data-visualization dashboard was implemented to monitor the data-collection routines in real-time. The operation of the data collection system allowed to create a three-month size dataset that can be used to train CLR diagnosis machine learning models. This result validates that the designed system can collect, store, and transfer reliable data of a test bench coffee-crop towards CLR diagnosis.

Model learning to identify systemic regulators of the peripheral circadian clock

MotivationPersonalized medicine aims at providing patient-tailored therapeutics based on multi-type data toward improved treatment outcomes. Chronotherapy that consists in adapting drug administration to the patient’s circadian rhythms may be improved by such approach. Recent clinical studies demonstrated large variability in patients’ circadian coordination and optimal drug timing. Consequently, new eHealth platforms allow the monitoring of circadian biomarkers in individual patients through wearable technologies (rest-activity, body temperature), blood or salivary samples (melatonin, cortisol) and daily questionnaires (food intake, symptoms). A current clinical challenge involves designing a methodology predicting from circadian biomarkers the patient peripheral circadian clocks and associated optimal drug timing. The mammalian circadian timing system being largely conserved between mouse and humans yet with phase opposition, the study was developed using available mouse datasets.ResultsWe investigated at the molecular scale the influence of systemic regulators (e.g. temperature, hormones) on peripheral clocks, through a model learning approach involving systems biology models based on ordinary differential equations. Using as prior knowledge our existing circadian clock model, we derived an approximation for the action of systemic regulators on the expression of three core-clock genes: Bmal1, Per2 and Rev-Erbα. These time profiles were then fitted with a population of models, based on linear regression. Best models involved a modulation of either Bmal1 or Per2 transcription most likely by temperature or nutrient exposure cycles. This agreed with biological knowledge on temperature-dependent control of Per2 transcription. The strengths of systemic regulations were found to be significantly different according to mouse sex and genetic background.Availability and implementation https://gitlab.inria.fr/julmarti/model-learning-mb21eccb.Supplementary information Supplementary data are available at Bioinformatics online.

Artificial Intelligence Techniques in Smart Grid: A Survey

The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in processing the data; thus, the applications of artificial intelligence (AI) techniques in the smart grid are becoming more apparent. This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in the smart grid and power systems. It also provides further research challenges for applying AI technologies to realize truly smart grid systems. Finally, this survey presents opportunities of applying AI to smart grid problems. The paper concludes that the applications of AI techniques can enhance and improve the reliability and resilience of smart grid systems.

Bridge damage detection from the equivalent damage load by multitype measurements

Traditional damage identification (DI) approaches are based on the structural modal information, which is unstable and affected by the environment. This study proposes a novel bridge DI algorithm for the elastically supported beams with a constant cross-section. The expression of the equivalent damage load (EDL) is deduced from the force–displacement relationship. The EDL only exists in the damaged areas, and it is a good damage indicator. Then, a principal component analysis-based load estimation method is adopted to estimate the external nodal force and the EDL. To reduce the influence of the measurement noise and errors, this study furtherly proposes a robust EDL-based damage indicator, which is calculated from multitype data. The numerical simulations and in-field experiments have discussed the influence of several factors, including damage scenarios and loading conditions, on the proposed damage indicator. All results have shown that the proposed damage indicator is applicable and useful. The proposed approach can detect the damage in a real-time manner with high computational efficiency, and it is useful for the in-operation bridges.

Scheduling for gathering multitype data with local computations

In this paper, we analyze scheduling gathering multitype data in a star network. Certain numbers of datasets of different types have to be collected on a single computer, either by downloading them from remote nodes, or by producing them locally. The time required to download a dataset depends on its type and initial location, and the time needed to produce a dataset depends only on its type. The scheduling problem is to decide which datasets should be downloaded from which nodes, and how many datasets of each type should be generated locally, in order to minimize the total time necessary for gathering all data. We show that this problem is NP-hard, indicate special cases solvable in polynomial time, and propose a fully polynomial time approximation scheme for a special case which is still NP-hard. For the general case, we present an integer linear programming formulation, a dynamic programming algorithm and a polynomial 2-approximation algorithm. The performance of these algorithms is tested in computational experiments.

Identifying Human Daily Activity Types with Time-Aware Interactions

Human activities embedded in crowdsourced data, such as social media trajectory, represent individual daily styles and patterns, which are valuable in many applications. However, the accurate identification of human activity types (HATs) from social media is challenging, possibly because interactions between posts and users at different time are overlooked. To fill this gap, we propose a novel model that introduces the interactions hidden in social media and synthesizes Graph Convolutional Network (GCN) for identifying HAT. The model first characterizes interactions among words, posts, dates, and users, and then derives a Time Gated Human Activity Graph Convolutional Network (TG-HAGCN) to predict the HATs of social media trajectory. To examine the proposed model performance, we built a new dataset including interactions between post content, post time, and users from the open Yelp dataset. Experimental results show that exploiting interactions hidden in social media to recognize HATs achieves state-of-the-art performance with high accuracy. The study indicates that interactions among social media promotes ability of machine learning on social media data mining and intelligent applications, and offers a reference solution for how to fuse multi-type heterogeneous data in social media.

Object-based island hierarchical land cover classification using unmanned aerial vehicle multitype data

The unmanned aerial vehicle (UAV) is an emerging technology applied recently in land cover classification, owing to its ability to acquire very high-resolution spatial data, that has provided an effective means for detailed land cover mapping, especially for a small island area. Selecting suitable UAV-acquired data and exploring the combined use of UAV multitype data are of significance for island mapping. Nine classification models were established through a fusion method of visible, multispectral, and light detection and ranging (LIDAR) data acquired by UAVs. A two-level hierarchical land cover classification (level 1 and level 2) of the Donkey Island in China was performed using geographic object-based image analysis with random forest classifier. We investigated the performance of land cover classification models containing different sets of features (spectral, height, intensity, and shape features extracted from UAV data) and evaluated the importance of various features. The results demonstrate that the overall accuracy (OA) of the models generally increase with decreasing detail and the amount of information entering the classification process. The OA achieved range from 82.08% to 92.54% and 74.12% to 85.08% across the nine models for level 1 and level 2, respectively. The best result was achieved with a model combining all features based on multispectral and LIDAR data. Height and intensity information input significantly affect the quality of classification models positively, with height apparently more significant than LIDAR information. When comparing different features, spectral features prominently assist in discriminating land cover classes. The importance of height and intensity features to classification accuracy varies for the classification models, showing greater importance in models based on visible data.

Learning Compact Multifeature Codes for Palmprint Recognition From a Single Training Image per Palm

In this article, we propose a multifeature learning method to jointly learn compact multifeature codes (LCMFCs) for palmprint recognition with a single training sample per palm. Unlike most existing hand-crafted methods that extract single-type features from raw pixels, we first form the multi-type data vectors such as the direction-data, and texture-data to completely sample the multiple information of a palmprint image. Then, we learn the discriminative multifeatures from multi-type data vectors by maximizing the inter-palm distance, and minimizing the energy loss between the learned codes, and the original data. Moreover, our LCMFC method adaptively learns the optimal weights of multi-type features to jointly learn the compact multifeature codes. Finally, we cluster the nonoverlapping blockwise histograms of the compact multifeature codes into a feature vector for palmprint representation. Extensive experimental results on six benchmark palmprint databases are presented to show the effectiveness of the proposed method.

The sparse group lasso for high-dimensional integrative linear discriminant analysis with application to alzheimer's disease prediction

In the big data era, high-dimensional data from multi-type data sources are ubiquitous and pose great challenge for statistical analysis. In this paper, we focus on two-class classification problem for this kind of data. Motivated by the least squares formulation of linear discriminant analysis, we propose an integrative discrimination analysis method by virtue of sparse group Lasso penalty. Thorough numerical studies are conducted and the results show that the proposed method compares favourably with other popular sparse discriminant proposals in terms of both variable selection accuracy and classification error. A real-data example is also given to illustrate its advantages.

GRACE-FO precise orbit determination and gravity recovery

The gravity recovery and climate experiment follow-on (GRACE-FO) satellites, launched in May of 2018, are equipped with geodetic quality GPS receivers for precise orbit determination (POD) and gravity recovery. The primary objective of the GRACE-FO mission is to map the time-variable and mean gravity field of the Earth. To achieve this goal, both GRACE-FO satellites are additionally equipped with a K-band ranging (KBR) system, accelerometers and star trackers. Data processing strategies, data weighting approaches and impacts of observation types and rates are investigated in order to determine the most efficient approach for processing GRACE-FO multi-type data for precise orbit determination and gravity recovery. Two GPS observation types, un-differenced (UD) and double-differenced (DD) observations in general can be used for GPS-based POD and gravity recovery. The GRACE-FO KBR observations are mainly used for gravity recovery, but they can be also used for POD to improve the relative orbit accuracy. The main purpose of this paper is to study the impacts of the DD, UD and KBR observations on GRACE-FO POD and gravity recovery. The precise orbit accuracy is assessed using several tests, which include analysis of orbital fits, satellite laser ranging residuals, KBR range residuals and orbit comparisons. The gravity recovery is validated by comparing different gravity solutions through coefficient-wise comparison, degree difference variances and water height variations over the whole Earth and selected area and river basins.

Mining latent academic social relationships by network fusion of multi-type data

The relationship extraction and network fusion are hotspots of current research in social network mining. Since the types of data are manifold, researchers can utilize multi-type data to construct multiple networks. In academic social network mining, present researches are mostly based on the single-type data, e.g., the co-authorship network constructed by academic co-authorship records. However, the relationships portrayed by single-type data are not sufficient to characterize the complex relationships of the real world. To solve this problem, we are the first to the best of our knowledge to use acknowledgment text to construct a semantic information-based academic social network. First, we extract named entities from multi-type data and implement network optimization and alignment. Second, a semi-supervised fusion framework for multiple networks (SFMN), using the gradient boosting decision tree algorithm to fuse the information of multiple networks into a single network, is proposed in this paper. Third, we implement the parallel version of SFMN on Spark to improve the performance of large-scale social network analysis. Experiments show the superiority of our framework over several state-of-the-art methods and prove our method can effectively integrate network information.

A Multi-type Data Acquisition Platform Based on LabVIEW

This paper presents a LabVIEW based platform for acquiring multi-type data. With the help of virtual instrument technique, the platform is capable of sampling the current signal, frequency signal, switching signal and resistance signal, judging the validity of data and analyzing the characters of data as well. The framework of the acquisition platform comprises of two parts, i.e., the software part is developed in LabVIEW language and the hardware part is built by National Instruments (NI). In addition to its major functions, the platform supports storing and reviewing the data and analysis results, and possesses a friendly graphical user interface (GUI), which facilitates the maintenance and function extensions. The engineering practices reveal that the proposed platform has advantages of readily-operating and stable functions, and satisfies the diverse requirements of data acquisition.

Generalized integrative principal component analysis for multi-type data with block-wise missing structure.

High-dimensional multi-source data are encountered in many fields. Despite recent developments on the integrative dimension reduction of such data, most existing methods cannot easily accommodate data of multiple types (e.g. binary or count-valued). Moreover, multi-source data often have block-wise missing structure, i.e. data in one or more sources may be completely unobserved for a sample. The heterogeneous data types and presence of block-wise missing data pose significant challenges to the integration of multi-source data and further statistical analyses. In this article, we develop a low-rank method, called generalized integrative principal component analysis (GIPCA), for the simultaneous dimension reduction and imputation of multi-source block-wise missing data, where different sources may have different data types. We also devise an adapted Bayesian information criterion (BIC) criterion for rank estimation. Comprehensive simulation studies demonstrate the efficacy of the proposed method in terms of rank estimation, signal recovery, and missing data imputation. We apply GIPCA to a mortality study. We achieve accurate block-wise missing data imputation and identify intriguing latent mortality rate patterns with sociological relevance.

Manufacture process quality control of interferometric fibre optic gyroscope using analyses of multi-type assembly and test data

ABSTRACTQuality control of manufacture process of a type of interferometric fibre optic gyroscope (IFOG) optical path, which uses computer analyses of multi-type assembly and test data, is proposed. Two quality control algorithms are developed. The first is the Monte Carlo (MC) and Support Vector Machine (SVM) iteration algorithm, i.e. the MC_SVM algorithm. The MC is used to generate random values of the assembly correction data, while the SVM is employed to forecast the assembly quality degree of IFOG. Then, the MC_SVM can tune the multi-type assembly errors. The second algorithm is the Series Feature Analysis (SFA) and Analytic Hierarchy Process (AHP) ranking algorithm, i.e. the SFA_AHP algorithm. The SFA is used to calculate the signal features of multi-type data from an IFOG environment adaptability test experiment. The AHP is employed to analyse the importance ranking of potential faults. Then, the SFA_AHP can detect and locate product faults. The above methods can correct errors before the product assembly and locate faults after the assembly. The experiential knowledge of assembly can be inherited and the multidisciplinary restriction of complex manufacturing can be broken. Their analysis results are easy to be understood by users. Many experiments have demonstrated the effectiveness of proposed algorithms.

A High-Performance and Hardware-Efficient PCIe Transmission for a Multi-Channel Video Using Command Caching and Dynamic Splicing on FPGA

With the increasing requirements of transmitting larger amounts of diverse video and image data, the PCI express (PCIe) interface has been extensively used in high-speed digital systems for multimedia processing and communication. However, the existing PCIe transmission methods are not efficient to transmit multi-channel videos on FPGA processors, as they waste PCIe bandwidth and hardware resources. In this paper, an efficient PCIe transmission method for multi-channel video is presented on an FPGA processor. First, to reduce the programmed I/O (PIO) write latency, the command caching method is adopted. The traditional DMA descriptors are transformed into DMA commands, which are smaller, and the command buffer is used to store these commands. Second, a dynamic splicing mechanism is proposed for data transmission and commands the scheduling separately. The multi-type data is transmitted concurrently in this dynamic splicing mechanism. Moreover, the adjacent commands are spliced into one command, which reduces the DMA initiation times. Finally, the direct kernel memory access technique and timer self-feedback monitor technique are applied to improve the transmission efficiency and enhance the system reliability. The experimental results show that the transmission efficiency of the proposed method is increased by nearly 30% compared with other conventional methods, and the highest measured transfer rate is 1631 Mbytes/s on write and 1582 Mbytes/s on read, which reaches 84.4% of the theoretical maximum.

Efficient PCIe transmission for Multi-Channel video using dynamic splicing and conditional prefetching

PCI Express (PCIe) interface has been extensively used in high-speed digital systems for multimedia communication. With the migration of the video processing algorithms from host to embedded hardware, multi-channel video capturing systems will produce not only different channels of raw video data but also different types of auxiliary data, such as analyzed data and compressed stream. In order to display multi-channel video in real-time and explore the auxiliary data, conventional transmission strategies are no longer applicable, due to the fact that heterogeneous data will cause frequent interactions and lead to the waste of PCIe bandwidth. In this paper, an efficient PCIe transmission method for multi-channel video is presented. Firstly, for the transmission of multi-type video data, a dynamic splicing mechanism is proposed to combine the video analyzed data and the compressed stream with the raw video to avoid the individual transmission of the auxiliary data. Secondly, as the spliced data are from different channels, a conditional prefetching mechanism is employed to determine whether there exists any entire video frame in other channel buffers, so that multi-channel video data can be transmitted possibly at one time. Finally, in the host-side driver, direct kernel buffer access technique is used to improve the application I/O request packet (IRP) performance. And to ensure the transmission efficiency of the conditional prefetching, DMA circular queue buffer and timer self-feedback monitor techniques are designed to avoid the possible visit bursts and abnormal interruptions. Experimental results demonstrate that compared with the conventional methods, the proposed method reduces the interrupt interactions by 60%, increases the transmission channel number by 94%, and also increases the application IRP number by 54%. The peak transmission speed of PCIe is up to 155 MB/s, which can meet 7 channels 704 × 576 YUV raw video and its auxiliary data transmission requirements using one 1-lane PCIe endpoint.

AnchoredDistr: a Package for the Bayesian Inversion of Geostatistical Parameters with Multi-type and Multi-scale Data

anchoredDistr: a Package for the Bayesian Inversion of Geostatistical Parameters with Multi-type and Multi-scale Data

Knowledge model for electric power big data based on ontology and semantic web

It is very important for the development of electric power big data technology to use the electric power knowledge. A new electric power knowledge theory model is proposed here to solve the problem of normalized modeled electric power knowledge for the management and analysis of electric power big data. Current modeling techniques of electric power knowledge are viewed as inadequate because of the complexity and variety of the relationships among electric power system data. Ontology theory and semantic web technologies used in electric power systems and in many other industry domains provide a new kind of knowledge modeling method. Based on this, this paper proposes the structure, elements, basic calculations and multidimensional reasoning method of the new knowledge model. A modeling example of the regulations defined in electric power system operation standard is demonstrated. Different forms of the model and related technologies are also introduced, including electric power system standard modeling, multi-type data management, unstructured data searching, knowledge display and data analysis based on semantic expansion and reduction. Research shows that the new model developed here is powerful and can adapt to various knowledge expression requirements of electric power big data. With the development of electric power big data technology, it is expected that the knowledge model will be improved and will be used in more applications.

Optimization of optical single sideband configurations for radio over fiber transmission and multi-type data communication over a DWDM link

Two new optical single side band (OSSB) configurations for radio frequency (RF) transmission of data are proposed based on the structures that have been designed for baseband digital data transmission. The performances of these schemes are evaluated based on the relevant eye diagrams and bit error rate (BER). The proposed configurations are studied and analyzed mathematically. Eye diagrams and BER’s indicate an acceptable performance for the suggested structures. Feasible settings for the RF electrical power of the links are considered and the link performance for each setting is evaluated. A 4-dense wavelength division multiplexing (4-DWDM) link is considered for transmitting different types of data over a single mode fiber (SMF). Baseband digital data in the format of OSSB and RF-OSSB signals are transmitted over a 4-DWDM link. Furthermore, the link performance is considered for different electrical power settings of OSSB signal in order to achieve efficient transmission for DWDM network.