Application Of Data Fusion Techniques Research Articles

Multi-sensor data fusion framework for energy optimization in smart homes

Advancements in Internet of Energy (IoE) technologies drive the development of several energy efficient frameworks for better energy optimization, economic savings, safety, and security in smart homes. However, certain challenges such as real-time operational data for each micro-moment, proper application of data fusion techniques, and end-to-end computing and deployment architecture prevent the establishment of an effective energy-efficient framework to provide personalized energy-saving recommendations. This work presents energy management for smart spaces (EMSS), the proposed energy efficiency framework implemented in an edge-cloud computing platform that fuses data from heterogeneous data sources (environmental sensors, camera, plug data, etc.) at appropriate data fusion levels and process them to generate actionable, explainable, personalized, and persuasive recommendations at the right moment. The user response to the generated recommendations triggers the actuators to perform respective energy-saving actions and provide more personalized future recommendations. Further, SMARTHome - a data generation framework based on configurable scenario files and a set of software codes was proposed to generate synthetic data with respect to different building types and micro-moments. The functionalities of the EMSS (device and user registration), user dashboard, analytics, and energy-saving recommendations were made accessible to the user through web and mobile applications. The validation analysis of the EMSS was performed by (i) comparative analysis of the machine learning and deep learning algorithms used by the decision engine to generate energy-saving recommendations and (ii) benchmarking of EMSS based on the taxonomy of data fusion-based energy efficiency frameworks for smart homes.

Application of Data Fusion Technique for the Analysis of Medical and Healthcare Consciousness

In a rapidly changing society, findings from social surveys serve as an important tool in identifying the social interests and the subjective social consciousness of the local residents, and for this reason, social surveys are being employed in various ways by KOSTAT (Statistics Korea) and local governments. In Gyeongsangnam-Do, social surveys are conducted annually, with survey questionnaire being modified biennially. As a previous study of the Gyeongsangnam-Do social survey, medical/healthcare items of the social survey surveyed in 2019 were studied, and the 2018 social survey also revealed that there are medical/healthcare related questions. Thus, this particular study will explore how data fusion method can be applied to collate the results from the 2019 and 2018 Gyeongsangnam-Do social survey to allow for an efficient analysis of the medical/healthcare questions, and also discuss the findings from this process. The data fusion and analysis method proposed in this study will also be useful in exploring the information obtained from social studies conducted by other local governments.

Application of Data Fusion Techniques to Improve Air Quality Forecast: A Case Study in the Northern Italy

Deterministic air quality forecasting models play a key role for regional and local authorities, being key tools to ensure that timely information about actual or near future exceedances of pollutant threshold values are provided to the public, as stated by the EU directive (2008/50/EC). One of the main problems of these models is that they usually underestimate some important pollutants, like PM10, especially in high-concentration areas. For this reason, the forecast of critical episodes (i.e., exceedance of 50 μ g/m 3 for PM10 concentration daily threshold) has low accuracy. To overcome this issue, several computationally fast techniques have been implemented in the last decade. In this work, two computational fast techniques are introduced, implemented and evaluated. The techniques are based on the off-line correction of the chemical transport model output in the forecasting window, estimated by means of the measurement data up to the beginning of the forecast. In particular, the techniques are based on the estimation of the correction performed as a linear combination of the corrections computed for the days when the measurements are available. The resulting system has been applied to the Lombardy region case (Northern Italy) for daily PM10 forecasting with good results.

A method for monthly mapping of wet and dry snow using Sentinel-1 and MODIS: Application to a Himalayan river basin

Satellite Remote Sensing, with both optical and SAR instruments, can provide distributed observations of snow cover over extended and inaccessible areas. Both instruments are complementary, but there have been limited attempts at combining their measurements. We describe a novel approach to produce monthly maps of dry and wet snow areas through application of data fusion techniques to MODIS fractional snow cover and Sentinel-1 wet snow mask, facilitated by Google Earth Engine. The method is demonstrated in a 55,000 km2 river basin in the Indian Himalayan region over a period of ∼2.5 years, although it can be applied to any areas of the world where Sentinel-1 data are routinely available. The typical underestimation of wet snow area by SAR is corrected using a digital elevation model to estimate the average melting altitude. We also present an empirical model to derive the fractional cover of wet snow from Sentinel-1. Finally, we demonstrate that Sentinel-1 effectively complements MODIS as it highlights a snowmelt phase which occurs with a decrease in snow depth but no/little decrease in snowpack area. Further developments are now needed to incorporate these high resolution observations of snow areas as inputs to hydrological models for better runoff analysis and improved management of water resources and flood risk.

Application of data fusion techniques to direct geographical traceability indicators

A hierarchical data fusion approach has been developed proposing multivariate curve resolution (MCR) as a variable reduction tool.The case study presented concerns the characterization of soil samples of the Modena District. It was performed in order to understand, at a pilot study stage, the geographical variability of the zone prior to planning a representative soils sampling to derive geographical traceability models for Lambrusco Wines. Soils samples were collected from four producers of Lambrusco Wines, located in in-plane and hill areas. Depending on the extension of the sampled fields the number of points collected varies from three to five and, for each point, five depth levels were considered.The different data blocks consisted of X-ray powder diffraction (XRDP) spectra, metals concentrations relative to thirty-four elements and the 87Sr/86Sr isotopic abundance ratio, a very promising geographical traceability marker.A multi steps data fusion strategy has been adopted. Firstly, the metals concentrations dataset was weighted and concatenated with the values of strontium isotopic ratio and compressed. The resolved components described common patterns of variation of metals content and strontium isotopic ratio. The X-ray powder spectra profiles were resolved in three main components that can be referred to calcite, quartz and clays contributions. Then, a high-level data fusion approach was applied by combining the components arising from the previous data sets.The results show interesting links among the different components arising from XRDP, the metals pattern and to which of these 87Sr/86Sr Isotopic Ratio variation is closer. The combined information allowed capturing the variability of the analyzed soil samples.

Application of Fuzzy Classifier Fusion in Determining Productive Zones in Oil Wells

This study is an application of data fusion techniques, especially fuzzy theory, in determining oil producing zones through four nearby wells, located on an oil field in south west of Iran. Two fusing techniques, used here are based on Bayesian and fuzzy theories. At first, two Bayesian classifiers are being constructed by training in two different wells; then a fuzzy operator, called Sugeno discrete integral, is used to fuse outputs of two mentioned Bayesian classifiers. Finally, it is concluded that using fuzzy classifier fusion improves not only certainty and confidence of decision making, but also generalization ability of determining productive zones.

A Machine Learning Approach to Weighting Schemes in the Data Fusion of Similarity Coefficients

The application of data fusion techniques for combining the results of similarity searches of chemical databases has been shown to improve search performance. When used to combine the results of searches using different similarity coefficients, the optimum combination is dependent on the size, in terms of substructural fragments present, of the molecules being compared. This paper describes preliminary simulation tests which aim to automatically deduce, using machine learning techniques, the optimum combination of similarity coefficient which may be combined using data fusion for a given class of active compounds.

Early warning model of network intrusion based on D-S evidence theory

Application of data fusion technique in intrusion detection is the trend of next-generation Intrusion Detection System (IDS). In network security, adopting security early warning technique is feasible to effectively defend against attacks and attackers. To do this, correlative information provided by IDS must be gathered and the current intrusion characteristics and situation must be analyzed and estimated. This paper applies D-S evidence theory to distributed intrusion detection system for fusing information from detection centers, making clear intrusion situation, and improving the early warning capability and detection efficiency of the IDS accordingly.

2D- and 3D-visualisation of NDT-data using data fusion technique

Non-destructive testing (NDT) of concrete structures plays an increasing role in civil engineering. At present there are several acoustic and electromagnetic methods, which cover a large area of application and which can be used complementarily. But in most cases, only one method is applied to solve a distinct problem. In this contribution, results will be presented based on the networked research project FOR384, funded by the Deutsche Forschungsgemeinschaft (DFG). Among others, the combiantion of NDT methods, and thus the application of data fusion techniques, has been investigated to demonstrate synergetic effects by merging complementary results and amplifying information about concrete structures. In order to be able to combine NDT-data from several methods recorded from the same volume, the different data sets must, be adapted. After the data sets are reconstructed and transferred into a common reference system, they can be processed in different ways according to the purpose of the investigation. The results, which have been achieved in concrete tets specimens and on the carriageway of a bridge with radar, ultrasonic echo and impact echo, will be presented and will show the feasibility of the data fusion method.