Concept Of Information Fusion Research Articles

Foreground Fusion-Based Liquefied Natural Gas Leak Detection Framework From Surveillance Thermal Imaging

A leak detection and repair survey (LDAR) is essential to ensure a reliable and safe liquefied natural gas (LNG) supply. Modern LDAR systems deploy numerous fixed thermal imaging devices to automatically monitor the risk of potential leaks empowered by computational intelligence frameworks. Existing frameworks employ either background subtraction-based (BGS-based) or deep neural network-based (DNN-based) frameworks for LNG leak detection from thermal images. However, the BGS-based frameworks feature high sensitivity to perceive LNG emissions with low precision. On the contrary, the DNN-based frameworks can precisely classify the LNG leak after training while the sensitivity is low. Additionally, conventional DNN-based frameworks are difficult in modeling non-rigid objects such as LNG gas due to limited perceptive fields. Therefore, this study proposes a hybrid framework, namely foreground fusion-based gas detection (FFBGD), combining the advantages of BGS-based and DNN-based detectors for improved detection robustness through newly introduced concept of information fusion to LNG industries. Specifically, a foreground fusion network (FFN) is designed to fuse information of original thermal and foreground images after BGS based on the visual attention mechanism. Meanwhile, several advanced modules, i.e. deformable convolution, feature pyramid network, and cascade region-of-interest (ROI) head are adopted to enhance leak detection by offering better perceptive fields. Extensive experiments are carried out in this study to demonstrate the significant improvement brought by the proposed FFBGD over leak detection accuracy and robustness. Hence, the proposed solution can be deployed in energy facilities and enable reliable visual surveillance of LNG leaks.

An integrated information fusion and grey multi-criteria decision-making framework for sustainable supplier selection

The recent changes in global business markets have forced supply chains to include sustainability and considerable uncertainty in their decision-making processes. In this study, we propose an integrated information fusion and grey multi-criteria decision-making (MCDM) framework for solving sustainable supplier selection problems with imprecise information. We identify a series of criteria in the literature based on the economic, social, and environmental aspects of sustainability and obtain the relative importance of these criteria from experts' opinions. We then propose a best-worst method (BWM) integrated with grey theory to calculate the weights of criteria. These weights are used to evaluate a set of suppliers based on three sustainability aspects. A hybrid method composed of weighted aggregated sum product assessment (WASPAS), the technique for order of preference by similarity to ideal solution (TOPSIS), and grey theory are proposed for ranking the suppliers. We aggregate the grey MCDM results with the concept of information fusion to find an integrated score for each supplier. The final ranking is obtained by incorporating expert opinions with the integrated scores. We also present a case study to demonstrate the applicability of the proposed framework. A sensitivity analysis is performed to test the reliability and robustness of the results.

Multiagent UAV Routing: A Game Theory Analysis With Tight Price of Anarchy Bounds

We study the multiagent unmanned aerial vehicle (UAV) routing problem where a set of UAVs needs to collect information via surveillance of an area of operation. Each UAV is autonomous and does not rely on a reliable communication medium to coordinate with other UAVs. We formulate the problem as a game where UAVs are players and their strategies are the different routes they can take. Our model also incorporates the useful concept of information fusion. This results in a new variant of weighted congestion-type games. We show that the price of anarchy (PoA) of the game is at most 2, irrespective of the number of UAVs and their sensor capabilities. This also validates the empirical results of earlier works. Furthermore, we identify classes of games for the existence of a pure Nash equilibrium. To the best of our knowledge, these are the first such theoretical results in the related literature. Finally, we conduct experimental studies using randomly generated instances with several multiagent UAV routing policies. Our insights are that PoA increases with the congestion level when the same number of UAVs search a smaller area or more UAVs search the same area, and on an average, our proposed policies are less than 10% worse than the centralized optimal for the problem scenarios attempted. Note to Practitioners —UAVs are becoming increasingly popular for information collection tasks in defense and civilian applications alike. When the collection area is large, it is not unusual that a fleet of UAVs is deployed. Routing of a fleet can be performed in a centralized or decentralized manner. Decentralized routing might be the only possibility when centralized situational awareness is not possible due to bandwidth limitations and centralized optimal routes for each UAV in the fleet are too complex to compute. Autonomous solutions have several other advantages, let alone simplicity. For managers of UAV systems, our work provides the first theoretical characterization of how bad could decentralized routing be. Under various scenarios of information fusion, specifically weak and strong, and the attribution of information collected to each UAV of a team, we prove that the fleet will collect at least 50% of the best-centralized solution. Empirically, we show that, in fact, the performance of the fleet is much better and generally not worse than 10% of the best-centralized solution. Hopefully, our routing strategies provide valuable guidance to the practicing engineer or manager of a UAV fleet.

Data fusion in cyber-physical-social systems: State-of-the-art and perspectives

Cyber-Physical-Social systems (CPSSs) are the extension of Cyber-Physical systems (CPS), which seamlessly integrate cyber space, physical space and social space. CPSSs promote the information resource from single space to tri-space, so as to lead a revolution in data science (DS). This paper aims to provide a comprehensive review of data fusion in CPSSs for readers. We firstly analyze data collection and representation in CPSS and propose to use tensors to represent CPSS data, then a general definition of CPSS data fusion is proposed to clarify the concept of information fusion in CPSS. After that, some representative data fusion methods related to CPSS are reviewed. Furthermore, we propose a series of tensor based data fusion methods for CPSS data. Also, we review the design of data fusion frameworks and propose a comprehensive data fusion framework for CPSS. Some challenges and future works are discussed as well.

Improved Estimation for Well-Logging Problems Based on Fusion of Four Types of Kalman Filters

The concept of information fusion has gained a widespread interest in many fields due to its complementary properties. It makes systems more robust against uncertainty. This paper presents a new approach for the well-logging estimation problem by using a fusion methodology. The natural gamma-ray tool (NGT) is considered as an important instrument in the well logging. The NGT detects changes in natural radioactivity emerging from the variations in concentrations of micronutrients as uranium (U), thorium (Th), and potassium (K). The main goal of this paper is to have precise estimation of the concentrations of $U$ , $Th$ , and $K$ . Four types of Kalman filters are designed to estimate the elements using the NGT sensor. Then, a fusion of the Kalman filters is utilized into an integrated framework by an ordered weighted averaging (OWA) operator to enhance the quality of the estimations. A real covariance of the output error based on the innovation matrix is utilized to design weighting factors for the OWA operator. The simulation studies indicate not only a reliable performance of the proposed method compared with the individual Kalman filters but also a better response in contrast with previous fusion methodologies.

Non-probabilistic information fusion technique for structural damage identification based on measured dynamic data with uncertainty

Based on measured natural frequencies and acceleration responses, a non-probabilistic information fusion technique is proposed for the structural damage detection by adopting the set-membership identification (SMI) and two-step model updating procedure. Due to the insufficiency and uncertainty of information obtained from measurements, the uncertain problem of damage identification is addressed with interval variables in this paper. Based on the first-order Taylor series expansion, the interval bounds of the elemental stiffness parameters in undamaged and damaged models are estimated, respectively. The possibility of damage existence (PoDE) in elements is proposed as the quantitative measure of structural damage probability, which is more reasonable in the condition of insufficient measurement data. In comparison with the identification method based on a single kind of information, the SMI method will improve the accuracy in damage identification, which reflects the information fusion concept based on the non-probabilistic set. A numerical example is performed to demonstrate the feasibility and effectiveness of the proposed technique.

Modelling input parameter interactions in the possibilistic harmonic load flow

In a previous study by the authors, a possibilistic harmonic load flow (PHLF) approach was presented. The PHLF was capable of modelling, through possibility distributions, uncertainties regarding magnitude and composition in both linear (LL) and non-linear loads (NLL) connected at the power system (PS). However, in that approach, possibility non-interaction between parameters for modelling LL and NLL was assumed. Such assumption is quite conservative and conduces to uncertainty overestimation. In fact, there are well-defined physical relationships among model parameters that constrain the variability of these. This study presents an improved approach which overcomes the aforementioned drawback by including both linear and non-linear equality constraints into the non-linear programming problem used to compute the PHLF. These linear and non-linear equality constraints are specifically used to model the balance of active and reactive power, respectively, at each bus of the PS. The equality constrains inclusion is supported from a possibility theory point of view by means of the ‘information fusion concept’. Influence of the possibility interaction consideration is evaluated in the IEEE 14-bus PS for harmonic analysis. Then, the complete PHLF is tested in a real 88-bus PS, and comparisons with Monte Carlo simulation results are performed and discussed.

정보융합 기술 기반의 지능형 항행안전정보 시스템

다양한 데이터들로부터 현재 상황의 인식을 돕는 정보융합기술 연구는 국방관련 상황인식에서 시작됐으며, 최근 다른 분야의 문제해결에 적용하는 시도가 진행 되고 있다. 해양 분야에서는 항해중인 선박이 다양한 항행 장비들을 통한 여러 유형의 선내.외 안전 정보를 전달받고, 이러한 정보들로 항해관련 안전상황의 인식 및 예측을 하게 된다. 하지만 지나치게 많은 정보가 빠르게 전달됨으로써 사람이 모든 정보를 판단하는 일이 쉽지 않고, 종종 매체 간 정보가 불일치하는 경우가 발생한다. 이 연구는 선박의 안전항행 상황을 진단하고 예측하기 위한 정보융합기술을 어떻게 적용 할 수 있는지 그 개념을 소개하고, 특정 상황 시나리오의 정보 융합 예를 통해 지능형 항행 안전 정보 시스템의 실현 가능성을 보인다. The study of information fusion technology, which merges various types of data to recognize a situation more exactly, has begun in the area of national defense. Recently, the concept of information fusion is getting applied to other fields, and we are interested in maritime safety. In navigation, officers receive data about inside and outside of ship from several devices in bridge, and use it to recognize and predict the safety situation. However, too much and fast updated data might even fatigue mates, and there is the problem of inconsistency among data from several types of devices. This paper introduce how can use information fusion technology for the situation awareness and prediction of navigation safety, and show the realization possibility of Intelligent Navigation Safety Information System through an information fusion example in a specific situation scenario.

Proposition of common classifier construction for pattern recognition with context task

This paper deals with the concept of information fusion and its application to the contextual pattern recognition task. The concept of the recognition based on the probabilistic model are presented. The machine learning algorithm based on statistical tests for the recognition of controlled Markov chains is shown. Idea of information unification via transforming the expert rules into the learning set is derived. Some experimental results of obtained methods are shown and proposed concepts are applied to the real medical decision problem.

An Integrative Approach for Monitoring Water Movement in the Vadose Zone

Electrical resistivity tomography (ERT), during the past few years, has emerged as a potentially cost-effective, noninvasive tool for imaging changes of moisture content in the vadose zone. The accuracy of ERT surveys, however, has been the subject of debate because of its nonunique inverse solution and spatial variability in the constitutive relation between resistivity and moisture content. In this paper, an integrative inverse approach for ERT, based on a stochastic information fusion concept of Yeh and Simůnek, was developed to derive the best unbiased estimate of the moisture content distribution. Unlike classical ERT inversion approaches, this new approach assimilates prior information about the geological and moisture content structures in a given geological medium, as well as sparse point measurements of the moisture content, electrical resistivity, and electric potential. Using these types of data and considering the spatial variability of the resistivity–moisture content relation, the new approach directly estimates three-dimensional moisture content distributions instead of simply changes in moisture content in the vadose zone. Numerical experiments were conducted to investigate the effect of uncertainties in the prior information on the estimate. The effects of spatial variability in the constitutive relation were then examined on the interpretation of the change in moisture content, based on the change in electrical resistivity from the ERT survey. Finally, the ability of the integrative approach was tested by directly estimating moisture distributions in three-dimensional, heterogeneous vadose zones. Results show that the integrative approach can produce accurate estimates of the moisture content distributions and that incorporating some measurements of the moisture content is essential to improve the estimate.

An Integrative Approach for Monitoring Water Movement in the Vadose Zone

Electrical resistivity tomography (ERT), during the past few years, has emerged as a potentially cost‐effective, noninvasive tool for imaging changes of moisture content in the vadose zone. The accuracy of ERT surveys, however, has been the subject of debate because of its nonunique inverse solution and spatial variability in the constitutive relation between resistivity and moisture content. In this paper, an integrative inverse approach for ERT, based on a stochastic information fusion concept of Yeh and Šimůnek, was developed to derive the best unbiased estimate of the moisture content distribution. Unlike classical ERT inversion approaches, this new approach assimilates prior information about the geological and moisture content structures in a given geological medium, as well as sparse point measurements of the moisture content, electrical resistivity, and electric potential. Using these types of data and considering the spatial variability of the resistivity–moisture content relation, the new approach directly estimates three‐dimensional moisture content distributions instead of simply changes in moisture content in the vadose zone. Numerical experiments were conducted to investigate the effect of uncertainties in the prior information on the estimate. The effects of spatial variability in the constitutive relation were then examined on the interpretation of the change in moisture content, based on the change in electrical resistivity from the ERT survey. Finally, the ability of the integrative approach was tested by directly estimating moisture distributions in three‐dimensional, heterogeneous vadose zones. Results show that the integrative approach can produce accurate estimates of the moisture content distributions and that incorporating some measurements of the moisture content is essential to improve the estimate.

Fusing information simultaneously corrupted by uncertainties with known bounds and random noise with known distribution

A new algorithm is derived for estimating the state of a linear dynamic system by fusing uncertain observations, which suffer from two types of uncertainties simultaneously. The first uncertainty is a stochastic process with given distribution. The second uncertainty is only known to be bounded, the exact underlying distribution is unknown. The new fusion algorithm combines set theoretic and stochastic estimation in a rigorous manner and provides a continuous transition between the two classical information fusion concepts. It converges to a set theoretic estimator, when the stochastic error goes to zero, and to a Kalman filter, when the bounded error vanishes. In the mixed noise case, solution sets are provided that are uncertain in a stochastic sense.

Information fusion for estimation of summer MIZ ice concentration from SAR imagery

The authors define the concept of information fusion and show how they used it to estimate summer sea ice concentration in the marginal ice zone (MIZ) from single-channel SAR satellite imagery. They used data about melt stage, wind speed, and surface temperature to generate temporally-accumulated information, and fused this information with the SAR image, resulting in an interpretation of summer MIZ imagery. They also used the results of previous classifications of the same area to guide and correct future interpretations, thus fusing historical information with imagery and nonimagery data. They chose to study the summer MIZ since summer melt conditions cause classification based upon backscatter intensity to fail, as the backscatter of open water, thin ice, first-year ice, and multiyear ice overlap to a large degree. This makes it necessary to fuse various information and data to achieve proper segmentation and automated classification of the image. Their results were evaluated qualitatively and showed that their approach produces very good ice concentration estimates in the summer MIZ.

Information fusion: application to data and model fusion for ultrasound image segmentation.

Nowadays, information fusion constitutes a challenging research topic. Our study proposes to achieve the fusion of several knowledge sources. This, in order to detect the esophagus inner wall from ultrasound medical images. After a brief description of information fusion concepts, we propose a system architecture including both model and data fusion. The data fusion is accomplished using fuzzy modeling, which can be seen as a monosensor/multiple sources data fusion system. The model fusion is performed using a full-adapted snake theory, which projects the fuzzy decision into the binary decision space.