Multiple sources geographic attribute data uncertainty and information fusion schemes

The classification accuracy of a classifier depends largely on the aggregation operator it uses in fusion of information. This paper presents a similarity classifier that utilizes a weighted ordered weighted averaging (WOWA) operator. The applicability of the WOWA operator for aggregation of similarities is studied and tested with several Regular Increasing Monotonic (RIM) type weight generators. The RIM quantifiers used here are of the basic, polynomial, trigonometric, exponential, and of the logarithmic type. The proposed approach is tested on three real world data sets. Results obtained with the new method are compared with those obtained using two previously presented similarity classifiers. The proposed new classifier shows better performance on the data sets studied, which indicates that benefits can be gained from using a WOWA operator in the aggregation.

The super-resolution algorithms based on deep learning can effectively increase optical remote sensing image (ORSI) details for further analysis tasks. Deep unfolding methods have been studied in recent years to bridge the gap between optimization-based and learning-based methods, which can take good advantage of the prior knowledge. However, these unfolding methods usually ignore the utilization of intermediate network features between different iteration stages, thereby limiting the performance of super-resolution results. We propose a multi-source information fusion network (MSFNet) for ORSI super-resolution to address this problem. We mainly consider three strategies to enhance the image super-resolution performance, including feature extraction strategy, information fusion strategy, and the structure of the unfolding network. Firstly, image information of various scales is helpful for mining potential features of images for image super-resolution. Therefore, we introduce multi-scale implicit constraints to the objective function. Secondly, we unfold the optimization process into a neural network by alternating direction method of multipliers (ADMM). The network framework follows the ADMM method's iteration process. This unfolding strategy can effectively utilize the prior information for image reconstruction. Thirdly, we propose a row-column decoupling Transformer module based on this unfolding framework for feature fusion. Specifically, the row Transformer block completes the feature fusion of various scales, and the column Transformer block completes the feature fusion of various channels. The fused features are transmitted to the next iteration stage for feature enhancement. And this fusion strategy enables the network to focus on the global information of the features. We perform experiments on three remote sensing image datasets to fully demonstrate the algorithm's effectiveness. Experiment results show that the proposed algorithm can achieve better image reconstruction performance.

A new fusion strategy is introduced in this article to estimate state for multi-rate multi-sensor systems with missing measurements. N sensors, which possess various sampling rates, render the measurements. Missing measurements with a certain probability pattern are also investigated. For these types of systems, Multi-rate Kalman filters are designed to estimate a target position at various sampling rates. Next, Ordered Weighted Averaging (OWA) operator is utilized to integrate multi-rate Kalman filters and improve the estimation quality. A new fusion strategy based on a real covariance matrix is introduced for updating the weighting factors, and proof of convergence is granted. Simulation studies on a tracking system verify the superior performance of the proposed fusion strategy in comparison with the Kalman filter, the multi-rate Kalman filters, and also the previous fusion methodology.

This paper reviews several information fusion techniques and strategies in the application of multimodal biometrics system using face and palmprint images. Multimodal biometric is able to overcome several limitations in single modal biometric such as intra-class variations, less discriminative power, noise data and redundant features. By consolidating two kinds of modality a better performance can be achieved. Information fusion in multimodal biometrics can be carried out at three possible levels, i.e. feature, matching score and decision levels. Fusions at these three levels have their own attributes, thus this paper is aimed to compare their effectiveness. A specific fusion rule is necessary to combine the information at each level. Several numbers of analyses on verification and identification shows matching score fusion is able to achieve the best performance which is 98% recognition rates and 98.5% GAR at 0.1% FAR when tested using AR face and PolyU palmprint datasets.

Orientation determination is an essential process planning task in additive manufacturing (AM) since it directly affects the part quality, part texture, mechanical properties, build time, fabrication cost, etc. Evaluation method provides a simple and effective way to determine the optimum orientation of a part. However, the candidates are predesigned in the evaluation method, which results in limited candidate space and makes the qualities of evaluation results highly dependent on the qualities of the predesigned candidates; bad outcomes will be obtained due to the poor candidates. To this end, a feedback multi-attribute decision-making (MADM) model is proposed in this work. The feedback MADM model is an integration of two sub-models: MADM model and proportional-integral-derivative (PID) control model. MADM model aims to calculate the score of a given build orientation. Three criteria, surface roughness, support volume, and build time, are considered in this model, and the ordered weighted averaging (OWA) operator is applied for aggregation. In the PID control model, the finite candidate space is first expanded to infinity by quaternion rotation, then PID controller is applied to match the build orientation with the user’s requirements, that is, search for the part orientation whose score is consistent with the user expected score in the infinite alternative orientations. Four parts with different geometric structures are tested in the experiments, and evaluation and control features of the feedback MADM model are discussed.

Flood hazard zone mapping incorporating geographic information system (GIS) and multi-criteria analysis (MCA) techniques

This study proposes a site location assessment model for citrus cropland using multi-criteria evaluation (MCE) and the combination of a set of factors for suitability mapping and delineating the suitable areas for citrus production in Ramsar, Iran. It defines an incorporated method for the suitability mapping of the most appropriate sites for citrus cultivars with an emphasis on the multi-criteria decision analysis (MCDA) process. The combination of geographic information system (GIS) and a modified version of the analytic hierarchy process (AHP) based on the ordered weighted averaging (OWA) technique is also emphasized. The OWA is based on two principles, namely: the weights of relative criterion significance and the order weights. Therefore, the participatory technique was employed to outline the set of standards and the important criterion. The results derived from the GIS–OWA technique indicate that the cultivation of citrus is feasible only in limited areas, which make up 6.7% of the total area near the Caspian Sea. This investigation has shown that the GIS–OWA model can be integrated into MCDA to select the optimal site for citrus production. The present research highlights how multi-criteria in GIS can play a considerable role in decision making for evaluating the suitability of selected sites for citrus production.

Accurate aircraft engine fault detection and diagnosis is vitally important reducing operating costs and improving safety. Various data and knowledge could be collected from manufacture, test bed measurement systems, on-board measurement systems, maintenance history and experts' experience. Integrating and fusing these data and information to provide intelligent fault diagnosis and maintenance schedules are essentially to both civil and military engines. Information fusion strategies and architectures have been developed over the last several years for improving upon the accuracy, robustness and overall effectiveness of diagnostic and prognostic technologies. Fusion of relevant sensor data, maintenance database information, and various diagnostic and prognostic technologies has been proven effective in reducing false alarm rates, increasing confidence levels in early fault detection, and predicting time to failure or degraded condition requiring maintenance action. In this paper, we researched on the architectures of fusion systems. A four-level model was presented to fit the usage in aircraft engines fault detection and diagnosis. To generate the deviations for gas path fault detection, baseline values should be chosen firstly. We analyzed choosing baseline values and generating deviations in detail. Then, the fuzzy set was introduced to descript the degree of symptoms belonging to fault patterns. A method based on fuzzy set to isolate fault and quantify the deterioration of performance of components was presented. We also demonstrated the method with an example. For this method, it is not necessary to have more measurements than fault patterns. The fault diagnosis system based on it is very easy to construct and extend too

To combine the strengths of Gaussian and non-Gaussian latent variable models, a novel information fusion strategy has recently been proposed under the deep learning framework. Although promising results have been obtained, the critical structure learning problem remains unsolved, which seriously hinders the automation of data-driven modeling and analytics. In this article, the maximal information coefficient (MIC) method is introduced as a measurement of the association strength between two latent variables, which has no restriction in the type of data distribution. Through an assessment on the necessity of adding a new hidden layer into the deep model in each step, an evaluation index is defined for automatic determination of the required hidden layers during the model training process. For time-varying industrial production environments, reconfiguration or updating of the model structure is frequently required. In this case, automated data-driven modeling and structure learning can significantly improve the efficiency of data analytics. Based on the study results obtained from two real industrial examples, the proposed structure learning algorithm is feasible, and the automated data analytics scheme has significantly improved the online prediction performance in time-varying industrial processes.

Emotion recognition has become an important research area for advanced human-robot interaction. Through recognizing facial expressions, a robot can interact with a person with a more friendly manner. In this paper, we proposed a bimodal emotion recognition system by combining image and speech information. A novel information fusion strategy is proposed to set proper weights to two feature modalities based on their recognition reliability. The fusion weights are determined by the distance between test data and the classification hyperplane and the standard deviation of training samples. After normalization using the mean distance between training samples and the hyperplane, the fusion weight is set to represent the classification reliability of individual modality. In the latter bimodal SVM classification, the recognition result with higher weight is selected. The complete procedure has been implemented in a DSP-based system to recognize five facial expressions on-line in real time. The experimental results show that a recognition rate of 86.9% is achieved, an improvement of 5% compared with using only image information.

Decision-making methods are widely used in renewable energy source (RES) investment. In practical applications, various conditions need to be considered during the decision-making process, such as uncertainty and reliability of information, dependence among criteria, and different risk preferences of the decision makers. However, there is currently a lack of effective consideration of all these conditions. In this article, a new decision-making method based on Z-DEMATEL and the maximal entropy OWA operator is proposed, where Z-number is used to characterize the uncertainty and reliability of the information, decision-making trial and evaluation laboratory (DEMATEL) technique, and the maximal entropy ordered weighted average (OWA) operator are used to deal with dependence and risk preference, respectively. The application example in RES investment and discussions show the effectiveness and the advantages of the proposed method.

HighlightsE-nose and computer vision combined with data fusion strategies were applied to trace tea origins.Pearson correlation analysis, IG, and F-scores were applied to modify the fusion strategies.The classification performances of different fusion strategies were compared.The strategies of IG_SVM_FL and IG_SVM_DS achieved the best results.Abstract. The traceability of tea origins is of great significance. In this study, an electronic nose (E-nose) and computer vision system (CVS) were jointly applied to acquire aroma and image signals of tea samples, aiming at identifying Longjing teas from different geographic origins including Jinyun (120° 7' E, 28° 65' N), Xihu (120° 13' E, 30° 27' N), Xinchang (120° 9' E, 29° 50' N), and Qian Daohu (119° 3' E, 29° 60' N). Data fusion was used to integrate the E-nose and CVS signals for comprehensively characterizing the tea samples. Four traditional fusion strategies including k-nearest neighbors (KNN) and support vector machine (SVM) based feature-level fusion (KNN_FL and SVM_FL) and Dempster-Shafer (D-S) evidence theory based decision-level strategies (KNN_DS and SVM_DS) were applied for classification modeling. Pearson analysis, information gain (IG), and F-scores were employed to modify the traditional fusion strategies to reduce inconsistent and redundant information in the fusion process. The results indicated that the original fusion strategies had no superiority over independent E-nose and CVS decision-making. With the feature selection methods, the modified fusion strategies generally exhibited better performance than the independent decision-making and original fusion strategies. Moreover, the IG-based fusion strategies, encompassing IG_SVM_FL and IG_SVM_DS, achieved the highest classification accuracy of 100%. Keywords: Computer vision, Electronic nose, Feature selection, Fusion strategies, Tea origins.

Assessment of flood susceptible areas using spatially explicit, probabilistic multi-criteria decision analysis

This paper presents a wireless distributed pyroelectric sensor system for tracking and identifying multiple humans based on their body heat radiation. This study aims to make pyroelectric sensors a low-cost alternative to infrared video sensors in thermal gait biometric applications. In this system, the sensor field of view (FOV) is specifically modulated with Fresnel lens arrays for functionality of tracking or identification, and the sensor deployment is chosen to facilitate the process of data-object-association. An Expectation-Maximization-Bayesian tracking scheme is proposed and implemented among slave, master, and host modules of a prototype system. Information fusion schemes are developed to improve the system identification performance for both individuals and multiple subjects. The fusion of thermal gait biometric information measured by multiple nodes is tested at four levels: sample, feature, score, and decision. Experimentally, the prototype system is able to simultaneously track two individuals in both follow-up and crossover scenarios with average tracking errors less than 0.5 m. The experimental results also demonstrate system's potential to be a reliable biometric system for the verification/identification of a small group of human subjects. The developed wireless distributed infrared sensor system can run as a standalone prisoner/patient monitoring system under any illumination conditions, as well as a complement for conventional video and audio human tracking and identification systems.

Integrating multi-criteria evaluation techniques with geographic information systems for landfill site selection: A case study using ordered weighted average