Fuzzy (And Interval) Techniques In The Age Of Big Data: An Overview With Applications To Environmental Science, Geosciences, Engineering, And Medicine
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paper_title: The role of judgment in macroeconomic forecasting accuracy

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Abstract This paper presents evidence on the role that judgmental adjustments play in macroeconomic forecast accuracy. It starts by contrasting the predictive records of four prominent forecasters who adjust their models with those of three models that are used mechanically. The adjusted forecasts tend to be more accurate overall, although important exceptions can be found. Next the article compares adjusted forecasts with those generated mechanically by the same model. Again, with some significant exceptions, judgmental adjustments improve accuracy more often than not. The article closes by considering whether macroeconomic forecasters should place more or less emphasis on their adjustments relative to their models. It finds a clear tendency for modelers to overadjust their models, illustrating what prominent psychologists have termed “the major error of intuitive prediction”. In short, model builders should not hesitate to adjust their models to offset their limitations but should also guard against the tendency to overestimate the value of their personal insights.
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paper_title: Eddy Covariance: A Practical Guide to Measurement and Data Analysis

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Preface Chapter 1 : The eddy covariance method 1.1 History 1.2 Preliminaries 1.3. One point conservation equations 1.4 Integrated relations 1.5 Spectral analysis Chapter 2 : Measurement set-up 2.1 Introduction 2.2 Tower considerations 2.3 Sonic Anemometer 2.4 Eddy CO2 / H2O analyzer 2.5 Profile measurement Chapter 3 : Data Acquisition and Flux Calculations 3.1 Data Transfer and Acquisition 3.2 Flux calculation from raw data 3.3 Flux Determination Chapter 4 : Corrections and data quality control 4.1. Flux data correction 4.2. Effect of the unclosed energy balance 4.3 Data quality analysis 4.4. Accuracy of turbulent fluxes after correction and quality control 4.5 Overview of available correction software Chapter 5 : Night time Flux correction 5.1 Introduction 5.2 Is this problem really important? 5.3. How to implement the filtering procedure ? 5.4 Correction procedures Chapter 6: Data gap filling 6.1 Introduction 6.2 Gap-filling: why and when is it needed? 6.3 Gap-filling methods 6.4 Uncertainty and quality flags 6.5 Final remarks Chapter 7: Uncertainty quantification 7.1 Introduction 7.2 Random errors in flux measurements 7.3 Systematic errors in flux measurements 7.4 Closing ecosystem carbon budgets Conclusion Chapter 8 : Footprint analysis 8.1 Concept of footprint 8.2 Footprint models for atmospheric boundary layer 8.3 Footprint models for high vegetation 8.4 Complicated landscapes and inhomogeneous canopies 8.5 Quality assessment using footprint models 8.6 Validation of footprint models Chapter 9: Partitioning of net fluxes 9.1 Motivation 9.2 Definitions 9.3 Standard methods 9.4 Additional considerations and new approaches 9.5 Recommendations Chapter 10 : Disjunct eddy covariance method 10.1 Introduction 10.2 Theory 10.3 Practical applications of DEC 10.4 DEC in spectral space 10.5 Uncertainty due to DEC 10.6 On the history of the DEC approach Chapter 11: Eddy covariance measurements over forests 11.1 Introduction 11.2 Flux computation, selection and dependence 11.3 Additional measurements 11.4 Impact of ecosystem management and manipulation Chapter 12: Eddy covariance measurements over crops 12.1 Introduction 12.2 Measurement system 12.3 Flux calculation 12.4 Flux corrections 12.5. Data gap filling and footprint evaluation 12.6. Cumulated carbon exchange 12.7. Additional measurements 12.8. Future experimentations Chapter 13: Eddy covariance measurements over grasslands 13.1 Historic overview of grassland EC flux measurements 13.2 Peculiarities of eddy covariance flux measurements over grasslands 13.3 Estimating grassland carbon sequestration from flux measurements 13. 4 Additional measurements 13.5 Other green house gases Chapter 14: Eddy covariance measurements over wetlands 14.1 Introduction 14.2 Historic overview 14.3 Ecosystem-specific considerations 14.4 Complementary measurements 14.5 EC measurements in the wintertime 14.6 Carbon balances and climate effects 14.7 Concluding remarks Chapter 15: Eddy covariance measurements over lakes 15.1. Introduction 15.2. Existing studies 15.3. Surface-specific siting problems Chapter 16: Eddy covariance measurements over urban areas 16.1 Introduction 16.2 Conceptual framework for urban EC measurements 16.3 Challenges in the siting of urban EC stations 16.4 Implications of the peculiarities of the urban boundary layer on EC measurements 16.5 Summary and conclusions Chapter 17: Database maintenance, data sharing policy, collaboration 17.1 Data Management 17.2 Data Practices 17.3 Data User Services 17.4 Data Sharing and Policy of Uses Symbol Index Subject Index
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paper_title: How to Gauge Accuracy of Measurements and of Expert Estimates: Beyond Normal Distributions

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To properly process data, we need to know the accuracy of different data points, i.e., accuracy of different measurement results and expert estimates. Often, this accuracy is not given. For such situations, we describe how this accuracy can be estimated based on the available data.
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paper_title: Assessment of functional impairment in human locomotion: A fuzzy-motivated approach

paper_content:

Many neurological disorders result in disordered motion. The effects of a disorder can be decrease by an appropriate rehabilitation. To make rehabilitation efficient, we need to monitor the patient and check how well he or she improves. In our previous papers, we proposed a fuzzy-based semi-heuristic method of gauging how well a patient improved. Surprisingly, this semi-heuristic method turned out to be more efficient that we expected. In this paper, we provide a justification for this efficiency. In the future, it is desirable to combine this fuzzy-assessment approach with results by Alavarez-Alvarez, Trivino, and Cordon who use fuzzy techniques for modeling human gait.
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paper_title: Nonlinear high-resolution three-dimensional seismic travel time tomography

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A tomographic inversion procedure is described and applied to a synthetic three-dimensional (3-D) seismic refraction data set, demonstrating that tomography is capable of determining a densely sampled velocity model with large velocity contrasts. Forward and inverse modeling procedures are chosen to minimize the computational costs of the inversion. Parameterizing the linearized inversion using functions defined along the ray paths, simple backprojection with zero pixel size is shown to exactly solve the linear problem, producing the smallest model for the slowness perturbation. For small grid cells, simple backprojection closely approximates the exact solution and is a sufficient solution for an iterative nonlinear inversion. This eliminates the need to store or solve a large system of linear equations. Accurate first arrival travel times are rapidly computed using a finite difference algorithm. Forward modeling between each simple backprojection allows the procedure to correctly account for the locations of the rays. This becomes more important as the spatial resolution of the model is improved. The computational efficiency of the entire nonlinear procedure allows the model to be densely sampled, providing a spatially well-resolved 3-D tomographic image. The synthetic refraction survey is designed to be similar to a published 3-D survey over the East Pacific Rise. Tests based on this example and others show that 3-D tomography is capable of inverting a large travel time data set for detailed earth structure with large lateral velocity variations and is stable in the presence of noisy data.
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paper_title: Application of wearable sensors for human gait analysis using fuzzy computational algorithm

paper_content:

Abstract The authors have developed and tested a wearable inertial sensor system for the acquisition of gait features. The sensors were placed on anatomical segments of the lower limb: foot, shank, thigh, and hip, and the motion data were then captured in conjunction with 3D ground reaction forces (GRFs). The method of relational matrix was applied to develop a rule-based system, an intelligent fuzzy computational algorithm. The rule-based system provides a feature matrix model representing the strength of association or interaction amongst the elements of the gait functions (limb-segments accelerations and GRFs) throughout the gait cycle. A comparison between the reference rule-based data and an input test data was evaluated using a fuzzy similarity algorithm. This system was tested and evaluated using two subject groups: 10 healthy subjects were recruited to establish the reference fuzzy rule-base, and 4 relapsing remitting multiple sclerosis subjects were used as an input test data; and the grade of similarity between them was evaluated. This similarity provides a quantitative assessment of mobility state of the impaired subject. This algorithmic tool may be helpful to the clinician in the identification of pathological gait impairments, prescribe treatment, and assess the improvements in response to therapeutic intervention.
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paper_title: Uncertainty handling in quantitative BDD-based fault-tree analysis by interval computation

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In fault-tree analysis, probabilities of failure of components are often assumed to be precise. However this assumption is seldom verified in practice. There is a large literature on the computation of the probability of the top (dreadful) event of the fault-tree, based on the representation of logical formulas in the form of binary decision diagrams (BDD). When probabilities of atomic propositions are ill-known and modelled by intervals, BDD-based algorithms no longer apply to the computation of the top probability interval. This paper investigates this question for general Boolean expressions, and proposes an approach based on interval methods, relying on the analysis of the structure of the Boolean formula. The considered application deals with the fault-tree-based analysis of the reliability of aircraft operations.
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paper_title: Computational Intelligence in Biomedical Engineering

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In recent years, there has been an explosion of interest in computational intelligence (CI) techniques as evidenced by numerous applications in various fields. Biomedical research and applications have benefited enormously as a result of applications of such tools and techniques. Most such scientific literature concerning the application of CI for modeling and pattern recognition are scattered around the world in different journals and conference proceedings. Computational Intelligence in Bioengineeringprovides a comprehensive account of the recent research efforts in this emerging area. It brings together cutting-edge research and applications of computational intelligence in a single volume.
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paper_title: Interval Computations as an Important Part of Granular Computing: An Introduction

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This chapter provides a general introduction to interval computations, especially to interval computations as an important part of granular computing. This introduction is aimed at researchers who would like to learn more – and eventually to use – the main ideas and techniques of granular computing. We explain how intervals naturally appear in data processing, which techniques exist for processing intervals, and how these techniques have been historically developed.
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paper_title: Biomechanics And Motor Control Of Human Movement

paper_content:

Thank you very much for downloading biomechanics and motor control of human movement. Maybe you have knowledge that, people have search hundreds times for their favorite books like this biomechanics and motor control of human movement, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some malicious virus inside their laptop.
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paper_title: Computational Intelligence in Gait Research: A Perspective on Current Applications and Future Challenges

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Our mobility is an important daily requirement so much so that any disruption to it severely degrades our perceived quality of life. Studies in gait and human movement sciences, therefore, play a significant role in maintaining the well-being of our mobility. Current gait analysis involves numerous interdependent gait parameters that are difficult to adequately interpret due to the large volume of recorded data and lengthy assessment times in gait laboratories. A proposed solution to these problems is computational intelligence (CI), which is an emerging paradigm in biomedical engineering most notably in pathology detection and prosthesis design. The integration of CI technology in gait systems facilitates studies in disorders caused by lower limb defects, cerebral disorders, and aging effects by learning data relationships through a combination of signal processing and machine learning techniques. Learning paradigms, such as supervised learning, unsupervised learning, and fuzzy and evolutionary algorithms, provide advanced modeling capabilities for biomechanical systems that in the past have relied heavily on statistical analysis. CI offers the ability to investigate nonlinear data relationships, enhance data interpretation, design more efficient diagnostic methods, and extrapolate model functionality. These are envisioned to result in more cost-effective, efficient, and easy-to-use systems, which would address global shortages in medical personnel and rising medical costs. This paper surveys current signal processing and CI methodologies followed by gait applications ranging from normal gait studies and disorder detection to artificial gait simulation. We review recent systems focusing on the existing challenges and issues involved in making them successful. We also examine new research in sensor technologies for gait that could be combined with these intelligent systems to develop more effective healthcare solutions.
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paper_title: Estimating Probability of Failure of a Complex System Based on Partial Information about Subsystems and Components , with Potential Applications to Aircraft Maintenance

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In many real-life applications (e.g., in aircraft maintenance), we need to estimate the probability of failure of a complex system (such as an aircraft as a whole or one of its subsystems). Complex systems are usually built with redundancy allowing them to withstand the failure of a small number of components. In this paper, we assume that we know the structure of the system, and, as a result, for each possible set of failed components, we can tell whether this set will lead to a system failure. For each component A, we know the probability P(A) of its failure with some uncertainty: e.g., we know the lower and upper bounds $\underline P(A)$ and $\overline P(A)$ for this probability. Usually, it is assumed that failures of different components are independent events. Our objective is to use all this information to estimate the probability of failure of the entire the complex system. In this paper, we describe a new efficient method for such estimation based on Cauchy deviates.
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paper_title: Measurement of functional impairments in human locomotion using pattern analysis

paper_content:

Abstract The authors have developed a measurement methodology for the efficient and reliable analysis of human gait dynamics at a level that quantifies variations from morphometrically adjusted normal in three dimensions and in real-time. The large quantities of acquired kinetic, kinematic and electromyographic data are dealt with as aggregate information granules, enabling an efficient partition of input space and more rapid analysis. The process of data aggregation or granulation is predicted on an understanding and application of the functional relationships of human movement and are expressed in an implication table. This implication table is the basis of a fuzzy relational matrix, a feature pattern, established between the dynamic activities during locomotion. The performance and efficiency of the system have been evaluated using two case studies of gait impairments involving two patients each with cerebral palsy and with multiple sclerosis, respectively.
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paper_title: Assessment of functional impairment in human locomotion: A fuzzy-motivated approach

paper_content:

Many neurological disorders result in disordered motion. The effects of a disorder can be decrease by an appropriate rehabilitation. To make rehabilitation efficient, we need to monitor the patient and check how well he or she improves. In our previous papers, we proposed a fuzzy-based semi-heuristic method of gauging how well a patient improved. Surprisingly, this semi-heuristic method turned out to be more efficient that we expected. In this paper, we provide a justification for this efficiency. In the future, it is desirable to combine this fuzzy-assessment approach with results by Alavarez-Alvarez, Trivino, and Cordon who use fuzzy techniques for modeling human gait.
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paper_title: Analysis of muscle activity during gait cycle using fuzzy rule-based reasoning

paper_content:

Abstract The purpose of this study was to determine the patterns of muscle activation as outcome measures of the ground reaction forces during normal walking tasks using optical motion analysis capture system, instrumented treadmill and electromyography (EMG), respectively. The recognition of the muscle patterns during gait dynamics offers insight into the control of skeletal position, joint stiffness, vibrations of the soft tissue packages, stability during ground contact, and propulsion for the movement task. Sixteen able-bodied participants were recruited to walk on a dual-belt instrumented treadmill with embedded force plates. A fuzzy rule-based reasoning algorithm for recognizing the activation patterns of the lower extremity muscles during normal walking maneuvers within the seven gait phases was developed. The resulting recognition will enable the determination of alterations in the locomotor control system, contribute to suggest symptoms of a neurological disease, disease severity, and also indications of improvements in response to therapeutic interventions.
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paper_title: Application of wearable sensors for human gait analysis using fuzzy computational algorithm

paper_content:

Abstract The authors have developed and tested a wearable inertial sensor system for the acquisition of gait features. The sensors were placed on anatomical segments of the lower limb: foot, shank, thigh, and hip, and the motion data were then captured in conjunction with 3D ground reaction forces (GRFs). The method of relational matrix was applied to develop a rule-based system, an intelligent fuzzy computational algorithm. The rule-based system provides a feature matrix model representing the strength of association or interaction amongst the elements of the gait functions (limb-segments accelerations and GRFs) throughout the gait cycle. A comparison between the reference rule-based data and an input test data was evaluated using a fuzzy similarity algorithm. This system was tested and evaluated using two subject groups: 10 healthy subjects were recruited to establish the reference fuzzy rule-base, and 4 relapsing remitting multiple sclerosis subjects were used as an input test data; and the grade of similarity between them was evaluated. This similarity provides a quantitative assessment of mobility state of the impaired subject. This algorithmic tool may be helpful to the clinician in the identification of pathological gait impairments, prescribe treatment, and assess the improvements in response to therapeutic intervention.
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paper_title: Processing interval sensor data in the presence of outliers, with potential applications to localizing underwater robots

paper_content:

Measurements are never absolutely accurate, the measurement result x is, in general, different from the actual (unknown) values x of the corresponding quantity. In many practical problems, we only know upper bounds Δ on the measurement errors equation. In such situations, once we know the measurement result, the only conclusion that we can make about the actual value x is that this value belongs to the interval [x − Δ, x + Δ]. There exist many efficient algorithms for processing such interval data. However, these algorithms usually assume that all the measurement results are valid. In reality, due to factors such as sensor malfunction, some measurement results may be way off (outliers), for which the difference between x and x is much larger than the upper bound Δ on the measurement error. In this paper, we overview the algorithmic problems related to processing interval sensor data in the presence of outliers. Our case study - for which we develop and analyze these algorithms - is localization of underwater robots, a problem in which a significant number of measurement results are outliers.
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paper_title: Using interval methods in the context of robust localization of underwater robots

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In this paper we will apply interval methods to solve the problem of robust localization of an underwater robot. The localization problem is cast into a constraint satisfaction problem (CSP) where constraint propagation algorithms are particularly efficient. The method is designed to work in real environments with numerous outliers. Besides, we used a new approach to represent the map by a binary image. This allow us to represent even unstructured maps. We tested the algorithm on a real data set gathered by an underwater robot in a marina located in Costa Brava.
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paper_title: If we take into account that constraints are soft, then processing constraints becomes algorithmically solvable

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Constraints are ubiquitous in science and engineering. Constraints describe the available information about the state of the system, constraints describe possible relation between current and future states of the system, constraints describe which future states we would like to obtain. To solve problems from engineering and science, it is therefore necessary to process constraints. We show that if we treat constraints as hard (crisp), with all the threshold values exactly known, then in the general case, all the corresponding computational problems become algorithmically unsolvable. However, these problems become algorithmically solvable if we take into account that in reality, constraints are soft: we do not know the exact values of the corresponding thresholds, we do not know the exact dependence between the present and future states, etc.
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paper_title: Towards fast and reliable localization of an underwater object: An interval approach

paper_content:

To localize an underwater object, we measure the distance to this object from several sonar sensors with ::: known locations. The problem is that the signal sent by some of the sonars is re ::: ected not by the desired ::: object(s), but by some auxiliary object and thus, the values measured by these sensors are drastically ::: di erent from the distance to the desired object. To solve this problem, currently probabilistic methods ::: are used; however, since we do not know the exact probability distributions, these methods may miss ::: the actual location of the object. There exist interval-based methods which provide guaranteed (reliable) ::: bounds on the object's location, but these methods sometimes require too much computation time. In ::: this paper, we propose a new faster algorithm for reliable localization of underwater objects.
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paper_title: Guaranteed robust nonlinear minimax estimation

paper_content:

Minimax parameter estimation aims at characterizing the set of all values of the parameter vector that minimize the largest absolute deviation between the experimental data and the corresponding model outputs. It is well known, however, to be extremely sensitive to outliers in the data resulting, e.g., of sensor failures. In this paper, a new method is proposed to robustify minimax estimation by allowing a prespecified number of absolute deviations to become arbitrarily large without modifying the estimates. By combining tools of interval analysis and constraint propagation, it becomes possible to compute the corresponding minimax estimates in an approximate but guaranteed way, even when the model output is nonlinear in its parameters. The method is illustrated on a problem where the parameters are not globally identifiable, which demonstrates its ability to deal with the case where the minimax solution is not unique.
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Title: Fuzzy (And Interval) Techniques In The Age Of Big Data: An Overview With Applications To Environmental Science, Geosciences, Engineering, And Medicine

Section 1: Introduction
Description 1: This section introduces the context of the study, discussing the importance of both data-supported decisions and expert opinions, and outlines the structure of the paper.

Section 2: Probabilistic and Fuzzy Techniques: A Brief Comparison
Description 2: This section provides a comparative overview of probabilistic techniques and fuzzy techniques for handling uncertainty.

Section 3: First Stage of Data Processing: Gauging Measurement Accuracy
Description 3: This section discusses the importance of gauging measurement accuracy and the methods employed, including both traditional statistical approaches and fuzzy techniques.

Section 4: Second Stage: Data Processing Itself
Description 4: This section delves into the data processing phase, explaining how to handle and estimate uncertainties in the data and the benefits of using fuzzy techniques over probabilistic ones.

Section 5: Possibility of Outliers
Description 5: This section focuses on the challenge of outliers in measurements, illustrating the problem with a case study on localizing underwater robots, and compares probabilistic approaches with fuzzy techniques.

Section 6: Conclusions
Description 6: This section summarizes the key findings of the paper, reiterating the value of expert knowledge and fuzzy techniques even in the age of big data, across various application domains.