Data fusion using expected output membership functions

Multi-sensor systems can improve accuracy, increase detection range, and enhance reliability compared to single sensor systems. The main problems in multi-sensor systems are how to select sensors, model the sensors, and combine the data;This dissertation proposes a new data fusion method based on fuzzy set methods. The expected output membership function (EOMF) method uses the fuzzy input set and the expected fuzzy output. This method uses the intersections of the fuzzy inputs with the expected fuzzy output in order to find relationships between the given inputs and the estimate of the output. The EOMF method creates a fuzzy confidence distance measurement by assessing the fusability of the data. The fusability measure is used for finding the best position of the EOMF and the best estimate of the system output. Adaptive methods can help deal with occasional bad measurements and set the EOMF to the proper width. The EOMF method can be used for both homogeneous and heterogeneous sensors, which give redundant, cooperative or complementary information. In addition, the EOMF method is robust in the sense that it can eliminate sensor measurements that are outliers. The EOMF method compares favorably with other methods of data fusion such as the weighted average method. An example from the control of automated vehicles shows the effectiveness of the adaptive EOMF method, compared to the fixed EOMF method and the weighted average method in the presence of Gaussian and impulsive noise. This method can also be applied to nondestructive evaluation (NDE) images from heterogeneous sensors

The application of data fusion to reinforced concrete NDT - Volume 1

Research into different approaches to concrete non-destructive testing is presented. However, the main consideration is how data fusion methods can add value to the interpretation of the data gathered from different sensor sources. Mathematical models for data fusion and simultaneous adjustment of inhomogeneous data are used, which increase the accuracy and reliability of the subsequent surface repair decision. The aim of the approach is to adjust any kind of data in a combined way by giving adequate weights to each measurement. An assessment of the quality of different sensor data is part of this. A comparison of different sensors is given. A Graphical User Interface, developed in the research, gives surface representations of the spatial data. This allows the differences between surface reconstruction results to be examined. The validation of the approach for multi sensor fusion for the reconstruction of surfaces is described and demonstrated by using data from areas of concrete that have been surveyed and subsequently repaired. The results of the numerical experiments are interpreted and conclusions for the processing chain of the approach drawn. With further development the experimental software could be useful for industrial applications

Six-Sigma Quality Management of Additive Manufacturing

Quality is a key determinant in deploying new processes, products, or services and influences the adoption of emerging manufacturing technologies. The advent of additive manufacturing (AM) as a manufacturing process has the potential to revolutionize a host of enterprise-related functions from production to the supply chain. The unprecedented level of design flexibility and expanded functionality offered by AM, coupled with greatly reduced lead times, can potentially pave the way for mass customization. However, widespread application of AM is currently hampered by technical challenges in process repeatability and quality management. The breakthrough effect of six sigma (6S) has been demonstrated in traditional manufacturing industries (e.g., semiconductor and automotive industries) in the context of quality planning, control, and improvement through the intensive use of data, statistics, and optimization. 6S entails a data-driven DMAIC methodology of five steps—define, measure, analyze, improve, and control. Notwithstanding the sustained successes of the 6S knowledge body in a variety of established industries ranging from manufacturing, healthcare, logistics, and beyond, there is a dearth of concentrated application of 6S quality management approaches in the context of AM. In this article, we propose to design, develop, and implement the new DMAIC methodology for the 6S quality management of AM. First, we define the specific quality challenges arising from AM layerwise fabrication and mass customization (even one-of-a-kind production). Second, we present a review of AM metrology and sensing techniques, from materials through design, process, and environment, to post-build inspection. Third, we contextualize a framework for realizing the full potential of data from AM systems and emphasize the need for analytical methods and tools. We propose and delineate the utility of new data-driven analytical methods, including deep learning, machine learning, and network science, to characterize and model the interrelationships between engineering design, machine setting, process variability, and final build quality. Fourth, we present the methodologies of ontology analytics, design of experiments (DOE), and simulation analysis for AM system improvements. In closing, new process control approaches are discussed to optimize the action plans, once an anomaly is detected, with specific consideration of lead time and energy consumption. We posit that this work will catalyze more in-depth investigations and multidisciplinary research efforts to accelerate the application of 6S quality management in AM

Modeling Autonomous Agents In Military Simulations

Simulation is an important tool for prediction and assessment of the behavior of complex systems and situations. The importance of simulation has increased tremendously during the last few decades, mainly because the rapid pace of development in the field of electronics has turned the computer from a costly and obscure piece of equipment to a cheap ubiquitous tool which is now an integral part of our daily lives. While such technological improvements make it easier to analyze well-understood deterministic systems, increase in speed and storage capacity alone are not enough when simulating situations where human beings and their behavior are an integral part of the system being studied. The problem with simulation of intelligent entities is that intelligence is still not well understood and it seems that the field of Artificial Intelligence (AI) has a long way to go before we get computers to think like humans. Behavior-based agent modeling has been proposed in mid-80\u27s as one of the alternatives to the classical AI approach. While used mainly for the control of specialized robotic vehicles with very specific sensory capabilities and limited intelligence, we believe that a behavior-based approach to modeling generic autonomous agents in complex environments can provide promising results. To this end, we are investigating a behavior-based model for controlling groups of collaborating and competing agents in a geographic terrain. In this thesis, we are focusing on scenarios of military nature, where agents can move within the environment and adversaries can eliminate each other through use of weapons. Different aspects of agent behavior like navigation to a goal or staying in group formation, are implemented by distinct behavior modules and the final observed behavior for each agent is an emergent property of the combination of simple behaviors and their interaction with the environment. Our experiments show that while such an approach is quite efficient in terms of computational power, it has some major drawbacks. One of the problems is that reactive behavior-based navigation algorithms are not well suited for environments with complex mobility constraints where they tend to perform much worse than proper path planning. This problem represents an important research question, especially when it is considered that most of the modern military conflicts and operations occur in urban environments. One of the contributions of this thesis is a novel approach to reactive navigation where goals and terrain information are fused based on the idea of transforming a terrain with obstacles into a virtual obstacle-free terrain. Experimental results show that our approach can successfully combine the low run-time computational complexity of reactive methods with the high success rates of classical path planning. Another interesting research problem is how to deal with the unpredictable nature of emergent behavior. It is not uncommon to have situations where an outcome diverges significantly from the intended behavior of the agents due to highly complex nonlinear interactions with other agents or the environment itself. Chances of devising a formal way to predict and avoid such abnormalities are slim at best, mostly because such complex systems tend to be be chaotic in nature. Instead, we focus on detection of deviations through tracking group behavior which is a key component of the total situation awareness capability required by modern technology-oriented and network-centric warfare. We have designed a simple and efficient clustering algorithm for tracking of groups of agent suitable for both spatial and behavioral domain. We also show how to detect certain events of interest based on a temporal analysis of the evolution of discovered clusters

From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers’ personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.This work was supported in part by the Basque Government for its funding support through the EMAITEK program (3KIA, ref. KK-2020/00049). It has also received funding support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the Basque Government

\u3cem\u3eGRASP News\u3c/em\u3e: Volume 9, Number 1

The past year at the GRASP Lab has been an exciting and productive period. As always, innovation and technical advancement arising from past research has lead to unexpected questions and fertile areas for new research. New robots, new mobile platforms, new sensors and cameras, and new personnel have all contributed to the breathtaking pace of the change. Perhaps the most significant change is the trend towards multi-disciplinary projects, most notable the multi-agent project (see inside for details on this, and all the other new and on-going projects). This issue of GRASP News covers the developments for the year 1992 and the first quarter of 1993

DevOps for Trustworthy Smart IoT Systems

ENACT is a research project funded by the European Commission under its H2020 program. The project consortium consists of twelve industry and research member organisations spread across the whole EU. The overall goal of the ENACT project was to provide a novel set of solutions to enable DevOps in the realm of trustworthy Smart IoT Systems. Smart IoT Systems (SIS) are complex systems involving not only sensors but also actuators with control loops distributed all across the IoT, Edge and Cloud infrastructure. Since smart IoT systems typically operate in a changing and often unpredictable environment, the ability of these systems to continuously evolve and adapt to their new environment is decisive to ensure and increase their trustworthiness, quality and user experience. DevOps has established itself as a software development life-cycle model that encourages developers to continuously bring new features to the system under operation without sacrificing quality. This book reports on the ENACT work to empower the development and operation as well as the continuous and agile evolution of SIS, which is necessary to adapt the system to changes in its environment, such as newly appearing trustworthiness threats

Multimodal Multisensor attention modelling

Introduction: Sustaining attention is one of the most important factors in determining successful outcomes and deep learning in students. Existing approaches to track student engagement involve periodic human observations that are subject to inter-rater reliability. Our solution uses real-time Multimodal Multisensor data labeled by objective performance outcomes to track the attention of students. Method: The study involves four students with a combined diagnosis of cerebral palsy and a learning disability who took part in a 3-month trial over 59 sessions. Multimodal Multisensor data were collected while they participated in a Continuous Performance Test (CPT). Eyegaze, electroencephalogram, body pose, and interaction data were used to create a model of student attention through objective labeling from the Continuous Performance Test outcomes. To achieve this, a type of continuous performance test is introduced, the Seek-X type. Nine features were extracted including High-Level handpicked Compound Features (HLCF). Using leave-one-out cross-validation, a series of different machine learning approaches were evaluated. Research questions: RQ1: Can we create a model of attention for PMLD/CP students using the CPT? RQ2: What are the main correlations found in the CPT outcomes and the Multimodal Multisensor data? Results: Overall, the random forest classification approach achieved the best classification results. Using random forest, 84.8% classification for attention and 65.4% accuracy for inattention were achieved. We compared these results to outcomes from different models: AdaBoost, decision tree, k-Nearest Neighbor, naïve Bayes, neural network, and support vector machine. We showed that using a multisensor approach achieved higher accuracy than using features from any reduced set of sensors. Incorporating person-specific data improved the classification outcome, compared to being participant neutral. We found that using HighLevel handpicked Compound Features (HLCF) can improve the classification accuracy in every sensor mode. Our approach is robust to both sensor fallout and occlusions. The single most important sensor feature to the classification of attention and inattention was shown to be eye-gaze. We have shown that we can accurately predict the level of attention of students with learning disabilities in a real-time approach that is not subject to inter-rater reliability, human observation, or reliant on a single mode of sensor input. In total, 2475 separate correlation tests were carried over 55 data points using Pearson’s correlation coefficient. Data points from the SDT, CPT outcomes measures, Multimodal Multisensor features, and participant characteristics were assessed longitudinally for cross-correlation significance. A strong positive correlation was found between participant ability to maintain sustained and selective attention in the CPT to their academic progress in school (d′), P < .01. Participants who showed more inhibition in tests had progressed further in their academic assessments P < .01. The Seek-X type CPT also showed specific physiological characteristics, including body movement range and eye-gaze that were significant in P scales such as ‘Reading’ and ‘Listening’ P < .05. We found that participant bias was overall liberal B″D < 0. Participants iii showed no significant bias change during the sessions, and we found no significant correlation between bias (B″D) and sensitivity (d′). Conclusion: An approach to labeling Multimodal Multisensor data to train machine-learning algorithms to track the attention of students with profound and multiple disabilities has been presented. We posit that this approach can overcome the variation in observer inter-rater reliability when using standardized scales in tracking the emotional expression of students with such profound disabilities. The accuracy of our approach increases with multiple modes of sensor input, and our method is robust to sensor occlusion and fall-out. Multiple sources of sensor input are provided, to accommodate a wide variety of users and their needs. Our model can reliably track the attention of students with profound disabilities, regardless of the sensors available. A system incorporating this model can help teachers design personalized interventions for a very heterogeneous group of students, where teachers cannot possibly attend to each of their individual needs. This approach could be used to identify those with the greatest learning challenges, to guarantee that all students are supported to reach their full potential. Keywords—Affective computing in education, affect detection, attention, continuous performance test, engagement, flow, HCI, interaction, learning disabilities, machine learning, multimodal, multisensor, physiological sensors, Signal Detection Theory, selective attention, sustained attention, student engagement

DevOps for Trustworthy Smart IoT Systems

ENACT is a research project funded by the European Commission under its H2020 program. The project consortium consists of twelve industry and research member organisations spread across the whole EU. The overall goal of the ENACT project was to provide a novel set of solutions to enable DevOps in the realm of trustworthy Smart IoT Systems. Smart IoT Systems (SIS) are complex systems involving not only sensors but also actuators with control loops distributed all across the IoT, Edge and Cloud infrastructure. Since smart IoT systems typically operate in a changing and often unpredictable environment, the ability of these systems to continuously evolve and adapt to their new environment is decisive to ensure and increase their trustworthiness, quality and user experience. DevOps has established itself as a software development life-cycle model that encourages developers to continuously bring new features to the system under operation without sacrificing quality. This book reports on the ENACT work to empower the development and operation as well as the continuous and agile evolution of SIS, which is necessary to adapt the system to changes in its environment, such as newly appearing trustworthiness threats

From data acquisition to data fusion : a comprehensive review and a roadmap for the identification of activities of daily living using mobile devices

This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs)