Flexibility is the keyword when preparing for the uncertainfuture tasks for the civilian and military defence. Supporttools relying on general principles will greatlyfacilitateflexible co-ordination and co-operation between differentcivilian and military organizations, and also between differentcommand levels. Further motivations for general solutionsinclude reduced costs for technical development and training,as well as faster and more informed decisionmaking. Mosttechnical systems that support military activities are howeverdesigned with specific work tasks in mind, and are consequentlyrather inflexible. There are large differences between forinstance fire fighting, disaster relief, calculating missiletrajectories, and navigating large battle-ships. Still, thereought to be much in common in the work of managing thesevarious tasks. We use the termCommand and Control(C2) to capture these commonfeatures in management of civilian and military, rescue anddefence operations. Consequently, this thesis describes a top-down approach tosupport systems for decision-making in the context of C2, as acomplement to the prevailing bottom-up approaches. DISCCO(Decision Support for Command and Control) is a set ofnetwork-based services includingCommand Supporthelping commanders in the human,cooperative and continuous process of evolving, evaluating, andexecuting solutions to their tasks. The command tools providethe means to formulate and visualize tasks, plans, andassessments, but also the means to visualize decisions on thedynamic design of organization. Also included in DISCCO isDecision Support, which, based on AI and simulationtechniques, improve the human process by integrating automaticand semiautomatic generation and evaluation of plans. The toolsprovided by DISCCO interact with aCommon Situation Modelcapturing the recursive structureof the situation, including the status, the dynamicorganization, and the intentions, of own, allied, neutral, andhostile resources. Hence, DISCCOprovides a more comprehensivesituation description than has previously been possible toachieve. DISCCO shows generic features since it is designed tosupport a decisionmaking process abstracted from the actualkinds and details of the tasks that are solved. Thus it will beuseful through all phases of the operation, through all commandlevels, and through all the different organizations andactivities that are involved. Keywords:Command and Control, Management, DecisionSupport, Data Fusion, Information Fusion, Situation Awareness,Network-Based Defence, Ontology. / <p>QCR 20161026</p>

In a three-dimensional world, for perception of the objects around us, we not only wish to classify them, but also know where these objects are. The task of object detection combines both classification and localization. In addition to predicting the object category, we also predict where the object is from sensor data. As it is not known ahead of time how many objects that we have interest in are in the sensor data and where are they, the output size of object detection may change, which makes the object detection problem difficult. In this dissertation, I focus on the task of object detection, and use fusion to improve the detection accuracy and robustness. To be more specific, I propose a method to calculate measure of conflict. This method does not need external knowledge about the credibility of each source. Instead, it uses the information from the sources themselves to help assess the credibility of each source. I apply the proposed measure of conflict to fuse independent sources of tracking information from various stereo cameras. Besides, I propose a computational intelligence system for more accurate object detection in real--time. The proposed system uses online image augmentation before the detection stage during testing and fuses the detection results after. The fusion method is computationally intelligent based on the dynamic analysis of agreement among inputs. Comparing with other fusion operations such as average, median and non-maxima suppression, the proposed methods produces more accurate results in real-time. I also propose a multi--sensor fusion system, which incorporates advantages and mitigate disadvantages of each type of sensor (LiDAR and camera). Generally, camera can provide more texture and color information, but it cannot work in low visibility. On the other hand, LiDAR can provide accurate point positions and work at night or in moderate fog or rain. The proposed system uses the advantages of both camera and LiDAR and mitigate their disadvantages. The results show that comparing with LiDAR or camera detection alone, the fused result can extend the detection range up to 40 meters with increased detection accuracy and robustness.

Information fusion techniques have been widely applied in many applications including clustering, classification, detection and etc. The major objective is to improve the performance using information derived from multiple sources as compared to using information obtained from any of the sources individually. In our previous work, we demonstrated the performance improvement of Electroencephalography(EEG) based seizure detection using information fusion. In the detection problem, the optimal fusion rule is usually derived under the assumption that local decisions are conditionally independent given the hypotheses. However, due to the fact that local detectors observe the same phenomenon, it is highly possible that local decisions are correlated. To address the issue of correlation, we implement the fusion rule sub-optimally by first estimating the unknown parameters under one of the hypotheses and then using them as known parameters to estimate the rest of unknown parameters. In the aforementioned scenario, the hypotheses are uniquely defined, i.e., all local detectors follow the same labeling convention. However, in certain applications, the regions of interest (decisions, hypotheses, clusters and etc.) are not unique, i.e., may vary locally (from sources to sources). In this case, information fusion becomes more complicated. Historically, this problem was first observed in classification and clustering. In classification applications, the category information is pre-defined and training data is required. Therefore, a classification problem can be viewed as a detection problem by considering the pre-defined classes as the hypotheses in detection. However, information fusion in clustering applications is more difficult due to the lack of prior information and the correspondence problem caused by symbolic cluster labels. In the literature, information fusion in clustering problem is usually referred to as clustering ensemble problem. Most of the existing clustering ensemble methods are unsupervised. In this thesis, we proposed two semi-supervised clustering ensemble algorithms (SEA). Similar to existing ensemble methods, SEA consists of two major steps: the generation and fusion of base clusterings. Analogous to distributed detection, we propose a distributed clustering system which consists of a base clustering generator and a decision fusion center. The role of the base clustering generator is to generate multiple base clusterings for the given data set. The role of the decision fusion center is to combine all base clusterings into a single consensus clustering. Although training data is not required by conventional clustering algorithms (usually unsupervised), in many applications expert opinions are always available to label a small portion of data observations. These labels can be utilized as the guidance information in the fusion process. Therefore, we design two operational modes for the fusion center according to the absence or presence of the training data. In the unsupervised mode, any existing unsupervised clustering ensemble methods can be implemented as the fusion rule. In the semi-supervised mode, the proposed semi-supervised clustering ensemble methods can be implemented. In addition, a parallel distributed clustering system is also proposed to reduce the computational times of clustering high-volume data sets. Moreover, we also propose a new cluster detection algorithm based on SEA. It is implemented in the system to provide feedback information. When data observations from a new class (other than existing training classes) are detected, signal is sent out to request new training data or switching from the semi-supervised mode to the unsupervised mode. / Thesis / Doctor of Philosophy (PhD)

The abundance of next-generation sequencing (NGS) data has encouraged the adoption of machine learning methods to aid in the diagnosis and treatment of human disease. In particular, the last decade has shown the extensive use of predictive analytics in cancer research due to the prevalence of rich cellular descriptions of genetic and transcriptomic profiles of cancer cells. Despite the availability of wide-ranging forms of genomic data, few predictive models are designed to leverage multidimensional data sources. In this paper, we introduce a deep learning approach using neural network based information fusion to facilitate the integration of multi-platform genomic data, and the prediction of cancer cell sub-class. We propose the dGMU (deep gated multimodal unit), a series of multiplicative gates that can learn intermediate representations between multi-platform genomic data and improve cancer cell stratification. We also provide a framework for interpretable dimensionality reduction and assess several methods that visualize and explain the decisions of the underlying model. Experimental results on nine cancer types and four forms of NGS data (copy number variation, simple nucleotide variation, RNA expression, and miRNA expression) showed that the dGMU model improved the classification agreement of unimodal approaches and outperformed other fusion strategies in class accuracy. The results indicate that deep learning architectures based on multiplicative gates have the potential to expedite representation learning and knowledge integration in the study of cancer pathogenesis. / Thesis / Master of Science (MSc)

Networked, multi-sensor array systems have proven to be advantageous in the sensor world. A large amount of research has been conducted with these systems, with a main interest in data fusion. Intelligently processing the large amounts of data collected by these systems is required in order to fully utilize the benefits of a multi-sensor array system. A robust but flexible simulation environment would provide a platform for accurately comparing current and future data fusion theories. This thesis proposes a simulator model for testing fusion theories for these acoustic multi-sensor networks. An iterative, lossless data fusion algorithm was presented as the model for simulation development. The arrangement and orientation of objects in the simulation environment, as well as most other system parameters are defined by the user before the simulation runs. The sensor data, including noise, is generated at the appropriate time delay and propagation loss before being processed by a delay and sum beamformer and a matched filter. The resulting range-Doppler maps are modified to probability density functions, and translated to a single point of reference. The data is then combined into a single world model. An iterative process is used to filter out false targets and amplify true target detections. Data is fused from each multi-sensor array and from each simulation run. Target amplitudes are gained if they are present in all combined world models, and are otherwise reduced. This thesis presents the results of the fusion algorithm used, including multiple iterations, to prove the algorithms effectiveness. / Master of Science

>Magister Scientiae - MSc / The recent years have seen an increase in the number of users accessing online services using communication devices such as computers, mobile phones and cards based credentials such as credit cards. This has prompted most governments and business organizations to change the way they do business and manage their identity information. The coming of the online services has however made most Internet users vulnerable to identity fraud and theft. This has resulted in a subsequent increase in the number of reported cases of identity theft and fraud, which is on the increase and costing the global industry excessive amounts. Today with more powerful and effective technologies such as artificial intelligence, wireless communication, mobile storage devices and biometrics, it should be possible to come up with a more effective multi-modal authentication system to help reduce the cases of identity fraud and theft. A multi-modal digital identity management system IS proposed as a solution for managing digital identity information in an effort to reduce the cases of identity fraud and theft seen on most online services today. The proposed system thus uses technologies such as artificial intelligence and biometrics on the current unsecured networks to maintain the security and privacy of users and service providers in a transparent, reliable and efficient way. In order to be authenticated in the proposed multi-modal authentication system, a user is required to submit more than one credential attribute. An artificial intelligent technology is used to implement a technique of information fusion to combine the user's credential attributes for optimum recognition. The information fusion engine is then used to implement the required multi-modal authentication system.

In military battlefield domain, decision making plays a very important part because safety and protection depends upon the accurate decisions made by the commanders in complex situations. In military and defense applications, there is a need of such technology that helps leaders to take good decisions in the critical situations with information overload. With the help of multi-sensor information fusion, the amount of information can be reduced as well as uncertainties in the information in the decision making of identifying and tracking targets in the military area.   Information fusion refers to the process of getting information from different sources and fusing this information, to supply an enhanced decision support. Decision making is the very core and a vital part in the field of information fusion and better decisions can be obtained by understanding how situation awareness can be enhanced. Situation awareness is about understanding the elements of the situation i.e. circumstances of the surrounding environment, their relations and their future impacts, for better decision making. Efficient situation awareness can be achieved with the effective use of the sensors. Sensors play a very useful role in the multi-sensor fusion technology to collect the data about, for instance, the enemy regarding their movements across the border and finding relationships between different objects in the battlefield that helps the decision makers to enhance situation awareness.   The purpose of this thesis is to understand and analyze the critical issue of uncertainties that results information in overload in military battlefield domain and benefits of using multi-sensor information fusion technology to reduce uncertainties by comparing uncertainty management methods of Bayesian and Dempster Shafer theories to enhance decision making and situation awareness for identifying the targets in battlefield domain.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18556号 / 工博第3917号 / 新制||工||1602(附属図書館) / 31456 / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 田村 正行, 准教授 須﨑 純一, 教授 小池 克明 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

<p>By purposefully utilising sensors, for instance by a datafusion system, the state of some system-relevant environmentmight be adequately assessed to support decision-making. Theever increasing access to sensors o.ers great opportunities,but alsoincurs grave challenges. As a result of managingmultiple sensors one can, e.g., expect to achieve a morecomprehensive, resolved, certain and more frequently updatedassessment of the environment than would be possible otherwise.Challenges include data association, treatment of con.ictinginformation and strategies for sensor coordination.</p><p>We use the term information acquisition to denote the skillof a data fusion system to actively acquire information. Theaim of this thesis is to instructively situate that skill in ageneral context, explore and classify related research, andhighlight key issues and possible future work. It is our hopethat this thesis will facilitate communication, understandingand future e.orts for information acquisition.</p><p>The previously mentioned trend towards utilisation of largesets of sensors makes us especially interested in large-scaleinformation acquisition, i.e., acquisition using many andpossibly spatially distributed and heterogeneous sensors.</p><p>Information acquisition is a general concept that emerges inmany di.erent .elds of research. In this thesis, we surveyliterature from, e.g., agent theory, robotics and sensormanagement. We, furthermore, suggest a taxonomy of theliterature that highlights relevant aspects of informationacquisition.</p><p>We describe a function, perception management (akin tosensor management), which realizes information acquisition inthe data fusion process and pertinent properties of itsexternal stimuli, sensing resources, and systemenvironment.</p><p>An example of perception management is also presented. Thetask is that of managing a set of mobile sensors that jointlytrack some mobile targets. The game theoretic algorithmsuggested for distributing the targets among the sensors proveto be more robust to sensor failure than a measurement accuracyoptimal reference algorithm.</p><p><b>Keywords:</b>information acquisition, sensor management,resource management, information fusion, data fusion,perception management, game theory, target tracking</p>

More and more complex semi-automated systems are being developed, aiding human operators to collect and analyze data and information and even to recommend decisions and act upon these. The goal of such development is often to support the operators make better decisions faster, while at the same time decrease their workload. However, these promises are not always fulfilled and several incidents have highlighted the fact that the introduction of automated technologies might instead increase the need for human involvement andexpertise in the tasks carried out. The significance of communicating information regarding an automated system's performance and to explain its strengths and limitations to its operators is strongly highlighted within the system transparencyand operator-centered automation literature. However, it is not common that feedback containing system qualifiers is incorporated into the primary displays of the automated system, obscuring its transparency. In this thesis, we deal with the investigation of the effects of explaining and visualizing system reasoning and performance parameters in different domains on the operators' trust, workload and performance. Different proof-of-concept prototypes have been designed with transparency characteristics in mind, and quantitative and qualitative evaluations together with operators of these systems have been carried out. Our results show that the effects of automation transparency can positively influence the performance and trust calibration of operators of complex systems, yet possibly at the costs of higher workload and longer decision-making times. Further, this thesis provides recommendations for designers and developers of automated systems in terms of general design concepts and guidelines for developing transparent automated systems for the future. / Fler och fler komplexa semiautomatiserade system utvecklas idag, vilka hjälper operatörer att samla in och analysera data och information och även att rekommendera beslut och agera på dessa. Det yttersta målet med implementeringen av automatiserade system är ofta att hjälpa operatörerna att fatta bättre beslut snabbare och samtidigt minska deras arbetsbelastning. Dock blir detta inte alltid fallet och flera olyckor har uppmärksammat faktumet att introduktionen av automatiserade system istället kan öka behovet av mänsklig inblandning och expertis. Inom forskningsområden såsom automationstransparens och operatörscentrerad automation har vikten av att kommunicera information angående automationens prestanda betonats, likaså att förklara dess styrkor och svagheter för operatörerna. Dock är det inte vanligt att sådan meta-information inkorporeras i de automatiska systemens primära användargränssnitt, vilket kan försvåra det för operatörerna att tillgodogöra sig denna information. I denna avhandling undersöks effekterna av att förklara och visualisera semiautomatiserade systems resonerande och prestanda i olika domäner på operatörernas tillit till systemen, deras upplevda arbetsbörda och deras prestation. Olika konceptprototyper har designats med inkorporerade transparensegenskaper och kvalitativa och kvantitativa utvärderingar tillsammans med operatörer av dessa system har genomförts. Resultaten visar att automationstransparens kan ha positiva effekter på operatörers prestanda och tillitskalibrering, dock med möjliga kostnader i form av högre upplevd arbetsbelastning och längre beslutstider. Avhandlingen erbjuder även rekommendationer till designers och utvecklare i form av generella riktlinjer och designegenskaper vid utvecklandet av framtida transparenta semiautomatiserade stödsystem. / <p>The author is also affiliated to the university of Skövde</p><p>This research has been founded by The Swedish Governmental Agency for InnovationSystems (Vinnova) through the National Aviation Engineering Research Program(NFFP5-2009-01315) and supported by Saab AB.</p>