• search hit 10 of 66
Back to Result List

Advancing the Automated Design of Integrated Intelligent Multi-Sensory Systems with Self-X Properties

  • The recently established technologies in the areas of distributed measurement and intelligent information processing systems, e.g., Cyber Physical Systems (CPS), Ambient Intelligence/Ambient Assisted Living systems (AmI/AAL), the Internet of Things (IoT), and Industry 4.0 have increased the demand for the development of intelligent integrated multi-sensory systems as to serve rapid growing markets [1, 2]. These increase the significance of complex measurement systems, that incorporate numerous advanced methodological implementations including electronics circuit, signal processing, and multi-sensory information fusion. In particular, in multi-sensory cognition applications, to design such systems, the skill-required tasks, e.g., method selection, parameterization, model analysis, and processing chain construction are elaborated with immense effort, which conventionally are done manually by the expert designer. Moreover, the strong technological competition imposes even more complicated design problems with multiple constraints, e.g., cost, speed, power consumption, exibility, and reliability. Thus, the conventional human expert based design approach may not be able to cope with the increasing demand in numbers, complexity, and diversity. To alleviate the issue, the design automation approach has been the topic for numerous research works [3-14] and has been commercialized to several products [15-18]. Additionally, the dynamic adaptation of intelligent multi-sensor systems is the potential solution for developing dependable and robust systems. Intrinsic evolution approach and self-x properties [19], which include self-monitoring, -calibrating/trimming, and -healing/repairing, are among the best candidates for the issue. Motivated from the ongoing research trends and based on the background of our research work [12, 13] among the pioneers in this topic, the research work of the thesis contributes to the design automation of intelligent integrated multi-sensor systems. In this research work, the Design Automation for Intelligent COgnitive system with self- X properties, the DAICOX, architecture is presented with the aim of tackling the design effort and to providing high quality and robust solutions for multi-sensor intelligent systems. Therefore, the DAICOX architecture is conceived with the defined goals as listed below. Perform front to back complete processing chain design with automated method selection and parameterization, Provide a rich choice of pattern recognition methods to the design method pool, Associate design information via interactive user interface and visualization along with intuitive visual programming, Deliver high quality solutions outperforming conventional approaches by using multi-objective optimization, Gain the adaptability, reliability and robustness of designed solutions with self-x properties, Derived from the goals, several scientific methodological developments and implementations, particularly in the areas of pattern recognition and computational intelligence, will be pursued as part of the DAICOX architecture in the research work of this thesis. The method pool is aimed to contain a rich choice of methods and algorithms covering data acquisition and sensor configuration, signal processing and feature computation, dimensionality reduction, and classification. These methods will be selected and parameterized automatically by the DAICOX design optimization to construct a multi-sensory cognition processing chain. A collection of non-parametric feature quality assessment functions for the purpose of Dimensionality Reduction (DR) process will be presented. In addition, to standard DR methods, the variations of feature selection method, in particular, feature weighting will be proposed. Three different classification categories shall be incorporated in the method pool. Hierarchical classification approach will be proposed and developed to serve as a multi-sensor fusion architecture at the decision level. Beside multi-class classification, one-class classification methods, e.g., One-Class SVM and NOVCLASS will be presented to extend functionality of the solutions, in particular, anomaly and novelty detection. DAICOX is conceived to effectively handle the problem of method selection and parameter setting for a particular application yielding high performance solutions. The processing chain construction tasks will be carried out by meta-heuristic optimization methods, e.g., Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), with multi-objective optimization approach and model analysis for robust solutions. In addition, to the automated system design mechanisms, DAICOX will facilitate the design tasks with intuitive visual programming and various options of visualization. Design database concept of DAICOX is aimed to allow the reusability and extensibility of the designed solutions gained from previous knowledge. Thus, the cooperative design of machine and knowledge from the design expert can also be utilized for obtaining fully enhanced solutions. In particular, the integration of self-x properties as well as intrinsic optimization into the system is proposed to gain enduring reliability and robustness. Hence, DAICOX will allow the inclusion of dynamically reconfigurable hardware instances to the designed solutions in order to realize intrinsic optimization and self-x properties. As a result from the research work in this thesis, a comprehensive intelligent multisensor system design architecture with automated method selection, parameterization, and model analysis is developed with compliance to open-source multi-platform software.It is integrated with an intuitive design environment, which includes visual programming concept and design information visualizations. Thus, the design effort is minimized as investigated in three case studies of different application background, e.g., food analysis (LoX), driving assistance (DeCaDrive), and magnetic localization. Moreover, DAICOX achieved better quality of the solutions compared to the manual approach in all cases, where the classification rate was increased by 5.4%, 0.06%, and 11.4% in the LoX, DeCaDrive, and magnetic localization case, respectively. The design time was reduced by 81.87% compared to the conventional approach by using DAICOX in the LoX case study. At the current state of development, a number of novel contributions of the thesis are outlined below. Automated processing chain construction and parameterization for the design of signal processing and feature computation. Novel dimensionality reduction methods, e.g., GA and PSO based feature selection and feature weighting with multi-objective feature quality assessment. A modification of non-parametric compactness measure for feature space quality assessment. Decision level sensor fusion architecture based on proposed hierarchical classification approach using, i.e., H-SVM. A collection of one-class classification methods and a novel variation, i.e., NOVCLASS-R. Automated design toolboxes supporting front to back design with automated model selection and information visualization. In this research work, due to the complexity of the task, neither all of the identified goals have been comprehensively reached yet nor has the complete architecture definition been fully implemented. Based on the currently implemented tools and frameworks, ongoing development of DAICOX is pursuing towards the complete architecture. The potential future improvements are the extension of method pool with a richer choice of methods and algorithms, processing chain breeding via graph based evolution approach, incorporation of intrinsic optimization, and the integration of self-x properties. According to these features, DAICOX will improve its aptness in designing advanced systems to serve the increasingly growing technologies of distributed intelligent measurement systems, in particular, CPS and Industrie 4.0.

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar
Author:Kittikhun Thongpull
URN (permanent link):urn:nbn:de:hbz:386-kluedo-42679
Advisor:Andreas König
Document Type:Doctoral Thesis
Language of publication:English
Publication Date:2016/01/11
Year of Publication:2016
Publishing Institute:Technische Universität Kaiserslautern
Granting Institute:Technische Universität Kaiserslautern
Acceptance Date of the Thesis:2015/12/23
Date of the Publication (Server):2016/01/12
Number of page:XXI, 196
Faculties / Organisational entities:Fachbereich Elektrotechnik und Informationstechnik
DDC-Cassification:6 Technik, Medizin, angewandte Wissenschaften / 620 Ingenieurwissenschaften und Maschinenbau
Licence (German):Standard gemäß KLUEDO-Leitlinien vom 30.07.2015