Integrated Dynamic Facade Control with an Agent-based Architecture for Commercial Buildings

Dynamic façades have significant technical potential to minimize heating, cooling, and lighting energy use and peak electric demand in the perimeter zone of commercial buildings, but the performance of these systems is reliant on being able to balance complex trade-offs between solar control, daylight admission, comfort, and view over the life of the installation. As the context for controllable energy-efficiency technologies grows more complex with the increased use of intermittent renewable energy resources on the grid, it has become increasingly important to look ahead towards more advanced approaches to integrated systems control in order to achieve optimum life-cycle performance at a lower cost. This study examines the feasibility of a model predictive control system for low-cost autonomous dynamic façades. A system architecture designed around lightweight, simple agents is proposed. The architecture accommodates whole building and grid level demands through its modular, hierarchical approach. Automatically-generated models for computing window heat gains, daylight illuminance, and discomfort glare are described. The open source Modelica and JModelica software tools were used to determine the optimum state of control given inputs of window heat gains and lighting loads for a 24-hour optimization horizon. Penalty functions for glare and view/ daylight quality were implemented as constraints. The control system was tested on a low-power controller (1.4 GHz single core with 2 GB of RAM) to evaluate feasibility. The target platform is a low-cost ($35/unit) embedded controller with 1.2 GHz dual-core cpu and 1 GB of RAM. Configuration and commissioning of the curtainwall unit was designed to be largely plug and play with minimal inputs required by the manufacturer through a web-based user interface. An example application was used to demonstrate optimal control of a three-zone electrochromic window for a south-facing zone. The overall approach was deemed to be promising. Further engineering is required to enable scalable, turnkey solutions

Modular System for Shelves and Coasts (MOSSCO v1.0) - a flexible and multi-component framework for coupled coastal ocean ecosystem modelling

Shelf and coastal sea processes extend from the atmosphere through the water column and into the sea bed. These processes are driven by physical, chemical, and biological interactions at local scales, and they are influenced by transport and cross strong spatial gradients. The linkages between domains and many different processes are not adequately described in current model systems. Their limited integration level in part reflects lacking modularity and flexibility; this shortcoming hinders the exchange of data and model components and has historically imposed supremacy of specific physical driver models. We here present the Modular System for Shelves and Coasts (MOSSCO, http://www.mossco.de), a novel domain and process coupling system tailored---but not limited--- to the coupling challenges of and applications in the coastal ocean. MOSSCO builds on the existing coupling technology Earth System Modeling Framework and on the Framework for Aquatic Biogeochemical Models, thereby creating a unique level of modularity in both domain and process coupling; the new framework adds rich metadata, flexible scheduling, configurations that allow several tens of models to be coupled, and tested setups for coastal coupled applications. That way, MOSSCO addresses the technology needs of a growing marine coastal Earth System community that encompasses very different disciplines, numerical tools, and research questions.Comment: 30 pages, 6 figures, submitted to Geoscientific Model Development Discussion

A generic architecture style for self-adaptive cyber-physical systems

Die aktuellen Konzepte zur Gestaltung von Regelungssystemen basieren auf dynamischen Verhaltensmodellen, die mathematische Ansätze wie Differentialgleichungen zur Ableitung der entsprechenden Funktionen verwenden. Diese Konzepte stoßen jedoch aufgrund der zunehmenden Systemkomplexität allmählich an ihre Grenzen. Zusammen mit der Entwicklung dieser Konzepte entsteht eine Architekturevolution der Regelungssysteme. In dieser Dissertation wird eine Taxonomie definiert, um die genannte Architekturevolution anhand eines typischen Beispiels, der adaptiven Geschwindigkeitsregelung (ACC), zu veranschaulichen. Aktuelle ACC-Varianten, die auf der Regelungstheorie basieren, werden in Bezug auf ihre Architekturen analysiert. Die Analyseergebnisse zeigen, dass das zukünftige Regelungssystem im ACC eine umfangreichere Selbstadaptationsfähigkeit und Skalierbarkeit erfordert. Dafür sind kompliziertere Algorithmen mit unterschiedlichen Berechnungsmechanismen erforderlich. Somit wird die Systemkomplexität erhöht und führt dazu, dass das zukünftige Regelungssystem zu einem selbstadaptiven cyber-physischen System wird und signifikante Herausforderungen für die Architekturgestaltung des Systems darstellt. Inspiriert durch Ansätze des Software-Engineering zur Gestaltung von Architekturen von softwareintensiven Systemen wird in dieser Dissertation ein generischer Architekturstil entwickelt. Der entwickelte Architekturstil dient als Vorlage, um vernetzte Architekturen mit Verfolgung der entwickelten Designprinzipien nicht nur für die aktuellen Regelungssysteme, sondern auch für selbstadaptiven cyber-physischen Systeme in der Zukunft zu konstruieren. Unterschiedliche Auslösemechanismen und Kommunikationsparadigmen zur Gestaltung der dynamischen Verhalten von Komponenten sind in der vernetzten Architektur anwendbar. Zur Bewertung der Realisierbarkeit des Architekturstils werden aktuelle ACCs erneut aufgenommen, um entsprechende logische Architekturen abzuleiten und die Architekturkonsistenz im Vergleich zu den originalen Architekturen basierend auf der Regelungstheorie (z. B. in Form von Blockdiagrammen) zu untersuchen. Durch die Anwendung des entwickelten generischen Architekturstils wird in dieser Dissertation eine künstliche kognitive Geschwindigkeitsregelung (ACCC) als zukünftige ACC-Variante entworfen, implementiert und evaluiert. Die Evaluationsergebnisse zeigen signifikante Leistungsverbesserungen des ACCC im Vergleich zum menschlichen Fahrer und aktuellen ACC-Varianten.Current concepts of designing automatic control systems rely on dynamic behavioral modeling by using mathematical approaches like differential equations to derive corresponding functions, and slowly reach limitations due to increasing system complexity. Along with the development of these concepts, an architectural evolution of automatic control systems is raised. This dissertation defines a taxonomy to illustrate the aforementioned architectural evolution relying on a typical example of control application: adaptive cruise control (ACC). Current ACC variants, with their architectures considering control theory, are analyzed. The analysis results indicate that the future automatic control system in ACC requires more substantial self-adaptation capability and scalability. For this purpose, more complicated algorithms requiring different computation mechanisms must be integrated into the system and further increase system complexity. This makes the future automatic control system evolve into a self-adaptive cyber-physical system and consistitutes significant challenges for the system’s architecture design. Inspired by software engineering approaches for designing architectures of software-intensive systems, a generic architecture style is proposed. The proposed architecture style serves as a template by following the developed design principle to construct networked architectures not only for the current automatic control systems but also for self-adaptive cyber-physical systems in the future. Different triggering mechanisms and communication paradigms for designing dynamic behaviors are applicable in the networked architecture. To evaluate feasibility of the architecture style, current ACCs are retaken to derive corresponding logical architectures and examine architectural consistency compared to the previous architectures considering the control theory (e.g., in the form of block diagrams). By applying the proposed generic architecture style, an artificial cognitive cruise control (ACCC) is designed, implemented, and evaluated as a future ACC in this dissertation. The evaluation results show significant performance improvements in the ACCC compared to the human driver and current ACC variants

Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements