Dietmar Bruckner

Communication and Computation in Buildings: A Short Introduction and Overview

Building automation (BA) and smart homes (SHs) have traditionally not been a unified field but varied by their origins, legal foundations, different applications, different goals, and national funding programs for basic research. Only within the last years that an international common focus appeared. The following overview gives not only an introduction into the topic of BA but also the distinction to other areas of automation, in which networks of the field level (the sensor and actuator level) play an important role. Finally, the scientific challenges will be mentioned. SHs are referred to when the differences to BA have to be explicitly stressed. This paper is an introduction for the special IEEE Transactions on Industrial Electronics section on BA and shall introduce the reader to this new topic. BA not only has a huge economic potential but also is of significant academic interest today.

Simulating the Mind: A Technical Neuropsychoanalytical Approach

Can psychoanalysis offer a new computer model? Can computer designers help psychoanalysts to understand their theory better?In contemporary publications human psyche is often related to neural networks. Why? The wiring in computers can also be related to application software. But does this really make sense? Artificial Intelligence has tried to implement functions of human psyche. The reached achievements are remarkable; however, the goal to get a functional model of the mental apparatus was not reached. Was the selected direction incorrect?The editors are convinced: yes, and they try to give answers here. If one accepts that the brain is an information processing system, then one also has to accept that computer theories can be applied to the brains functions, the human mental apparatus. The contributors of this book - Solms, Panksepp, Sloman and many others who are all experts in computer design, psychoanalysis and neurology are united in one goal: finding synergy in their interdisciplinary fields.

A hidden Markov model based procedure for identifying household electric loads

In automated energy management systems, to make instantaneous decisions based on the appliance status information, continuous data access is a key requirement. With the advances in sensor and communication technologies, it is now possible to remotely monitor the power consumption data. However, before an appliance is actively monitored, it must be identified using the obtained power consumption data. Appropriate methods are required to analyse power consumption patterns for proper appliance recognition. The focus of this work is to provide the model structure for storing and distinguishing the recurring footprints of the household appliances. Hidden Markov model based method is proposed to recognize the individual appliances from combined load. It is found that the proposed method can efficiently differentiate the power consumption patterns of appliances from their combined profiles.

Hierarchical Semantic Processing Architecture for Smart Sensors in Surveillance Networks

Data acquisition by multidomain data acquisition provides means for environment perception usable for detecting unusual and possibly dangerous situations. When being automated, this approach can simplify surveillance tasks required in, for example, airports or other security sensitive infrastructures. This paper describes a novel architecture for surveillance networks based on combining multimodal sensor information. Compared to previous methodologies using only video information, the proposed approach also uses audio data thus increasing its ability to obtain valuable information about the sensed environment. A hierarchical processing architecture for observation and surveillance systems is proposed, which recognizes a set of predefined behaviors and learns about normal behaviors. Deviations from “normality” are reported in a way understandable even for staff without special training. The processing architecture, including the physical sensor nodes, is called smart embedded network of sensing entities (SENSE).

Behavior Learning in Dwelling Environments With Hidden Markov Models

Building automation systems (BASs) have seen widespread distribution also in private residences over the past few years. The ongoing technological developments in the fields of sensors, actuators, as well as embedded systems lead to more and more complex and larger systems. These systems allow ever-better observations of activities in buildings with a rapidly growing number of possible applications. Unfortunately, control systems with lots of parameters, which would be normally utilized, are hard to describe and-from a context-deriving view-hard to understand with standard control engineering techniques. This paper presents an approach to how statistical methods can be applied to (future) BASs to extract semantic and context information from sensor data. A hierarchical model structure based on hidden Markov models is proposed to establish a framework. The lower levels of the model structure are used to observe the sensor values themselves, whereas the higher levels provide a basis for the semantic interpretation of what is happening in the building. Ultimately, the system should be able to give a condensed overview of the daily routine of a sensor or the process that the sensor observes. While knowing the context of the sensor, a human operator can easily interpret the result.

Psychoanalytical model for automation and robotics

Research in automation focuses on systems which are capable of solving very complex tasks and problems. Artificial Intelligence and especially Cognitive Science have brought remarkable successes; however, in some areas the boarders of feasibility and further extension are reached. Compared to human intelligence the range of capabilities of the solutions is still modest. In the following we will argue why we see the necessity to introduce a novel approach for creating models, which possibilities and tools computer engineering can offer, why a psychoanalytical template is considered meaningful, and which open problems could be tackled or even broken through with this approach, respectively. The article is based on comprehensive research results in the course of several research projects including a European one. Involved persons originate from a number of research institutions in Austria, South Africa, and Canada.

High-level hierarchical semantic processing framework for smart sensor networks

This paper presents the framework of a novel approach to combine multi-modal sensor information from audio and video modalities to gain valuable supplementary information compared to traditional video-based observation systems or even just CCTV systems. A hierarchical, multi-modal sensor processing architecture for observation and surveillance systems is proposed. It recognizes a set of pre-defined behavior and learns about usual behavior. Deviations from ldquonormalityrdquo are reported in a way understandable even for staff without special training. The processing architecture including the physical sensor nodes is called SENSE (smart embedded network of sensing entities) (Zucker and Frangu, 2007).

Cognitive Automation—Survey of Novel Artificial General Intelligence Methods for the Automation of Human Technical Environments

Automation, the utilization of control and information technologies for reducing the need for human intervention in the production process is about to meet Cognition-the science concerned with human thinking-and related sciences. More and more processes require analysis and insights that allow controlling them beyond the mere execution of rules and beyond prefitted controllers in order to automatically keep them within the desired conditions. Automatic and flexible decision making based on challenging conditions such as increasing amounts of information, lacking prior knowledge of data, incomplete, missing or contradicting data, becomes the key challenges for future automation technologies.

Emulating the perceptual system of the brain for the purpose of sensor fusion

This work presents a bionic model derived from research findings about the perceptual system of the human brain to build next generation intelligent sensor fusion systems. Therefore, a new information processing principle called neuro-symbolic information processing is introduced. According to this method, sensory data are processed by so-called neuro-symbolic networks. The basic processing units of neuro-symbolic networks are neuro-symbols. Correlations between neuro-symbols of a neuro-symbolic network can be learned from examples. Perception is based on sensor data as well as on interaction with cognitive processes like focus of attention, memory, and knowledge. Additionally, a mechanism for evaluating perception by emotions is suggested.

Daily activity learning from motion detector data for Ambient Assisted Living

In an intelligent environment one important task is to observe and analyze persons daily activities. Through analyzing the corresponding time series sensor data the persons daily activity model should be build. To build such a model some problems have to be overcome: the sensor data count increase sharp with time and the distribution of the data is dynamically according the persons daily activities. In an Ambient Assisted Living (AAL) project we handle this kind of time series sensor data from a motion detector. At first we reduce the data count through a predefined threshold value and build data “states” in time interval. Secondly, we analyze the states using a hidden Markov model, the forward algorithm, and the Viterbi Algorithm to build the persons daily activity model. To test the correctness of the model some special and random days activities routine will be given.