With the active development of IES in standards since the mid-2010s, the society has made considerable progress with multiple standards’ developments. This paper presents the results of engaging in standards’ development within and across borders of an IEEE society. In particular, the hands-on INTEROP Plugfests, coupled with the CoEs, provide platforms to create ideas for standards, develop standards, initiate interoperability among multiple vendors, providing competitive time-to-market advantage for involved industry partners.

The industrial communication market is dominated by Ethernet-based fieldbus systems. Although they share similar requirements and market segments, their implementations and ecosystems differ considerably. As a result, end customers and device manufacturers are faced with a multitude of technologies that need to be produced, run, diagnosed, maintained, and kept in stock. Although the availability of products and services is largely satisfactory, dealing with multiple solutions generates high costs and limits IoT capability. This paper introduces Open Platform Communication Unified Architecture Time-Sensitive Networking (OPC UA TSN) as a new technology and presents the current view. This time, the industrial prospects of fulfilling industrial communication requirements while leveraging the cost benefits of standard Ethernet hardware in the midterm are in reach. We anticipate that OPC UA TSN will reveal itself as a game changer in the field of industrial automation, being a candidate for establishing a holistic communication infrastructure from the sensor to the cloud.

The Standards Group of the IEEE Industrial Electronics Society (IES) has been active in standards for industrial electronics in recent years, focusing on sensors and sensors networks, real-time industrial communications and industrial agents in the automation fields. It has also participated and collaborated with other IEEE societies such as the Instrumentation and Measurement Society (IMS) in the IEEE 1451 sensor networks standards family, and with government entities such as the US National Institute of Standards and Technology (NIST). This paper gives a brief synopsis of IES standards activities and the trends it sees in industrial electronics standardization in the coming emerging technologies such as Internet of Things (IoT)/Industrial IoT (IIoT), 5G communications, industrial wireless and possibly transportation electrification. All these technologies are expected to be disruptive to the industry in the coming years and standards must be generated to be effective and beneficial to industry and society. The IES Standards Group anticipates more contributions to the IEEE 1451 standards family, industrial agents, industrial wireless applications with NIST, and possibly with standards activities within Industry 4.0 in the coming years.

Building automation is the key to sustainable, safe and comfortable buildings as well as to the integration of buildings into smart grids and with other external applications such as cloud computing. In a typical smart community scenario, various household appliances of multiple residential users are connected via a Home Area Network. HANs are further connected to the local power distribution network via smart meters, forming a LAN, where also renewables communicate. Methods are needed to design and integrate networks with hundred thousands of nodes in a cost-efficient way. The key to providing improved services in building automation is to process complex scenarios in an adequate way. Furthermore, building automation systems must be seen as dependable systems covering both safety and security aspects. The main objective of this Special Section is to bring the ideas of the worldwide research community into a common platform, to present the latest advances and developments.

Complexity of technology is constantly increasing; for the field of automation this means that economic considerations dictate a need for corresponding measures. Artificial intelligence boasts noteworthy successes in this area; however, its achievements appear modest when compared to the faculties of human intelligence. This paper will demonstrate that a new modeling approach is required via possibilities offered by the mindset and tools of computer technology, thereby demonstrating why a psychoanalytic approach seems sensible and necessary. The paramount goal of the research introduced in the following is a formal approach to describing the human psyche based on the neuro- symbolic and neuronal functional layers. Questions associated with this approach include: To what extent can the psychoanalytic model be confirmed by computer technology and the possibility of simulation? To what extent is an axiomatically developed terminology in psychoanalysis a prerequisite for the better integration of the natural-scientific way of thinking in psychoanalysis? The paper is based on the results of several fundamental research projects within the framework of ARS (Artificial Recognition System) which were funded in part nationally and in part by the EU.

The Semantic Web is envisioned to be the next evolutionary step of the Word Wide Web. It will allow programs to access information and interact with services similar to the way people use the Internet today. The required standards have already been developed, and also large databases containing factual information have been published in the appropriate format. However, more casual, every-day information like that published by used car dealerships, resellers, and other data contained in web databases is not available yet. We believe that bringing this type of information to the Semantic Web could greatly increase its use and help bring it into people's everyday lives. In this paper we present a model for such web databases and show how to integrate these sites into the landscape of the Semantic Web by transparently converting and forwarding queries to conventional web databases.

This paper is about problems in the area of the artificial intelligence (AI) of an embodied agent, i.e. a robot or any other autonomous machine, whether simulated or real. We discuss the necessity of grounding the concepts that form relations between symbols in the decision unit of such an agent. We explore existing concepts for knowledge engineering in the field of the Semantic Technologies to determine which grounded base vocabulary might be necessary, we detect that besides bodily experienced basics intelligence also deals with mental experiences and show a possible way to ground the concept for subclass as an example.

So far the worlds of building automation and power networks have co-existed electrically attached but without data interaction, with different, and sometimes divergent, goals and requirements. The smartgrid community is coping nowadays with a new phase in the deployment of its vision: the integration of smart buildings, making use of their services and advantages to enable more complex functionalities at a global level. The main objective of this paper is to review latest results of the research community of industrial electronics, whose society within the IEEE, the IES, acts as the organizer of this conference. At the conference, latest advances and developments in design, modelling, simulating and implementing tools for, or systems of, sensor and/or actuator networks with advances towards user orientation, wireless connectivity, dependability, energy efficiency, context awareness and ubiquitous computing will be presented.

When developing autonomous agents, the problem of motivation poses one of the key questions. Following a bionic approach inspired by psychoanalysis, a multi-level motivational system to enable autonomous agents to pursue their own agenda and cope with their internal and external environment is presented. Considering embodiment, both homeostatic (extrinsic) and non-homeostatic (intrinsic) internal motivations are modeled. In particular, drives, which are represented on multiple levels, are modeled as the motivational element for agents' actions. Representing an additional level of motivations, emotions are integrated as a co-determinant of decision making.

When developing a model of perceptual categorization, a functional and holistic approach is required. Therefore, considering the influence of an agent's current needs, perceptual categorization is modeled as the valuation of a stimulus regarding its potential to satisfy an agent's needs. Based on an exemplar model, an activation-based model is developed that considers two kinds of categorization criteria, namely perceptual similarity and expectation, which represents bottom-up and top-down approaches of perception, respectively. The aggregation of these two criteria then corresponds to the integration of these approaches into a holistic model. A simplified simulation of the categorization model shows that the multi-criteria model leads to a more confident valuation of stimulus objects. In particular, the model is able to eliminate ambiguity in perceptual categorization. Although used equivalently, the simulation shows that the bottom-up similarity criterion is more significant than the top-down expectation-based criterion. In particular, the latter is only significant in case of ambiguity in an object's appearance.

For cognitive agents that decide based on experiences, memory retrieval is a significant issue. In this article a contribution to this field is made by introducing the concept of psychic spreading activation for memory retrieval in an autonomous agent. Using the psychoanalytical foundation of that as a constraint, a variant of spreading activation was created to regard perceptional as well as motivational inputs and considering the emotional component of a situation. Results show how this type of memory retrieval can influence the decision making process.

Previously mostly independently considered topics grow more and more together to form the building blocks of the future smart city. The building itself needs to be built as a zero average energy consuming building, posing requirements mainly on thermal and electrical parameters. Those parameters need to be known and controlled, requiring intelligent control techniques and cross-layer optimized sensor networks. Finally, very differently used buildings together participate in the infrastructure of the city, which gives them freedom to passively burden the grids, or actively help stabilizing them, while benefitting from different tariffs in the smart grids. This paper summarizes the state of the art of those topics with respect to industrial electronics.

Conflicting goals, rules, and/or input data can cause problems in the decision making unit of software agents. Hence, in this paper we introduce psychoanalytic defense mechanisms to be implemented in multi-agent systems to resolve these conflicts. We give a general insight into defense mechanisms in psychoanalysis and how we transfer this theory to multi-agent systems. We describe the kinds of conflicts in the decision making process of software agents where the implementation of psychoanalytic defense mechanisms help to improve the stability of the system and to enhance the performance of the decision making unit. We explain different aspects and important notions which have to be obeyed while implementing defense mechanisms in cognitive systems. Therefore, we provide some example detection rules to find conflicting data, conflicting drives, conflicting action plans, and conflicting intentions in cognitive systems. These detection rules help to exclude unreal input data from the environment by performing a reality check. Defense mechanisms which we implemented are repression, denial, and reaction formation. Finally, we give examples for applications of defense mechanisms in cognitive systems.

In this paper, we propose a human daily activity recognition method that is used for Ambient Assisted Living. The proposed system is able to learn a user's activities using the data from motion and door sensors. We extract low level features from the sensor data and feed the features to a model that combines support vector machines (SVMs) and conditional random fields (CRFs) to give accurate recognition results. We propose to combine SVM and CRF classifiers in a hierarchical model which results in better accuracies and can also make use of high level features. We conducted experiments and presented the effectiveness and accuracies of the proposed method.

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).

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.

Size of objects in scenes is an important parameter of video surveillance systems. From the analysis of object's size we can build an objects size model in scenes. The basic idea derives from automatic calibration to different perspectives. To build such a model of object's size from the real time video data we utilize Gaussians and real-time fast learning algorithm from literature. The built model is used for real-time surveillance systems.