Smart Solution for Smart Factory
University for Business and Technology, Lagjja Kalabria p.n.,
10000 Prishtina, Kosovo (e-mail: [email protected])
Abstract: Modern Industry systems are mostly complex systems with a highly sophisticated level and with high potential for effectiveness and efficiency. To be part of the Industry 4.0 or so called the fourth Industrial Revolution, it needs a business transformation model through digitalization and smart solution. The Design and Management of such systems required Large (Big) Data Processing, Complex Models, Discrete Event Simulation, On Line Control, Multi criteria Optimization tools and also the appropriate knowledge and capacity building for it.
In this paper the integrative approach of modeling, simulation, control and optimization to create a smart solution for complex systems with INTSCHED is presented. Some action taken in UBT (University for Business and Technology) in order to generate knowledge and capacity buildings for Industry 4.0 and the relevant mentioned topics in transition countries like Kosova are shown.
Internet of Things and Big Data – The Disruption of the Value Chain and the Rise of New Software Ecosystems
Abstract: IoT closely connects devices, humans, places, and even abstract items like events. Driven by smart sensors, powerful embedded microelectronics, high-speed connectivity and the standards of the internet, IoT is on the brink of disrupting today’s value chains. Big Data, characterized by high volume, high velocity and a high variety of information, is result of and driving force for IoT. A datafication of business poses completely new opportunities and risks. To hedge technical risks of the interaction between “everything”, IoT requires comprehensive modelling tools. Furthermore, new IT platforms and architectures are necessary to process and store the unprecedented flow of structured and unstructured, repetitive and non-repetitive data in real-time. Finally, only powerful analytics tools are able to extract “sense” from the exponentially growing amount of data and, consequently, data science becomes a strategic asset.
The era of IoT heavily relies on standards for technologies which guarantee the interoperability of everything. This paper outlines some fundamental activities of standardization. Big Data architectures for real-time processing are sketched and, finally, tools for analytics addressed. It is emphasized that IoT is a (fast) evolutionary process whose success to penetrate all dimensions of life heavily depends on a close cooperation between standardization organization, open source communities and IT experts.
Deep Learning vs. Wise Learning: A Critical and Challenging Overview
Peter P. Groumpos
In this Plenary paper the most important scientific challenge of knowledge learning is reviewed thought two different approaches: Deep Learning (DL) and Wise Learning (WL).
Learning is the most important thing that living creatures do. As far as any living creature is concerned, any action that does not involve learning is pretty much a waste of time. This is especially so for a human one. An organism cannot properly animate itself without first learning how to. Humans, before they can satisfy their own needs, first have to learn how to do it.
Deep Learning (DL) is a new branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers, with complex structures or otherwise, composed of multiple non-linear transformations. Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain. In this keynote paper DL is extensively reviewed. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks, recurrent neural networks among other ones are reviewed. The way that different DL algorithms have been applied to a number of fields like computer vision, automatic speech recognition, natural language processing, audio recognition, expert systems and bioinformatics where they have been shown to produce state-of-the-art results on various tasks are presented and analyzed. Specific examples are presented.
Wise Learning (WL) is a simple new emerging mathematical approach for modeling Complex Dynamic Systems (CDS). It is new but very challenging. It is exploring and combining theories of fuzzy logic, neural networks, artificial intelligence, intelligent control, Decision trees, fuzzy cognitive maps, Hebbian learning and other advanced state space methods in a new integrated approach. The new main ingredient of this new approach is that “wise” constraints must be taken into considerations. The basis of the new approach is Fuzzy Cognitive Maps which explores simultaneously: Knowledge Base Systems (KBS), computational complexity, Smart Logistic Systems (SLS), Cloud Computing and Internet of Things, multiyear historical data, Multiagent Systems using many experts and discrete state space techniques. A critical comparison between Deep Learning (DL) and Wise Learning (WL) approaches is presented and their relation to World Peace and Stability is analyzed and discussed. Finaly future research directions in these two different approaches are presented.
Development Trends in Robotics
Abstract: Abstract: Current developing trends are humanoid robots and robots supporting people in everyday life. Other intensive research areas are cooperative robots, bio-inspired robots, ubiquitous robots, cloud robots, modular robots,…….. Micro-, Nano- and Femto-robots are in development and Ato-robots are knocking on the door.
This paper is an “upgrade” to Kopacek (2015) because the field of robotics is dramatically changing. Therefore an overview as well as an outlook on future developments will be given with special emphasis to the demands and relations to TECIS..
3D METAL PRINTING TECHNOLOGY
(Authors: L. Venkat Raghavan, Material Specialist, GEC, Bangalore & Thomas Duda, Engineering Leader GGCC, Schaffhausen)
Abstract:3D Printing or Additive manufacturing is a novel method of manufacturing parts directly from digital model by using layer by layer material build-up approach. This tool-less manufacturing method can produce fully dense metallic parts in short time, with high precision. Features of additive manufacturing like freedom of part design, part complexity, light weighting, part consolidation and design for function are garnering particular interests in metal additive manufacturing for aerospace, oil & gas, marine and automobile applications. Powder bed fusion, in which each powder bed layer is selectively fused by using energy source like laser, is the most promising additive manufacturing technology that can be used for manufacturing small, low volume, complex metallic parts. This review presents overview of 3D Printing technologies, materials, applications, advantages, disadvantages, challenges, economics and applications of 3D metal printing technology.