Innovative Service-Based Business Concepts for the Machine Tool Building Industry

Organised by: Cranfield UniversityDuring the last decade, machine tool building companies have been forced to put innovative offers on the market. Due to the technical features of their products and the prevailing organizational structures in this sector, especially product-service systems are a promising way of creating a unique selling point. In this paper, potential new business concepts for machine tool builders will be presented which aim at fulfilling basic customer needs like the increase in quality, flexibility, productivity and the reduction of lead times, costs and risks. For the implementation of these product-service systems, practical examples are given.Mori Seiki – The Machine Tool Compan

Kompics: a message-passing component model for building distributed systems

The Kompics component model and programming framework was designedto simplify the development of increasingly complex distributed systems. Systems built with Kompics leverage multi-core machines out of the box and they can be dynamically reconfigured to support hot software upgrades. A simulation framework enables deterministic debugging and reproducible performance evaluation of unmodified Kompics distributed systems. We describe the component model and show how to program and compose event-based distributed systems. We present the architectural patterns and abstractions that Kompics facilitates and we highlight a case study of a complex distributed middleware that we have built with Kompics. We show how our approach enables systematic development and evaluation of large-scale and dynamic distributed systems

Deep Contextual Bandit and Reinforcement Learning for IRS-Assisted MU-MIMO Systems

© 2023 IEEE. This version of the article has been accepted for publication, after peer review. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The Version of Record is available online at: https://doi.org/10.1109/TVT.2023.3249353.[Abstract]: The combination of multiple-input multiple-output (MIMO) systems and intelligent reflecting surfaces (IRSs) is foreseen as a critical enabler of beyond 5G (B5G) and 6G. In this work, two different approaches are considered for the joint optimization of the IRS phase-shift matrix and MIMO precoders of an IRS-assisted multi-stream (MS) multi-user MIMO (MU-MIMO) system. Both approaches aim to maximize the system sum-rate for every channel realization. The first proposed solution is a novel contextual bandit (CB) framework with continuous state and action spaces called deep contextual bandit-oriented deep deterministic policy gradient (DCB-DDPG). The second is an innovative deep reinforcement learning (DRL) formulation where the states, actions, and rewards are selected such that the Markov decision process (MDP) property of reinforcement learning (RL) is appropriately met. Both proposals perform remarkably better than state-of-the-art heuristic methods in scenarios with high multi-user interference.This work has been supported by grants ED431C 2020/15 and ED431G 2019/01 (to support the Centro de Investigación de Galicia “CITIC”) funded by Xunta de Galicia and ERDF Galicia 2014-2020; and by grants PID2019-104958RB-C42 (ADELE) and BES-2017-081955 funded by MCIN/AEI/10.13039/501100011033.Xunta de Galicia; ED431C 2020/15Xunta de Galicia; ED431G 2019/0

Reinforcement learning in real-time geometry assurance

To improve the assembly quality during production, expert systems are often used. These experts typically use a system model as a basis for identifying improvements. However, since a model uses approximate dynamics or imperfect parameters, the expert advice is bound to be biased. This paper presents a reinforcement learning agent that can identify and limit systematic errors of an expert systems used for geometry assurance. By observing the resulting assembly quality over time, and understanding how different decisions affect the quality, the agent learns when and how to override the biased advice from the expert software

Business models as systemic instruments for the evolution of traditional districts?

This paper aims to explore the potential role of Innovation Intermediaries in the evolution of a traditional cluster toward a service-oriented perspective. In particular, we will highlight the generative function of business models, here as market devices, in stimulating the co- evolution of Intermediary and target firms’ strategies.Business Models, Innovation Intermediaries, Entrepreneurship, Manufacturing, Systemic Instruments

Ambient-Oriented Programming in Fractal

International audienceAmbient-Oriented Programming (AmOP) comprises suits of challenges that are hard to meet by current software development techniques. Although Component-Oriented Programming (COP) represents promising approach, the state-of-the-art component models do not provide sufficient adaptability towards specific constraints of the Ambient field. In this position paper we argue that merging AmOP and COP can be achieved by introducing the Fractal component model and its new feature : Component-Based Controlling Membranes. The proposed solution allows dynamical adaptation of component systems towards the challenges of the Ambient world