An evolutionary scheduling approach for trading-off accuracy vs. verifiable energy in multicore processors

This work addresses the problem of energy-efficient scheduling and allocation of tasks in multicore environments, where the tasks can allow a certain loss in accuracy in the output, while still providing proper functionality and meeting an energy budget. This margin for accuracy loss is exploited by using computing techniques that reduce the work load, and thus can also result in significant energy savings. To this end, we use the technique of loop perforation, that transforms loops to execute only a subset of their original iterations, and integrate this technique into our existing optimization tool for energy-efficient scheduling. To verify that a schedule meets an energy budget, both safe upper and lower bounds on the energy consumption of the tasks involved are needed. For this reason, we use a parametric approach to estimate safe (and tight) energy bounds that are practical for energy verification (and optimization applications). This approach consists in dividing a program into basic (?branchless?) blocks, establishing the maximal (resp. minimal) energy consumption for each block using an evolutionary algorithm, and combining the obtained values according to the program control flow, by using static analysis to produce energy bound functions on input data sizes. The scheduling tool uses evolutionary algorithms coupled with the energy bound functions for estimating the energy consumption of different schedules. The experiments with our prototype implementation were performed on multicore XMOS chips, but our approach can be adapted to any multicore environment with minor changes. The experimental results show that our new scheduler enhanced with loop perforation improves on the previous one, achieving significant energy savings (31% on average for the test programs) for acceptable levels of accuracy loss

An early-stage decision-support framework for the implementation of intelligent automation

The constant pressure on manufacturing companies to improve productivity, reduce the lead time and progress in quality requires new technological developments and adoption.The rapid development of smart technology and robotics and autonomous systems (RAS) technology has a profound impact on manufacturing automation and might determine winners and losers of the next generation’s manufacturing competition. Simultaneously, recent smart technology developments in the areas enable an automation response to new production paradigms such as mass customisation and product-lifecycle considerations in the context of Industry 4.0. New paradigms, like mass customisation, increased both the complexity of the tasks and the risk due to smart technology integration. From a manufacturing automation perspective, intelligent automation has been identified as a possible response to arising demands. The presented research aims to support the industrial uptake of intelligent automation into manufacturing businesses by quantifying risks at the early design stage and business case development. An early-stage decision-support framework for the implementation of intelligent automation in manufacturing businesses is presented in this thesis.The framework is informed by an extensive literature review, updated and verified with surveys and workshops to add to the knowledge base due to the rapid development of the associated technologies. A paradigm shift from cost to a risk-modelling perspective is proposed to provide a more flexible and generic approach applicable throughout the current technology landscape. The proposed probabilistic decision-support framework consists of three parts:• A clustering algorithm to identify the manufacturing functions in manual processes from task analysis to mitigate early-stage design uncertainties• A Bayesian Belief Network (BBN) informed by an expert elicitation via the DELPHI method, where the identified functions become the unit of analysis.• A Markov-Chain Monte-Carlo method modelling the effects of uncertainties on the critical success factors to address issues of factor interdependencies after expert elicitation.Based on the overall decision framework a toolbox was developed in Microsoft Excel. Five different case studies are used to test and validate the framework. Evaluation of the results derived from the toolbox from the industrial feedback suggests a positive validation for commercial use. The main contributions to knowledge in the presented thesis arise from the following four points:• Early-stage decision-support framework for business case evaluation of intelligent automation.• Translating manual tasks to automation function via a novel clustering approach• Application of a Markov-Chain Monte-Carlo Method to simulate correlation between decision criteria• Causal relationship among Critical Success Factors has been established from business and technical perspectives.The implications on practise might be promising. The feedback arising from the created tool was promising from the industry, and a practical realisation of the decision-support tool seems to be desired from an industrial point of view.With respect to further work, the decision-support tool might have established a ground to analyse a human task automatically for automation purposes. The established clustering mechanisms and the related attributes could be connected to sensorial data and analyse a manufacturing task autonomously without the subjective input of task analysis experts. To enable such an autonomous process, however, the psychophysiological understanding must be increased in the future.</div

Ecosystemic Evolution Feeded by Smart Systems

Information Society is advancing along a route of ecosystemic evolution. ICT and Internet advancements, together with the progression of the systemic approach for enhancement and application of Smart Systems, are grounding such an evolution. The needed approach is therefore expected to evolve by increasingly fitting into the basic requirements of a significant general enhancement of human and social well-being, within all spheres of life (public, private, professional). This implies enhancing and exploiting the net-living virtual space, to make it a virtuous beneficial integration of the real-life space. Meanwhile, contextual evolution of smart cities is aiming at strongly empowering that ecosystemic approach by enhancing and diffusing net-living benefits over our own lived territory, while also incisively targeting a new stable socio-economic local development, according to social, ecological, and economic sustainability requirements. This territorial focus matches with a new glocal vision, which enables a more effective diffusion of benefits in terms of well-being, thus moderating the current global vision primarily fed by a global-scale market development view. Basic technological advancements have thus to be pursued at the system-level. They include system architecting for virtualization of functions, data integration and sharing, flexible basic service composition, and end-service personalization viability, for the operation and interoperation of smart systems, supporting effective net-living advancements in all application fields. Increasing and basically mandatory importance must also be increasingly reserved for human–technical and social–technical factors, as well as to the associated need of empowering the cross-disciplinary approach for related research and innovation. The prospected eco-systemic impact also implies a social pro-active participation, as well as coping with possible negative effects of net-living in terms of social exclusion and isolation, which require incisive actions for a conformal socio-cultural development. In this concern, speed, continuity, and expected long-term duration of innovation processes, pushed by basic technological advancements, make ecosystemic requirements stricter. This evolution requires also a new approach, targeting development of the needed basic and vocational education for net-living, which is to be considered as an engine for the development of the related ‘new living know-how’, as well as of the conformal ‘new making know-how’

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

The optimisation and integration of AGVs with the manufacturing process

In recent years, the manufacturing environment, driven by the growth of advanced technologies and the increasing demand for customised products, has becomes increasingly competitive. In this context, manufacturing systems are now required to be more automated, flexible and reconfigurable. Thus, Autonomous Guided Vehicle (AGV), as a key enabler of dynamic shop floor logistics, are being increasingly widely deployed into the manufacturing sector for the lineside materials supplying, work-in-progress transportation, and finished products collection. A large number of companies and institutions are researching on different AGV systems to integrate AGVs-based shop floor logistics with manufacturing equipment and processes. However, these AGV systems are typically equipped with various communication protocols and utilise ad-hoc communication methods. They lack a generic framework to integrate the AGV systems into the manufacturing systems with minimal engineering effort and system reconfiguration. Current scheduling optimisation methods for multiple AGVs in shop floor logistics now support effective task allocation, shortest route planning, and conflict-free supervision, allocating the delivery tasks based on the location and availability of AGVs. However, these current methods do not give enough consideration to real-time operational information during the manufacturing process and have difficulties in analysing the real-time delivery requests from manufacturing work stations. This not only reduces the efficiency and flexibility of the shop floor logistics, ii but also significantly impacts on the overall performance of manufacturing processes. This thesis presents a generic integration approach, called Smart AGV Management System (SAMS), to support the integration of AGVs with manufacturing processes. The proposed framework enables enhanced interoperability between AGVs-based shop floor logistics and the manufacturing process through a generic data-sharing platform. Moreover, a Digital Twin (DT)-based optimisation method is developed in SAMS that can simulate and analyse the real-time manufacturing process to schedule AGVs for optimising multiple objectives, including the utilisation of work stations, delivery Justin- time (JIT) performance, charging of AGVs and overall energy consumption. This approach is experimentally deployed and evaluated from various perspectives to identify its integration and optimisation capabilities during the reconfiguration and operational phases. The results show that the proposed integration framework can enable a more effective integration with manufacturing process compared to traditional integration methods. In addition, the results demonstrate that the proposed optimisation method can schedule and reschedule AGV-based shop floor logistics when facing a range of system disruptions

Proceedings /5th International Symposium on Industrial Engineering – SIE2012, June 14-15, 2012., Belgrade

editors Dragan D. Milanović, Vesna Spasojević-Brkić, Mirjana Misit

Proceedings /5th International Symposium on Industrial Engineering – SIE2012, June 14-15, 2012., Belgrade

editors Dragan D. Milanović, Vesna Spasojević-Brkić, Mirjana Misit

Natural Disaster Detection Using Wavelet and Artificial Neural Network

Indonesia, by the location of its geographic and geologic, it have more potential encounters for natural disasters. This nation is traversed by three tectonic plates, namely: IndoAustralian, the Eurasian and the Pacific plates. One of the tools employed to detect danger and send an early disaster warning is sensor device for ocean waves, but it has drawbacks related to the very limited time gap between information/warnings obtained and the real disaster event, which is only less than 30 minutes. Natural disaster early detection information system is essential to prevent potential danger. The system can make use of the pattern recognition of satellite imagery sequences that take place before and during the natural disaster. This study is conducted to determine the right wavelet to compress the satellite image sequences and to perform the pattern recognition process of a natural disaster employing an artificial neural network. This study makes use of satellite imagery sequences of tornadoes and hurricanes