Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Cost reduction using process analysis in company PEGRES obuv s.r.o.

Firma PEGRES obuv s.r.o. se již delší dobu potýká se stagnací v oblasti plánování a řízení výroby. Některé podnikové procesy jsou nyní značně zastaralé a v aktuálních podmínkách již neefektivní. Cíl práce je snížení nákladů s využitím procesní analýzy. Pro dosažení tohoto cíle bude provedena analýza současného stavu zastaralých procesů a budou popsány vybrané metody řízení výroby, které jsou svou povahou relevantní pro výrobu obuvi. Výstupem práce je sada doporučení a návrhů na změny v existujících procesech. Vybrané návrhy budou v prostředí firmy implementovány a práce zahrne zhodnocení výsledků po zavedení těchto změn.Company PEGRES obuv s.r.o. has been long time struggling with stagnation in production planning and control. Some of the internal processes are now obsolete and in current conditions no longer effective. The goal of the paper is to reduce the costs using process analysis. To achieve this goal, analysis of the current state of outdated processes will be performed, followed by description of selected methods of production management, which by their nature are relevant to the production of the shoes. Output of the work is a set of recommendations and proposals for changes to existing processes. Selected proposals will be implemented in the company and paper will include evaluation of results after the implementation of these changes.

Mining Event Logs to Support Workflow Resource Allocation

Workflow technology is widely used to facilitate the business process in enterprise information systems (EIS), and it has the potential to reduce design time, enhance product quality and decrease product cost. However, significant limitations still exist: as an important task in the context of workflow, many present resource allocation operations are still performed manually, which are time-consuming. This paper presents a data mining approach to address the resource allocation problem (RAP) and improve the productivity of workflow resource management. Specifically, an Apriori-like algorithm is used to find the frequent patterns from the event log, and association rules are generated according to predefined resource allocation constraints. Subsequently, a correlation measure named lift is utilized to annotate the negatively correlated resource allocation rules for resource reservation. Finally, the rules are ranked using the confidence measures as resource allocation rules. Comparative experiments are performed using C4.5, SVM, ID3, Na\"ive Bayes and the presented approach, and the results show that the presented approach is effective in both accuracy and candidate resource recommendations.Comment: T. Liu et al., Mining event logs to support workflow resource allocation, Knowl. Based Syst. (2012), http://dx.doi.org/ 10.1016/j.knosys.2012.05.01

Structuring the decision process

This chapter includes a discussion of leadership decisions and stress. Many leaders are daily exposed to stress when they must make decisions, and there are often social reasons for this. Social standards suggest that a leader must be proactive and make decisions and not flee the situation. Conflict often creates stress in decision-making situations. It is important for leaders to understand that it is not stress in itself that leads to bad decisions, rather, bad decisions may be the result of time pressure in the sense that leaders have not been able to gather enough relevant information. Thus, it is worthwhile for leaders to be able to prioritize properly in order to cope with stressful situations. In some situations, a leader chooses to delegate the decisions to his/her team and then it is important to guard against «groupthink», a phenomenon where members of a team put consensus before anything else as a result of the peer pressure. A number of methods are presented that enable leaders to avoid this phenomenon. Often leaders are involved in decision-making situations where they are forced to navigate between objectives that are in strong conflict with each other. We are talking about «decision dilemmas». These are characterized by the existence of a conflict between the top leadership's desire to control the activities and their wish to give autonomy and independence to the various units. It is important for leaders to be able to strike a balance in different dilemma situations and understand how to best manage conflicts when they aris

Scientific knowledge and scientific uncertainty in bushfire and flood risk mitigation: literature review

EXECUTIVE SUMMARY The Scientific Diversity, Scientific Uncertainty and Risk Mitigation Policy and Planning (RMPP) project aims to investigate the diversity and uncertainty of bushfire and flood science, and its contribution to risk mitigation policy and planning. The project investigates how policy makers, practitioners, courts, inquiries and the community differentiate, understand and use scientific knowledge in relation to bushfire and flood risk. It uses qualitative social science methods and case studies to analyse how diverse types of knowledge are ordered and judged as salient, credible and authoritative, and the pragmatic meaning this holds for emergency management across the PPRR spectrum. This research report is the second literature review of the RMPP project and was written before any of the case studies had been completed. It synthesises approximately 250 academic sources on bushfire and flood risk science, including research on hazard modelling, prescribed burning, hydrological engineering, development planning, meteorology, climatology and evacuation planning. The report also incorporates theoretical insights from the fields of risk studies and science and technology studies (STS), as well as indicative research regarding the public understandings of science, risk communication and deliberative planning. This report outlines the key scientific practices (methods and knowledge) and scientific uncertainties in bushfire and flood risk mitigation in Australia. Scientific uncertainties are those ‘known unknowns’ and ‘unknown unknowns’ that emerge from the development and utilisation of scientific knowledge. Risk mitigation involves those processes through which agencies attempt to limit the vulnerability of assets and values to a given hazard. The focus of this report is the uncertainties encountered and managed by risk mitigation professionals in regards to these two hazards, though literature regarding natural sciences and the scientific method more generally are also included where appropriate. It is important to note that while this report excludes professional experience and local knowledge from its consideration of uncertainties and knowledge, these are also very important aspects of risk mitigation which will be addressed in the RMPP project’s case studies. Key findings of this report include: Risk and scientific knowledge are both constructed categories, indicating that attempts to understand any individual instance of risk or scientific knowledge should be understood in light of the social, political, economic, and ecological context in which they emerge. Uncertainty is a necessary element of scientific methods, and as such risk mitigation practitioners and researchers alike should seek to ‘embrace uncertainty’ (Moore et al., 2005) as part of navigating bushfire and flood risk mitigation

The role of learning on industrial simulation design and analysis

The capability of modeling real-world system operations has turned simulation into an indispensable problemsolving methodology for business system design and analysis. Today, simulation supports decisions ranging from sourcing to operations to finance, starting at the strategic level and proceeding towards tactical and operational levels of decision-making. In such a dynamic setting, the practice of simulation goes beyond being a static problem-solving exercise and requires integration with learning. This article discusses the role of learning in simulation design and analysis motivated by the needs of industrial problems and describes how selected tools of statistical learning can be utilized for this purpose

Forecasting Player Behavioral Data and Simulating in-Game Events

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors