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A covariance matrix self-adaptation evolution strategy for optimization under linear constraints
(2018)
A modified matrix adaptation evolution strategy with restarts for constrained real-world problems
(2020)
In combination with successful constraint handling techniques, a Matrix Adaptation Evolution Strategy (MA-ES) variant (the εMAg-ES) turned out to be a competitive algorithm on the constrained optimization problems proposed for the CEC 2018 competition on constrained single objective real-parameter optimization. A subsequent analysis points to additional potential in terms of robustness and solution quality. The consideration of a restart scheme and adjustments in the constraint handling techniques put this into effect and simplify the configuration. The resulting BP-εMAg-ES algorithm is applied to the constrained problems proposed for the IEEE CEC 2020 competition on Real-World Single-Objective Constrained optimization. The novel MA-ES variant realizes improvements over the original εMAg-ES in terms of feasibility and effectiveness on many of the real-world benchmarks. The BP-εMAg-ES realizes a feasibility rate of 100% on 44 out of 57 real-world problems and improves the best-known solution in 5 cases.
A multi-recombinative active matrix adaptation evolution strategy for constrained optimization
(2019)
With Cloud Computing and multi-core CPUs parallel computing resources are becoming more and more affordable and commonly available. Parallel programming should as well be easily accessible for everyone. Unfortunately, existing frameworks and systems are powerful but often very complex to use for anyone who lacks the knowledge about underlying concepts. This paper introduces a software framework and execution environment whose objective is to provide a system which should be easily usable for everyone who could benefit from parallel computing. Some real-world examples are presented with an explanation of all the steps that are necessary for computing in a parallel and distributed manner.
Analysis of the (μ/μI,λ)-CSA-ES with repair by projection applied to a conically constrained problem
(2019)
The usage of data gathered for Industry 4.0 and smart factory scenarios continues to be a problem for companies of all sizes. This is often the case because they aim to start with complicated and time-intensive Machine Learning scenarios. This work evaluates the Process Capability Analysis (PCA) as a pragmatic, easy and quick way of leveraging the gathered machine data from the production process. The area of application considered is injection molding. After describing all the required domain knowledge, the paper presents an approach for a continuous analysis of all parts produced. Applying PCA results in multiple key performance indicators that allow for fast and comprehensible process monitoring. The corresponding visualizations provide the quality department with a tool to efficiently choose where and when quality checks need to be performed. The presented case study indicates the benefit of analyzing whole process data instead of considering only selected production samples. The use of machine data enables additional insights to be drawn about process stability and the associated product quality.
Blood and breath profiles of volatile organic compounds in patients with end-stage renal disease
(2014)
Comparison of constraint-handling mechanisms for the (1,λ)-ES on a simple constrained problem
(2016)
Complementarities and synergies of quadruple helix innovation design in smart city development
(2020)
Increased urbanization trends are stimulating regional needs to support transitions from urban environments to smart cities, using its holistic perspective as a source to innovation. Strong relations between smart cities, urban and regional development, are getting increased attention both at policy and implementation level, providing fertile ground for execution of the new European policy frameworks that supports quadruple helix approaches to innovation. Smart specialization strategies (RIS3) encompass such initiatives, placing ICT and collaboration between academia, industry, government, and citizen at the center of urban innovation. However, there is still lack of research on effects of such approaches to innovation, involving both quadruple helix clusters and ICT in utilizing innovation potentials for developing smart cities. This study aims to increase the understanding on how quadruple helix urban innovation strengthens competitiveness of regions by improving its local smart areas – RIS3. We identified smart specialization patterns and applied comparative benchmark between nine smallmedium sized urban regions in Central Europe. Building on these results, the study provides an overview of the effects of RIS3 strategies implemented through quadruple helix innovation clusters on competitiveness of regions and Smart City development.
A step change is needed in the deployment of renewable energy if the triple challenge of ensuring climate change mitigation, energy security, and energy affordability is to be met. Yet, social acceptance of infrastructure projects and policies remains a key concern. While there has been decades of fruitful research on the social acceptance of wind energy and other renewables, much of the extant research is cross-sectional in nature, failing to capture the important dynamic processes that can make or break renewable energy projects. This paper introduces a Special Issue of Energy Policy which focuses on the neglected topic of the dynamics of social acceptance of renewable energy, drawing on contributions made at an international research conference held in St. Gallen (Switzerland) in June 2022. In addition to introducing these papers and drawing out common themes, we also seek to offer some conceptual clarity on the issue of dynamics in social acceptance, taking into account the influence of time, power, and scale in shaping decision-making processes. We conclude by highlighting a number of avenues of potential future research.
Small and medium-sized enterprises often face resource deficits and there- fore depend on cooperating with other actors to stay innovative in a competitive environment. Establishing and maintaining actual co-creation and service inter- action strategies however is challenging. A reason for this is the complexity of finding methodologies and tools to create valuable outcome and the lack of knowledge of collaboration toolsets, also in virtual environments. This paper introduces an Innovation-Method-Framework consisting of innovation methods for increased service interaction and value co-creation among service stakeholders. Also, toolsets for the framework’s practical application are provided.
Purpose: Although there is an apparent potential in using data for advanced services in manufacturing environments, SMEs are reluctant to share data with their ecosystem partners, which prevents them from leveraging this potential. Therefore, the purpose of this paper is to analyse the reasons behind these resistances. The argumentation paves the way for elaborating countermeasures that are adequate for the specific situation and the typical capabilities of SMEs.
Design/Methodology/Approach: The analysis is based on literature research and in-depth interviews with management representatives of 15 companies in manufacturing service ecosystems. Half of these are manufacturers and the other half technology or service providers for manufacturers. They are SMEs or partly larger companies operating in structures that are typical for SMEs.
Findings: Data sharing hurdles are investigated in the five dimensions, 1. quantifying the value of data, 2. willingness to share data and trust, 3. organizational culture and mindset, 4. legal aspects, and 5. security and privacy. The ability to quantify the value of data is a necessary but not sufficient precondition for data sharing, which must be enabled by adequate measures in the other four dimensions.
Originality/Value: The findings of this empirical study and the solution approach provide an SME-specific framework to analyze hurdles that must be overcome for sharing data in an ecosystem.
Manufacturing SMEs can apply the framework to overcome the hurdles by specific insights and solution approaches. Furthermore, the analysis illustrates the future research direction of the project towards a comprehensive solution approach for data sharing in a manufacturing ecosystem.
In 2021, a prominent Austria dairy producer suffered from an IT attack and was completely paralysed. Without clearly defined mitigation measures in place, major disruptions were caused alongside the whole supply chain, including logistics service providers, governmental food safety bodies, as well as retailers (i.e., supermarkets and convenience stores). In this paper, we ask the question how digitisation and digital transformation impact IT security, especially when considering the complex company ecosystems of food production and food supply chains in Austria. The problem statement stems from a gap in knowledge of key differences in approaches towards IT security, resilience, risk management and especially business interfaces between food suppliers, supermarkets, distributors, logistics and other service providers. In order to answer related research questions, firstly, the authors conduct literature research, and highlight common guidelines and standardisation as well as look at state-based recommendations for critical infrastructure. In a second step, the paper describes a quantitative and qualitative survey with Austrian food companies (producers and retailers) which is described in detail in the paper. A description of recommended measures for the industry, further steps, as well as an outlook conclude the paper.
In this paper, we consider the question of data aggregation using the practical example of emissions data for economic activities for the sustainability assessment of regional bank clients. Given the current scarcity of company-specific emission data, an approximation relies on using available public data. These data are reported in different standards in different sources. To determine a mapping between the different standards, an adaptation to the Covariance Matrix Self-Adaptation Evolution Strategy is proposed. The obtained results show that high-quality mappings are found. Nevertheless, our approach is transferable to other data compatibility problems. These can be found in the merging of emissions data for other countries, or in bridging the gap between completely different data sets.
Modeling the dynamic of breath methane concentration profiles during exercise on an ergometer
(2015)
Recent developments in the area of Natural Language Processing (NLP) increasingly allow for the extension of such techniques to hitherto unidentified areas of application. This paper deals with the application of state-of-the-art NLP techniques to the domain of Product Safety Risk Assessment (PSRA). PSRA is concerned with the quantification of the risks a user is exposed to during product use. The use case arises from an important process of maintaining due diligence towards the customers of the company OMICRON electronics GmbH.
The paper proposes an approach to evaluate the consistency of human-made risk assessments that are proposed by potentially changing expert panels. Along the stages of this NLP-based approach, multiple insights into the PSRA process allow for an improved understanding of the related risk distribution within the product portfolio of the company. The findings aim at making the current process more transparent as well as at automating repetitive tasks. The results of this paper can be regarded as a first step to support domain experts in the risk assessment process.
On the extension of digital ecosystems for SCM and customs with distributed ledger technologies
(2019)
Global supply chains represent the backbone of the modern manufacturing industry. Planning of global supply chains still represents a major hurdle, mainly because of the high complexity and unforeseen disruptions that have to be mastered for meeting the different logistics windows in a globally distributed production environment. Trust in supply chains is an additional challenge. A major – albeit sometimes overlooked - part of Supply Chain Management (SCM) is the management and integration of customs processes, clearing of tariffs, (re-)billing of customers, and fulfilling other legal requirements related to crossing borders, ranging from environmental standards over goods inspection to general paper work. With the exception of work offered by the World Customs Organization (WCO) the issue of customs and blockchain is still underrepresented in research and practice. In this paper, we look at innovations that drive the current ICTenabled SCM research and how these can be combined with smart customs management. After a literature review and introduction to the state-of-the-art, we list potential trust-based innovations for SCM and customs in digital business ecosystems. Based upon the innovations we also describe a requirements analysis of existing distributed ledger technologies (requirements for system layout, system configuration, system governance). A description of the prototype for the Lake Constance region – on which we are currently working – concludes the paper.
On the integration of intelligent logistics ecosystems in production and industry 4.0 settings
(2017)
The design and development of smart products and services with data science enabled solutions forms a core topic of the current trend of digitalisation in industry. Enabling skilled staff, employees, and students to use data science in their daily work routine of designing such products and services is a key concern of higher education institutions, including universities, company workshop providers and in further education. The scope and usage scenario of this paper is to assess software modules (‘tools’) for integrated data and analytics as service (DAaaS). The tools are usually driven by machine learning, may be deployed in cloud infrastructures, and are specifically targeted at particular needs of the industrial manufacturing, production, or supply chain sector.
The paper describes existing theories and previous work, namely methods used in didactics, work done for visually designing and using machine learning algorithms (no-code / low- code tools), as well as combinations of these two topics. For tools available on the market, an extended assessment of their suitability for a set of learning scenarios and personas is discussed.
Blood flow and ventilatory flow strongly influence the concentrations of volatile organic compounds (VOCs) in exhaled breath. The physicochemical properties of a compound (e.g., water solubility) additionally determine if the concentration of the compound in breath reflects the alveolar concentration, the concentration in the upper airways, or a mixture of both. Mathematical modeling based on mass balance equations helps to understand how measured breath concentrations are related to their corresponding blood concentrations and physiological parameters, such as metabolic rates and endogenous production rates. In addition, the influence of inhaled compounds on their exhaled concentrations can be quantified and appropriate correction formulas can be derived. Isoprene and acetone, two endogenous VOCs with very different water solubility, have been modeled to explain the essential features of their behavior in breath. This chapter introduces the theory of physiological modeling of exhaled VOCs, with examples of isoprene and acetone.