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Microstructure of Selective Laser Melted 316L under Non-Equilibrium Solidification Conditions
(2023)
XAutoML : A Visual Analytics Tool for Understanding and Validating Automated Machine Learning
(2023)
The absolute value of recruitment-to-inflation ratio does not correlate with the recruited volume
(2023)
Increasing Resilience of Production Systems by Dynamic Context Modelling and Process Adaption
(2023)
Selected case studies regarding research-based education in the area of machine and civil assemblies
(2023)
Industrial Internet of Things (IIoT) systems are enhancing the delivery of services and boosting productivity in a wide array of industries, from manufacturing to healthcare. However, IIoT devices are susceptible to cyber-threats such as the leaking of important information, products becoming compromised, and damage to industrial controls. Recently, blockchain technology has been used to increase the trust between stakeholders collaborating in the supply chain in order to preserve privacy, ensure the provenance of material, provide machine-led maintenance, etc. In all cases, such industrial blockchains establish a novel foundation of trust for business transactions which could potentially streamline and expedite economic processes to a significant extent. This paper presents an examination of “Schloss”, an industrial blockchain system architecture designed for multi-factory environments. It proposes an innovative solution to increase trust in industrial networks by incorporating a fairness concept as a subsystem of an industrial blockchain. The proposed mechanism leverages the concept of taxes imposed on blockchain nodes to enforce ethical conduct and discipline among participants. In this paper, we propose a game theory-based mechanism to address security and trust difficulties in industrial networks. The mechanism, inspired by the ultimatum game, progressively punishes malicious actors to increase the cost of fraud, improve the compensation system, and utilise the reward reporting capabilities of blockchain technology to further discourage fraudulent activities. Furthermore, the blockchain’s incentive structure is utilised to reduce collusion and speed up the process of reaching equilibrium, thereby promoting a secure and trustworthy environment for industrial collaboration. The objective of this paper is to address lack of trust among industrial partners and introduce a solution that brings security and trust to the forefront of industrial blockchain applications.
The Industrial Internet of Things (IIoT) holds significant potential for improving efficiency, quality, and flexibility. In decentralized systems, there are no trust based centralized authentication techniques, which are unsuitable for distributed networks or subnets, as they have a single point of failure. However, in a decentralized system, more emphasis is needed on trust management, which presents significant challenges in ensuring security and trust in industrial devices and applications. To address these issues, industrial blockchain has the potential to make use of trustless and transparent technologies for devices, applications, and systems. By using a distributed ledger, blockchains can track devices and their data exchanges, improving relationships between trading partners, and proving the supply chain. In this paper, we propose a model for cross-domain authentication between the blockchain-based infrastructure and industrial centralized networks outside the blockchain to ensure secure communication in industrial environments. Our model enables cross authentication for different sub-networks with different protocols or authentication methods while maintaining the transparency provided by the blockchain. The core concept is to build a bridge of trust that enables secure communication between different domains in the IIoT ecosystem. Our proposed model enables devices and applications in different domains to establish secure and trusted communication channels through the use of blockchain technology, providing an efficient and secure way to exchange data within the IIoT ecosystem. Our study presents a decentralized cross-domain authentication mechanism for field devices, which includes enhancements to the standard authentication system. To validate the feasibility of our approach, we developed a prototype and assessed its performance in a real-world industrial scenario. By improving the security and efficiency in industrial settings, this mechanism has the potential to inspire this important area.
Influence of Reconstruction Algorithms on Harmonic Analysis in Electrical Impedance Tomography
(2023)
The common corpus optimization method “stop words removal” is based on the assumption that text tokens with high occurrence frequency can be removed without affecting classification performance. Linguistic information regarding sentence structure is ignored as well as preferences of the classification technology. We propose the Weighted Unimportant Part-of-Speech Model (WUP-Model) for token removal in the pre-processing of text corpora. The weighted relevance of a token is determined using classification relevance and classification performance impact. The WUP-Model uses linguistic information (part of speech) as grouping criteria. Analogous to stop word removal, we provide a set of irrelevant part of speech (WUP-Instance) for word removal. In a proof-of-concept we created WUP-Instances for several classification algorithms. The evaluation showed significant advantages compared to classic stop word removal. The tree-based classifier increased runtime by 65% and 25% in performance. The performance of the other classifiers decreased between 0.2% and 2.4%, their runtime improved between −4.4% and −24.7%. These results prove beneficial effects of the proposed WUP-Model.
In Vehicular Ad Hoc Networks (VANETs), promoting cooperative behavior is a challenging problem for mechanism designers. Cooperative actions, such as disseminating data, can seem at odds with rationality and may benefit other vehicles at a cost to oneself. Without additional mechanisms, it is expected that cooperative behavior in the population will decrease and eventually disappear. Classical game theoretical models for cooperation, such as the public goods game, predict this outcome, but they assume fixed population sizes and overlook the ecological dynamics of the interacting vehicles. In this paper, we propose an evolutionary public goods game that incorporates VANET ecological dynamics and offers new insights for promoting cooperation. Our model considers free spaces, population density, departure rates of vehicles, and randomly composed groups for each data sender. Theoretical analysis and simulation results show that higher population densities and departure rates, due to minimum differences between pay-offs of vehicles, promote cooperative behavior. This feedback between ecological dynamics and evolutionary game dynamics leads to interesting results. Our proposed model demonstrates a new extension of evolutionary dynamics to vehicles of varying densities. We show that it is possible to promote cooperation in VANETs without the need for any supporting mechanisms. Future research can investigate the potential for using this model in practical settings.
Specification of Neck Muscle Dysfunction through Digital Image Analysis Using Machine Learning
(2023)
Data processed in context is more meaningful, easier to understand and has higher information content, hence it derives its semantic meaning from the surrounding context. Even in the field of acoustic signal processing. In this work, a Deep Learning based approach using Ensemble Neural Networks to integrate context into a learning system is presented. For this purpose, different use cases are considered and the method is demonstrated using acoustic signal processing of machine sound data for valves, pumps and slide rails. Mel-spectrograms are used to train convolutional neural networks in order to analyse acoustic data using image processing techniques.
Existing literature (Erling & Hingeldorf, 2006; Earls, 2014) indicates that there is a lack of formal policies at the macro- or meso-level governing the use of English in German higher education. This has led to a situation in which higher education institutions (HEIs) are required to formulate and implement their own policies and guidelines regarding English-medium instruction (EMI). As a growing number of HEIs adopt EMI (Wächter & Maiworm, 2014; Macaro et al., 2018) without access to policy guidelines, there is an urgent need to scrutinize the policy formulation and implementation processes at the institutional level. Such investigation is crucial to understand the complexities that come with tailoring EMI to unique institutional contexts, objectives, and stakeholder needs. We believe that this will enable more effective and equitable implementations, while also providing insights that could inform future policy recommendations. In this article, we analyze the motivations for drafting a language policy at a medium-sized German university of applied sciences1 (UAS) and investigate the attitudes and opinions towards EMI of three stakeholder groups: faculty members, administrative staff, and the student body. We were especially interested in exploring the rationales for implementing Bilingual Degree Programs (BDPs), as a variant of EMI, and how each stakeholder group influenced the formulation and implementation of the policy. To get an initial overview, we read institutional policy documents outlining the proposed language policy. We then complemented the documentary analysis by conducting a survey investigating the attitudes and opinions of the stakeholder groups using a questionnaire format (n=207). Finally, to gain deeper insights and triangulate data from the questionnaire, we conducted semi-structured interviews (n=18). Analysis of the data indicates that the primary motivation for implementing BDPs is to attract greater numbers of international as well as domestic applicants to make up for an ongoing decline in student numbers. We also discovered that stakeholder groups hold different beliefs about BDPs, impacting their level of support for their implementation. We argue this is due to some groups within the institution being more influential in policy formulation, leading to feelings of disempowerment in individuals tasked with implementing BDPs, but not being consulted in the policy formulation process. Finally, it also seems that the institutional policy is driven by experience in implementation, resulting in policy enhancement over time. We assume this approach is a direct outcome of the lack of policy guidelines and consider the issues that arise from such an approach and share implications of the current practice.
Feasibility of Parylene C for encapsulating piezoelectric actuators in active medical implants
(2023)
Parylene C is well-known as an encapsulation material for medical implants. Within the approach of miniaturization and automatization of a bone distractor, piezoelectric actuators were encapsulated with Parylene C. The stretchability of the polymer was investigated with respect to the encapsulation functionality of piezoelectric chips. We determined a linear yield strain of 1% of approximately 12-μm-thick Parylene C foil. Parylene C encapsulation withstands the mechanical stress of a minimum of 5×105 duty cycles by continuous actuation. The experiments demonstrate that elongation of the encapsulation on piezoelectric actuators and thus the elongation of Parylene C up to 0.8 mm are feasible.
Novel method for plasma etching of printed circuit boards as alternative for fluorocarbon gases
(2023)
The Present and Future of a Digital Montenegro: Analysis of C-ITS, Agriculture, and Healthcare
(2023)
Laparoscopic Video Analysis Using Temporal, Attention, and Multi-Feature Fusion Based-Approaches
(2023)
Quality assurance (QA) plays a crucial role in manufacturing to ensure that products meet their specifications. However, manual QA processes are costly and time-consuming, thereby making artificial intelligence (AI) an attractive solution for automation and expert support. In particular, convolutional neural networks (CNNs) have gained a lot of interest in visual inspection. Next to AI methods, the explainable artificial intelligence (XAI) systems, which achieve transparency and interpretability by providing insights into the decision-making process of the AI, are interesting methods for achieveing quality inspections in manufacturing processes. In this study, we conducted a systematic literature review (SLR) to explore AI and XAI approaches for visual QA (VQA) in manufacturing. Our objective was to assess the current state of the art and identify research gaps in this context. Our findings revealed that AI-based systems predominantly focused on visual quality control (VQC) for defect detection. Research addressing VQA practices, like process optimization, predictive maintenance, or root cause analysis, are more rare. Least often cited are papers that utilize XAI methods. In conclusion, this survey emphasizes the importance and potential of AI and XAI in VQA across various industries. By integrating XAI, organizations can enhance model transparency, interpretability, and trust in AI systems. Overall, leveraging AI and XAI improves VQA practices and decision-making in industries.
Relationships between External, Wearable Sensor-Based, and Internal Parameters: A Systematic Review
(2023)
Vertebral motion reveals complex patterns, which are not yet understood in detail. This applies to vertebral kinematics in general but also to specific motion tasks like gait. For gait analysis, most of existing publications focus on averaging characteristics of recorded motion signals. Instead, this paper aims at analyzing intra- and inter-individual variation specifically and elaborating motion parameters, which are consistent during gait cycles of particular persons. For this purpose, a study design was utilized, which collected motion data from 11 asymptomatic test persons walking at different speed levels (2, 3, and 4 km/h). Acquisition of data was performed using surface topography. The motion signals were preprocessed in order to separate average vertebral orientations (neutral profiles) from basic gait cycles. Subsequently, a k-means clustering technique was applied to figure out, whether a discrimination of test persons was possible based on the preprocessed motion signals. The paper shows that each test sequence could be assigned to the particular test person without additional prior information. In particular, the neutral profiles appeared to be highly consistent intra-individually (across the gait cycles as well as speed levels), but substantially different between test persons. A full discrimination of test persons was achieved using the neutral profiles with respect to flexion/extension data. Based on this, these signals can be considered as individual characteristics for the particular test persons.
Keywords: Gait analysis; Human spine; Intra- and interindividual variation; Motion analysis; Rasterstereography; Surface topography; k-means algorithm.
In this paper, we derive set constraints for a reduced order model and augment them into a model predictive control (MPC) scheme to ensure safe operation of the large-scale ensemble system. For the control feedback, only the aggregated information of the whole system is required. For the constraint satisfaction, we consider an adaptive tube formulation to characterize the deviation between the reduced order model and the ensemble system. Employing the robust control invariant set, we ensure recursive feasibility and initial feasibility under an easily verifiable condition.