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Standard assembly time is an important piece of data in product development that is used to compare different product variants or manufacturing variants. In the presented approach, standard time is created with the use of a decision tree regarding standard manual and machine-manual operations, taking into consideration product characteristics and typical tools, equipment and layout. The analysed features include, among others: information determined during product development, such as product structure, parts characteristics (e.g. weight, size), connection type, as well as the information determined during assembly planning: tools (e.g. hand screw driver, power screw driver, pliers), equipment (e.g. press, heater), workstation layout (e.g. distance, way of feeding). The object-attribute-value (OAV) framework was applied for the assembly characteristic. An example of the decision tree application to predict standard assembly time was presented for a mechanical subassembly. The case study was dedicated to standard time prediction for a bearing assembly. The presented approach is particularly important for the enterprises which offer customized products.
We use machine learning for the selection and classification of single–molecule trajectories to replace commonly used user–dependent sorting algorithms. Measured fluorescence time series of labelled single molecules need to be sorted into ’good molecules’ and ’bad’ molecules before further kinetic and thermodynamic analysis.
Currently, processing, sorting and analysis of the data is mainly done with the help of laboratory specific programs.
Although there are freely available programs for processing smFRET data, they do not offer ’molecular sorting’ or it is purely empirical. Only recently, new approaches came up to solve this problem by means of machine learning. Here, we describe a sound terminology for molecular sorting of smFRET data and present an efficient workflow for manual annotation followed by the training of the ML algorithm. Descriptive statistics of our generated dataset are provided and will serve as the basis for supervised ML-based molecular sorting algorithms yet to be developed.
Smart ultrafast laser processing with rotating beam – Laser micro drilling, cutting and turning
(2021)
Current micro drilling, cutting and turning processes are mainly based on EDM, milling, stamping, honing or grinding. All these technologies are using a tool with a predefined geometry that is transferred to the working piece. In contrast the laser is a highly flexible tool, which can adapt its size very fast by changing only a software setting. Thanks to the efforts in laser development during the last years, stable ultrafast lasers with sufficient average power and high repetition rates became industrially available. For using as many pulses as possible, a cost-efficient production demands for innovative processes and machining setups with fast axes movement and special optics for beam manipulation. GFH has developed a helical drilling optics, which rotates the beam up to 30.000 rpm in a very precise circle and allows furthermore to adjust the diameter and the incidence angle. This enables the laser to be used for high precision drilling and cutting and also for micro turning processes.
Sensor fusion is an important and crucial topic in many industrial applications. One of the challenging problems is to find an appropriate sensor combination for the dedicated application or to weight their information adequately. In our contribution, we focus on the application of the sensor fusion concept together with the reference to the distance-based learning for object classification purposes. The developed machine learning model has a bi-functional architecture, which learns on the one side the discrimination of the data regarding their classes and, on the other side, the importance of the single signals, i.e., the contribution of each sensor to the decision. We show that the resulting bi-functional model is interpretative, sparse, and simple to integrate in many standard artificial neural networks.
Current research in identity management is focusing on decentralized trust establishment for distributed identities. One of these decentralized trust models is Self-Sovereign Identities (SSI). With SSI each entity should be able to independently present and manage provable information about itself as well as request and review evidence from other entities. Using a distributed blockchain, information for verifying the authenticity of this evidence can be obtained from any other entity. This concept can be used not only for people, but also for authentication and authorization during the life cycle of devices in the Internet of Things (IoT). This paper presents an SSI-based concept for authentication and authorization of IoT devices among each other, intended to contribute to the change in trust on the internet. The SSI methodology employing a blockchain offers the possibility to establish mutual trust and proof of ownership without relying on any third party. The paper describes the concept, offers a reference implementation, and gives a discussion of the approach.
Global challenges like climate change, food security, and infectious diseases such as the COVID-19 pandemic are nearly impossible to tackle when established experts and upstart innovators work in silos. If research organizations, governments, universities, NGOs, and the private sector could collaborate on these challenges more easily, lasting solutions would certainly come more quickly. Aligned with the United Nations’ Sustainable Development Goals, SAIRA connects key players in different arenas: scientists and engineers at research and technology organizations (RTOs) looking to collaborate on sustainable development projects, companies seeking R&D support to tackle their most challenging problems, and startups with innovative ideas and a desire to scale. The platform is a blockchain-secured open innovation platform, anchored on Max Plank Digital Library's blockchain network bloxberg, that assures the authenticity and integrity of all user-generated content and collaboration processes.
With the advancement in cryptography and emerging internet technology, electronic voting is gaining popularity since it ensures ballot secrecy, voter security, and integrity. Many commercial startups and e-Voting systems have been proposed, but due to lack of trust, privacy, transparency, and hacking issues, many solutions have been suspended. Blockchain, along with cryptographic primitives, has emerged as a promising solution due to its transparent, immutable, and decentralized nature. In this paper, we summarized the properties that existing solutions should satisfy and explained some cryptographic primitives like ZKP, Ring signatures along with their security limitations. We gave a comprehensive review of some blockchain-based e-Voting systems and discussed their strengths and weaknesses based on the given properties with table of comparison.
With the increasing usage of blockchain technology, legal challenges such as GDPR compliance arise. Especially the right of erasure is considered challenging as blockchains are tamperproof by design. Several approaches investigated
possibilities to weaken the tamperproof aspect of blockchains in favor of GDPR compliance. This paper presents several approaches, then focuses on chameleon hash functions by evaluating the possibility to use these specific functions in a private blockchain. The goal of the built system is to take a step towards the digitization of the bill of lading used in international trade. This paper describes the developed software as well as the core considerations around the system such as network design or block structure.
Learning Vector Quantization (LVQ) methods have been popular choices of classification models ever since its introduction by T. Kohonen in the 90s. These days, LVQ is combined with Deep Learning methods to provide powerful yet interpretable machine-learning solutions to some of the most challenging computational problems.
However, techniques to model recurrent relationships in the data using prototype methods still remain quite unsophisticated. In particular, we are not aware of any modification of LVQ that allows the input data to have different lengths. Needless to say, such data is abundant in today's digital world and demands new processing techniques to extract useful information. In this paper, we propose the use of the Siamese architecture to not only model recurrent relationships within the prototypes but also the ability to handle prototypes of various dimensions simultaneously.
Prototype-based Vector Quantization is one of the key methods in data processing like data compression or interpretable classification learning. Prototype vectors serve as references for data and data classes. The data are given as vectors representing objects by numerical features. Famous approaches are the Neural Gas Vector Quantizer (NGVQ) for data compression and Learning Vector Quantizers (LVQ) for classification tasks. Frequently, training of those models is time consuming. In the contribution we discuss modifications of these algorithms adopting ideas from quantum computing. The aim for this is a least twofold: First quantum computing provides ideas for enormous speedup making use of quantum mechanical systems and inherent parallelization.
Second, considering data and prototype vectors in terms of quantum systems, implicit data processing is performed, which frequently results in better data separation. We will highlight respective ideas and difficulties when equipping vector quantizers with quantum computing features.