293 research outputs found

    Pristup specifikaciji i generisanju proizvodnih procesa zasnovan na inΕΎenjerstvu voΔ‘enom modelima

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    In this thesis, we present an approach to the production process specification and generation based on the model-driven paradigm, with the goal to increase the flexibility of factories and respond to the challenges that emerged in the era of Industry 4.0 more efficiently. To formally specify production processes and their variations in the Industry 4.0 environment, we created a novel domain-specific modeling language, whose models are machine-readable. The created language can be used to model production processes that can be independent of any production system, enabling process models to be used in different production systems, and process models used for the specific production system. To automatically transform production process models dependent on the specific production system into instructions that are to be executed by production system resources, we created an instruction generator. Also, we created generators for different manufacturing documentation, which automatically transform production process models into manufacturing documents of different types. The proposed approach, domain-specific modeling language, and software solution contribute to introducing factories into the digital transformation process. As factories must rapidly adapt to new products and their variations in the era of Industry 4.0, production must be dynamically led and instructions must be automatically sent to factory resources, depending on products that are to be created on the shop floor. The proposed approach contributes to the creation of such a dynamic environment in contemporary factories, as it allows to automatically generate instructions from process models and send them to resources for execution. Additionally, as there are numerous different products and their variations, keeping the required manufacturing documentation up to date becomes challenging, which can be done automatically by using the proposed approach and thus significantly lower process designers' time.Π£ овој Π΄ΠΈΡΠ΅Ρ€Ρ‚Π°Ρ†ΠΈΡ˜ΠΈ прСдстављСн јС приступ ΡΠΏΠ΅Ρ†ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΡ˜ΠΈ ΠΈ Π³Π΅Π½Π΅Ρ€ΠΈΡΠ°ΡšΡƒ ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ… процСса заснован Π½Π° ΠΈΠ½ΠΆΠ΅ΡšΠ΅Ρ€ΡΡ‚Π²Ρƒ Π²ΠΎΡ’Π΅Π½ΠΎΠΌ ΠΌΠΎΠ΄Π΅Π»ΠΈΠΌΠ°, Ρƒ Ρ†ΠΈΡ™Ρƒ ΠΏΠΎΠ²Π΅Ρ›Π°ΡšΠ° флСксибилности ΠΏΠΎΡΡ‚Ρ€ΠΎΡ˜Π΅ΡšΠ° Ρƒ Ρ„Π°Π±Ρ€ΠΈΠΊΠ°ΠΌΠ° ΠΈ Π΅Ρ„ΠΈΠΊΠ°ΡΠ½ΠΈΡ˜Π΅Π³ Ρ€Π°Π·Ρ€Π΅ΡˆΠ°Π²Π°ΡšΠ° ΠΈΠ·Π°Π·ΠΎΠ²Π° који сС ΠΏΠΎΡ˜Π°Π²Ρ™ΡƒΡ˜Ρƒ Ρƒ Π΅Ρ€ΠΈ Π˜Π½Π΄ΡƒΡΡ‚Ρ€ΠΈΡ˜Π΅ 4.0. Π—Π° ΠΏΠΎΡ‚Ρ€Π΅Π±Π΅ Ρ„ΠΎΡ€ΠΌΠ°Π»Π½Π΅ ΡΠΏΠ΅Ρ†ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΡ˜Π΅ ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ… процСса ΠΈ ΡšΠΈΡ…ΠΎΠ²ΠΈΡ… Π²Π°Ρ€ΠΈΡ˜Π°Ρ†ΠΈΡ˜Π° Ρƒ Π°ΠΌΠ±ΠΈΡ˜Π΅Π½Ρ‚Ρƒ Π˜Π½Π΄ΡƒΡΡ‚Ρ€ΠΈΡ˜Π΅ 4.0, ΠΊΡ€Π΅ΠΈΡ€Π°Π½ јС Π½ΠΎΠ²ΠΈ намСнски јСзик, Ρ‡ΠΈΡ˜Π΅ ΠΌΠΎΠ΄Π΅Π»Π΅ Ρ€Π°Ρ‡ΡƒΠ½Π°Ρ€ ΠΌΠΎΠΆΠ΅ Π΄Π° ΠΎΠ±Ρ€Π°Π΄ΠΈ Π½Π° Π°ΡƒΡ‚ΠΎΠΌΠ°Ρ‚ΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°Ρ‡ΠΈΠ½. ΠšΡ€Π΅ΠΈΡ€Π°Π½ΠΈ јСзик ΠΈΠΌΠ° могућност модСловања ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ… процСса који ΠΌΠΎΠ³Ρƒ Π±ΠΈΡ‚ΠΈ нСзависни ΠΎΠ΄ ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ… систСма ΠΈ Ρ‚ΠΈΠΌΠ΅ ΡƒΠΏΠΎΡ‚Ρ€Π΅Π±Ρ™Π΅Π½ΠΈ Ρƒ Ρ€Π°Π·Π»ΠΈΡ‡ΠΈΡ‚ΠΈΠΌ ΠΏΠΎΡΡ‚Ρ€ΠΎΡ˜Π΅ΡšΠΈΠΌΠ° ΠΈΠ»ΠΈ Ρ„Π°Π±Ρ€ΠΈΠΊΠ°ΠΌΠ°, Π°Π»ΠΈ ΠΈ ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ… процСса који су спСцифични Π·Π° ΠΎΠ΄Ρ€Π΅Ρ’Π΅Π½ΠΈ систСм. Како Π±ΠΈ ΠΌΠΎΠ΄Π΅Π»Π΅ ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ… процСса зависних ΠΎΠ΄ ΠΊΠΎΠ½ΠΊΡ€Π΅Ρ‚Π½ΠΎΠ³ ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΎΠ³ систСма Π±ΠΈΠ»ΠΎ ΠΌΠΎΠ³ΡƒΡ›Π΅ Π½Π° Π°ΡƒΡ‚ΠΎΠΌΠ°Ρ‚ΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°Ρ‡ΠΈΠ½ трансформисати Ρƒ ΠΈΠ½ΡΡ‚Ρ€ΡƒΠΊΡ†ΠΈΡ˜Π΅ којС рСсурси ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΎΠ³ систСма ΠΈΠ·Π²Ρ€ΡˆΠ°Π²Π°Ρ˜Ρƒ, ΠΊΡ€Π΅ΠΈΡ€Π°Π½ јС Π³Π΅Π½Π΅Ρ€Π°Ρ‚ΠΎΡ€ ΠΈΠ½ΡΡ‚Ρ€ΡƒΠΊΡ†ΠΈΡ˜Π°. Π’Π°ΠΊΠΎΡ’Π΅ су ΠΊΡ€Π΅ΠΈΡ€Π°Π½ΠΈ ΠΈ Π³Π΅Π½Π΅Ρ€Π°Ρ‚ΠΎΡ€ΠΈ Ρ‚Π΅Ρ…Π½ΠΈΡ‡ΠΊΠ΅ Π΄ΠΎΠΊΡƒΠΌΠ΅Π½Ρ‚Π°Ρ†ΠΈΡ˜Π΅, који Π½Π° Π°ΡƒΡ‚ΠΎΠΌΠ°Ρ‚ΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°Ρ‡ΠΈΠ½ Ρ‚Ρ€Π°Π½ΡΡ„ΠΎΡ€ΠΌΠΈΡˆΡƒ ΠΌΠΎΠ΄Π΅Π»Π΅ ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ… процСса Ρƒ Π΄ΠΎΠΊΡƒΠΌΠ΅Π½Ρ‚Π΅ Ρ€Π°Π·Π»ΠΈΡ‡ΠΈΡ‚ΠΈΡ… Ρ‚ΠΈΠΏΠΎΠ²Π°. Π£ΠΏΠΎΡ‚Ρ€Π΅Π±ΠΎΠΌ ΠΏΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎΠ³ приступа, намСнског јСзика ΠΈ софтвСрског Ρ€Π΅ΡˆΠ΅ΡšΠ° доприноси сС ΡƒΠ²ΠΎΡ’Π΅ΡšΡƒ Ρ„Π°Π±Ρ€ΠΈΠΊΠ° Ρƒ процСс Π΄ΠΈΠ³ΠΈΡ‚Π°Π»Π½Π΅ Ρ‚Ρ€Π°Π½ΡΡ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΡ˜Π΅. Како Ρ„Π°Π±Ρ€ΠΈΠΊΠ΅ Ρƒ Π΅Ρ€ΠΈ Π˜Π½Π΄ΡƒΡΡ‚Ρ€ΠΈΡ˜Π΅ 4.0 ΠΌΠΎΡ€Π°Ρ˜Ρƒ Π±Ρ€Π·ΠΎ Π΄Π° сС ΠΏΡ€ΠΈΠ»Π°Π³ΠΎΠ΄Π΅ Π½ΠΎΠ²ΠΈΠΌ ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΠΌΠ° ΠΈ ΡšΠΈΡ…ΠΎΠ²ΠΈΠΌ Π²Π°Ρ€ΠΈΡ˜Π°Ρ†ΠΈΡ˜Π°ΠΌΠ°, Π½Π΅ΠΎΠΏΡ…ΠΎΠ΄Π½ΠΎ јС Π΄ΠΈΠ½Π°ΠΌΠΈΡ‡ΠΊΠΈ Π²ΠΎΠ΄ΠΈΡ‚ΠΈ ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄ΡšΡƒ ΠΈ Π½Π° Π°ΡƒΡ‚ΠΎΠΌΠ°Ρ‚ΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°Ρ‡ΠΈΠ½ слати ΠΈΠ½ΡΡ‚Ρ€ΡƒΠΊΡ†ΠΈΡ˜Π΅ рСсурсима Ρƒ Ρ„Π°Π±Ρ€ΠΈΡ†ΠΈ, Ρƒ зависности ΠΎΠ΄ ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄Π° који сС ΠΊΡ€Π΅ΠΈΡ€Π°Ρ˜Ρƒ Ρƒ ΠΊΠΎΠ½ΠΊΡ€Π΅Ρ‚Π½ΠΎΠΌ ΠΏΠΎΡΡ‚Ρ€ΠΎΡ˜Π΅ΡšΡƒ. Π’ΠΈΠΌΠ΅ ΡˆΡ‚ΠΎ јС Ρƒ ΠΏΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎΠΌ приступу ΠΌΠΎΠ³ΡƒΡ›Π΅ ΠΈΠ· ΠΌΠΎΠ΄Π΅Π»Π° процСса Π°ΡƒΡ‚ΠΎΠΌΠ°Ρ‚ΠΈΠ·ΠΎΠ²Π°Π½ΠΎ гСнСрисати ΠΈΠ½ΡΡ‚Ρ€ΡƒΠΊΡ†ΠΈΡ˜Π΅ ΠΈ послати ΠΈΡ… рСсурсима, доприноси сС ΠΊΡ€Π΅ΠΈΡ€Π°ΡšΡƒ јСдног Π΄ΠΈΠ½Π°ΠΌΠΈΡ‡ΠΊΠΎΠ³ ΠΎΠΊΡ€ΡƒΠΆΠ΅ΡšΠ° Ρƒ саврСмСним Ρ„Π°Π±Ρ€ΠΈΠΊΠ°ΠΌΠ°. Π”ΠΎΠ΄Π°Ρ‚Π½ΠΎ, услСд Π²Π΅Π»ΠΈΠΊΠΎΠ³ Π±Ρ€ΠΎΡ˜Π° Ρ€Π°Π·Π»ΠΈΡ‡ΠΈΡ‚ΠΈΡ… ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄Π° ΠΈ ΡšΠΈΡ…ΠΎΠ²ΠΈΡ… Π²Π°Ρ€ΠΈΡ˜Π°Ρ†ΠΈΡ˜Π°, ΠΏΠΎΡΡ‚Π°Ρ˜Π΅ ΠΈΠ·Π°Π·ΠΎΠ²Π½ΠΎ ΠΎΠ΄Ρ€ΠΆΠ°Π²Π°Ρ‚ΠΈ Π½Π΅ΠΎΠΏΡ…ΠΎΠ΄Π½Ρƒ Ρ‚Π΅Ρ…Π½ΠΈΡ‡ΠΊΡƒ Π΄ΠΎΠΊΡƒΠΌΠ΅Π½Ρ‚Π°Ρ†ΠΈΡ˜Ρƒ, ΡˆΡ‚ΠΎ јС Ρƒ ΠΏΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎΠΌ приступу ΠΌΠΎΠ³ΡƒΡ›Π΅ ΡƒΡ€Π°Π΄ΠΈΡ‚ΠΈ Π½Π° Π°ΡƒΡ‚ΠΎΠΌΠ°Ρ‚ΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°Ρ‡ΠΈΠ½ ΠΈ Ρ‚ΠΈΠΌΠ΅ Π·Π½Π°Ρ‡Π°Ρ˜Π½ΠΎ ΡƒΡˆΡ‚Π΅Π΄Π΅Ρ‚ΠΈ Π²Ρ€Π΅ΠΌΠ΅ ΠΏΡ€ΠΎΡ˜Π΅ΠΊΡ‚Π°Π½Π°Ρ‚Π° процСса.U ovoj disertaciji predstavljen je pristup specifikaciji i generisanju proizvodnih procesa zasnovan na inΕΎenjerstvu voΔ‘enom modelima, u cilju poveΔ‡anja fleksibilnosti postrojenja u fabrikama i efikasnijeg razreΕ‘avanja izazova koji se pojavljuju u eri Industrije 4.0. Za potrebe formalne specifikacije proizvodnih procesa i njihovih varijacija u ambijentu Industrije 4.0, kreiran je novi namenski jezik, čije modele računar moΕΎe da obradi na automatizovan način. Kreirani jezik ima moguΔ‡nost modelovanja proizvodnih procesa koji mogu biti nezavisni od proizvodnih sistema i time upotrebljeni u različitim postrojenjima ili fabrikama, ali i proizvodnih procesa koji su specifični za odreΔ‘eni sistem. Kako bi modele proizvodnih procesa zavisnih od konkretnog proizvodnog sistema bilo moguΔ‡e na automatizovan način transformisati u instrukcije koje resursi proizvodnog sistema izvrΕ‘avaju, kreiran je generator instrukcija. TakoΔ‘e su kreirani i generatori tehničke dokumentacije, koji na automatizovan način transformiΕ‘u modele proizvodnih procesa u dokumente različitih tipova. Upotrebom predloΕΎenog pristupa, namenskog jezika i softverskog reΕ‘enja doprinosi se uvoΔ‘enju fabrika u proces digitalne transformacije. Kako fabrike u eri Industrije 4.0 moraju brzo da se prilagode novim proizvodima i njihovim varijacijama, neophodno je dinamički voditi proizvodnju i na automatizovan način slati instrukcije resursima u fabrici, u zavisnosti od proizvoda koji se kreiraju u konkretnom postrojenju. Time Ε‘to je u predloΕΎenom pristupu moguΔ‡e iz modela procesa automatizovano generisati instrukcije i poslati ih resursima, doprinosi se kreiranju jednog dinamičkog okruΕΎenja u savremenim fabrikama. Dodatno, usled velikog broja različitih proizvoda i njihovih varijacija, postaje izazovno odrΕΎavati neophodnu tehničku dokumentaciju, Ε‘to je u predloΕΎenom pristupu moguΔ‡e uraditi na automatizovan način i time značajno uΕ‘tedeti vreme projektanata procesa

    Interstitial null-distance time-domain diffuse optical spectroscopy using a superconducting nanowire detector

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    Significance: Interstitial fiber-based spectroscopy is gaining interest for real-time in vivo optical biopsies, endoscopic interventions, and local monitoring of therapy. Different from other photonics approaches, time-domain diffuse optical spectroscopy (TD-DOS) can probe the tissue at a few cm distance from the fiber tip and disentangle absorption from the scattering properties. Nevertheless, the signal detected at a short distance from the source is strongly dominated by the photons arriving early at the detector, thus hampering the possibility of resolving late photons, which are rich in information about depth and absorption. Aim: To fully benefit from the null-distance approach, a detector with an extremely high dynamic range is required to effectively collect the late photons; the goal of our paper is to test its feasibility to perform TD-DOS measurements at null source-detector separations (NSDS). Approach: In particular, we demonstrate the use of a superconducting nanowire single photon detector (SNSPD) to perform TD-DOS at almost NSDS formula presented by exploiting the high dynamic range and temporal resolution of the SNSPD to extract late arriving, deep-traveling photons from the burst of early photons. Results: This approach was demonstrated both on Monte Carlo simulations and on phantom measurements, achieving an accuracy in the retrieval of the water spectrum of better than 15%, spanning almost two decades of absorption change in the 700- to 1100-nm range. Additionally, we show that, for interstitial measurements at null source-detector distance, the scattering coefficient has a negligible effect on late photons, easing the retrieval of the absorption coefficient. Conclusions: Utilizing the SNSPD, broadband TD-DOS measurements were performed to successfully retrieve the absorption spectra of the liquid phantoms. Although the SNSPD has certain drawbacks for use in a clinical system, it is an emerging field with research progressing rapidly, and this makes the SNSPD a viable option and a good solution for future research in needle guided time-domain interstitial fiber spectroscopy

    Is there a Moore's law for quantum computing?

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    There is a common wisdom according to which many technologies can progress according to some exponential law like the empirical Moore's law that was validated for over half a century with the growth of transistors number in chipsets. As a still in the making technology with a lot of potential promises, quantum computing is supposed to follow the pack and grow inexorably to maturity. The Holy Grail in that domain is a large quantum computer with thousands of errors corrected logical qubits made themselves of thousands, if not more, of physical qubits. These would enable molecular simulations as well as factoring 2048 RSA bit keys among other use cases taken from the intractable classical computing problems book. How far are we from this? Less than 15 years according to many predictions. We will see in this paper that Moore's empirical law cannot easily be translated to an equivalent in quantum computing. Qubits have various figures of merit that won't progress magically thanks to some new manufacturing technique capacity. However, some equivalents of Moore's law may be at play inside and outside the quantum realm like with quantum computers enabling technologies, cryogeny and control electronics. Algorithms, software tools and engineering also play a key role as enablers of quantum computing progress. While much of quantum computing future outcomes depends on qubit fidelities, it is progressing rather slowly, particularly at scale. We will finally see that other figures of merit will come into play and potentially change the landscape like the quality of computed results and the energetics of quantum computing. Although scientific and technological in nature, this inventory has broad business implications, on investment, education and cybersecurity related decision-making processes.Comment: 32 pages, 24 figure

    Adaptive Automated Machine Learning

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    The ever-growing demand for machine learning has led to the development of automated machine learning (AutoML) systems that can be used off the shelf by non-experts. Further, the demand for ML applications with high predictive performance exceeds the number of machine learning experts and makes the development of AutoML systems necessary. Automated Machine Learning tackles the problem of finding machine learning models with high predictive performance. Existing approaches incorporating deep learning techniques assume that all data is available at the beginning of the training process (offline learning). They configure and optimise a pipeline of preprocessing, feature engineering, and model selection by choosing suitable hyperparameters in each model pipeline step. Furthermore, they assume that the user is fully aware of the choice and, thus, the consequences of the underlying metric (such as precision, recall, or F1-measure). By variation of this metric, the search for suitable configurations and thus the adaptation of algorithms can be tailored to the user’s needs. With the creation of a vast amount of data from all kinds of sources every day, our capability to process and understand these data sets in a single batch is no longer viable. By training machine learning models incrementally (i.ex. online learning), the flood of data can be processed sequentially within data streams. However, if one assumes an online learning scenario, where an AutoML instance executes on evolving data streams, the question of the best model and its configuration remains open. In this work, we address the adaptation of AutoML in an offline learning scenario toward a certain utility an end-user might pursue as well as the adaptation of AutoML towards evolving data streams in an online learning scenario with three main contributions: 1. We propose a System that allows the adaptation of AutoML and the search for neural architectures towards a particular utility an end-user might pursue. 2. We introduce an online deep learning framework that fosters the research of deep learning models under the online learning assumption and enables the automated search for neural architectures. 3. We introduce an online AutoML framework that allows the incremental adaptation of ML models. We evaluate the contributions individually, in accordance with predefined requirements and to state-of-the- art evaluation setups. The outcomes lead us to conclude that (i) AutoML, as well as systems for neural architecture search, can be steered towards individual utilities by learning a designated ranking model from pairwise preferences and using the latter as the target function for the offline learning scenario; (ii) architectual small neural networks are in general suitable assuming an online learning scenario; (iii) the configuration of machine learning pipelines can be automatically be adapted to ever-evolving data streams and lead to better performances

    2022 Review of Data-Driven Plasma Science

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    Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required
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