293 research outputs found
Pristup specifikaciji i generisanju proizvodnih procesa zasnovan na inΕΎenjerstvu voΔenom modelima
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£ ΠΎΠ²ΠΎΡ Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠΈ ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΡΠ΅Π½ ΡΠ΅ ΠΏΡΠΈΡΡΡΠΏ ΡΠΏΠ΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡΠΈ ΠΈ Π³Π΅Π½Π΅ΡΠΈΡΠ°ΡΡ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ
ΠΏΡΠΎΡΠ΅ΡΠ° Π·Π°ΡΠ½ΠΎΠ²Π°Π½ Π½Π° ΠΈΠ½ΠΆΠ΅ΡΠ΅ΡΡΡΠ²Ρ Π²ΠΎΡΠ΅Π½ΠΎΠΌ ΠΌΠΎΠ΄Π΅Π»ΠΈΠΌΠ°, Ρ ΡΠΈΡΡ ΠΏΠΎΠ²Π΅ΡΠ°ΡΠ° ΡΠ»Π΅ΠΊΡΠΈΠ±ΠΈΠ»Π½ΠΎΡΡΠΈ ΠΏΠΎΡΡΡΠΎΡΠ΅ΡΠ° Ρ ΡΠ°Π±ΡΠΈΠΊΠ°ΠΌΠ° ΠΈ Π΅ΡΠΈΠΊΠ°ΡΠ½ΠΈΡΠ΅Π³ ΡΠ°Π·ΡΠ΅ΡΠ°Π²Π°ΡΠ° ΠΈΠ·Π°Π·ΠΎΠ²Π° ΠΊΠΎΡΠΈ ΡΠ΅ ΠΏΠΎΡΠ°Π²ΡΡΡΡ Ρ Π΅ΡΠΈ ΠΠ½Π΄ΡΡΡΡΠΈΡΠ΅ 4.0. ΠΠ° ΠΏΠΎΡΡΠ΅Π±Π΅ ΡΠΎΡΠΌΠ°Π»Π½Π΅ ΡΠΏΠ΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡΠ΅ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ
ΠΏΡΠΎΡΠ΅ΡΠ° ΠΈ ΡΠΈΡ
ΠΎΠ²ΠΈΡ
Π²Π°ΡΠΈΡΠ°ΡΠΈΡΠ° Ρ Π°ΠΌΠ±ΠΈΡΠ΅Π½ΡΡ ΠΠ½Π΄ΡΡΡΡΠΈΡΠ΅ 4.0, ΠΊΡΠ΅ΠΈΡΠ°Π½ ΡΠ΅ Π½ΠΎΠ²ΠΈ Π½Π°ΠΌΠ΅Π½ΡΠΊΠΈ ΡΠ΅Π·ΠΈΠΊ, ΡΠΈΡΠ΅ ΠΌΠΎΠ΄Π΅Π»Π΅ ΡΠ°ΡΡΠ½Π°Ρ ΠΌΠΎΠΆΠ΅ Π΄Π° ΠΎΠ±ΡΠ°Π΄ΠΈ Π½Π° Π°ΡΡΠΎΠΌΠ°ΡΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°ΡΠΈΠ½. ΠΡΠ΅ΠΈΡΠ°Π½ΠΈ ΡΠ΅Π·ΠΈΠΊ ΠΈΠΌΠ° ΠΌΠΎΠ³ΡΡΠ½ΠΎΡΡ ΠΌΠΎΠ΄Π΅Π»ΠΎΠ²Π°ΡΠ° ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ
ΠΏΡΠΎΡΠ΅ΡΠ° ΠΊΠΎΡΠΈ ΠΌΠΎΠ³Ρ Π±ΠΈΡΠΈ Π½Π΅Π·Π°Π²ΠΈΡΠ½ΠΈ ΠΎΠ΄ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ
ΡΠΈΡΡΠ΅ΠΌΠ° ΠΈ ΡΠΈΠΌΠ΅ ΡΠΏΠΎΡΡΠ΅Π±ΡΠ΅Π½ΠΈ Ρ ΡΠ°Π·Π»ΠΈΡΠΈΡΠΈΠΌ ΠΏΠΎΡΡΡΠΎΡΠ΅ΡΠΈΠΌΠ° ΠΈΠ»ΠΈ ΡΠ°Π±ΡΠΈΠΊΠ°ΠΌΠ°, Π°Π»ΠΈ ΠΈ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ
ΠΏΡΠΎΡΠ΅ΡΠ° ΠΊΠΎΡΠΈ ΡΡ ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ½ΠΈ Π·Π° ΠΎΠ΄ΡΠ΅ΡΠ΅Π½ΠΈ ΡΠΈΡΡΠ΅ΠΌ. ΠΠ°ΠΊΠΎ Π±ΠΈ ΠΌΠΎΠ΄Π΅Π»Π΅ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ
ΠΏΡΠΎΡΠ΅ΡΠ° Π·Π°Π²ΠΈΡΠ½ΠΈΡ
ΠΎΠ΄ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΠΎΠ³ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΎΠ³ ΡΠΈΡΡΠ΅ΠΌΠ° Π±ΠΈΠ»ΠΎ ΠΌΠΎΠ³ΡΡΠ΅ Π½Π° Π°ΡΡΠΎΠΌΠ°ΡΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°ΡΠΈΠ½ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠΈΡΠ°ΡΠΈ Ρ ΠΈΠ½ΡΡΡΡΠΊΡΠΈΡΠ΅ ΠΊΠΎΡΠ΅ ΡΠ΅ΡΡΡΡΠΈ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΎΠ³ ΡΠΈΡΡΠ΅ΠΌΠ° ΠΈΠ·Π²ΡΡΠ°Π²Π°ΡΡ, ΠΊΡΠ΅ΠΈΡΠ°Π½ ΡΠ΅ Π³Π΅Π½Π΅ΡΠ°ΡΠΎΡ ΠΈΠ½ΡΡΡΡΠΊΡΠΈΡΠ°. Π’Π°ΠΊΠΎΡΠ΅ ΡΡ ΠΊΡΠ΅ΠΈΡΠ°Π½ΠΈ ΠΈ Π³Π΅Π½Π΅ΡΠ°ΡΠΎΡΠΈ ΡΠ΅Ρ
Π½ΠΈΡΠΊΠ΅ Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ°ΡΠΈΡΠ΅, ΠΊΠΎΡΠΈ Π½Π° Π°ΡΡΠΎΠΌΠ°ΡΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°ΡΠΈΠ½ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠΈΡΡ ΠΌΠΎΠ΄Π΅Π»Π΅ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ
ΠΏΡΠΎΡΠ΅ΡΠ° Ρ Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ΅ ΡΠ°Π·Π»ΠΈΡΠΈΡΠΈΡ
ΡΠΈΠΏΠΎΠ²Π°. Π£ΠΏΠΎΡΡΠ΅Π±ΠΎΠΌ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎΠ³ ΠΏΡΠΈΡΡΡΠΏΠ°, Π½Π°ΠΌΠ΅Π½ΡΠΊΠΎΠ³ ΡΠ΅Π·ΠΈΠΊΠ° ΠΈ ΡΠΎΡΡΠ²Π΅ΡΡΠΊΠΎΠ³ ΡΠ΅ΡΠ΅ΡΠ° Π΄ΠΎΠΏΡΠΈΠ½ΠΎΡΠΈ ΡΠ΅ ΡΠ²ΠΎΡΠ΅ΡΡ ΡΠ°Π±ΡΠΈΠΊΠ° Ρ ΠΏΡΠΎΡΠ΅Ρ Π΄ΠΈΠ³ΠΈΡΠ°Π»Π½Π΅ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΠΈΡΠ΅. ΠΠ°ΠΊΠΎ ΡΠ°Π±ΡΠΈΠΊΠ΅ Ρ Π΅ΡΠΈ ΠΠ½Π΄ΡΡΡΡΠΈΡΠ΅ 4.0 ΠΌΠΎΡΠ°ΡΡ Π±ΡΠ·ΠΎ Π΄Π° ΡΠ΅ ΠΏΡΠΈΠ»Π°Π³ΠΎΠ΄Π΅ Π½ΠΎΠ²ΠΈΠΌ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΠΌΠ° ΠΈ ΡΠΈΡ
ΠΎΠ²ΠΈΠΌ Π²Π°ΡΠΈΡΠ°ΡΠΈΡΠ°ΠΌΠ°, Π½Π΅ΠΎΠΏΡ
ΠΎΠ΄Π½ΠΎ ΡΠ΅ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠΊΠΈ Π²ΠΎΠ΄ΠΈΡΠΈ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡ ΠΈ Π½Π° Π°ΡΡΠΎΠΌΠ°ΡΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°ΡΠΈΠ½ ΡΠ»Π°ΡΠΈ ΠΈΠ½ΡΡΡΡΠΊΡΠΈΡΠ΅ ΡΠ΅ΡΡΡΡΠΈΠΌΠ° Ρ ΡΠ°Π±ΡΠΈΡΠΈ, Ρ Π·Π°Π²ΠΈΡΠ½ΠΎΡΡΠΈ ΠΎΠ΄ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π° ΠΊΠΎΡΠΈ ΡΠ΅ ΠΊΡΠ΅ΠΈΡΠ°ΡΡ Ρ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΠΎΠΌ ΠΏΠΎΡΡΡΠΎΡΠ΅ΡΡ. Π’ΠΈΠΌΠ΅ ΡΡΠΎ ΡΠ΅ Ρ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎΠΌ ΠΏΡΠΈΡΡΡΠΏΡ ΠΌΠΎΠ³ΡΡΠ΅ ΠΈΠ· ΠΌΠΎΠ΄Π΅Π»Π° ΠΏΡΠΎΡΠ΅ΡΠ° Π°ΡΡΠΎΠΌΠ°ΡΠΈΠ·ΠΎΠ²Π°Π½ΠΎ Π³Π΅Π½Π΅ΡΠΈΡΠ°ΡΠΈ ΠΈΠ½ΡΡΡΡΠΊΡΠΈΡΠ΅ ΠΈ ΠΏΠΎΡΠ»Π°ΡΠΈ ΠΈΡ
ΡΠ΅ΡΡΡΡΠΈΠΌΠ°, Π΄ΠΎΠΏΡΠΈΠ½ΠΎΡΠΈ ΡΠ΅ ΠΊΡΠ΅ΠΈΡΠ°ΡΡ ΡΠ΅Π΄Π½ΠΎΠ³ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠΊΠΎΠ³ ΠΎΠΊΡΡΠΆΠ΅ΡΠ° Ρ ΡΠ°Π²ΡΠ΅ΠΌΠ΅Π½ΠΈΠΌ ΡΠ°Π±ΡΠΈΠΊΠ°ΠΌΠ°. ΠΠΎΠ΄Π°ΡΠ½ΠΎ, ΡΡΠ»Π΅Π΄ Π²Π΅Π»ΠΈΠΊΠΎΠ³ Π±ΡΠΎΡΠ° ΡΠ°Π·Π»ΠΈΡΠΈΡΠΈΡ
ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π° ΠΈ ΡΠΈΡ
ΠΎΠ²ΠΈΡ
Π²Π°ΡΠΈΡΠ°ΡΠΈΡΠ°, ΠΏΠΎΡΡΠ°ΡΠ΅ ΠΈΠ·Π°Π·ΠΎΠ²Π½ΠΎ ΠΎΠ΄ΡΠΆΠ°Π²Π°ΡΠΈ Π½Π΅ΠΎΠΏΡ
ΠΎΠ΄Π½Ρ ΡΠ΅Ρ
Π½ΠΈΡΠΊΡ Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ°ΡΠΈΡΡ, ΡΡΠΎ ΡΠ΅ Ρ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎΠΌ ΠΏΡΠΈΡΡΡΠΏΡ ΠΌΠΎΠ³ΡΡΠ΅ ΡΡΠ°Π΄ΠΈΡΠΈ Π½Π° Π°ΡΡΠΎΠΌΠ°ΡΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°ΡΠΈΠ½ ΠΈ ΡΠΈΠΌΠ΅ Π·Π½Π°ΡΠ°ΡΠ½ΠΎ ΡΡΡΠ΅Π΄Π΅ΡΠΈ Π²ΡΠ΅ΠΌΠ΅ ΠΏΡΠΎΡΠ΅ΠΊΡΠ°Π½Π°ΡΠ° ΠΏΡΠΎΡΠ΅ΡΠ°.U ovoj disertaciji predstavljen je pristup specifikaciji i generisanju proizvodnih procesa zasnovan na inΕΎenjerstvu voΔenom modelima, u cilju poveΔanja 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, Δije modele raΔunar moΕΎe da obradi na automatizovan naΔin. Kreirani jezik ima moguΔnost modelovanja proizvodnih procesa koji mogu biti nezavisni od proizvodnih sistema i time upotrebljeni u razliΔitim postrojenjima ili fabrikama, ali i proizvodnih procesa koji su specifiΔni za odreΔeni sistem. Kako bi modele proizvodnih procesa zavisnih od konkretnog proizvodnog sistema bilo moguΔe na automatizovan naΔin transformisati u instrukcije koje resursi proizvodnog sistema izvrΕ‘avaju, kreiran je generator instrukcija. TakoΔe su kreirani i generatori tehniΔke dokumentacije, koji na automatizovan naΔin transformiΕ‘u modele proizvodnih procesa u dokumente razliΔitih tipova. Upotrebom predloΕΎenog pristupa, namenskog jezika i softverskog reΕ‘enja doprinosi se uvoΔenju fabrika u proces digitalne transformacije. Kako fabrike u eri Industrije 4.0 moraju brzo da se prilagode novim proizvodima i njihovim varijacijama, neophodno je dinamiΔki voditi proizvodnju i na automatizovan naΔin slati instrukcije resursima u fabrici, u zavisnosti od proizvoda koji se kreiraju u konkretnom postrojenju. Time Ε‘to je u predloΕΎenom pristupu moguΔe iz modela procesa automatizovano generisati instrukcije i poslati ih resursima, doprinosi se kreiranju jednog dinamiΔkog okruΕΎenja u savremenim fabrikama. Dodatno, usled velikog broja razliΔitih proizvoda i njihovih varijacija, postaje izazovno odrΕΎavati neophodnu tehniΔku dokumentaciju, Ε‘to je u predloΕΎenom pristupu moguΔe uraditi na automatizovan naΔin i time znaΔajno uΕ‘tedeti vreme projektanata procesa
Interstitial null-distance time-domain diffuse optical spectroscopy using a superconducting nanowire detector
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?
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
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 userβs 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
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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