Relevant bibliographies by topics / Artificial production

Academic literature on the topic 'Artificial production'

Author: Grafiati
Published: 22 June 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "Artificial production":

1

Çelik, Şenol. "MODELING AVOCADO PRODUCTION IN MEXICO WITH ARTIFICIAL NEURAL NETWORKS." Engineering and Technology Journal 07, no. 10 (October 31, 2022): 1605–9. http://dx.doi.org/10.47191/etj/v7i10.08.

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An Artificial Neural Network (ANN) model was created in this research to estimate and predict the amount of avocado production in Mexico. In the development of the ANN model, the years that are time variable were used as the input parameter, and the avocado production amount (tons) was used as the output parameter. The research data includes avocado production in Mexico for 1961-2020 period. Mean Squared Error (MSE) and Mean Absolut Error (MAE) statistics were calculated using hyperbolic tangent activation function to determine the appropriate model. ANN model is a network architecture with 12 hidden layers, 12 process elements (12-12-1) and Levenberg-Marquardt back propagation algorithm. The amount of avocado production was estimated between 2021 and 2030 with the ANN. As a result of the prediction, it is expected that the amount of avocado production for the period 2021-2030 will be between 2,410,741-2,502,302 tons.
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Fagundes Netto, Eduardo Barros, and Ilana Rosental Zalmon. "Artificial reefs: "Attraction versus Production"." Revista Vértices 13, no. 1 (2011): 179–90. http://dx.doi.org/10.5935/1809-2667.20110010.

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Kagawa, S., F. Ito, and K. Kagawa. "Production of artificial snow crystals." Physics Education 34, no. 2 (January 1, 1999): 92–95. http://dx.doi.org/10.1088/0031-9120/34/2/020.

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Bannat, Alexander, Thibault Bautze, Michael Beetz, Juergen Blume, Klaus Diepold, Christoph Ertelt, Florian Geiger, et al. "Artificial Cognition in Production Systems." IEEE Transactions on Automation Science and Engineering 8, no. 1 (January 2011): 148–74. http://dx.doi.org/10.1109/tase.2010.2053534.

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Gupta, Shanker. "Artificial Intelligence and Pharmaceutical Production." PDA Journal of Pharmaceutical Science and Technology 77, no. 3 (2023): 145. http://dx.doi.org/10.5731/pdajpst.2023.001423.

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Farzana Bashir, Farzana Bashir, Yumna Sadef Yumna Sadef, Iqra Nadeem Iqra Nadeem, Romana Shahzadi Romana Shahzadi, and Rubina Nelofer and Muhammad Tariq Rubina Nelofer and Muhammad Tariq. "Application of Artificial Neural Network Technique for the Production of Biotoxin." Journal of the chemical society of pakistan 44, no. 6 (2022): 568. http://dx.doi.org/10.52568/001181/jcsp/44.06.2022.

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In this study. a locally isolated strain of Bacillus thuringiensis that has insecticidal activity against dengue vector (larvae of Aedes aegypti), was cultivated. Different carbon and nitrogen sources were screened for enhanced bacterial growth. The factors affecting Bacillus thuringiensis’s biomass production like concentration of carbon, nitrogen, pH and temperature were optimized by one parameter at a time technique. The optimal levels of the selected parameters were also obtained by using an Artificial Neural Network (ANN). Peptone and molasses were selected as the best nitrogen and carbon sources respectively. The optimal levels obtained for nitrogen, carbon, pH and temperature by using the one parameter at a time technique were 1%, 0.25%, 8, and 37 ℃ respectively with 0.53 mg/mL biomass production. The ANN predicted levels were 1% for nitrogen, 0.25% for carbon, 9 pH and 31 ℃ for temperature with the predicted value of biomass being 0.85 mg/ml. The biomass produced at predicted optimum levels of variables was 0.82 mg/ml, very close to the predicted value of 0.85 mg/ml.
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Sase, S., H. Ikeda, and T. Takezono. "PLANT PRODUCTION IN THE ARTIFICIAL ENVIRONMENT." Acta Horticulturae, no. 230 (September 1988): 323–28. http://dx.doi.org/10.17660/actahortic.1988.230.42.

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Mould, Richard F. "Artificial Production of Radioactive Elements 1934." Zeitschrift für Medizinische Physik 19, no. 3 (August 2009): 155. http://dx.doi.org/10.1016/j.zemedi.2009.05.001.

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De Toni, Alberto, Guido Nassimbeni, and Stefano Tonchia. "An artificial, intelligence‐based production scheduler." Integrated Manufacturing Systems 7, no. 3 (June 1996): 17–25. http://dx.doi.org/10.1108/09576069610116896.

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Goswami, Saurabh, and Dr Tej Singh Chouhan. "Artificial Lift to Boost Oil Production." International Journal of Engineering Trends and Technology 26, no. 1 (August 25, 2015): 1–5. http://dx.doi.org/10.14445/22315381/ijett-v26p201.

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You might also be interested in the extended bibliographies on the topic 'Artificial production' for particular source types:
Journal articles Dissertations / Theses Books

Dissertations / Theses on the topic "Artificial production":

1

Ranawake, Manoja, and n/a. "Development of the artificial heart for serial production." University of Canberra. Industrial Design, 1995. http://erl.canberra.edu.au./public/adt-AUC20061113.151545.

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Heart disease is the principal cause of death in most industrialised countries. In the U.S.A. for example, 2.3 million individuals suffer from chronic heart failure, with an annual increase in numbers of 17%. It is estimated that 17,000 to 35,000 of them per year will die from this disease if they are not given either a heart transplant or an artificial heart. Unfortunately, the numbers of heart donors cannot meet the demand for transplantation, and, at present, the artificial heart is a prohibitively expensive alternative. The total artificial heart (TAH) intended for the total replacement of the natural heart is still some years away from realisation. However, the ventricular assist device (VAD) which is used temporarily to maintain an ailing heart is available now, although only in restricted numbers due to difficulties in processing the biocompatible materials used during manufacture. Consequently, such devices are expensive, costing anywhere from AUS$30,000 for the pump head to AUS$200,000 for a complete system. In this study, the Australian designed $quot;Spiral Vortex$quot; VAD was used to investigate fabrication techniques for use in the eventual cost-effective manufacture of a pump head costing approximately AUS$4,000. A second VAD originally designed at the Kolff Laboratory, University of Utah, U.S.A. was also used for comparative evaluation. The hard-shell Spiral Vortex VAD is intended to be used outside the body, while the soft-shell Kolff VAD has the advantage of being implantable for long-term use. They were cast from epoxy resin and vacuum formed from polyurethane, respectively. Several units of each were fabricated, including 60 of the Kolff VAD, for use in vitro and in vivo experiments. From these experiments it was found that both the Spiral Vortex and Kolff VADs could be fabricated to quality controllable standards. The Kolff VAD was used exclusively in chronic animal experiments, and was able to sustain sheep and goats for periods of up to five weeks. Furthermore, it became evident that techniques used in fabrication of the Kolff VAD could be adopted for use in the mass production of the Spiral Vortex VAD. The two other areas investigated in this study were the prosthetic heart valves and drive systems used for an artificial heart. A high percentage of the cost of an artificial heart is accounted for by the inflow/outflow valves. The trileaflet valve used in the Kolff VAD, which mimics the natural heart valve, was fabricated using inexpensive vacuum-forming techniques. Quality control was found to be adequate, with good flow characteristics which could be maintained for several weeks in animal experiments. Both the Spiral Vortex and Kolff VADs are pulsation pumps which require a pneumatic driver unit. This driver is the single most expensive component in a VAD system, costing upwards of AUS$150,000. The theoretical efficiency of a compact hydromechanical drive unit was investigated using a test rig to simulate an original design based primarily on proprietary components. Results obtained so far indicate that the proposed driver can operate only under limited conditions as a result of its severe reduction in size. By adopting mass production techniques wherever possible in the fabrication of the VAD (pump head) and valves, and by reducing the cost and size of the driver unit, it may therefore be possible to produce a cost effective ventricular assist device system.
2

CECCONI, BIANCA. "Artificial Photosynthesis: Molecular Approaches for Photocatalytic Hydrogen Production." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2016. http://hdl.handle.net/10281/100472.

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The humankind today needs to face an epochal transition from a fossil fuel to a renewable source-based economy. Renewable sources are our chance to build a clean world with unlimited and widespread energy. Nowadays renewable energies could be properly harvested to produce electricity, while the development of a future clean fuel is less advanced. Since our energetic consumption is made essentially of fuels we need to build devices to transform renewable energy, such as solar radiation, into chemical energy of bonds. A promising future fuel is hydrogen since its carbon footprint is zero and it can be obtained from an abundant source such as water. Nature, through the photosynthesis, could inspire us to build our feed in the form of fuels. In this research project DSSC (dye-sensitized solar cells) have been modified to produce chemical energy instead of electricity. Attention has been focused on hydrogen production semi-reaction, thus the use of a sacrificial electron donor has been adopted. Such system is composed of TiO2 nanoparticles covered by a reduction catalyst and a metal-free organic sensitizer to harvest the visible spectrum of solar radiation. The aim of this research has been the development of molecular approaches to provide efficient light harvesting systems and reduction catalysts. Molecular design allowed a fine tuning of materials properties and a deep understanding of structure/performances relationships. The first part of the project has focused on designing push-pull structures to harvest visible light portion of solar spectrum. Fine molecular tuning of metal-free dyes afforded enhanced performances depending on the kind of modification. We modified a known phenothiazine dye in the donor, spacer and acceptor units in order to derive structure/performances relationships. Enhanced light harvesting properties and photo-stability have been afforded through π-spacer modification with various mono- and polycyclic simple and fused thiophene derivatives, while decoration of the donor core with glycolic or sugar chains gave better hydrophilicity and surface wettability. Lastly hydroxamic acids have been introduced as alternative anchoring groups to give stronger ester bonds on TiO2 surface and prevent hydrolysis in aqueous media. The second part of the research has concerned the study of cobaloximes as alternative noble metal free reduction catalysts. Starting from a mini-library of cobaloximes bearing various equatorial bridges, axial ligands, and starting oxidation numbers, molecular structure/efficiency studies have been done, while UV/Vis spectroscopy has been used to investigate the nature of the eventual Co(I) species transiently formed. For cobaloximes a Co(I) species is hypothesized but not confirmed in photocatalytic experiments and optimization of efficiency and stability of new catalysts need a deep understanding of the catalytic cycle in order to intervene in the critical intermediates.
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Daniel, John W. H. "Exploiting application parallelism in production systems." Thesis, Cranfield University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.279737.

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Abukhader, Rami, and Samer Kakoore. "ARTIFICIAL INTELLIGENCE FOR VERTICAL FARMING – CONTROLLING THE FOOD PRODUCTION." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-53355.

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The Covid-19 crisis has highlighted the vulnerability of access to food and the need for local and circular food supply chains in urban environments. Nowadays, Indoor Vertical Farming has been increased in large cities and started deploying Artificial Intelligence to control vegetations remotely. This thesis aims to monitor and control the vertical farm by scheduling the farming activities by solving a newly proposed Job-shop scheduling problem to enhance food productivity. The Job-shop scheduling problem is one of the best-known optimization problems as the execution of an operation may depend on the completion of another operation running at the same time. This paper presents an efficient method based on genetic algorithms developed to solve the proposed scheduling problem. To efficiently solve the problem, a determination of the assignment of operations to the processors and the order of each operation so that the execution time is minimized. An adaptive penalty function is designed so that the algorithm can search in both feasible and infeasible regions of the solution space. The results show the effectiveness of the proposed algorithm and how it can be applied for monitoring the farm remotely.

The presentation was held in zoom

5

Wiegmann, Lars. "Cost-based shop control using artificial neural networks." Diss., This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-06062008-165820/.

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Duttala, Satish. "Virtual material processing artificial intelligence based process selection." Ohio : Ohio University, 2002. http://www.ohiolink.edu/etd/view.cgi?ohiou1174590077.

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Andersen, Mats Grønning. "Reservoir Production Optimization Using Genetic Algorithms and Artificial Neural Networks." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9985.

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This master's thesis has investigated how methods from artificial intelligence (AI) can be used to perform and augment production optimization of sub-sea oil reservoirs. The methods involved in this work are genetic algorithms (GAs) and artificial neural networks (ANNs). Different optimization schemes were developed by the author to perform production optimization on oil reservoir simulator models. The optimization involves finding good input parameter values for certain properties of the model, relating to how the wells in the oil reservoir operate. The research involves straightforward optimization using GAs, model approximations using ANNs, and also more advanced schemes using these methods together with other available technology to perform and augment reservoir optimization. With this work, the author has attempted to make a genuine contribution to all the research areas this master's thesis has touched upon, ranging from computer science and AI to process and petroleum engineering. The methods and approaches developed through this research were compared to the performance of each other and also to other approaches and methods used on the same challenges. The comparison found some of the developed optimization schemes to be very successful, while others were found to be less appropriate for solving the problem at hand. Some of the less successful approaches still showed considerable promise for simpler problems, leading the author to conclude that the developed schemes are suited for solving optimization problems in the petroleum industry.

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Grip, Stefan. "Artificial spider silk : recombinant production and determinants for fiber formation /." Uppsala : Dept. of Biomedical Sciences and Veterinary Public Health and Dept. of Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, 2008. http://epsilon.slu.se/2008100.pdf.

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Qiu, Xueni, and 邱雪妮. "Artificial immune systems for job shop scheduling problems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B49617576.

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Effective process scheduling is very important to the modern manufacturing production. This research addresses a classical scheduling problem — the job shop scheduling problem from the standpoint of both static and dynamic environment. In this study, the job shop scheduling problem (JSSP) is investigated in three aspects: (1) static JSSP that operates under a static scheduling environment with known information about the jobs and machines without unexpected events; (2) semi-dynamic JSSP which is developed based on static JSSP but violating the non-operation disruption assumption due to the presence of uncertainties occurring in the dynamic scheduling process; (3) dynamic online JSSP that operates under a dynamic operating environment in which jobs continuously arrive that are accompanied by unpredictable disruptions, such as machine failures. In the thesis, these three types of JSSP are solved by artificial immune systems (AIS) based algorithms. For static JSSP, a hybrid algorithm is proposed based on clonal selection theory and immune network theory of AIS, and particle swarm optimization (PSO). The clonal selection theory establishes the framework of the hybrid algorithm, while the immune network theory is applied to increase the diversity of antibody set which represents the solution candidates. The proposed framework involves the processes of selection, cloning, hypermutation, memory, and receptor editing. The PSO is designed to optimize the hypermutation process of the antibodies to accelerate the search procedure. This hybrid algorithm is tested with benchmark problems of different sizes and is compared with other methods. The results demonstrate the efficiency of the proposed algorithm, the effectiveness of PSO, and the contribution of long-lasting memory which is one of the key features of AIS. The semi-dynamic JSSP is handled by the rescheduling process. An extended deterministic dendritic cell algorithm (dDCA) is proposed to control the rescheduling process under considerations of the stability and efficiency of the scheduling system. The main role of the extended dDCA is to quantify the negative effect generated from the unexpected disturbances and to determine the best time to trigger the rescheduling process. This algorithm is tested on static benchmark problems with the existence of different kinds of disruptions. The experimental results demonstrate its capability of timely triggering the rescheduling process. The dynamic online JSSP is modeled as a multi-objective optimization problem. In this case, the immune network theory of AIS is hybridized with priority dispatching rules (PDRs) to establish the idiotypic network model for dispatching rules. This idiotypic network model drives the dispatching rule selection process under a dynamic scheduling environment. Based on the job shop situations represented by the antigens, the dispatching rules that perform best under specific conditions are selected as the antibodies of the idiotypic network model. Finally, the thesis proposes a generic framework of JSSP that combines the three different aspects studied in this research with corresponding scheduling strategies. The scheduling framework for a job shop system consists of four collaborating modules and is designed to solve various scheduling situations efficiently under a dynamic operating environment.
published_or_final_version
Industrial and Manufacturing Systems Engineering
Doctoral
Doctor of Philosophy
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Amado, António Correia de Campos Jordão. "An ontology to support evolvable production systems." Master's thesis, Faculdade de CIências e Tecnologia, 2008. http://hdl.handle.net/10362/3662.

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Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores
Ontologias são cada vez mais um conceito fundamental no suporte à interoperabilidade. Além disso, elas também são fundamentais no suporte aos sistemas evolutivos de produção por duas razões principais. A primeira está relacionada com o facto de a clara identificação e formalização dos processos ser importante para a criação de módulos inteligentes. A segunda razão está relacionada com o facto de os sistemas evolutivos de produção (SEP) serem baseados em sistemas multi-agente que depende em muito, da construção das ontologias de modo a permitir a comunicação entre os agentes pertencentes ao sistema. Os principais conceitos por detrás da ontologia aqui desenvolvida serão os conceitos de processos, tarefas, produto e componentes de manufactura. Esta tese pretende mostrar não só a criação de uma ontologia, mas também de um agente de modo a ser possível a integração da ontologia num sistema multi-agente, no âmbito da manufactura inteligente respondendo às questões envolventes ao paradigma dos sistemas evolutivos de produção. Sabendo que os SEP são baseados em sistemas multi-agente, será também mostrado um agente que irá ter todo o controlo da ontologia e irá pertencer ao sistema de manufactura.
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Books on the topic "Artificial production":

1

Northwest Power Planning Council (U.S.), ed. Artificial production review. Portland, Or: Northwest Power Planning Council, 1999.

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Jake, Krakauer, and Computer and Automated Systems Association of SME. Publications Development Dept. Marketing Services Dept., eds. Smart manufacturing with artificial intelligence. Dearborn, Mich: Computer and Automated Systems Association of SME, Publications Development Dept., Marketing Services Division, 1987.

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Wittrisch, Christian. Progressing cavity pumps: Oil well production artificial lift. Paris: Éditions Technip, 2013.

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Dolgui, Alexandre, Alain Bernard, David Lemoine, Gregor von Cieminski, and David Romero, eds. Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85874-2.

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Dolgui, Alexandre, Alain Bernard, David Lemoine, Gregor von Cieminski, and David Romero, eds. Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85910-7.

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Dolgui, Alexandre, Alain Bernard, David Lemoine, Gregor von Cieminski, and David Romero, eds. Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85914-5.

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Dolgui, Alexandre, Alain Bernard, David Lemoine, Gregor von Cieminski, and David Romero, eds. Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85902-2.

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Dolgui, Alexandre, Alain Bernard, David Lemoine, Gregor von Cieminski, and David Romero, eds. Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85906-0.

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Northwest Power and Conservation Council (U.S.), ed. Artificial production review and evaluation: Draft basin-level report. [Portland, Or.]: Northwest Power and Conservaiton Council, 2003.

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Fazel, Famili A., Nau Dana S, and Kim Steven H, eds. Artificial intelligence applications in manufacturing. Menlo Park, CA: AAAI Press/MIT Press, 1992.

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Book chapters on the topic "Artificial production":

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Monostori, Laszlo. "Artificial Intelligence." In CIRP Encyclopedia of Production Engineering, 1–4. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-642-35950-7_16703-4.

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Monostori, Laszlo. "Artificial Intelligence." In CIRP Encyclopedia of Production Engineering, 47–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-20617-7_16703.

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Monostori, Laszlo. "Artificial Intelligence." In CIRP Encyclopedia of Production Engineering, 73–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-53120-4_16703.

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de Jong, Carmen. "Artificial Production of Snow." In Encyclopedia of Earth Sciences Series, 61–66. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-90-481-2642-2_29.

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Kobayashi, Takeshi, and Nobuyuki Uozumi. "Production of Artificial Seeds." In Biochemical Engineering for 2001, 270–73. Tokyo: Springer Japan, 1992. http://dx.doi.org/10.1007/978-4-431-68180-9_72.

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Perez-Castanos, Sergi, Ausias Prieto-Roig, David Monzo, and Javier Colomer-Barbera. "Holistic Production Overview: Using XAI for Production Optimization." In Artificial Intelligence in Manufacturing, 423–36. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46452-2_24.

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AbstractThis chapter introduces the work performed in XMANAI to address the need of explainability in manufacturing AI systems applied to optimize production lines. The XMANAI platform is designed to meet the needs of manufacturing factories, offering them a unified framework to leverage their data and extract valuable insights. Within the project, the Ford use case is focused on forecasting production in a dynamically changing manufacturing line, serving as a practical illustration of the platform capabilities. This chapter focuses on the application of explainability using Hybrid Models and Heterogeneous Graph Machine Learning (ML) techniques. Hybrid Models combine traditional AI models with eXplainable AI (XAI) tools and Heterogeneous Graph ML techniques using Graph Attention (GAT) layers to extract explainability in complex manufacturing scenarios where data that can be represented as a graph. To understand explainability applied to the Ford use case, this chapter describes the initial needs of the scenario, the infrastructure behind the use case and the results obtained, showcasing the effectiveness of this approach, where models are trained in the XMANAI platform. Specifically, a description is given on the results of production forecasting in an engine assembly plant while providing interpretable explanations when deviations from expected are predicted.
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Esquivel, Gabriel. "Agency and Artificial Intelligence." In Design Technology and Digital Production, 63–86. New York: Routledge, 2024. http://dx.doi.org/10.4324/9781003251675-5.

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Ayel, Jacqueline. "Decision coordination in production management." In Artificial Social Systems, 295–310. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58266-5_17.

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Wichert, Andreas. "Quantum Production System." In Quantum Artificial Intelligence with Qiskit, 106–18. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003374404-8.

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Amaro, Helena M., M. GlóRia Esquível, Teresa S. Pinto, and F. Xavier Malcata. "Hydrogen Production by Microalgae." In Natural and Artificial Photosynthesis, 231–41. Hoboken, NJ, USA: John Wiley & Sons Inc., 2013. http://dx.doi.org/10.1002/9781118659892.ch8.

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Conference papers on the topic "Artificial production":

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Kahali, K., R. Rai, and R. K. Mukerjie. "Artificial Lift Methods for Marginal Fields." In SPE Production Operations Symposium. Society of Petroleum Engineers, 1991. http://dx.doi.org/10.2118/21696-ms.

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"Artificial organelle for energy production in artificial cell." In ECAL 2011: The 11th European Conference on Artificial Life. MIT Press, 2011. http://dx.doi.org/10.7551/978-0-262-29714-1-ch067.

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Heinze, Lloyd R., Herald W. Winkler, and James F. Lea. "Decision Tree for Selection of Artificial Lift Method." In SPE Production Operations Symposium. Society of Petroleum Engineers, 1995. http://dx.doi.org/10.2118/29510-ms.

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Olivares Velazquez, Gonzalo Jesus, Carlos Javier Escalona Quintero, and Eddy Ramon Gimenez. "Production Monitoring Using Artificial Intelligence." In SPE Intelligent Energy International. Society of Petroleum Engineers, 2012. http://dx.doi.org/10.2118/149594-ms.

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"Agent-based Manufacturing in a Production Grid - Adapting a Production Grid to the Production Paths." In International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004758803420349.

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Nicholls, Steven, Stuart Cunningham, and Richard Picking. "Collaborative Artificial Intelligence in Music Production." In AM'18: Sound in Immersion and Emotion. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3243274.3243311.

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Ager, Joel W., Min-Hyung Lee, and Ali Javey. "Solar fuels production by artificial photosynthesis." In SOLAR CHEMICAL ENERGY STORAGE: SolChES. AIP, 2013. http://dx.doi.org/10.1063/1.4848078.

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Baykal, Gokhan, and Murat Cenk Erdurak. "Artificial Sand Production for Geotechnical Uses." In International Symposium on Advances in Ground Technology & Geo-Information. Singapore: Research Publishing Services, 2011. http://dx.doi.org/10.3850/978-981-07-0188-8_p078.

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Barree, Robert D., and Hemanta Mukherjee. "Design Guidelines for Artificial Barrier Placement and Their Impact on Fracture Geometry." In SPE Production Operations Symposium. Society of Petroleum Engineers, 1995. http://dx.doi.org/10.2118/29501-ms.

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Gupta, Siddhartha. "Staircase Lifting of Oil using Venturi Principle - A New Artificial Lift Technique." In Production and Operations Symposium. Society of Petroleum Engineers, 2007. http://dx.doi.org/10.2118/106092-ms.

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Reports on the topic "Artificial production":

1

Davis, Wayne J., and Albert T. Jones. Artificial intelligence techniques in real-time production scheduling. Gaithersburg, MD: National Institute of Standards and Technology, 1989. http://dx.doi.org/10.6028/nist.ir.88-3891.

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2

Myhre, Kristian, Ethan Balkin, Draguna Vrabie, and Danda Rawat. Artificial Intelligence for Isotopes: Report on the 2022 Workshop on Artificial Intelligence for Isotope R&D and Production. Office of Scientific and Technical Information (OSTI), January 2023. http://dx.doi.org/10.2172/1909817.

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3

Gyrya, Vitaliy, Evan Lieberman, Mark Kenamond, and Mikhail Shashkov. Optimization of artificial viscosity in production codes based on Gaussian Regression surrogate models. Office of Scientific and Technical Information (OSTI), August 2022. http://dx.doi.org/10.2172/1881779.

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4

Ndoye, Aïssatou, Khadim Dia, and Racine Ly. AAgWa Crop Production Forecasts Brief Series - Issue N.03. AKADEMIYA2063, February 2023. http://dx.doi.org/10.54067/acpf.03.

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Abstract:
Brief 3 presents forecasts on millet production levels in Côte d’Ivoire based on AKADEMIYA2063’s Africa Crop Production (AfCP) model. The AfCP is an artificial intelligence (AI) based forecasting model applied to remotely sensed bio-geophysical data to produce estimates of crop production for nine crops in 47 African countries before the harvesting period. Thus, millet production statistics in Côte d’Ivoire are presented in this brief.
5

Ndoye, Aïssatou, Khadim Dia, and Racine Ly. AAgWa Crop Production Forecasts Brief Series - Issue N.10. AKADEMIYA2063, February 2023. http://dx.doi.org/10.54067/acpf.10.

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Abstract:
The Africa Agriculture Watch (AAgWa) Crop Production Forecast Brief 10 aims to provide more accurate and timely statistics about millet production in Ghana using the Africa Crop Production (AfCP) model. Developed at AKADEMIYA2063, the AfCP is an artificial intelligence (AI) based forecasting model applied to remotely sensed geo-biophysical data to produce estimates of expected crop yields and harvests at the beginning of every growing season.
6

Ndoye, Aïssatou, Khadim Dia, and Racine Ly. AAgWa Crop Production Forecasts Brief Series - Issue N.04. AKADEMIYA2063, February 2023. http://dx.doi.org/10.54067/acpf.04.

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The Africa Agriculture Watch (AAgWa) Crop Production Forecast Brief 4 by AKADEMIYA2063 seeks to provide accurate production levels in Burkina Faso using the Africa Food Crop Production (AfCP) model. The AfCP, developed by AKADEMIYA2063, is an artificial intelligence (AI) based predictive model applied to remotely sensed bio-geophysical data to estimate crop yields and harvests before the harvesting period for nine crops across 47 African countries.
7

Ndoye, Aïssatou, Khadim Dia, and Racine Ly. AAgWa Crop Production Forecasts Brief Series - Issue N.08. AKADEMIYA2063, February 2023. http://dx.doi.org/10.54067/acpf.08.

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The Africa Agriculture Watch (AAgWa) Crop Production Forecast Brief 8 by AKADEMIYA2063 provides accurate and timely millet production statistics for Guinea using the Africa Crop Production (AfCP) model. The AfCP developed at AKADEMIYA2063 is an artificial intelligence (AI) based forecasting model applied to remotely sensed geo-biophysical data to produce estimates of crop yields and harvests at the beginning of every growing season for nine crops in 47 African countries.
8

Ndoye, Aissatou, Khadim Dia, and Racine Ly. The AAgWa Crop Production Forecasts Brief Series - Issue N.02. AKADEMIYA2063, February 2023. http://dx.doi.org/10.54067/acpf.02.

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The Africa Agriculture Watch (AAgWa) Crop Production Brief 2, produced by AKADEMIYA2063, aims to provide more accurate and timely statistics on millet production in Gambia using the Africa Food Crop Production (AfCP) model. The AfCP developed at AKADEMIYA2063 is an artificial intelligence (AI) based forecasting model used to produce yield and harvest forecasts at the beginning of each growing season for nine crops in 47 African countries.
9

Ndoye, Aïssatou, Khadim Dia, and Racine Ly. AAgWa Crop Production Forecasts Brief Series - Issue N.07. AKADEMIYA2063, February 2023. http://dx.doi.org/10.54067/acpf.07.

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The Africa Agriculture Watch (AAgWa) Crop Production Forecast Brief 7 by AKADEMIYA2063 provides accurate and timely statistics about millet production in Mali. The Africa Crop Production (AfCP) model developed at AKADEMIYA2063 is used to forecast millet production. The AfCP is an artificial intelligence (AI) based forecasting model applied to remotely sensed bio-geophysical data to estimate expected crop yields and harvests at the beginning of every growing season for nine crops across nearly 47 African countries.
10

Ndoye, Aïssatou, Khadim Dia, and Racline Ly. AAgWa Crop Production Forecasts Brief Series - Issue N.05. AKADEMIYA2063, February 2023. http://dx.doi.org/10.54067/acpf.05.

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The Africa Agriculture Watch (AAgWa) Crop Production Forecast Brief 5 by AKADEMIYA2063 provides timely and accurate statistics on millet production in Sierra Leone using the Africa Crop Production (AfCP) model. Developed at AKADEMIYA2063, the AfCP is an artificial intelligence (AI) based forecasting model applied to remotely sensed bio-geophysical data to produce estimates of expected crop yields and harvests at the beginning of every growing season for nine crops across 47 African countries.

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