Letteratura scientifica selezionata sul tema "Experience-Driven Optimization"
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Articoli di riviste sul tema "Experience-Driven Optimization"
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Cheng, Yihua, Xu Zhang e Junchen Jiang. "Enabling Perception-Driven Optimization in Networking". ACM SIGMETRICS Performance Evaluation Review 51, n. 2 (28 settembre 2023): 103–5. http://dx.doi.org/10.1145/3626570.3626608.
Testo completoAbstract (sommario):
Service providers struggle to catch up with the rapid growth in bandwidth and latency demand of Internet videos and other applications. An essential contributor to this resource contention is the assumption that users are equally sensitive to service quality everywhere, so any low-quality incidents must be avoided. However, this assumption is not true. For example, our work and other parallel efforts have shown that more video users can be served with better quality of experience (QoE) if we embrace the fact that the QoE's sensitivity to video quality varies greatly with the video content. To unleash such benefits, the application systems must be driven by not only system measurement data but also user feedback data that capture users' perceptions of service quality. In this short paper, I will highlight some of our recent efforts toward the efficient collection of user feedback and enabling perception-driven optimization for Internet applications.
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P, Dr Periasamy, e Dr Dinesh N. "Data Driven Marketing Strategic Trends in 2022". International Journal of Science, Engineering and Management 9, n. 6 (13 giugno 2022): 24–27. http://dx.doi.org/10.36647/ijsem/09.06.a005.
Testo completoAbstract (sommario):
This paper is an attempt to find out the significant data driven marketing strategic trends. The author has developed a model which will help the readers to under the data driven marketing strategies in the recents trends in the world very easily. This model is called EPCMASQ , a model of the author, That is Ethical Information, Personalized Marketing Automation, Better Customer Experience, Multi-channel Experience, Artificial Intelligence, Search Engine Optimization and Qualitative Data. With which the concept of data driven marketing strategies explained well.
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Shailesh, K. S., e P. V. Suresh. "Performance Driven Development Framework for Web Applications". Global Journal of Enterprise Information System 9, n. 1 (5 maggio 2017): 75. http://dx.doi.org/10.18311/gjeis/2017/15870.
Testo completoAbstract (sommario):
The performance of web applications is of paramount importance as it can impact end-user experience and the business revenue. Web Performance Optimization (WPO) deals with front-end performance engineering. Web performance would impact customer loyalty, SEO, web search ranking, SEO, site traffic, repeat visitors and overall online revenue. In this paper we have conducted the survey of state of the art tools, techniques, methodologies of various aspects of web performance optimization. We have identified key web performance patterns and proposed novel web performance driven development framework. We have elaborated on various techniques related to different phases of web performance driven development framework.
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SONG, WEI, DIAN TJONDRONEGORO e MICHAEL DOCHERTY. "EXPLORATION AND OPTIMIZATION OF USER EXPERIENCE IN VIEWING VIDEOS ON A MOBILE PHONE". International Journal of Software Engineering and Knowledge Engineering 20, n. 08 (dicembre 2010): 1045–75. http://dx.doi.org/10.1142/s0218194010005067.
Testo completoAbstract (sommario):
Compared with viewing videos on PCs or TVs, mobile users have different experiences in viewing videos on a mobile phone due to different device features such as screen size and distinct usage contexts. To understand how mobile user's viewing experience is impacted, we conducted a field user study with 42 participants in two typical usage contexts using a custom-designed iPhone application. With user's acceptance of mobile video quality as the index, the study addresses four influence aspects of user experiences, including context, content type, encoding parameters and user profiles. Accompanying the quantitative method (acceptance assessment), we used a qualitative interview method to obtain a deeper understanding of a user's assessment criteria and to support the quantitative results from a user's perspective. Based on the results from data analysis, we advocate two user-driven strategies to adaptively provide an acceptable quality and to predict a good user experience, respectively. There are two main contributions from this paper. Firstly, the field user study allows a consideration of more influencing factors into the research on user experience of mobile video. And these influences are further demonstrated by user's opinions. Secondly, the proposed strategies — user-driven acceptance threshold adaptation and user experience prediction — will be valuable in mobile video delivery for optimizing user experience.
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Wu, Xiaojing, Zijun Zuo e Long Ma. "Aerodynamic Data-Driven Surrogate-Assisted Teaching-Learning-Based Optimization (TLBO) Framework for Constrained Transonic Airfoil and Wing Shape Designs". Aerospace 9, n. 10 (17 ottobre 2022): 610. http://dx.doi.org/10.3390/aerospace9100610.
Testo completoAbstract (sommario):
The surrogate-assisted optimization (SAO) process can utilize the knowledge contained in the surrogate model to accelerate the aerodynamic optimization process. The use of this knowledge can be regarded as the primary form of intelligent optimization design. However, there are still some difficulties in improving intelligent design levels, such as the insufficient utilization of optimization process data and optimization parameters’ adjustment that depends on the designer’s intervention and experience. To solve the above problems, a novel aerodynamic data-driven surrogate-assisted teaching-learning-based optimization (TLBO) framework is proposed for constrained aerodynamic shape optimization (ASO). The main contribution of the study is that ASO is promoted using historically aerodynamic process data generated during the gradient free optimization process. Meanwhile, nonparametric adjustment of the TLBO algorithm can help relieve manual design experience for actual engineering applications. Based on the structure of the TLBO algorithm, a model optimal prediction method is proposed as the new surrogate-assisted support strategy to accelerate the ASO process. The proposed method is applied to airfoil and wing shape designs to verify the optimization effect and efficiency. A benchmark aerodynamic design optimization is employed for the drag minimization of the RAE2822 airfoil. The optimized results indicate that the proposed method has advantages of high efficiency, strong optimization ability, and nonparametric characteristics for ASO. Moreover, the results of the wing shape optimization verify the advantages of the proposed methods over the surrogate-based optimization and direct optimization frameworks.
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Pairet, Eric, Constantinos Chamzas, Yvan R. Petillot e Lydia Kavraki. "Path Planning for Manipulation Using Experience-Driven Random Trees". IEEE Robotics and Automation Letters 6, n. 2 (aprile 2021): 3295–302. http://dx.doi.org/10.1109/lra.2021.3063063.
Testo completo
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Endress, Felix, Jasper Rieser e Markus Zimmermann. "ON THE TREATMENT OF REQUIREMENTS IN DFAM: THREE INDUSTRIAL USE CASES". Proceedings of the Design Society 3 (19 giugno 2023): 2815–24. http://dx.doi.org/10.1017/pds.2023.282.
Testo completoAbstract (sommario):
AbstractOptimization-driven design offers advantages over traditional experience-based mechanical design. As an example, topology optimization can be a powerful tool to generate body shapes for Additive Manufacturing (AM). This is helpful, when (1) load paths are non-intuitive due to complex design domains or boundary conditions, or (2) the design process is to be automated to minimize effort associated with experience-based design. However, practically relevant boundary conditions are often difficult to put into a formal mathematical language to, for example, either feed it into a topology optimization algorithm, or provide precise quantitative criteria for CAE-supported manual design. This paper presents a survey of three industry use cases and identifies three types of requirements: the first can be directly cast into parts of an optimization problem statement (∼ 40%), the second is considered indirectly by adapting the optimization problem without explicit reference to the requirement (∼ 20%), and the third is only assessed after the design is finalized (∼ 40%). For categories 2 and 3 we propose directions of improvement to support formulating complex design tasks as unambiguous design problems.
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Borba Evangelista, Gustavo, Guilherme Conceição Rocha e Wlamir Olivares Loesch Vianna. "Aircraft Troubleshooting Optimization Using Case-based Reasoning and Decision Analysis". Annual Conference of the PHM Society 12, n. 1 (3 novembre 2020): 8. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1170.
Testo completoAbstract (sommario):
The Fault Isolation Manual (FIM) can be seen as a specialist system that carries the expectations and expertise of engineers and technical team concerning the aircraft components and systems operation. It is basically a manual that supports the maintainers regarding the actions to perform in determined situations to properly isolate a fault. Although the FIM is the most common tool that assists maintainer on the troubleshooting process today, it does not adequately consider field experience and it does not explore situations where the maintenance operator has limited resources, such as a lack of tools and equipment. These drawbacks are essentially caused by the lack of flexibility or adaptability of this method since it is a static manual. There are several dynamic methods studied in the field of system troubleshooting and aircraft maintenance such as Artificial Neural Networks, Support Vector Machine, K Nearest Neighbor and many other machine learning algorithms. These techniques are considered very powerful and useful; however, the training process of the data-driven strategies requires a large amount of data to provide a reliable result.
In this context, the present work proposes a combination of data-driven with legacy knowledge-based approaches. The following techniques are employed to integrate the concepts mentioned: decision trees that explore the legacy knowledge with its topology based on the FIM, truth tables and decision analysis that explores Bayes’ rule to assist the decision- making process and case-based reasoning, technique that enables the learning from the field experience.
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Na, Chongzheng, e Huixin Liu. "A Historical Experience Surrogate Model Assisted Particle Swarm Optimization for Expensive Black-box Problems". Highlights in Science, Engineering and Technology 7 (3 agosto 2022): 83–88. http://dx.doi.org/10.54097/hset.v7i.1021.
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This paper proposed a new historical experience surrogate model assisted particle swarm optimization method. This method extends the particle swarm optimization by adding new surrogate-based phase. In the classic phase, the particle swarm optimization runs the same way as the original algorithm, and the real function value evaluated are collected into the global database. In the surrogate phase, sub-swarms are generated following the distribution of the history data and evaluated by the surrogate model(s). The purpose of the surrogate phase is to explore the possible better solutions of the searching history. Also, the surrogate model(s) have the ability of accelerating the intelligence algorithms. Nevertheless, considering the time complexity of training and evaluating the surrogate model(s), the original problem should be expensive to evaluate or driven by data, which are same as many real-world problems.
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Enríquez-Urbano, Juana, Marco Antonio Cruz-Chávez, Rafael Rivera-López, Martín H. Cruz-Rosales, Yainier Labrada-Nueva e Marta Lilia Eraña-Díaz. "Metaheuristic to Optimize Computational Convergence in Convection-Diffusion and Driven-Cavity Problems". Mathematics 9, n. 7 (31 marzo 2021): 748. http://dx.doi.org/10.3390/math9070748.
Testo completoAbstract (sommario):
This work presents an optimization proposal to better the computational convergence time in convection-diffusion and driven-cavity problems by applying a simulated annealing (SA) metaheuristic, obtaining optimal values in relaxation factors (RF) that optimize the problem convergence during its numerical execution. These relaxation factors are tested in numerical models to accelerate their computational convergence in a shorter time. The experimental results show that the relaxation factors obtained by the SA algorithm improve the computational time of the problem convergence regardless of user experience in the initial low-quality RF proposal.
Tesi sul tema "Experience-Driven Optimization"
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Mossina, Luca. "Applications d'apprentissage automatique à la résolution de problèmes récurrents en optimisation combinatoire". Electronic Thesis or Diss., Toulouse, ISAE, 2020. http://www.theses.fr/2020ESAE0043.
Testo completoAbstract (sommario):
Ce travail s'intéresse aux problèmes de décision pour lesquels on cherche une solution optimale ou quasi-optimale et dont il faut résoudre plusieurs instances successives (problèmes récurrents) lesquelles sont des variantes d'un même problème d'origine.On analyse la structure de tels problèmes afin de dégager les caractéristiques pouvant être exploitées efficacement et transférées d'une résolution à l'autre, afin d'améliorer incrémentalement la qualité de l'optimisation.On se place donc dans le cadre d'une interaction entre un processus d'apprentissage automatique (fouille de données d'optimisation) et un processus d'optimisation.D'une part, étant donné l'expérience de résolutions passées, on cherche à apprendre ce que l'on peut généraliser au problème courant.D'autre part, on cherche à utiliser ces connaissances au sein de l'algorithme d'optimisation afin de rendre son exécution plus efficace.En particulier, cette thèse présente trois contributions. La première introduit une méthode pour générer des sous-problèmes plus simple pour un instance d’un problème récurrent, en utilisant la classification multi-étiquette. Un sous-ensemble de variables décision est sélectionné et figé à une valeur de référence.La solution au sous-problème qui reste, même en étant pas garantie optimale pour le problème originale, peut être obtenu plus rapidement.La deuxième, emploie l’apprentissage supervisé, classification et régression, pour prédire et ajouter une contrainte additionnelle au problème récurrent modélisé par programmation mathématique. Au moment de résoudre une nouvelle instance, le modèle prédit en quelle mesure la solution au problème de référence est applicable, en permettant d’obtenir une résolution plus rapide.Dans la troisième, le contrôle dynamique de paramètres d’un algorithme évolutionnaire est encadré comme un problème d’apprentissage par renforcement. Les politiques de contrôle ainsi obtenues garantissent que l’algorithme d’optimisation atteint, en moyenne, la solution optimale dans le plus court délaiThe interest is on those decision problems for which an optimal or quasi-optimal solution is sought, and for which it is necessary to solve successive instances (recurrent problems) that are variations of a common original problem.The structure of such problems is analysed to identify the characteristics that can be exploited and transferred from one resolution to another, to incrementally improve the quality of the optimization process. The research is characterized by the interaction between a process of statistical learning (from optimization data) and a process of optimization. The information extracted from past resolutions is generalized to the current problem and integrated into the optimization algorithm to make its execution more resource-efficient.In particular, this thesis presents three contributions.The first, introduces a method that generates a simpler sub-problem to an instance of a recurrent problem, using multi-label classification. A subset of decision variables is selected and set to a reference value. The solution to the remaining sub-problem, while not guaranteed to be optimal for the original problem, can be obtained faster.The second employs Supervised Learning, classification and regression, to predict an additional constraint to a reference recurrent problem modelled via Mathematical Programming. When a new instance is solved, the model predicts how much of the solution to the reference problem is still applicable, allowing for a more rapid resolution.In the third, the dynamic control of the parameters of Evolutionary Algorithms is framed as a Reinforcement Learning problem. The control policies obtained guarantee that the optimization algorithm reaches an optimal solution within the shortest, average time
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Teng, Sin Yong. "Intelligent Energy-Savings and Process Improvement Strategies in Energy-Intensive Industries". Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-433427.
Testo completoAbstract (sommario):
S tím, jak se neustále vyvíjejí nové technologie pro energeticky náročná průmyslová odvětví, stávající zařízení postupně zaostávají v efektivitě a produktivitě. Tvrdá konkurence na trhu a legislativa v oblasti životního prostředí nutí tato tradiční zařízení k ukončení provozu a k odstavení. Zlepšování procesu a projekty modernizace jsou zásadní v udržování provozních výkonů těchto zařízení. Současné přístupy pro zlepšování procesů jsou hlavně: integrace procesů, optimalizace procesů a intenzifikace procesů. Obecně se v těchto oblastech využívá matematické optimalizace, zkušeností řešitele a provozní heuristiky. Tyto přístupy slouží jako základ pro zlepšování procesů. Avšak, jejich výkon lze dále zlepšit pomocí moderní výpočtové inteligence. Účelem této práce je tudíž aplikace pokročilých technik umělé inteligence a strojového učení za účelem zlepšování procesů v energeticky náročných průmyslových procesech. V této práci je využit přístup, který řeší tento problém simulací průmyslových systémů a přispívá následujícím: (i)Aplikace techniky strojového učení, která zahrnuje jednorázové učení a neuro-evoluci pro modelování a optimalizaci jednotlivých jednotek na základě dat. (ii) Aplikace redukce dimenze (např. Analýza hlavních komponent, autoendkodér) pro vícekriteriální optimalizaci procesu s více jednotkami. (iii) Návrh nového nástroje pro analýzu problematických částí systému za účelem jejich odstranění (bottleneck tree analysis – BOTA). Bylo také navrženo rozšíření nástroje, které umožňuje řešit vícerozměrné problémy pomocí přístupu založeného na datech. (iv) Prokázání účinnosti simulací Monte-Carlo, neuronové sítě a rozhodovacích stromů pro rozhodování při integraci nové technologie procesu do stávajících procesů. (v) Porovnání techniky HTM (Hierarchical Temporal Memory) a duální optimalizace s několika prediktivními nástroji pro podporu managementu provozu v reálném čase. (vi) Implementace umělé neuronové sítě v rámci rozhraní pro konvenční procesní graf (P-graf). (vii) Zdůraznění budoucnosti umělé inteligence a procesního inženýrství v biosystémech prostřednictvím komerčně založeného paradigmatu multi-omics.
Capitoli di libri sul tema "Experience-Driven Optimization"
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Can, Alperen, Hendrik Schulz, Ali El-Rahhal, Gregor Thiele e Jörg Krüger. "A Practical Approach to Realize a Closed Loop Energy Demand Optimization of Milling Machine Tools in Series Production". In Lecture Notes in Mechanical Engineering, 499–507. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28839-5_56.
Testo completoAbstract (sommario):
AbstractEnergy efficiency is becoming increasingly important for industry. Many approaches for energy efficiency improvements lead to the purchase of new hardware, which could neglect the sustainability. Therefore, optimizing the energy demand of existing machine tools (MT) is a promising approach. Nowadays energy demand optimization of MT in series production is mainly done manually by the operators, based on implicit knowledge gained by experience. This involves manual checks to ensure that production targets like product quality or cycle time are met. With data analytics it is possible to check these production targets autonomously, which allows optimizing production systems data driven. This paper presents the approach and evaluation of a closed loop energy demand optimization of auxiliary units for milling MT during series production. The approach includes, inter alia, a concept for machine connectivity using edge devices and a concept for validating production targets.
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Millan, Michael, Annika Becker, Ester Christou, Roman Flaig, Leon Gorißen, Christian Hinke, István Koren et al. "Design Elements of a Platform-Based Ecosystem for Industry Applications". In Internet of Production, 1–22. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-98062-7_20-1.
Testo completoAbstract (sommario):
AbstractMany companies in the Industry 4.0 (I4.0) environment are still lacking knowledge and experience of how to enter and participate in a platform-based ecosystem to gain long-term competitive advantages. This leads to uncertainty among firms when transforming into platform-based ecosystems. The article presents a structuralist approach to conceptualize the platform-based ecosystem construct, giving an overview of the literature landscape in a model bundled with unified terminology and different perspectives. The holistic process model aggregates the findings of 130 papers regarding platform-based ecosystem literature. It consists of 4 phases and 16 design elements that unify different terminologies from various research disciplines in one framework and provide a structured and process-oriented approach. Besides, use cases for different design elements were developed to make the model apply in an I4.0 context. Use Case I is a methodology that can be used to model and validate usage hypotheses based on usage data to derive optimization potential from identified deviations from real product usage. By collecting and refining data for analyzing different manufacturing applications and machine tool behavior the importance of specific data is shown in Use Case II and it is highlighted which data can be shared from an external perspective. Use Case III deals with strategic modeling of platform-based ecosystems and the research identifies control points that platform players can actively set to adjust their business models within alliance-driven cooperation to create and capture value jointly. Use Case IV investigates the status quo and expectations regarding platform-based ecosystems in the field of laser technology with the help of structured expert interviews. Overall, this chapter presents a framework on industrial platform-based ecosystems that gives researchers and practitioners a tool and specific examples to get started in this emerging topic.
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Azzam, Hammad. "Corporates in the Digital Age". In Technology Optimization and Change Management for Successful Digital Supply Chains, 39–52. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7700-3.ch003.
Testo completoAbstract (sommario):
A proposition for digital transformation of global groups into efficient enterprises is introduced. At the heart of the proposition is a transformational practice aimed at creating a customer-focused, data-driven global culture in any customer-serving company. The digital age has added a level of complexity to the way we acquire and serve customers. Doing a good job in the traditional channels is not enough anymore. Online is increasingly becoming the channel of choice with the two main customer-interaction paradigms: sell and service. And building a great customer experience is probably the most essential factor of success for both functions.
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Janssens, Jürgen. "Managing and Shaping Change in International Projects". In Research Anthology on Digital Transformation, Organizational Change, and the Impact of Remote Work, 1199–222. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7297-9.ch060.
Testo completoAbstract (sommario):
Companies are either international by nature, either their workforce is, or business dynamics and agile optimization create an extended web making it international. This obliges managers to gear up for the needs of this evolving DNA. This is especially the case in strategy critical project portfolio contexts focusing on organizational change, process transformation, or roll-out of headquarter-driven products/services. This chapter will address the required hard and soft assets: integrating the cultural essence and maturity of the ecosystem, combining experience with a pragmatic approach, and being dedicated to continuous shaping of collaborations. Additional focus will be set on managing in the digital age. To give more depth to the tangible value, different real-life cases will be integrated. Together, the theoretical insights and the empirical examples will offer a big picture view that will benefit the management of hybrid portfolios in geographically blended environments.
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Janssens, Jürgen. "Managing and Shaping Change in International Projects". In Managerial Competencies for Multinational Businesses, 150–73. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5781-4.ch008.
Testo completoAbstract (sommario):
Companies are either international by nature, either their workforce is, or business dynamics and agile optimization create an extended web making it international. This obliges managers to gear up for the needs of this evolving DNA. This is especially the case in strategy critical project portfolio contexts focusing on organizational change, process transformation, or roll-out of headquarter-driven products/services. This chapter will address the required hard and soft assets: integrating the cultural essence and maturity of the ecosystem, combining experience with a pragmatic approach, and being dedicated to continuous shaping of collaborations. Additional focus will be set on managing in the digital age. To give more depth to the tangible value, different real-life cases will be integrated. Together, the theoretical insights and the empirical examples will offer a big picture view that will benefit the management of hybrid portfolios in geographically blended environments.
Atti di convegni sul tema "Experience-Driven Optimization"
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Xie, Hui, Ze Zhang e Kang Song. "A Self-optimization Algorithm of Multi-style Smart Parking Driven by Experience, Knowledge and Data". In 2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI). IEEE, 2021. http://dx.doi.org/10.1109/cvci54083.2021.9661152.
Testo completo
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Agrawal, Sunil K., Venketesh N. Dubey, John J. Gangloff, Elizabeth Brackbill e Vivek Sangwan. "Optimization and Design of a Cable Driven Upper Arm Exoskeleton". In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86516.
Testo completoAbstract (sommario):
This paper presents the design of a wearable upper arm exoskeleton that can be used to assist and train arm movements of stroke survivors or subjects with weak musculature. In the last ten years, a number of upper-arm training devices have emerged. However, due to their size and weight, their use is restricted to clinics and research laboratories. Our proposed wearable exoskeleton builds upon our extensive research experience in wire driven manipulators and design of rehabilitative systems. The exoskeleton consists of three main parts: (i) an inverted U-shaped cuff that rests on the shoulder, (ii) a cuff on the upper arm, and (iii) a cuff on the forearm. Six motors, mounted on the shoulder cuff, drive the cuffs on the upper arm and forearm, using cables. In order to assess the performance of this exoskeleton, prior to use on humans, a laboratory test-bed has been developed where this exoskeleton is mounted on a model skeleton, instrumented with sensors to measure joint angles and transmitted forces to the shoulder. This paper describes design details of the exoskeleton and addresses the key issue of parameter optimization to achieve useful workspace based on kinematic and kinetic models.
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Burns, Cliff, e Mike Wiegand. "Practical Considerations for Optimization of Propulsion Efficiency in Commercial Vessels". In SNAME 13th Propeller and Shafting Symposium. SNAME, 2012. http://dx.doi.org/10.5957/pss-2012-007.
Testo completoAbstract (sommario):
In this paper the focus is not regarding any one particular type of vessel, but is to call attention to the various vessel and propulsor features which affect the propulsive efficiency of most any vessel. There is also some consideration of the various propulsor types and their effectiveness and appropriateness in the various vessel missions. The main focus, because most of the authors’ experience has been in design and manufacture of commercial marine propellers and nozzles, will be on propeller driven vessels. The paper will address, somewhat, various vessel design features and specific details which have an effect on efficient use of the power available. The author’s point of reference comes from decades of working to suggest, design, and provide efficient propellers for particular use in both new and existing vessels. Over the years, it seems common that in any field we learn the most from the most challenging vessel propulsion problems. It seems appropriate to share some of the experience and learning from the author’s work to improve propulsion efficiency and smoothness of operation in commercial vessels.
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Vennelakanti, Ravigopal, Malarvizhi Sankaranarayanasamy, Ramyar Saeedi, Rahul Vishwakarma, Prasun Singh, Jian Sun, Yushi Akiyama e Hisao Adachi. "Multimodal Mobility Framework: Towards Seamless Mobility Experience". In 2021 Joint Rail Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/jrc2021-58377.
Testo completoAbstract (sommario):
Abstract Mobility is no longer just a necessity for travelers, but choices among several possible routes and transportation modes. Urban passenger rail transport plays an essential role because it is affordable, convenient, safe, and fast. On the other hand, rail lines are limited to high passenger density corridors. Inevitably, rail has to be placed together with different transport modes, forming a multimodal network. However, to enable this integration with other modes of transport, numerous practical problems remain, such as making a smooth transition from the existing siloed, mode specific operational structure towards an interconnected system of transportation modes and business models for a seamless connected journey. The current isolated operational structure lacks a single truth and accurate visibility, which further discourages participation from augmenting transportation modes and leads to the extended reaction time for new technology integration. This research article introduces a Multimodal Mobility (MMM) solution framework that provides a functional interface to integrate and synchronize the railroad operations with other public transit networks (including train-bus-rapid transits) and micro-mobility services. The known approach to addressing the users’ seamless mobility experience entails a centralized, prearranged, a priori knowledge and mechanism for operating intermodal transport systems. In contrast, the method defined in this paper focuses on a market-driven demand-responsive system that allows for dis-intermediation in a network of peer-level transportation modes operations. The framework facilitates blockchain-based decentralized and multi-organizational engagement. The focus here is the role of railroad in the multimodal ecosystem and its performance advancements in this integrated solutions framework. Leveraging a combination of graph analytics and machine learning algorithms, we provide methods to address challenges in encoding spatial and temporal dependencies of multimodal transit networks and handle complex optimization problems such as mixed time window and volume variation for resource allocation and transit operational analytics. This enables operation of different transit modes with varied resolution and flexibility for operational parameters like time, capacity, ridership, revenue management, etc. The analytics enable solutions for recommendations on synchronizing and integrating operations of transportation systems. Further, the network’s decentralization and modular handling enable market-driven co-optimization of operational resources across various transportation modes to ensure seamless transit experience for users.
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Canchucaja, Ramiro. "Fast Real-Time Production Optimization for Integrated Asset Modelling Using Mixed-Integer Non-Linear Programming in Julia Language". In SPE Latin American and Caribbean Petroleum Engineering Conference. SPE, 2023. http://dx.doi.org/10.2118/213138-ms.
Testo completoAbstract (sommario):
Abstract This study proposes a method for integrated asset modelling by using machine learning along with operations research algorithms to perform real-time constrained production optimization and maximize operational profit in a real-time basis. The methodology, which is mainly about the transformation of field and well performance to equations, inequalities, and matrixes, was tested successfully in the operation of a gas condensate field where operational profit increased, in abnormal conditions when personnel normally act mostly based on experience, pre-conceived notion, or solutions to previously solved problems. The study provides a solution with full data-driven objectivity for decision-making using the results of a mixed integer non-linear programming problem.
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Choi, Young H., Jin H. Hong e Sung H. Jang. "A Study on the Feed Rate Optimization of a Ball Screw Feed Drive System for Minimum Vibrations". In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85473.
Testo completoAbstract (sommario):
In order to prevent machine tool feed slide system from transient vibrations during operation, machine tool designers usually adopt some typical design solutions; box-in-box typed feed slides, optimizing moving body for minimum weight and dynamic compliance, and so on. Despite all efforts for optimizing design, a feed drive system may experience severe transient vibrations during high-speed operation if its feed-rate control is not suitable. A rough feed-rate curve having discontinuity in its acceleration profile causes serious vibrations in the feed slides system. This paper presents a feed-rate optimization of a ball screw driven machine tool feed slide system for its minimum vibrations. A ball screw feed drive system was mathematically modeled as a 6-degree-of-freedom lumped parameter model. Then, a feed-rate optimization of the system was carried out for minimum vibrations. The main idea of the feed-rate optimization is to find out the most appropriate smooth acceleration profile having jerk continuity. A genetic algorithm, G.A., was used in this feed rate optimization.
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Antani, Kavit, Alireza Madadi, Mary E. Kurz, Laine Mears, Kilian Funk e Maria E. Mayorga. "Robust Work Planning and Development of a Decision Support System for Work Distribution on a Mixed-Model Automotive Assembly Line". In ASME 2012 International Manufacturing Science and Engineering Conference collocated with the 40th North American Manufacturing Research Conference and in participation with the International Conference on Tribology Materials and Processing. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/msec2012-7350.
Testo completoAbstract (sommario):
Line balancing is a very resource-intensive and time consuming process which is highly reliant on the experience and expertise of a few employees. Line balancing is made even more complex due to the high level of option content in premium automobiles. The current phase of this study involves hands-on training on the automotive assembly line, precedence relationship mapping of all the tasks involved on a pilot assembly line, identification of constraints, and development of a strategy to manage option content and constraints. The second phase will include the generation of an optimal line balance through optimization on expected station utilization. The current line balancing process relies significantly on the experience level of the utility workers and team leaders. Although initially labor intensive, the precedence mapping exercise and option coding strategy will facilitate the development of a decision support system to aid the human decision-maker in making data-driven decisions about work distribution.
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Park, Youn, Dragi Gasevski, Marlow Springer, Milos Stanic, Viraj Kulkarni e Dhiren Marjadi. "Simulation Driven Design Workflow for Aircraft Gearbox". In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-15091.
Testo completoAbstract (sommario):
Abstract The aerospace industry is driven more than ever to reduce time and cost of developing aircrafts while reducing weight without compromising performance. This challenge cascades down to design of aircraft engines, where manufacturers are striving to deliver engines with higher fuel efficiency and power per unit weight. The challenge cascades down further to aerospace systems such as gearboxes. The focus of this paper is to addresses this challenge through application of model-based systems engineering and a design workflow based on simulation and optimization. A typical gearbox design starts with system level performance requirements such as power requirements, speed, torques and operating conditions. The goal is to design a minimum weight, manufacturable gearbox in the shortest possible time that meets the performance requirements. In this paper, authors present a design workflow developed for meeting this goal. The efficiency of the workflow presented here comes from the use of advanced simulation technologies, seamless integration of the workflow tasks, and modern and intuitive user experience. The proposed workflow focuses on design of housing and lubrication. This workflow is demonstrated on an aircraft engine accessories gearbox. Gear and bearing design are currently out of the scope of this paper and can be addressed in future work. Demonstrating a fully detailed workflow on a real-life gearbox housing with all the physics covered here would require many man-months of work and is outside the scope of authors’ IRAD funding. Instead, authors have presented a proof of concept of the workflow while providing key statistics to support the value and feasibility of the workflow.
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Maheshwari, Nitin, Sultan Lobari e Ali Awadh Saary. "Production Optimization and Reservoir Monitoring Through Virtual Flow Metering". In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211233-ms.
Testo completoAbstract (sommario):
Abstract Development of marginal oil fields having limited production rate are typically very challenging, especially for the fields where reservoir behavior could be very sensitive to the operating envelop. Also, minimizing the operational expenses is the key for economic development of such marginal fields. Bu Haseer is one of the marginal fields in offshore concession area of Al Yasat Petroleum Operations Company. Al Yasat has implemented hybrid virtual flow meter solution in Bu Haseer field, to enhance the reservoir management plan while minimizing the operational expenses. Hybrid virtual flowmeter solution adopted by Al Yasat utilizes first principle based high fidelity multi-phase flow assurance simulator, leveraging its machine learning model for fast and robust flow estimation. Dynamic analysis and data driven approach of the virtual flow meter solution ensures the estimation accuracy and would highlight the system failure, if any. Utilizing available real time data from field, the virtual flow meter estimates the real time oil, water and gas flow rates from producers and expected down-hole parameters, within accuracy acceptable for reservoir surveillance and production optimization. Typically, well performance is being monitored through periodic well tests and downhole monitoring through wireline activities. Frequency of such reservoir surveillance activities could be significant, especially for marginal fields due to reservoir sensitivity. Continuous real time reservoir monitoring through virtual flow meter provides better understanding of reservoir for timely corrective actions and maximizes the oil recovery in the field lifecycle. With the implementation of virtual flow metering technology, physical well testing and downhole monitoring will be limited for periodic validation and calibration of the virtual flow meter. Therefore, operational expenses for reservoir monitoring activities and associated HSE risks are also significantly optimized. Virtual flow measurement using conventional steady state principles of physics is widely used in oil and gas industry. However, advancing the conventional methodology with a combined machine learning & dynamic simulation approach not only enhances the measurement accuracies, but allows the solution to be a tool for analyzing and identifying potential failures and their root causes. This paper presents Al Yasat experience in hybrid virtual flow measurement technology implemented in one of its marginal fields.
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Taruvai Sankaran, Raghuraman, Arunkumar S, Muthukumar Arunachalam e harinadh Gudla. "Simulation Driven Optimization of Automotive Floor Console Mounting Brackets – An Overview". In WCX World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2018. http://dx.doi.org/10.4271/2018-01-1020.
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