Journal articles: 'Model-driven performance engineering'

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Nambiar, Manoj, Ajay Kattepur, Gopal Bhaskaran, Rekha Singhal, and Subhasri Duttagupta. "Model Driven Software Performance Engineering." ACM SIGMETRICS Performance Evaluation Review 43, no. 4 (February 25, 2016): 53–62. http://dx.doi.org/10.1145/2897356.2897363.

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Sousa, Paul, and Eric J. Miller. "Performance-Driven Transit Funding Model." Transportation Research Record: Journal of the Transportation Research Board 1927, no. 1 (January 2005): 72–81. http://dx.doi.org/10.1177/0361198105192700109.

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This paper presents a new funding model for urban transit systems. The model is performance driven in that it captures the performance of transit systems in attracting riders in a cost-effective manner and recognizes that transit system funding needs vary with transit systems’ scale of operations. The model also allocates funding on a weighted per capita basis and thereby addresses equity concerns. Recent data for Canadian transit systems are used to illustrate the application of this funding model to real-world operations.

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Hu, Guang-Zhong, Xin-Jian Xu, Shou-Ne Xiao, Guang-Wu Yang, and Fan Pu. "Product Data Model for Performance-driven Design." Chinese Journal of Mechanical Engineering 30, no. 5 (August 4, 2017): 1112–22. http://dx.doi.org/10.1007/s10033-017-0173-6.

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Boonma, P., and J. Suzuki. "Moppet: A Model-Driven Performance Engineering Framework for Wireless Sensor Networks." Computer Journal 53, no. 10 (February 16, 2010): 1674–90. http://dx.doi.org/10.1093/comjnl/bxp129.

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KATASONOV, ARTEM. "ONTOLOGY-DRIVEN SOFTWARE ENGINEERING: BEYOND MODEL CHECKING AND TRANSFORMATIONS." International Journal of Semantic Computing 06, no. 02 (June 2012): 205–42. http://dx.doi.org/10.1142/s1793351x12500031.

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This paper introduces a novel framework for Ontology-Driven Software Engineering. This framework is grounded on the prior related work that studied the interplay between the model-driven engineering and the ontological modeling. Our framework makes a contribution by incorporating a more flexible means for ontological modeling that also has a higher performance in processing, and by incorporating a wider range of ontology types into ODSE. As a result, it extends the power and speed of the classification and the model consistency checking ontological services enabled by the prior work, and brings new ontological services: semantic search in model repositories, three kinds of semi-automated model composition services: task-based, result-based, and opportunistic, and the policy enforcement service. The primary intended use for this framework is to be implemented as part of model-driven engineering tools to support software engineers. We describe our reference implementation of such a tool called Smart Modeller, and report on a performance evaluation of our framework carried out with the help of it.

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Cortellessa, Vittorio, Daniele Di Pompeo, Vincenzo Stoico, and Michele Tucci. "Software Model Refactoring Driven by Performance Antipattern Detection." ACM SIGMETRICS Performance Evaluation Review 49, no. 4 (June 2, 2022): 53–58. http://dx.doi.org/10.1145/3543146.3543159.

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The satisfaction of ever more stringent performance requirements is one of the main reasons for software evolution. However, determining the primary causes of performance degradation is generally challenging, as they may depend on the joint combination of multiple factors (e.g., workload, software deployment, hardware utilization). With the increasing complexity of software systems, classical bottleneck analysis seems to show limitations in capturing complex performance problems. Hence, in the last decade, the detection of performance antipatterns has gained momentum as an effective way to identify performance degradation causes. In this tool paper we introduce PADRE (Performance Antipattern Detection and REfactoring), a tool for: (i) detecting performance antipattern in UML models, and (ii) refactoring models with the aim of removing the detected antipatterns. PADRE has been implemented within Epsilon, which is an open-source platform for model-driven engineering, and it grounds on a methodology that allows performance antipattern detection and refactoring within the same implementation context.

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Cortellessa, Vittorio, Daniele Di Pompeo, Vincenzo Stoico, and Michele Tucci. "Software Model Refactoring Driven by Performance Antipattern Detection." ACM SIGMETRICS Performance Evaluation Review 49, no. 4 (June 2, 2022): 53–58. http://dx.doi.org/10.1145/3543146.3543159.

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The satisfaction of ever more stringent performance requirements is one of the main reasons for software evolution. However, determining the primary causes of performance degradation is generally challenging, as they may depend on the joint combination of multiple factors (e.g., workload, software deployment, hardware utilization). With the increasing complexity of software systems, classical bottleneck analysis seems to show limitations in capturing complex performance problems. Hence, in the last decade, the detection of performance antipatterns has gained momentum as an effective way to identify performance degradation causes. In this tool paper we introduce PADRE (Performance Antipattern Detection and REfactoring), a tool for: (i) detecting performance antipattern in UML models, and (ii) refactoring models with the aim of removing the detected antipatterns. PADRE has been implemented within Epsilon, which is an open-source platform for model-driven engineering, and it grounds on a methodology that allows performance antipattern detection and refactoring within the same implementation context.

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Mateen, Ahmad, and Alia Tabassum. "A Framework for Model Driven Transformation Engineering towards Software Architecture and Performance." International Journal of Computer Applications 143, no. 8 (June 17, 2016): 6–9. http://dx.doi.org/10.5120/ijca2016908959.

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Lea, Michelle, Donald Thompson, BillVan Blarcom, Jon Eaton, Juergen Friesch, and John Richards. "Scale Model Testing of a Commercial Rim-Driven Propulsor Pod." Journal of Ship Production 19, no. 02 (May 1, 2003): 121–30. http://dx.doi.org/10.5957/jsp.2003.19.2.121.

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Podded propulsion is gaining more widespread use in the marine industry and is prevalent in newer cruise ships in particular. This propulsion system can provide many advantages to the ship owner that include increased propulsion efficiency, arrangement flexibility, payload, and harbor maneuverability. A new, unique podded propulsor concept is being developed that allows optimization of each element of the system. The concept comprises a ducted, multiple-blade row propulsor with a permanent magnet, radial field motor rotor mounted on the tips of the propulsor rotor blades, and the motor stator mounted within the duct of the propulsor. This concept, designated a commercial rim-driven propulsor pod (CRDP), when compared to a conventional hub-driven pod (HDP), offers improved performance in a number of areas, including equal or improved efficiency, cavitation, and hull unsteady pressures. The combination of these CRDP performance parameters allows the ship designer much greater flexibility to provide improved ship performance as compared to that of an HDP. A CRDP is being developed to power a panamax-size cruise vessel. The paper addresses the hydrodynamic performance of that CRDP design demonstrated at 1/25th scale as tested at the Hamburg Ship Model Basin, Hamburg Germany (HSVA).

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Deng, Xiuqi, Xin Bian, and Mingqi Li. "Data-Driven and Model-Driven Joint Detection Algorithm for Faster-Than-Nyquist Signaling in Multipath Channels." Sensors 22, no. 1 (December 30, 2021): 257. http://dx.doi.org/10.3390/s22010257.

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In recent years, Faster-than-Nyquist (FTN) transmission has been regarded as one of the key technologies for future 6G due to its advantages in high spectrum efficiency. However, as a price to improve the spectrum efficiency, the FTN system introduces inter-symbol interference (ISI) at the transmitting end, whicheads to a serious deterioration in the performance of traditional receiving algorithms under high compression rates and harsh channel environments. The data-driven detection algorithm has performance advantages for the detection of high compression rate FTN signaling, but the current related work is mainly focused on the application in the Additive White Gaussian Noise (AWGN) channel. In this article, for FTN signaling in multipath channels, a data and model-driven joint detection algorithm, i.e., DMD-JD algorithm is proposed. This algorithm first uses the traditional MMSE or ZFinear equalizer to complete the channel equalization, and then processes the serious ISI introduced by FTN through the deepearning network based on CNN or LSTM, thereby effectively avoiding the problem of insufficient generalization of the deepearning algorithm in different channel scenarios. The simulation results show that in multipath channels, the performance of the proposed DMD-JD algorithm is better than that of purely model-based or data-driven algorithms; in addition, the deepearning network trained based on a single channel model can be well adapted to FTN signal detection under other channel models, thereby improving the engineering practicability of the FTN signal detection algorithm based on deepearning.

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Lelandais, Benoît, Marie-Pierre Oudot, and Benoît Combemale. "Applying model-driven engineering to high-performance computing: Experience report, lessons learned, and remaining challenges." Journal of Computer Languages 55 (December 2019): 100919. http://dx.doi.org/10.1016/j.cola.2019.100919.

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Treesatayapun, Chidentree. "Discrete‐time prescribed performance controller based on affine data‐driven model." International Journal of Adaptive Control and Signal Processing 34, no. 11 (September 2020): 1625–41. http://dx.doi.org/10.1002/acs.3168.

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Ortiz, Francisco J., Carlos C. Insaurralde, Diego Alonso, Francisco Sánchez, and Yvan R. Petillot. "Model-driven analysis and design for software development of autonomous underwater vehicles." Robotica 33, no. 08 (April 29, 2014): 1731–50. http://dx.doi.org/10.1017/s0263574714001027.

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SUMMARYSoftware engineering plays a key role in state-of-the-art robotics where more effective and efficient software development solutions are needed for implementation and integration of advanced robotics capabilities. Component-based software engineering and model-driven software development are two paradigms suitable to deal with such challenges. This paper presents the analysis, design, and implementation of control software for an Autonomous Underwater Vehicle (AUV). The software development stages are carried out by using a model-driven toolchain that provides support to design and build component-based software for robotics applications. A case study of a high-performance AUV control application and experimental results from a software schedulability analysis are presented.

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Zhang, Weixin, Ye Li, Yulei Liao, Qi Jia, and Kaiwen Pan. "Hydrodynamic Analysis of Self-Propulsion Performance of Wave-Driven Catamaran." Journal of Marine Science and Engineering 9, no. 11 (November 5, 2021): 1221. http://dx.doi.org/10.3390/jmse9111221.

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The wave-driven catamaran is a small surface vehicle driven by ocean waves. It consists of a hull and hydrofoils, and has a multi-body dynamic structure. The process of moving from static state to autonomous navigation driven by ocean waves is called “self-propulsion”, and reflects the ability of the wave-driven catamaran to absorb oceanic wave energy. Considering the importance of the design of the wave-driven catamaran, its self-propulsion performance should be comprehensively analysed. However, the wave-driven catamaran’s multi-body dynamic structure, unpredictable dynamic and kinematic responses driven by waves make it difficult to analyse its self-propulsion performance. In this paper, firstly, a multi-body dynamic model is established for wave-driven catamaran. Secondly, a two-phase numerical flow field containing water and air is established. Thirdly, a numerical simulation method for the self-propulsion process of the wave-driven catamaran is proposed by combining the multi-body dynamic model with a numerical flow field. Through numerical simulation, the hydrodynamic response, including the thrust of the hydrofoils, the resistance of the hull and the sailing velocity of the wave-driven catamaran are identified and comprehensively analysed. Lastly, the accuracy of the numerical simulation results is verified through a self-propulsion test in a towing tank. In contrast with previous research, this method combines multi-body dynamics with computational fluid dynamics (CFD) to avoid errors caused by artificially setting the motion mode of the catamaran, and calculates the real velocity of the catamaran.

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Sharma, Surya Prakash, Laxman Singh, and Rajdev Tiwari. "Prediction of Customer Review's Helpfulness Based on Feature Engineering Driven Deep Learning Model." International Journal of Software Innovation 11, no. 1 (January 1, 2023): 1–16. http://dx.doi.org/10.4018/ijsi.315734.

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Online consumer reviews play a pivotal role in boosting online shopping. After Covid-19, the e-commerce industry has been grown exponentially. The e-commerce industry is greatly impacted by the online customer reviews, and a lot of work has been done in this regard to identify the usefulness of reviews for purchasing online products. In this proposed work, predicting helpfulness is taken as binary classification problem to identify the helpfulness of a review in context to structural, sentimental, and voting feature sets. In this study, the authors implemented various leading ML algorithms such as KNN, LR, GNB, LDA and CNN. In comparison to these algorithms and other existing state of art methods, CNN yielded better classification results, achieving highest accuracy of 95.27%. Besides, the performance of these models was also assessed in terms of precision, recall, F1 score, etc. The results shown in this paper demonstrate that proposed model will help the producers or service providers to improve and grow their business.

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Freeman, Paul, Rohit Pandita, Nisheeth Srivastava, and Gary J. Balas. "Model-Based and Data-Driven Fault Detection Performance for a Small UAV." IEEE/ASME Transactions on Mechatronics 18, no. 4 (August 2013): 1300–1309. http://dx.doi.org/10.1109/tmech.2013.2258678.

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Yamasaki, Shintaro, Kentaro Yaji, and Kikuo Fujita. "Data-driven topology design using a deep generative model." Structural and Multidisciplinary Optimization 64, no. 3 (June 30, 2021): 1401–20. http://dx.doi.org/10.1007/s00158-021-02926-y.

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AbstractIn this paper, we propose a sensitivity-free and multi-objective structural design methodology called data-driven topology design. It is schemed to obtain high-performance material distributions from initially given material distributions in a given design domain. Its basic idea is to iterate the following processes: (i) selecting material distributions from a dataset of material distributions according to eliteness, (ii) generating new material distributions using a deep generative model trained with the selected elite material distributions, and (iii) merging the generated material distributions with the dataset. Because of the nature of a deep generative model, the generated material distributions are diverse and inherit features of the training data, that is, the elite material distributions. Therefore, it is expected that some of the generated material distributions are superior to the current elite material distributions, and by merging the generated material distributions with the dataset, the performances of the newly selected elite material distributions are improved. The performances are further improved by iterating the above processes. The usefulness of data-driven topology design is demonstrated through numerical examples.

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Zheng, Zhao, and Kew Si Na. "A Data-Driven Emotion Model for English Learners Based on Machine Learning." International Journal of Emerging Technologies in Learning (iJET) 16, no. 08 (April 23, 2021): 34. http://dx.doi.org/10.3991/ijet.v16i08.22127.

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Learning confusion is a common emotion among learners. With the aid of machine learning, this paper develops a data-driven emotion model that automatically recognizes learning confusion in facial expression images. The data on learning behaviors and learning confusion of multiple subjects were collected through an online English evaluation experiment, and imported to the proposed model to derive the relationship between learning confusion and academic performance, which is measured by the correctness of the students’ answers to the test questions. The experimental results show that the students with learning confusion had relatively low correct rate of answering test questions. The research findings reveal the relationship between learning confusion and academic performance, laying the basis for predicting the academic performance of English learners through machine learning.

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Tan, Qingyuan, Xiang Chen, Ying Tan, and Ming Zheng. "Model-Guided Data-Driven Optimization for Automotive Compression Ignition Engine Systems." Mechanical Engineering 141, no. 03 (March 1, 2019): S16—S23. http://dx.doi.org/10.1115/1.2019-mar-5.

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Essentially, the performance improvement of automotive systems is a multi-objective optimization problem [1–4] due to the challenges in both operation management and control. The interconnected dynamics inside the automotive system normally requires precise tuning and coordination of accessible system inputs. In the past, such optimization problems have been approximately solved through expensive calibration procedures or an off-line local model-based approaches where either a regressive model or a first-principle model is used. The model-based optimization provides the advantage of finding the optimal model parameters to allow the model to be used to predict the real system behavior reasonably [5]. However, other than the model complexities, there are practically two issues facing the integrity of these models: modeling uncertainty due to inaccurate parameter values and/or unmodeled dynamics, and locally effective range around operating points. As a result, the optimum solutions extracted from the model-based approach could be subject to failure of expected performance [6].

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Nagy, Peter, and Marco Fossati. "Adaptive Data-Driven Model Order Reduction for Unsteady Aerodynamics." Fluids 7, no. 4 (April 6, 2022): 130. http://dx.doi.org/10.3390/fluids7040130.

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A data-driven adaptive reduced order modelling approach is presented for the reconstruction of impulsively started and vortex-dominated flows. A residual-based error metric is presented for the first time in the framework of the adaptive approach. The residual-based adaptive Reduced Order Modelling selects locally in time the most accurate reduced model approach on the basis of the lowest residual produced by substituting the reconstructed flow field into a finite volume discretisation of the Navier–Stokes equations. A study of such an error metric was performed to assess the performance of the resulting residual-based adaptive framework with respect to a single-ROM approach based on the classical proper orthogonal decomposition, as the number of modes is varied. Two- and three-dimensional unsteady flows were considered to demonstrate the key features of the method and its performance.

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Bao, Hanqiu, Qi Kang, Jing An, Xianghua Ma, and Mengchu Zhou. "A Performance-Driven MPC Algorithm for Underactuated Bridge Cranes." Machines 9, no. 8 (August 20, 2021): 177. http://dx.doi.org/10.3390/machines9080177.

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A crane system often works in a complex environment. It is difficult to model or learn its true dynamics by traditional system identification approaches. If a dynamics model is created by minimizing its prediction error, its use tends to introduce inaccuracies and thus lead to suboptimal performance. Is it possible to learn the dynamics model of a crane that can achieve the best performance, instead of learning its true dynamics? This work answers the question by presenting a performance-driven model predictive control (P-MPC) algorithm for a two-dimensional underactuated bridge crane. In the proposed dual-layer control architecture, an inner-loop controller uses a proportional–integral–derivative controller to achieve anti-sway rapidly. An outer-loop controller uses MPC to ensure accurate trolley positioning under control constraints. Compared with classical MPC, this work proposes a data-driven method for plant modeling and controller parameter updating. By considering the control target at the learning stage, the method can avoid adjusting the controller to deal with uncertainty. We use Bayesian optimization in an active learning framework where a locally linear dynamics model is learned with the intent of maximizing control performance and then used in conjunction with optimal control schemes to efficiently design a controller for a given task. The model is updated directly based on the performance observed in experiments on the physical system in an iterative manner till a desired performance is achieved. The controller parameters and prediction models of the best closed-loop performance can be found through continuous experiments and iterative optimization. Simulation and experiment results show that we can explicitly find the dynamics model that produces the best performance for an actual system, and the method can quickly suppress swing and realize accurate trolley positioning. The results verified its effectiveness, feasibility, and superior performance on comparing it with state-of-the-art methods.

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Fernandes, Fábio A. O., António B. Pereira, Bernardo Guimarães, and Tiago Almeida. "Laser Welding of Transmitting High-Performance Engineering Thermoplastics." Polymers 12, no. 2 (February 10, 2020): 402. http://dx.doi.org/10.3390/polym12020402.

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Laser processing is a rapidly growing key technology driven by several advantages such as cost and performance. Laser welding presents numerous advantages in comparison with other welding technologies, providing high reliability and cost-effective solutions. Significant interest in this technology, combined with the increasing demand for high-strength lightweight structures has led to an increasing interest in joining high-performance engineering thermoplastics by employing laser technologies. Laser transmission welding is the base method usually employed to successfully join two polymers, a transmitting one through which the laser penetrates, and another one responsible for absorbing the laser radiation, resulting in heat and melting of the two components. In this work, the weldability of solely transmitting high-performance engineering thermoplastic is analyzed. ERTALON® 6 SA, in its white version, is welded by a pulsed Nd:YAG laser. Tensile tests were performed in order to evaluate the quality of each joint by assessing its strength. A numerical model of the joint is also developed to support the theoretical approaches employed to justify the experimental observations.

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Pan, Yuyan Annie, Jifu Guo, Yanyan Chen, Siyang Li, and Wenhao Li. "Incorporating Traffic Flow Model into A Deep Learning Method for Traffic State Estimation: A Hybrid Stepwise Modeling Framework." Journal of Advanced Transportation 2022 (November 4, 2022): 1–17. http://dx.doi.org/10.1155/2022/5926663.

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Traffic state estimation (TSE), which reconstructs the traffic variables (e.g., speed, flow) on road segments using partially observed data, plays an essential role in intelligent transportation systems. Generally, traffic estimation problems can be divided into two categories: model-driven approaches and data-driven approaches. The model-driven method is commonly used to solve TSE efficiently and calibrate the parameters of these models. The data-driven method requires a large amount of historical observed traffic data in order to improve performance accurately. In order to combine the advantages of model-driven and data-driven methods, this paper proposed a hybrid framework incorporating the traffic flow model into deep learning (TFMDL) modeling that contains both model-driven and data-driven components. This paper focuses on highway TSE with observed data from loop detectors. We build a hybrid cost function to adjust the weights of model-driven and data-driven proportions. We then evaluate the proposed framework using the open-access performance measurement system (PMS) dataset on a corridor of US I-405 in Los Angeles, California. The experimental results show the advantages of the proposed TFMDL approach in performing better than several benchmark models in terms of estimation accuracy and data efficiency.

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Palmtag, A., H. Gröschl, L. Polte, and A. Jupke. "Model‐Based and Data‐Driven Performance Monitoring of Pulsed Sieve‐Plate Extraction Columns." Chemie Ingenieur Technik 94, no. 9 (August 25, 2022): 1311. http://dx.doi.org/10.1002/cite.202255013.

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ZHU, Wenyue, Yihua LIU, and Yongjuan WANG. "A complex machinery design-oriented digital prototype model driven method." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 40, no. 4 (August 2022): 901–8. http://dx.doi.org/10.1051/jnwpu/20224040901.

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Because of information loss during the conversion between a geometric digital prototype and a performance digital prototype in developing a complex mechanical product, the model-driven architecture of a digital prototype was constructed. By analyzing the constituent elements of a STEP file, the platform independent model based on the digital prototype was established. The platform specific model conversion method from the model-driven platform independent model to the UG software, the Adams software and the Abaqus software was proposed. Based on the heterogeneous software interface and the scripting language, the model-driven modules of the above three types of software were developed and verified with numerical instances. The verification results show that the use of the model-driven method can share the model information on the special software for a mechanical digital prototype and that the model-driven modules thus developed can perform the quick simulation analysis and output its results in time.

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Pan, Ruming, and Gérald Debenest. "A robust model of smoldering-driven pyrolysis reactor – Part 1: Thermal performance evaluation." Fuel 320 (July 2022): 123935. http://dx.doi.org/10.1016/j.fuel.2022.123935.

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Tsuruhara, Satoshi, and Kazuhisa Ito. "Data-Driven Model-Free Adaptive Displacement Control for Tap-Water-Driven Artificial Muscle and Parameter Design Using Virtual Reference Feedback Tuning." Journal of Robotics and Mechatronics 34, no. 3 (June 20, 2022): 664–76. http://dx.doi.org/10.20965/jrm.2022.p0664.

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A McKibben artificial muscle has strong asymmetric hysteresis characteristics, which depend on the load applied to the muscle. Thus, designing a controller for high-performance displacement is difficult. In a previous study, model predictive control with a servomechanism combining an inverse optimization algorithm with adaptive model matching, and a data-driven model-free adaptive control (MFAC) were introduced. As a result, a high tracking control performance was achieved in both control methods. However, model-based and data-driven approaches require a highly accurate mathematical model and a large number of design parameters, making them time-consuming, respectively. To solve these problems, in the present study, a controller design that requires no precise mathematical model and less design parameter tuning with trial and error was developed by combining conventional MFAC and virtual reference feedback tuning, which is a data-driven control method. Experimental results indicated that important design parameters, such as the initial pseudo-gradient vector and weighting factor, can be readily obtained. Compared with conventional MFAC, higher tracking control performance without overshoot was achieved in transient response, while the same level of control performance was maintained in steady-state response.

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Condrachi, Larisa, Ramón Vilanova, Montse Meneses, and Marian Barbu. "Anaerobic Digestion Process Control Using a Data-Driven Internal Model Control Method." Energies 14, no. 20 (October 16, 2021): 6746. http://dx.doi.org/10.3390/en14206746.

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Anaerobic digestion processes offer the possibility for wastewater treatment while obtaining a benefit through the obtained biogas. This paper aims to continue the effort to adopt data-driven control methods in the case of anaerobic digestion processes. The paper proposes a data-based Internal Model Control approach applied to an anaerobic digestion process. The paper deals extensively with the issue of choosing the reference model and proposing an engineering approach to this issue. The paper also addresses the issue of verifying robust stability, a very important aspect considering the uncertainties that characterize bioprocesses in general. The approach proposed in the paper is validated through a numerical simulation using the Anaerobic Digestion Model No. 1. During the validation of the proposed control solution, the main operating conditions were analyzed, such as the setpoint tracking performance, the rejection of disturbance generated by variations in the influent concentration, and the effect of the measurement noise on the controlled variable.

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Zhu, Shuo, Xianzhi Song, Zhaopeng Zhu, Xuezhe Yao, and Muchen Liu. "Intelligent Prediction of Stuck Pipe Using Combined Data-Driven and Knowledge-Driven Model." Applied Sciences 12, no. 10 (May 23, 2022): 5282. http://dx.doi.org/10.3390/app12105282.

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Stuck pipe phenomena can have disastrous effects on drilling performance, with outcomes that can range from time delays to loss of expensive machinery. In this work, we provide three methods for the prediction of stuck pipe. The first method targets the detection of friction coefficient which can represent the trend of stuck pipe. The second method targets the prediction of probability for stuck pipe using ANN (artificial neural network). The last model establishes a comprehensive indicator based on the first and the second method using fuzzy mathematics which can give more accurate probability for stuck pipe. The results show that the best model is the last one which can predict stuck pipe events with a F1 of 0.98 and a FAR (false alarm rate) of 1%. Preliminary experimental results on the available dataset indicate that the use of the proposed model and can help mitigate the stuck pipe issue.

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Zhang, Jinhui, Maoyu Lin, Wei Zhou, Tao Luo, and Lifeng Qin. "Modeling of a Rope-Driven Piezoelectric Vibration Energy Harvester for Low-Frequency and Wideband Energy Harvesting." Micromachines 12, no. 3 (March 15, 2021): 305. http://dx.doi.org/10.3390/mi12030305.

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In this work, a mechanical model of a rope-driven piezoelectric vibration energy harvester (PVEH) for low-frequency and wideband energy harvesting was presented. The rope-driven PVEH consisting of one low-frequency driving beam (LFDB) and one high-frequency generating beam (HFGB) connected with a rope was modeled as two mass-spring-damper suspension systems and a massless spring, which can be used to predict the dynamic motion of the LFDB and HFGB. Using this model, the effects of multiple parameters including excitation acceleration, rope margin and rope stiffness in the performance of the PVEH have been investigated systematically by numerical simulation and experiments. The results show a reasonable agreement between the simulation and experimental study, which demonstrates the validity of the proposed model of rope-driven PVEH. It was also found that the performance of the PVEH can be adjusted conveniently by only changing rope margin or stiffness. The dynamic mechanical model of the rope-driven PVEH built in this paper can be used to the further device design or optimization.

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Chen, Xin, Shancheng Fang, Zhendong Mao, and Yongdong Zhang. "A data-driven model for social media fake news detection." JUSTC 52, no. 3 (2022): 7. http://dx.doi.org/10.52396/justc-2021-0215.

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The rapid development of social media leads to the spread of a large amount of false news, which not only affects people’s daily life but also harms the credibility of social media platforms. Therefore, detecting Chinese fake news is a challenging and meaningful task. However, existing fake news datasets from Chinese social media platforms have a relatively small amount of data and data collection in this field is relatively old, thus being unable to meet the requirements of further research. In consideration of this background, we release a new Chinese Weibo Fake News dataset, which contains 26320 fake news data collected from Weibo. In addition, we propose a fake news detection model based on data augmentation that can effectively solve the problem of a lack of fake news, and we improve the generalization ability and robustness of the model. We conduct numerous experiments on our Chinese Weibo Fake News dataset and successfully deploy the model on the web page. The experimental performance proves the effectiveness of the proposed end-to-end model for detecting fake news on social media platforms.

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Jasiulewicz-Kaczmarek, Małgorzata, Patryk Żywica, and Arkadiusz Gola. "Fuzzy set theory driven maintenance sustainability performance assessment model: a multiple criteria approach." Journal of Intelligent Manufacturing 32, no. 5 (January 27, 2021): 1497–515. http://dx.doi.org/10.1007/s10845-020-01734-3.

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AbstractDuring the last five decades, manufacturing has radically changed not only due to the technology development but also because of the new market and environmental requirements. Nowadays, companies are focused not only on cost-reduction and effectiveness or realized processes but also on reducing the negative impact on natural and social environment. Therefore, the maintenance is also transforming its role in order to better support value creation, both contributing to the economic dimension as well as extending its care for the environmental and social aspects. The paper presents a new method to solve the problem of the initial maintenance indicator merge into a new synthetic index that allows measuring the level of maintenance sustainability. The proposed approach allows to look at the process of combining indicators from a different perspective (i.e. through interactions between criteria) to help decision-makers in improving economic, social and environmental results of maintenance system. To justify effectiveness of proposed approach, it was applied to Composite Maintenance Sustainability Indicator, which was therefore generalized and expanded, so that it could be used in decision support system. The included case study shows the real benefit of using the proposed approach to analyse the actual results of maintenance system from sustainability point of view and forecasting future actions.

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Asadi, Yasin, Amirhossein Ahmadi, Sasan Mohammadi, Ali Moradi Amani, Mousa Marzband, and Behnam Mohammadi-ivatloo. "Data-Driven Model-Free Adaptive Control of Z-Source Inverters." Sensors 21, no. 22 (November 9, 2021): 7438. http://dx.doi.org/10.3390/s21227438.

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The universal paradigm shift towards green energy has accelerated the development of modern algorithms and technologies, among them converters such as Z-Source Inverters (ZSI) are playing an important role. ZSIs are single-stage inverters which are capable of performing both buck and boost operations through an impedance network that enables the shoot-through state. Despite all advantages, these inverters are associated with the non-minimum phase feature imposing heavy restrictions on their closed-loop response. Moreover, uncertainties such as parameter perturbation, unmodeled dynamics, and load disturbances may degrade their performance or even lead to instability, especially when model-based controllers are applied. To tackle these issues, a data-driven model-free adaptive controller is proposed in this paper which guarantees stability and the desired performance of the inverter in the presence of uncertainties. It performs the control action in two steps: First, a model of the system is updated using the current input and output signals of the system. Based on this updated model, the control action is re-tuned to achieve the desired performance. The convergence and stability of the proposed control system are proved in the Lyapunov sense. Experiments corroborate the effectiveness and superiority of the presented method over model-based controllers including PI, state feedback, and optimal robust linear quadratic integral controllers in terms of various metrics.

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Fatima, Syeda Kounpal, Manzar Abbas, Imran Mir, Faiza Gul, Suleman Mir, Nasir Saeed, Abdullah Alhumaidi Alotaibi, Turke Althobaiti, and Laith Abualigah. "Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis." Processes 10, no. 7 (June 21, 2022): 1236. http://dx.doi.org/10.3390/pr10071236.

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Unmanned Aerial Vehicles (UAVs) are important tool for various applications, including enhancing target detection accuracy in various surface-to-air and air-to-air missions. To ensure mission success of these UAVs, a robust control system is needed, which further requires well-characterized dynamic system model. This paper aims to present a consolidated framework for the estimation of an experimental UAV utilizing flight data. An elaborate estimation mechanism is proposed utilizing various model structures, such as Autoregressive Exogenous (ARX), Autoregressive Moving Average exogenous (ARMAX), Box Jenkin’s (BJ), Output Error (OE), and state-space and non-linear Autoregressive Exogenous. A perspective analysis and comparison are made to identify the salient aspects of each model structure. Model configuration with best characteristics is then identified based upon model quality parameters such as residual analysis, final prediction error, and fit percentages. Extensive validation to evaluate the performance of the developed model is then performed utilizing the flight dynamics data collected. Results indicate the model’s viability as the model can accurately predict the system performance at a wide range of operating conditions. Through this, to the best of our knowledge, we present for the first time a model prediction analysis, which utilizes comprehensive flight dynamics data instead of simulation work.

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Soelami, FX Nugroho, Putu Handre Kertha Utama, Irsyad Nashirul Haq, Justin Pradipta, Edi Leksono, and Meditya Wasesa. "Data Driven Building Electricity Consumption Model Using Support Vector Regression." Journal of Engineering and Technological Sciences 53, no. 3 (July 12, 2021): 210313. http://dx.doi.org/10.5614/j.eng.technol.sci.2021.53.3.13.

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Every building has certain electricity consumption patterns that depend on its usage. Building electricity budget planning requires a consumption forecast to determine the baseline electricity load and to support energy management decisions. In this study, an algorithm to model building electricity consumption was developed. The algorithm is based on the support vector regression (SVR) method. Data of electricity consumption from the past five years from a selected building object in ITB campus were used. The dataset unexpectedly exhibited a large number of anomalous points. Therefore, a tolerance limit of hourly average energy consumption was defined to obtain good quality training data. Various tolerance limits were investigated, that is 15% (Type 1), 30% (Type 2), and 0% (Type 0). The optimal model was selected based on the criteria of mean absolute percentage error (MAPE) < 20% and root mean square error (RMSE) < 10 kWh. Type 1 data was selected based on its performance compared to the other two. In a real implementation, the model yielded a MAPE value of 14.79% and an RMSE value of 7.48 kWh when predicting weekly electricity consumption. Therefore, the Type 1 data-based model could satisfactorily forecast building electricity consumption.

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Choi, K.-B., and J. J. Lee. "Static model for flexure-based compliant mechanism driven by piezo stacks." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 222, no. 4 (April 1, 2008): 703–9. http://dx.doi.org/10.1243/09544062jmes630.

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Flexure-based compliant mechanisms have been used in ultra-precision stages, stages for nano-imprint machines, AFM scanners, and MEMS structures, etc. In this paper, a mathematical static model for flexure-based compliant mechanism driven by piezo stacks is derived. The force vector generated from piezo stacks was linearized in terms of displacement and applied voltage vectors. Then, the piezo force vector was substituted into the static model of a compliant mechanism. Finally, the force and the displacement vectors of input bodies, and the block force and the displacement vectors of output bodies were derived. The derived mathematical model was verified by FEM analyses and experiments. The derived mathematical model can well describe the static performance of a flexure-based compliant mechanism driven by piezo stacks.

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Huang, You Rui, Yi Ming Tian, and Li Guo Qu. "Design of Model Driven Cascade PID Controller Using Quantum Neural Network." Advanced Materials Research 108-111 (May 2010): 1486–91. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.1486.

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PID optimal parameters selection has been extensively studied, In order to improve some strict performance requirements for complex system. A new design scheme of PID controllers is proposed in this paper. This scheme is designed based on the IMC which is a kind of the model driven controllers. The internal model consists of the full model. The full model is deigned by using the quantum neural network. The PID control system is first constructed for the augmented system which is composed of the controlled object and the internal model. Simulation and experiment results show that the method outperforms the typical model-based approach despite its simplicity and it is therefore suitable to implement in distributed control system as well as in single-station controller.

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Zaiwu, Mei, Chen Liping, and Ding Jianwan. "Feed-forward control of elastic-joint industrial robot based on hybrid inverse dynamic model." Advances in Mechanical Engineering 13, no. 9 (September 2021): 168781402110381. http://dx.doi.org/10.1177/16878140211038102.

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A novel feedforward control method of elastic-joint robot based on hybrid inverse dynamic model is proposed in this paper. The hybrid inverse dynamic model consists of analytical model and data-driven model. Firstly, the inverse dynamic analytical model of elastic-joint robot is established based on Lie group and Lie algebra, which improves the efficiency of modeling and calculation. Then, by coupling the data-driven model with the analytical model, a feed-forward control method based on hybrid inverse dynamics model is proposed. This method can overcome the influence of the inaccuracy of the analytical inverse dynamic model on the control performance, and effectively improve the control accuracy of the robot. The data-driven model is used to compensate for the parameter uncertainties and non-parameter uncertainties of the analytical dynamic model. Finally, the proposed control method is proved to be stable and the multi-domain integrated system model of industrial robot is developed to verify the performance of the control scheme by simulation. The simulation results show that the proposed control method has higher control accuracy than the traditional torque feed-forward control method.

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Haji Mohammad, Fatemeh, Maha Benali, and Pierre Baptiste. "An optimization model for demand-driven distribution resource planning DDDRP." Journal of Industrial Engineering and Management 15, no. 2 (March 21, 2022): 338. http://dx.doi.org/10.3926/jiem.3825.

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Purpose: Demand-Driven Distribution Resource Planning (DDDRP) has recently been proposed in the literature to deal with higher supply networks complexity, shorter customer tolerance times, and inaccurate forecasts. The DDDRP requires to position inventory buffers in critical network nodes, where the inventory level in each buffer is replenished based on actual demands rather than on demand forecasts. This paper aims to identify optimal buffer positions in a distribution network driven by the DDDRP approach and to assess the performance of the DDDRP approach compared to the conventional Distribution Resource Planning (DRP) approach.Design/methodology/approach: First, a mixed-integer non-linear model is proposed to optimize buffer positioning under supply network constraints and with the objective of minimizing supply chain holding costs. Then, a case study is investigated to validate the optimization model and to evaluate the performance of the optimized distribution network driven by the DDDRP approach, compared to the DRP approach.Findings: Results of the considered case study demonstrate that the distribution network optimized and driven by the DDDRP approach achieves savings of 75% in terms of total holding costs and 67% in terms of inventory amounts, compared to a distribution network driven by the DRP approach.Research limitations/implications: Results of this paper cannot be generalized since several assumptions have been considered. Thus, addressing real case studies in different industrial contexts may be of theoretical and practical interest.Originality/value: This paper is the first to propose a mathematical model to optimize buffer positioning in a distribution network driven by the DDDRP approach.

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Zhang, Shiliang, Hui Cao, Yanbin Zhang, Lixin Jia, Zonglin Ye, and Xiali Hei. "Data-Driven Optimization Framework for Nonlinear Model Predictive Control." Mathematical Problems in Engineering 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/9402684.

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The structure of the optimization procedure may affect the control quality of nonlinear model predictive control (MPC). In this paper, a data-driven optimization framework for nonlinear MPC is proposed, where the linguistic model is employed as the prediction model. The linguistic model consists of a series of fuzzy rules, whose antecedents are the membership functions of the input variables and the consequents are the predicted output represented by linear combinations of the input variables. The linear properties of the consequents lead to a quadratic optimization framework without online linearisation, which has analytical solution in the calculation of control sequence. Both the parameters in the antecedents and the consequents are calculated by a hybrid-learning algorithm based on plant data, and the data-driven determination of the parameters leads to an optimization framework with optimized controller parameters, which could provide higher control accuracy. Experiments are conducted in the process control of biochemical continuous sterilization, and the performance of the proposed method is compared with those of the methods of MPC based on linear model, the nonlinear MPC with neural network approximator, and MPC nonlinear with successive linearisations. The experimental results verify that the proposed framework could achieve higher control accuracy.

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Zhao, Qi, Chen Wang, and Yu Fei Tan. "Performance Analysis of the First Absorption Heat Pump System Driven by Steam." Advanced Materials Research 204-210 (February 2011): 1207–10. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.1207.

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A sea source adsorption heat pump system driven by steam was put forward in this paper. A mathematic model of the system was established with thermodynamic method by appropriate assumptions.Three parameters were chosen for analysis the system.The results shows that the lower heating temperature,higher sea water circulation temperature and steam pressure will improve the system parameters especially on the conditions of low te and high tc. It may be very useful for design and operation of the absorption heat pump system driven by steam.

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Zhang, Wenjia, Weiwei Shang, Bin Zhang, Fei Zhang, and Shuang Cong. "Stiffness-based trajectory planning of a 6-DOF cable-driven parallel manipulator." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 21 (July 14, 2016): 3999–4011. http://dx.doi.org/10.1177/0954406216659893.

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The stiffness of the cable-driven parallel manipulator is usually poor because of the cable flexibility, and the existing methods on trajectory planning mainly take the minimum time and the optimal energy into account, not the stiffness. To solve it, the effects of different trajectories on stiffness are studied for a six degree-of-freedom cable-driven parallel manipulator, according to the kinematic model and the dynamic model. The condition number and the minimum eigenvalue of the dimensionally homogeneous stiffness matrix are selected as performance indices to analyze the stiffness changes during the motion. The simulation experiments are implemented on a six degree-of-freedom cable-driven parallel manipulator, to study the stiffness of three different trajectory planning approaches such as S-type velocity profile, quintic polynomial, and trigonometric function. The accelerations of different methods are analyzed, and the stiffness performances for the methods are compared after planning the point-to-point straight and the curved trajectories. The simulation results indicate that the quintic polynomial and S-type velocity profile have the optimal performance to keep the stiffness stable during the motion control and the travel time of the quintic polynomial can be optimized sufficiently while keeping stable.

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Wu, Yang, Si Cheng Qin, Xue Lin Zhang, Xin Feng, and Bin Bin Chen. "Research on Speed Control Model with Temperature in Hydraulically-Driven Cooling Fan." Advanced Materials Research 850-851 (December 2013): 350–54. http://dx.doi.org/10.4028/www.scientific.net/amr.850-851.350.

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In order to obtain excellent cooling module, it is necessary to make air flow flowing into the radiator vary with ambient temperature. Cooling fan speed must vary with cooling power continuously. In this paper, the relation was obtained between fan speed and flow, static pressure, torque, power based on calculation and performance test data related to cooling fan. The mathematical model between fan speed and heat dissipation power in cooling module was deduced. Driving characteristics of cooling fan were analyzed.

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Bavaresco, Milena, and Kevin Jose. "Developing high-performance heat exchangers for low-emission power systems." Project Repository Journal 14, no. 1 (August 27, 2022): 30–33. http://dx.doi.org/10.54050/prj1419215.

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Developing high-performance heat exchangers for low-emission power systems The InDEStruct project is a collaborative engineering design doctoral training programme that provides a model for developing technology leaders, enabling them to apply scientific methods from academia to interdisciplinary industrial design. InDEStruct is driven by the need to develop more efficient and lower emission engine systems, with air charge cooling identified as the key enabling technology. To meet the challenge, InDEStruct brought together a consortium of industrial and academic partners in a novel and bespoke doctoral training programme, with four doctorates covering diverse aspects of mechanical engineering: structural vibration, stress and thermal analysis, additive manufacturing, multifunctional metamaterials, fatigue and materials development.

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Guo, Zhenyu, Albert C. Reynolds, and Hui Zhao. "A Physics-Based Data-Driven Model for History Matching, Prediction, and Characterization of Waterflooding Performance." SPE Journal 23, no. 02 (December 19, 2017): 367–95. http://dx.doi.org/10.2118/182660-pa.

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Summary We develop and use a new data-driven model for assisted history matching of production data from a reservoir under waterflood and apply the history-matched model to predict future reservoir performance. Although the model is developed from production data and requires no prior knowledge of rock-property fields, it incorporates far more fundamental physics than that of the popular capacitance–resistance model (CRM). The new model also represents a substantial improvement on an interwell-numerical-simulation model (INSIM) that was presented previously in a paper coauthored by the latter two authors of the current paper. The new model, which is referred to as INSIM-FT, eliminates the three deficiencies of the original data-driven INSIM. The new model uses more interwell connections than INSIM to increase the fidelity of history matching and predictions and replaces the ad hoc computation procedure for computing saturation that is used in INSIM by a theoretically sound front-tracking procedure. Because of the introduction of a front-tracking method for the calculation of saturation, the new model is referred to as INSIM-FT. We compare the performance of CRM, INSIM, and INSIM-FT in two synthetic examples. INSIM-FT is also tested in a field example.

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Cantelmi, F. J., D. Gedeon, and A. A. Kornhauser. "An Analytical Model for Turbulent Compression-Driven Heat Transfer." Journal of Heat Transfer 120, no. 3 (August 1, 1998): 617–23. http://dx.doi.org/10.1115/1.2824319.

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Compression-driven heat transfer is important to the performance of many reciprocating energy-conversion machines. For small pressure variations in cylinder spaces without inflow, heat transfer and power losses are well predicted using a simple heat transfer model which neglects turbulence. In actual engine cylinders, where significant turbulence levels can be generated by high-velocity inflow, a model which neglects turbulence may not be adequate. In this paper, a heat transfer model having an analytical solution is developed for turbulent cylinder spaces based on a mixing length turbulence model. The model is then used to develop expressions for heat-transfer-related power loss and heat transfer coefficient. Predicted results compare favorably with experimental data for two in-flow configurations.

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Ng, K. C., and X. L. Wang. "Thermodynamic methods for performance analysis of chillers." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 219, no. 2 (May 1, 2005): 109–16. http://dx.doi.org/10.1243/095440805x28249.

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This paper presents a simple thermodynamic model that captures accurately the behaviour of all types of real chillers or cooling devices. In addition to its predictive capability, it also provides a diagnostic means for the in situ monitoring of chiller performance. To enhance the understanding of the chiller cycles, a novel way of formulating the classic T-s diagrams is provided for all chiller types, ranging from the mechanical, heat-driven and solid-state chillers.

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Jin, Xue-Bo, Ruben Jonhson Robert Jeremiah, Ting-Li Su, Yu-Ting Bai, and Jian-Lei Kong. "The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods." Sensors 21, no. 6 (March 16, 2021): 2085. http://dx.doi.org/10.3390/s21062085.

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State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

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Ouni, Bassem, Christophe Aussagues, Saadia Dhouib, and Chokri Mraidha. "Model-Driven Architectural Framework towards Safe and Secure Nuclear Power Reactors." Sensors 21, no. 15 (July 29, 2021): 5136. http://dx.doi.org/10.3390/s21155136.

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Sensor-based digital systems for Instrumentation and Control (I&C) of nuclear reactors are quite complex in terms of architecture and functionalities. A high-level framework is highly required to pre-evaluate the system’s performance, check the consistency between different levels of abstraction and address the concerns of various stakeholders. In this work, we integrate the development process of I&C systems and the involvement of stakeholders within a model-driven methodology. The proposed approach introduces a new architectural framework that defines various concepts, allowing system implementations and encompassing different development phases, all actors, and system concerns. In addition, we define a new I&C Modeling Language (ICML) and a set of methodological rules needed to build different architectural framework views. To illustrate this methodology, we extend the specific use of an open-source system engineering tool, named Eclipse Papyrus, to carry out many automation and verification steps at different levels of abstraction. The architectural framework modeling capabilities will be validated using a realistic use case system for the protection of nuclear reactors. The proposed framework is able to reduce the overall system development cost by improving links between different specification tasks and providing a high abstraction level of system components.

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Peng, Shilin, Canjun Yang, Shuangshuang Fan, Shaoyong Zhang, Pinfu Wang, and Ying Chen. "Hybrid Underwater Glider for Underwater Docking: Modeling and Performance Evaluation." Marine Technology Society Journal 48, no. 6 (November 1, 2014): 112–24. http://dx.doi.org/10.4031/mtsj.48.6.5.

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AbstractThe development of a novel type of hybrid underwater glider that combines the advantages of buoyancy-driven gliders and propeller-driven autonomous underwater vehicles has recently received considerable interest. However, few studies have considered a hybrid glider with docking capability, which would expand the glider's applications. This study presents a hybrid glider with a rotatable thruster for realizing underwater docking. A tailored dynamic model of the hybrid glider is derived, and the motion performance is evaluated by simulations and experimental tests. A comparison between the experiments and simulations shows that results are in agreement, thus indicating the feasibility of the dynamic model and the accuracy of the hydrodynamic coefficients. In addition, the hybrid glider open-loop docking tests validate the feasibility of the mechanical docking system. Moreover, the experimental tests also validate the glider's different functions and indicate that the hybrid glider with rotatable thruster has high maneuverability even at low speeds. Thus, this type of hybrid glider can be used for underwater docking.

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Journal articles on the topic 'Model-driven performance engineering'

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