دانلود و نمایش مقالات مرتبط با شبیه سازی::صفحه 1
دانلود بهترین مقالات isi همراه با ترجمه فارسی
نتیجه جستجو - شبیه سازی

تعداد مقالات یافته شده: 456
ردیف عنوان نوع
1 روش ردیابی خودرو بهبود یافته برای IEEE 802:11p
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 5 - تعداد صفحات فایل doc فارسی: 10
توسعه روش های موقعیت یابی با قابلیت تحرک- بالا با استفاده از استاندارد IEEE 802.11p در شبکه های ادهاک وسایل نقلیه (VANETs) به دلیل نقاط ضعف در ناحیه های GNSS-dark مانند جنگل ها، تونل و غیره، و اشتباهات ناشی از GNSS-dark در نتایج ، ضروری است. برآورد زمان دقیق رسیدن(TOA) مبتنی بر مدل مسافت یابی ، به عنوان یکی از چالش های سیستم پیشگیری از برخورد اتومبیل ها، توجه زیادی را به خود جلب است. در این مقاله، روش پیشنهادی TOA یا روش تخمین مسافت با راهنمای کوتاه IEEE 802.11p پیشنهاد شد تا اثربخشی اندازه گیری های وسایل نقلیه چندکاره و نسبت نویز سیگنال کم (SNR) را کاهش دهد. ابتدا، TOA با استفاده از همبستگی خودکار و همبستگی-متقاطع برآورد شد. سپس، رویکرد sum برای یافتن مبدا دقیق زمان ارائه شد. نتایج شبیه سازی در کانال اتحادیه بین المللی مخابرات خودرو (ITUA) و کانال نویز گاوسی سفید افزایشی (AWGN)، برتری الگوریتم پیشنهادی را در شرایط SNR کم و محیط چندکاره ثابت می کند.
کليدواژه: برآورد TOA | IEEE 802.11p | VANETS | دامنه | همبستگی خودکار | همبستگی- متقابل
مقاله ترجمه شده
2 تحلیل لبه ای مبتنی بر موجک چند جهته برای تشخیص سطح توسط پروفیلومتری نوری
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 18
دانشمندان، مهندسان و تولید کنندگان نیاز ضروری به تکنیک های بهتر تشخیص و کنترل کیفیت دارند. مترولوژی نوری با استفاده از علوم نور و علوم کامپیوتر به دنبال شبیه سازی، طراحی، محاسبات و بازرسی برای بسیاری از برنامه های کاربردی علمی و صنعتی مانند اپتیک، مکانیک، هواپیما، الکترونیک و … است. آنالیز الگوی fringe روشی برای انجام برخی عملیات در تصاویر نوری و به منظور دریافت نقشه فاز اینترفرومتری و سپس استخراج برخی اطلاعات مفید از آن است. در این مقاله، بهبود محرک الگوریتم دمدولاسیون fringe محلی ارائه شده است، که بر اساس موجک جدید چند جهته است. کارهای عددی و تجربی در مقایسه با سایر الگوریتم های استاندارد، سود جالبی را نشان می دهد. رویکرد ما به سرعت به عنوان فاز روش های بازیابی پرطرفدار اجرا می شود، اما با دقت قابل توجهی دمدولاسیون fringe های نویز را بهبود می دهد. همه این مسائل بدون هیچ پیش پردازش توسط فیلتر کردن مدل ها رخ می دهد.
کليدواژه ها: تصویربرداری نوری | علوم کامپیوتر | پردازش تصویر | موجک چند جهته | فاز بازیابی | طرح ریزی fringe .
مقاله ترجمه شده
3 Artificial Intelligence in Medical Education: Best Practices Using Machine Learning to Assess Surgical Expertise in Virtual Reality Simulation
هوش مصنوعی در آموزش پزشکی: بهترین روش هایی که با استفاده از یادگیری ماشینی برای ارزیابی تخصص جراحی در شبیه سازی واقعیت مجازی انجام می شود-2019
OBJECTIVE: Virtual reality simulators track all movements and forces of simulated instruments, generating enormous datasets which can be further analyzed with machine learning algorithms. These advancements may increase the understanding, assessment and training of psychomotor performance. Consequently, the application of machine learning techniques to evaluate performance on virtual reality simulators has led to an increase in the volume and complexity of publications which bridge the fields of computer science, medicine, and education. Although all disciplines stand to gain from research in this field, important differences in reporting exist, limiting interdisciplinary communication and knowledge transfer. Thus, our objective was to develop a checklist to provide a general framework when reporting or analyzing studies involving virtual reality surgical simulation and machine learning algorithms. By including a total score as well as clear subsections of the checklist, authors and reviewers can both easily assess the overall quality and specific deficiencies of a manuscript. DESIGN: The Machine Learning to Assess Surgical Expertise (MLASE) checklist was developed to help computer science, medicine, and education researchers ensure quality when producing and reviewing virtual reality manuscripts involving machine learning to assess surgical expertise. SETTING: This study was carried out at the McGill Neurosurgical Simulation and Artificial Intelligence Learning Centre. PARTICIPANTS: The authors applied the checklist to 12 articles using machine learning to assess surgical expertise in virtual reality simulation, obtained through a systematic literature review. RESULTS: Important differences in reporting were found between medical and computer science journals. The medical journals proved stronger in discussion quality and weaker in areas related to study design. The opposite trends were observed in computer science journals. CONCLUSIONS: This checklist will aid in narrowing the knowledge divide between computer science, medicine, and education: helping facilitate the burgeoning field of machine learning assisted surgical education. ( J Surg Ed 000:110.  2019 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.)
KEY WORDS: simulation| surgery | education | artificial intelligence | assessment | machine learning
مقاله انگلیسی
4 Towards an integrated machine-learning framework for model evaluation and uncertainty quantification
به سمت یک چارچوب یکپارچه یادگیری ماشین برای ارزیابی مدل و کمیت عدم اطمینان-2019
We introduce a new paradigm for treating and exploiting simulation data, serving in parallel as an alternative workflow for model evaluation and uncertainty quantification. Instead of reporting simulations of base-case and specific variations scenarios, databases covering a wide spectrum of operational conditions are built by means of machine-learning using sophisticated mathematical algorithms. While the approach works for all sorts of computer-aided engineering applications, the present contribution addresses the CFD/CMFD sub-branch, with application to a widely used benchmark of convective flow boiling. In addition to comparing simulation and experimental results on a case-by-case basis, machine-learning is used to create their respective (CFD and experiment) data-driven models (DDM), which will in a later stage serve for assessing the predictive performance of the CFD models over a wider range of experimental conditions, hence providing a high-level classification of their range of applicability.
Keywords: Fluid flow simulation | Wall boiling | Data analytics | Digital Twin | Machine-learning | Data-driven models (DDM)
مقاله انگلیسی
5 An integrated stochastic fuzzy MCDM approach to the balanced scorecard-based service evaluation
یک رویکرد MCDM فازی تصادفی یکپارچه برای ارزیابی خدمات مبتنی بر کارت امتیازی متعادل-2019
The purpose of the study is to analyse the balanced scorecard (BSC)-based evaluation of the new service development (NSD) in Turkish banking sector. The proposed model includes fuzzy ANP (FANP), Monte Carlo Simulation, fuzzy TOPSIS (FTOPSIS), and fuzzy VIKOR (FVIKOR) respectively. FANP has been used for weighting the criteria, Monte Carlo Simulation has been applied to provide the stochastic values of BSC-based dimensions of NSD in banking sector. FTOPSIS and FVIKOR have been considered to rank the banks by their dimension performances. The novelty of the study is to provide an integrated model including FANP, FTOPSIS, FVIKOR, and Monte Carlo Simulation respectively. Additionally, BSC-based analysis of NSD has been applied for evaluating Turkish banking sector. The results demonstrate that the comparative analysis is coherent for ranking the alternatives and the stochastic values facilitate to obtain the immense expert evaluations under the fuzzy environment. It is identified that the performance of the foreign banks is lower than private and state banks. Hence, it can be said that especially foreign banks should develop new services to attract the attention of their customers. Within this framework, customer expectations should be defined by conducting a detailed analysis. As a result, it can be possible to increase comparative advantage in comparison with the other banks.
Keywords: Balanced scorecard | New service development | Turkey | Banking | Monte Carlo simulation | Fuzzy sets
مقاله انگلیسی
6 A simulation-based heuristic that promotes business profit while increasing the perceived quality of service industries
اکتشاف مبتنی بر شبیه سازی که باعث افزایش سود تجاری ضمن افزایش کیفیت درک شده از صنایع خدمات می شود-2019
This paper tackles the problem of minimising the direct server costs of complex multiple-server queueing systems while improving the quality perceived by customers. The problem is solved by finding the server roster, defined as the allocation of active individual servers in each time interval of the working day. As no closed solution is available at this time, it was obtained by simulation modelling. The complex queueing system was solved by a simulation-based heuristic that includes the dynamic arrivals, entity reneging and/or balking, multi-servicing, and the individual service-time distributions of servers. The distributions of the random variables of single or multiple-branch businesses were identified by sampling procedures for multiple company-branch deployment. The beneficial financial impact of the proposed solution is confirmed in numerical real-life banking scenarios. The quality perceived by customers (evaluated through the expected queueing time) was also improved. Finally, additional insights that might improve the system performance are highlighted.
Keywords: Heuristics | OR in banking | Productivity and competitiveness | Queueing | Simulation
مقاله انگلیسی
7 Machine learning for predicting thermodynamic properties of pure fluids and their mixtures
یادگیری ماشین برای پیش بینی خواص ترمودینامیکی مایعات خالص و مخلوط های آنها-2019
Establishing a reliable equation of state for largely non-ideal or multi-component liquid systems is challenging because the complex effects of molecular configurations and/or interactions on the thermodynamic properties must generally be taken into account. In this regard, machine learning holds great potential for directly learning the thermodynamic mappings from existing data, thereby bypassing the use of equations of state. The present study outlines a general machine learning framework based on high-efficiency support vector regression for predicting the thermodynamic properties of pure fluids and their mixtures. The proposed framework is adopted in conjunction with training data obtained from a high-fidelity database to successfully predict the thermodynamic properties of three common pure fluids. The predictions demonstrate extremely low mean square errors. Moreover, little loss in the prediction accuracy is obtained for ternary mixtures of the pure fluids at the cost of a modest increase in the volume of training data provided by state-of-the-art molecular dynamics simulations. Our results demonstrate the promising potential of machine learning for building accurate thermodynamic mappings of pure fluids and their mixtures. The proposed methodology may pave the way in the future for the rapid exploration of novel or complex systems with potentially exceptional thermodynamic properties.
Keywords: Thermodynamic properties | Machine learning | Support vector regression | Mixtures | Molecular dynamics simulation
مقاله انگلیسی
8 روش شبیه سازی موازی جدید برای جمعیت عظیم
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 5 - تعداد صفحات فایل doc فارسی: 11
پردازش محاسباتی وسیع موازی، با ظهور پردازنده های هسته ای و حتی چند هسته ای، روند ارزان قیمت و محبوب را ارائه می دهد. ابزار جدید و راه حل های قابل اتکا برای محاسبه سریع شبیه سازی حرکات گروه های بزرگ –مقیاس و مشکل مشابه در حرکات گروه های فوق العاده-بزرگ ارائه می دهد. در فرآیند محاسبات موازی، طراحی معماری موازی و الگوریتم موازی تقریبا مرتبط با الگوریتم شبیه سازی حرکت گروهی است و به دلیل انجام وظیفه اصلی باید تجزیه شود. در این مقاله یک الگوریتم شبیه سازی موازی برای جمعیت عظیم ارائه شده است. از الگوریتم قطعه سازی صحنه مبتنی بر بلوک در گره مدیریت برای تقسیم صحنه شبیه سازی شده پس از مقدار دهی اولیه صحنه مجازی و اختصاص هر بلوک به یک گره محاسباتی متفاوت برای پردازش استفاده کردیم. سپس گره محاسبه ،اطلاعات مربوط به فردی را که مسئول آن است، دریافت می کند. بر اساس روش، سیستم نمونه اولیه شبیه سازی موازی بر روی پلت فرم محاسبات Sugon با کارایی بالا ، توسعه داده شد. نتایج تجربی نشان می دهد که الگوریتم شبیه سازی موازی ما می تواند کارایی صحنه -رندر را افزایش دهد و محدودیت عملیات را در مقیاس گروهی حل کند.
کليدواژه: محاسبات موازی | جمعیت عظیم | گره محاسبه | قطعه سازی صحنه
مقاله ترجمه شده
9 Applying a machine learning interatomic potential to unravel the effects of local lattice distortion on the elastic properties of multi-principal element alloys
استفاده از پتانسیل متقابل یادگیری ماشینی برای آشکار کردن اثرات اعوجاج شبکه محلی بر خصوصیات الاستیک آلیاژهای عنصر چند اصلی-2019
The concept of local lattice distortion (LLD) is of fundamental importance in the understanding of properties of high-entropy alloys and, more generally, of multi-principal element alloys (MPEAs). Despite previous experimental and computational efforts, the unambiguous evaluation of the static (due to atomic size difference) and dynamic (due to thermal fluctuation) LLD is still elusive. Here, as a first step, we develop a machine learning interatomic potential based on an efficient “learning-on-the-fly” scheme for CoFeNi, a prototypical ternary MPEA. Using this potential, we perform molecular dynamics simulations to calculate the elastic moduli of single- and polycrystalline CoFeNi. The results are in excellent agreement with theoretical and experimental data. As a second step, we design a simulation framework allowing the determination of the effects of static and dynamic LLD, thermal expansion, and chemical short-range order on the elastic properties of our prototypical MPEA. The results indicate that not only the average value of LLD, but also its probability distribution affect the elastic properties of MPEAs. In addition, we show that a variety of commonly used LLD indicators, e.g., atomic strain, pair distribution function, and bond-length distribution, correlate with each other. Our results not only shed light on the of LLD in MPEAs, but also demonstrate the capabilities of our machine learning potential as a powerful tool for the development and characterization of novel alloys with designed properties.
Keywords: Multi-principal element alloys | High-entropy alloys | Elastic properties | Atomistic simulations | Machine learning
مقاله انگلیسی
10 High-performance fiber reinforced concrete as a repairing material to normal concrete structures: Experiments, numerical simulations and a machine learning-based prediction model
بتن مسلح با فیبر با کارایی بالا به عنوان یک ماده ترمیم کننده سازه های بتونی عادی: آزمایش ها ، شبیه سازی عددی و یک مدل پیش بینی مبتنی بر یادگیری ماشین-2019
High-performance fiber reinforced concrete (HPFRC) has been reported as a repairing material to normal concrete (NC) structures due to its predominant mechanical performance. Here, we investigate the debonding behavior between HPFRC and NC subjected to direct shear loading. HPFRC specimens are fabricated and experimentally calibrated to determine the compressive and bending (i.e., flexural) strengths. HPFRC-NC samples are fabricated using two bonding strategies, i.e., mechanical surface treatments with and without chemical agent. Direct shear loading is applied to test the HPFRC-NC debonding behavior. A finite element (FE) model is developed to predict the direct shear debonding response. The FE model is validated by the experimental observations and then used to characterize the debonding behavior with various geometric and material parameters, as well as bonding interface treatments. Subsequently, a robust machine learning model is developed to formulate the shear debonding strength of HPFRC-NC with those influencing parameters. Design examples are presented to illustrate the efficiency of the proposed machine learning model in describing the debonding response of HPFRC-NC. A sensitivity analysis is further conducted to investigate the contribution of the chosen predictors to the debonding behavior of HPFRC-NC. The reported HPFRC and machine learning-based prediction model provide powerful tools to address repairing issues in various existing normal concrete structures.
Keywords: High-performance fiber reinforced concrete | (HPFRC) | Normal concrete (NC) | Debonding behavior | Machine learning | Prediction model | Direct shear test
مقاله انگلیسی
rss مقالات ترجمه شده rss مقالات انگلیسی rss کتاب های انگلیسی rss مقالات آموزشی