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نتیجه جستجو - simulation

تعداد مقالات یافته شده: 799
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1 Data Mining Strategies for Real-Time Control in New York City
استراتژی داده کاوی برای کنترل زمان واقعی در شهر نیویورک-2105
The Data Mining System (DMS) at New York City Department of Transportation (NYCDOT) mainly consists of four database systems for traffic and pedestrian/bicycle volumes, crash data, and signal timing plans as well as the Midtown in Motion (MIM) systems which are used as part of the NYCDOT Intelligent Transportation System (ITS) infrastructure. These database and control systems are operated by different units at NYCDOT as an independent database or operation system. New York City experiences heavy traffic volumes, pedestrians and cyclists in each Central Business District (CBD) area and along key arterial systems. There are consistent and urgent needs in New York City for real-time control to improve mobility and safety for all users of the street networks, and to provide a timely response and management of random incidents. Therefore, it is necessary to develop an integrated DMS for effective real-time control and active transportation management (ATM) in New York City. This paper will present new strategies for New York City suggesting the development of efficient and cost-effective DMS, involving: 1) use of new technology applications such as tablets and smartphone with Global Positioning System (GPS) and wireless communication features for data collection and reduction; 2) interface development among existing database and control systems; and 3) integrated DMS deployment with macroscopic and mesoscopic simulation models in Manhattan. This study paper also suggests a complete data mining process for real-time control with traditional static data, current real timing data from loop detectors, microwave sensors, and video cameras, and new real-time data using the GPS data. GPS data, including using taxi and bus GPS information, and smartphone applications can be obtained in all weather conditions and during anytime of the day. GPS data and smartphone application in NYCDOT DMS is discussed herein as a new concept. © 2014 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of Elhadi M. Shakshu Keywords: Data Mining System (DMS), New York City, real-time control, active transportation management (ATM), GPS data
مقاله انگلیسی
2 Machine learning estimates of plug-in hybrid electric vehicle utility factors
تخمین یادگیری ماشین فاکتورهای وسیله نقلیه الکتریکی هیبریدی توکار-2019
Plug-in hybrid electric vehicles (PHEV) combine an electric drive train with a conventional one and are able to drive on gasoline when the battery is fully depleted. They can thus electrify many vehicle miles travelled (VMT) without fundamental range limits. The most important variable for the electrification potential is the ratio of electric VMT to total VMT, the so-called utility factor (UF). However, the empirical assessment of UFs is difficult since important factors such as daily driving, re-charging behaviour and frequency of long-distance travel vary noteworthy between drivers and large data collections are required. Here, we apply machine learning techniques (regression tree, random forest, support vector machine, and neural nets) to estimate real-world UF and compare the estimates to actual long-term average UF of 1768 individual Chevrolet Volt PHEV. Our results show that UFs can be predicted with high accuracy from individual summary statistics to noteworthy accuracy with a mean absolute error of five percentage points. The accuracy of these methods is higher than a simple simulation with electric driving until the battery is discharged and one full daily recharge. The most important variables in estimating UF according to a linear regression model are the variance and skewness of the daily VMT distributions as well as the frequency of long-distance driving. Thus, our findings make UF predictions from existing data sets for driving of conventional vehicles more accurate.
Keywords: Electric vehicles | Plug-in hybrid electric vehicle | Utility factor | Machine learning
مقاله انگلیسی
3 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
4 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
5 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
6 Assessing environmental performance in early building design stage: An integrated parametric design and machine learning method
ارزیابی عملکرد محیطی در مرحله طراحی اولیه ساختمان: یکپارچه طراحی پارامتری و یک روش یادگیری ماشین-2019
Decisions made at early design stage have major impacts on buildings’ life-cycle environmental performance. However, when only a few parameters are determined in early design stages, the detailed design decisions may still vary significantly. This may cause same early design to have quite different environmental impacts. Moreover, default settings for unknown detailed design parameters clearly cannot cover all possible variations in impact, and Monte Carlo analysis is sometimes not applicable as parameters’ probability distributions are usually unknown. Thus, uncertainties about detailed design make it difficult for existing environmental assessment methods to support early design decisions. Thus, this study developed a quantitative method using parametric design technology and machine learning algorithms for assessing buildings’ environmental performance in early decision stages, considering uncertainty associated with detailed design decisions. The parametric design technology creates design scenarios dataset, then associated environmental performances are assessed using environmental assessment databases and building performance simulations. Based on the generated samples, a machine learning algorithm integrating fuzzy C-means clustering and extreme learning machine extracts the case-specific knowledge regarding designed buildings’ early design associated with environmental uncertainty. Proposed method is an alternative but more generally applicable method to previous approaches to assess buildings environmental uncertainty in early design stages.
Keywords: Building early design | Parametric design | Machine learning | Environmental impact | Prediction intervals
مقاله انگلیسی
7 Predicting complexation performance between cyclodextrins and guest molecules by integrated machine learning and molecular modeling techniques
پیش بینی عملکرد پیچیدگی بین سیکلودکسترین ها و مولکول های مهمان با یادگیری ماشین یکپارچه و تکنیک های مدل سازی مولکولی-2019
Most pharmaceutical formulation developments are complex and ideal formulations are generally obtained after extensive experimentation. Machine learning is increasingly advancing many aspects in modern society and has achieved significant success in multiple subjects. Current research demonstrated that machine learning can be adopted to build up high-accurate predictive models in drugs/cyclodextrins (CDs) systems. Molecular descriptors of compounds and experimental conditions were employed as inputs, while complexation free energy as outputs. Results showed that the light gradient boosting machine provided significantly improved predictive performance over random forest and deep learning. The mean absolute error was 1.38 kJ/mol and squared correlation coefficient was 0.86. The evaluation of relative importance of molecular descriptors further demonstrated the key factors affecting molecular interactions in drugs/CD systems. In the specific ketoprofeneCD systems, machine learning model showed better predictive performance than molecular modeling calculation, while molecular simulation could provide structural, dynamic and energetic information. The integration of machine learning and molecular simulation could produce synergistic effect for interpreting and predicting pharmaceutical formulations. In conclusion, the developed predictive models were able to quickly and accurately predict the solubilizing capacity of CD systems. Current research has taken an important step toward the application of machine learning in pharmaceutical formulation design.
KEY WORDS : Machine learning | Deep learning | LightGBM | Random forest | Cyclodextrin | Binding free energy | Molecular modeling | Ketoprofen
مقاله انگلیسی
8 sGDML: Constructing accurate and data efficient molecular force fields using machine learning
sGDML: ساخت زمینه های نیروی مولکولی دقیق و کارآمد با استفاده از یادگیری ماشین-2019
We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce global potential energy surfaces (PES) for molecules with a few dozen atoms from a limited number of user-provided reference molecular conformations and the associated atomic forces. Here, we introduce a Python software package to reconstruct and evaluate custom sGDML force fields (FFs), without requiring in-depth knowledge about the details of the model. A user-friendly command-line interface offers assistance through the complete process of model creation, in an effort to make this novel machine learning approach accessible to broad practitioners. Our paper serves as a documentation, but also includes a practical application example of how to reconstruct and use a PBE0+MBD FF for paracetamol. Finally, we show how to interface sGDML with the FF simulation engines ASE (Larsen et al., 2017) and i-PI (Kapil et al., 2019) to run numerical experiments, including structure optimization, classical and path integral molecular dynamics and nudged elastic band calculations.
Keywords: Machine learning potential | Machine learning force field | Ab initio molecular dynamics | Path integral molecular dynamics | Coupled cluster calculations | Molecular property prediction | Quantum chemistry | Gradient domain machine learning
مقاله انگلیسی
9 Predicting the onset of void swelling in irradiated metals with machine learning
پیش بینی شروع تورم خلا در فلزات تحت تابش با یادگیری ماشین-2019
Radiation-induced void swelling is a serious mode of degradation in nuclear structural materials. Much effort has been spent to predict swelling resistance, with the goal of increasing the void swelling incubation dose so as to postpone the consequences of radiation damage. However, this trial-and-error approach is highly inefficient due to the time- and resource-intensive nature of both experiments and physics-based multiscale simulations. In this work, as a first attempt, machine learning is applied to perform this prediction based on available experimental data. Of the multiple techniques applied, the gradient boosting ensemble method best predicts experimental onset doses for swelling in test datasets, and identifies the main contributing factors such as temperature, Fe and Cr content, and dose rate, which are consistent with established understanding. This work demonstrates the feasibility of machine learning to predict macroscale radiation effects based on material and environmental parameters, and has practical significance in guiding further material optimization for nuclear applications.
Keywords: Void swelling | Incubation dose | Machine learning | Radiation damage
مقاله انگلیسی
10 Using agent-based modelling to investigate diffusion of mobile-based branchless banking services in a developing country
استفاده از مدل سازی مبتنی بر عامل برای بررسی انتشار خدمات بانکی بدون شعبه مبتنی بر موبایل در یک کشور در حال توسعه-2019
Branchless Banking Services (BBS) were launched in 2009 in Pakistan with the promise of providing banking services to the unbanked. Since then the overall size of BBS has grown. Despite the popularity of the over-thecounter (OTC) channel, growth in m-wallets or mobile accounts (MA) has been slow. We investigate diffusion of MA through the development of an agent-based simulation model that captures the dynamics of the multi-sided BBS platform market. We identify important factors that drive MA diffusion and illustrate main and interaction effects between these factors. Furthermore, we examine how the relative effects of the different sides of the market have evolved over the course of the MA diffusion across both rural and urban consumer segments. The proposed model helps to understand the dynamics of diffusion of an important financial technology innovation. It can also serve as the starting point for future -research on technology diffusion in multi-sided markets.
Keywords: Branchless banking | Agent-based modelling | Innovation diffusion
مقاله انگلیسی
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