با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
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61 |
Optimizing hyperparameters of deep learning in predicting bus passengers based on simulated annealing
بهینه سازی پارامترهای یادگیری عمیق در پیش بینی مسافران اتوبوس مبتنی بر بازپخت شبیه سازی شده-2020 Bus is certainly one of the most widely used public transportation systems in a modern city because it
provides an inexpensive solution to public transportation users, such as commuters and tourists. Most
people would like to avoid taking a crowded bus on the way. That is why forecasting the number
of bus passengers has been a critical problem for years. The proposed method is inspired by the fact
that there is no easy way to know the suitable parameters for most of the deep learning methods
in solving the optimization problem of forecasting the number of passengers on a bus. To address
this issue, the proposed algorithm uses a simulated annealing (SA) to find out a suitable number of
neurons for each layer of a fully connected deep neural network (DNN) to enhance the accuracy rate in
solving this particular optimization problem. The proposed method is compared with support vector
machine, random forest, eXtreme gradient boosting, deep neural network, and deep neural network
with dropout for the data provided by the Taichung city smart transportation big data research center,
Taiwan (TSTBDRC). Our simulation results indicate that the proposed method outperforms all the other
forecasting methods for forecasting the number of bus passengers in terms of the accuracy rate and
the prediction time. Keywords: Bus transportation system | Simulated annealing | Deep learning | Hyperparameter optimization |
مقاله انگلیسی |
62 |
A distributed computing framework for wind speed big data forecasting on Apache Spark
چارچوب محاسباتی توزیع شده برای پیش بینی داده های بزرگ سرعت باد در Apache Spark-2020 The randomness of the wind speed leads to the intermittency of wind power, which is a challenge to realize wind
power energy as reliable and renewable power. The prediction of wind speed time series can promote the use of
wind energy. However, the traditional stand-alone methods are unable to meet the requirements of wind speed
big data environments. In the study, a hybrid distributed computing framework on Apache Spark is applied for
wind speed big data forecasting. Using the distributed computing strategy, the framework can divide the wind
speed big data into RDD groups and operate them in parallel. In the framework, a modified wind speed extreme
learning machine predictor is built using the Distributed Computing Process strategy, enhanced by the data
decomposition and result reconstruction components on Apache Spark. The experimental results indicate that
the proposed distributed computing framework on Spark can forecast wind speed big data in multi-step accurately.
Besides, the effectiveness of different components in the framework is verified. It is also proved that the
proposed distributed computing framework has a faster computation speed when processing big data, compared
to the stand-alone method. Keywords: Wind speed big data forecasting | Distributed computing | Apache Spark | Multi-step forecasting |
مقاله انگلیسی |
63 |
Management Forecast Errors and Corporate Investment Efficiency
خطاهای پیش بینی مدیریت و بهره وری سرمایه گذاری شرکت-2020 This paper examines the relation between management forecast errors and corporate investment
efficiency. If managers’ forecasting biases systematically affect both external earnings forecasts
and internal investment project payoff forecasts, errors in management earnings forecasts will be
linked to corporate investment decisions. Consistent with this conjecture, I find that signed
management forecast errors are associated with abnormal investment; specifically, more
optimistic forecasts are associated with more over-investment, while more pessimistic forecasts
are associated with more under-investment. Furthermore, I find that the link between forecast
errors and abnormal investment is stronger when analyst feedback reinforces managers’ forecast
errors and when managers display persistent forecast errors. Additional tests that consider
managers’ disclosure incentives reveal that the link between forecast errors and abnormal
investment is stronger when forecast errors are less likely driven by managers’ intentional
distortions. Keywords: Management earnings forecasts | managerial bias | investment efficiency |
مقاله انگلیسی |
64 |
Toward a trans-regional vulnerability assessment for Alps. A methodological approach to land cover changes over alpine landscapes, supporting urban adaptation
به سمت ارزیابی آسیب پذیری فرا منطقه ای برای رشته کوه های آلپ. یک رویکرد روششناختی برای تغییر پوشش زمین نسبت به مناظر کوهستانی ، حمایت از سازگاری شهری-2020 The contribution presents a possible assessment methodology for land cover change over ice and
snow, between 1990 and 2018 in the Dolomites and the Alpi Giulie. The methodology aims to
build surface atlas to assess the land cover changes. The tool is intended as a support for environmental
management, forecasting and, as support for territorial government systems in climate-
proof planning processes. In the “business as usual” global warming scenario, ice and snow
resources will become one of the most affected subjects by Climate Change, with heavy consequences
on ecosystems, urban environments and socioeconomic. Current monitoring and assessment
systems are fragmented both by survey methodology and by local distribution. The
methodology is developed in using GIS, following remote sensing (RS) processes and spatial
analysis tools to manage multispectral satellite images. The process uses spectral signatures from
satellite images to identify homogeneous areas in material and morphology. The process takes
into account the actual systems of assessment and local socioeconomic exposures. The methodology
takes a proactive approach to future hazards and impacts considering their management
in alpine habitats to support local administrations. The project develops transboundary assessment
techniques and aids the adaptation of planning strategies in the context of Climate Change. Keywords: Urban planning 1 | Transboundary governance 2 | Remote sensing analysis 3 | Climate change 4 | Adaptation strategies 5 | Alps monitoring 6 |
مقاله انگلیسی |
65 |
A non-canonical hybrid metaheuristic approach to adaptive data stream classification
یک روش متاوریستی ترکیبی غیر متعارف برای طبقه بندی جریان داده تطبیقی-2020 Data stream classification techniques have been playing an important role in big data analytics
recently due to their diverse applications (e.g. fraud and intrusion detection, forecasting and healthcare
monitoring systems) and the growing number of real-world data stream generators (e.g. IoT devices
and sensors, websites and social network feeds). Streaming data is often prone to evolution over time.
In this context, the main challenge for computational models is to adapt to changes, known as concept
drifts, using data mining and optimisation techniques. We present a novel ensemble technique called
RED-PSO that seamlessly adapts to different concept drifts in non-stationary data stream classification
tasks. RED-PSO is based on a three-layer architecture to produce classification types of different size,
each created by randomly selecting a certain percentage of features from a pool of features of the target
data stream. An evolutionary algorithm, namely, Replicator Dynamics (RD), is used to seamlessly adapt
to different concept drifts; it allows good performing types to grow and poor performing ones to shrink
in size. In addition, the selected feature combinations in all classification types are optimised using a
non-canonical version of the Particle Swarm Optimisation (PSO) technique for each layer individually.
PSO allows the types in each layer to go towards local (within the same type) and global (in all types)
optimums with a specified velocity. A set of experiments are conducted to compare the performance
of the proposed method to state-of-the-art algorithms using real-world and synthetic data streams in
immediate and delayed prequential evaluation settings. The results show a favourable performance of
our method in different environments. Keywords: Ensemble learning | Data stream mining | Concept drifts | Bio-inspired algorithms | Non-stationary environments | Particle swarm optimisation | Replicator dynamics |
مقاله انگلیسی |
66 |
An integrated predictive energy management for light-duty range-extended plug-in fuel cell electric vehicle
یک مدیریت انرژی پیش بینی یکپارچه برای وسیله نقلیه الکتریکی سلول سوختی با پلاگین با دامنه محدود-2020 A reliable power distribution strategy is of great significance towards the performance enhancement of fuel cell
electric vehicles. In this work, a novel model predictive control-based energy management is developed for a fuel
cell based light-duty range-extended hybrid electric vehicle. To fulfill the model predictive control framework, a
cooperative speed forecasting method based on Markov Chain and fuzzy C-means clustering technique is proposed,
which contains multiple predictive sub-models for handling different driving patterns. The final prediction
results are obtained by synthesizing the forecasted speed profiles from all sub-models with the quantified
fuzzy membership degrees. Besides, an adaptive battery State-of-Charge reference generator is built, which can
regulate the SoC depleting rates against various power requirements. Combined with the forecasted speed and
SoC reference, the desirable control actions are derived through minimizing the performance index over each
finite time horizon. As a result, under the realistic urban-based postal-delivery mission profiles, the proposed
strategy can achieve over 3.79% equivalent hydrogen consumption saving and over 40.04% fuel cell power
dynamics decrement against benchmark strategy. Moreover, the presented predictive energy management is
robust to certain level of trip duration estimation errors, further indicating its suitability for real applications. Keywords: Energy management strategy | Multi-objective optimization | Velocity forecasting | PHEV | Fuel cell |
مقاله انگلیسی |
67 |
Development and application of the Activity-BAsed Traveler Analyzer (ABATA) system
توسعه و کاربرد سیستم تجزیه و تحلیل مسافرتی Activity-BAsed (ABATA)-2020 While advanced technologies and big data are widely used in the transportation study, most transportation
plans still rely on some variant of traditional four-step demand forecasting models. The most
significant limitations of the four-step model are spatiotemporal aggregation of data and difficulty of
considering individual travel behaviors. To address these drawbacks, activity-based modeling systems
have increasingly been developed. In this paper, we present a new activity-based analytical system,
called Activity-BAsed Traveler Analyzer (ABATA). The distinguishing feature of ABATA is the simulation
of the present hourly service population that is determined from mobile phone data instead of a
synthetic population. ABATA comprises multiple components, including an hourly total population
estimator, activity profile constructor, hourly activity population estimator, spatial activity population
estimator, and origin–destination estimator. To demonstrate the proposed method, a future aged
society in Gangnam, Korea is evaluated as a case study. The results indicate that the hourly activity
populations engaged in work, school, and private education decreased, while those engaged in home,
shopping, recreation and other activities increased with the aging of the population. The associated
changes in mobility were found to be rational and reasonable: older people tend to have a more flexible
working time, make shorter-distance trips, undertake more trips for shopping, recreation, home, and
other activities, and finish their trips earlier, before evening. The proposed ABATA system is expected to
provide a valuable tool for simulating the impacts of future changes in population, activity schedules,
and land use on activity populations and travel demands. Keywords: Activity-based model | Mobile phone data | Hourly service population | Mobility | Aged society |
مقاله انگلیسی |
68 |
Remote Human-to-Machine Distance Emulation through AI-Enhanced Servers for Tactile Internet Applications
شبیه سازی فاصله انسان از ماشین از راه دور از طریق سرورهای تقویت شده هوش مصنوعی برای برنامه های لمسی اینترنت-2020 We alleviate the master-slave distance limitation of human-to-machine applications by
forecasting and pre-empting haptic feedback transmission. Results show 99% accuracy in
detecting touch events and 96% accuracy in forecasting feedback from different slave materials. |
مقاله انگلیسی |
69 |
Towards efficient energy management in smart grids considering microgrids with day-ahead energy forecasting
مدیریت انرژی کارآمد در شبکه های هوشمند با توجه به ریز شبکه با پیش بینی انرژی روزانه-2020 This study proposes an efficient energy management method to systematically manage the energy consumption
in the residential area to alleviate the peak to average ratio and mitigate electricity cost along with user comfort
maximization. We developed an efficient energy management scheme using mixed integer linear programming
(MILP), which schedules smart appliances and charging/discharging of electric vehicles (EVs) optimally in order
to mitigate energy costs. In the proposed model, consumer is able to generate its own energy from microgrid
consisting of solar panels and wind turbines. We also consider an energy storage system (ESS) for efficient energy
utilization. This work also performs energy forecasting using wind speed and solar radiation prediction for
efficient energy management. Moreover, we perform extensive simulations to validate our developed MILP
based scheme and results affirm the effectiveness and productiveness of our proposed energy efficient technique. Keywords: Artificial neural network | Efficient energy utilization | Energy forecasting | Home energy management | Mixed integer linear programming | Renewable energy generation |
مقاله انگلیسی |
70 |
Hydrological modelling of karst catchment using lumped conceptual and data mining models
مدل سازی هیدرولوژیکی حوضه کارست با استفاده از مدلهای مفهومی و داده کاوی بهم چسبیده-2019 Hydrological modelling is a challenging and significant issue, especially in nonhomogeneous catchments in
terms of geology, and it is an essential part of water resources management. In this study, daily rainfall-runoff
modelling was carried out using the lumped conceptual model, the artificial neural network (ANN), the deepneural
network (DNN), and regression tree (RT) data mining models for the nonhomogeneous karst Ljubljanica
catchment and four of its sub-catchments in Slovenia with different geological characteristics. Model performance
was evaluated using several performance criteria and additional investigation of low and high flows was
carried out. The results of the study indicate that the Génie Rural à 4 paramètres Journalier (GR4J) lumped
conceptual model yielded better modelling performance compared to the data-driven models, namely ANN, DNN
and RT models. Moreover, the enhanced version of the GR4J model (i.e. GR6J) also yielded good performance in
terms of the recession part. The RT model yielded the worst performance regarding runoff forecasting among the
examined models in the case of all five investigated catchments. However, ANN and DNN data-driven models
were slightly more successful in modelling the hydrograph recession in the case of karst sub-catchments compared
to the GR4J lumped conceptual model structure. Inclusion of additional meteorological variables to ANN
and DNN does not significantly improve modelling results. Keywords: Hydrological model | Lumped conceptual model | Data mining | Karst | Nonhomogeneous catchment | Ljubljanica River |
مقاله انگلیسی |