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 |
AI-based Reference Ankle Joint Torque Trajectory Generation for Robotic Gait Assistance: First Steps
تولید مسیر حرکت گشتاور مفصل مچ پا مبتنی بر هوش مصنوعی برای کمک به راه رفتن رباتیک: اولین قدم ها-2020 Robotic-based gait rehabilitation and assistance
have been growing to augment and to recover motor function in
subjects with lower limb impairments. There is interest in
developing user-oriented control strategies to provide
personalized assistance. However, it is still needed to set the
healthy user-oriented reference joint trajectories, namely,
reference ankle joint torque, that would be desired under healthy
conditions. Considering the potential of Artificial Intelligence (AI)
algorithms to model nonlinear relationships of the walking
motion, this study implements and compares two offline AI-based
regression models (Multilayer Perceptron and Long-Short Term
Memory-LSTM) to generate healthy reference ankle joint torques
oriented to subjects with a body height ranging from 1.51 to 1.83
m, body mass from 52.0 to 83.7 kg and walking in a flat surface
with a walking speed from 1.0 to 4.0 km/h. The best results were
achieved for the LSTM, reaching a Goodness of Fit and a
Normalized Root Mean Square Error of 79.6 % and 4.31 %,
respectively. The findings showed that the implemented LSTM
has the potential to be integrated into control architectures of
robotic assistive devices to accurately estimate healthy useroriented
reference ankle joint torque trajectories, which are
needed in personalized and Assist-As-Needed conditions. Future
challenges involve the exploration of other regression models and
the reference torque prediction for remaining lower limb joints,
considering a wider range of body masses, heights, walking speeds,
and locomotion modes. Keywords: Ankle Joint Torque Prediction | Artificial Intelligence | Control Strategies | Regression Models | Robotic Gait Rehabilitation |
مقاله انگلیسی |
62 |
Derivation and validation of wind tunnel free-flight similarity law for store separation from aircraft
اشتقاق و اعتبار قانون تشابه پرواز آزاد تونل باد برای جداسازی فروشگاه از هواپیما-2020 This paper describes a design method for a similarity law for free-flight tests of aircraft load separation. The effect of the initial separation velocity on the motion similarity is considered. For the first time, the initial separation velocity is introduced into the equation of motion to identify similar trajectories. Finally, the model mass parameter characteristics and separation velocity equation are solved to determine similarity laws for wind tunnel tests, greatly improving the accuracy and applicability of test results from wind tunnels. The proposed derivation overcomes the problems faced by the traditional light model method and the traditional heavy model method, namely that they are limited in terms of ejection separation and cannot be realized in wind tunnel tests. The typical separation state under wind load scenarios is simulated using computational fluid dynamics (CFD). Separation data from real aircraft and previous test methods are compared with the simulation data obtained by the new similarity law design method. The improvement of the new similarity law in terms of trajectory simulation is verified through a comprehensive data comparison. The data show that the new similarity law greatly improves the accuracy of wind tunnel tests. Keywords: Similarity law derivation | High-speed weapon delivery | Carrier and missile interference | Multi-body separation | Free-flight wind tunnel test |
مقاله انگلیسی |
63 |
Is justice delayed justice denied? An empirical approach
آیا عدالت با تاخیر عدالت انکار می شود؟ رویکرد تجربی-2020 Improving judicial performance in order to enhance the business environment has been a policy goal for
many governments in the last decades. Following the suggestions of several international organizations,
most countries have tried to speed up their case resolution systems by streamlining judicial procedure.
However, not as much attention has been devoted to test the potential drawbacks of similar reforms in
terms of supplying a quicker but yet qualitatively inferior justice, thus contradicting the well-known legal
maxim justice delayed is justice denied. The present work wishes to contribute to the empirical literature
on the topic by proposing two alternative ways to further disentangle the relationship between judicial
performance and judicial quality. Exploiting a dataset of 171 countries for the 2003–2016 time period,
we find statistically significant evidence of a strong and negative relationship between courts’ delay
and countries’ quality of the justice. While the intrinsic limits of this kind of institutional empirical
analysis suggest caution when interpreting our estimates as proof of causality, we present more robust
evidence suggesting that countries characterized by faster judiciaries seem to be equally not affected by a
deterioration of the quality of justice, thus confirming the aforementioned maxim, at least descriptively.
Keywords: Judicial delay | Judicial quality | Empirical institutional analysis |
مقاله انگلیسی |
64 |
Optimal planning of distributed photovoltaic generation for the traction power supply system of high-speed railway
برنامه ریزی بهینه از تولید فتوولتائیک توزیع شده برای سیستم منبع تغذیه کششی راه آهن با سرعت بالا-2020 The ever-increasing electricity price and energy consumption in high-speed railway industry push
railway companies to seek a promising way to realize their sustainable developments. Making full use of
the solar resource along with high-speed railways can be a potential solution to cut the electricity bill,
bring more profit to railway companies and realize the decarbonization of high-speed railway industry.
This paper studies the optimal planning of distributed photovoltaic generation (DPVG) and energy
storage system (ESS) for the traction power supply system (TPSS) of high-speed railway. A quantitative
method is proposed to study the time and space characteristics of photovoltaic generation and electricity
demand of high-speed trains. An integrated cost-benefit analysis framework is developed to evaluate the
effect of DPVG and ESS on the economy of TPSS. To derive the optimal planning scheme and energy
management strategy of DPVG and ESS, a mathematical programming model with the objective of
minimizing the total cost is proposed to seek the most economical solution. A hybrid global optimal
solution approach is developed to solve the model. A real-world case of Beijing-Baoding high-speed
railway in China is used to illustrate the capability and characteristics of the proposed model. The
computational results show that DPVG is able to supply 32:5% electricity demand of high-speed trains.
The integration of DPVG and ESS can help railway company save 4.2 million CNY each year in Beijing-
Baoding high-speed railway. This paper demonstrates the potential and applicability of DPVG and ESS
in high-speed railway industry. Keywords: High-speed railway | Photovoltaic generation | Energy storage system | Traction power supply system |
مقاله انگلیسی |
65 |
Laminar flame speeds of methane/air mixtures at engine conditions: Performance of different kinetic models and power-law correlations
سرعت شعله چند لایه مخلوط های متان / هوا در شرایط موتور: عملکرد مدل های مختلف جنبشی و همبستگی قدرت قانون-2020 The laminar flame speed is an important input in turbulent premixed combustion modelling of spark ignition engines. At engine-relevant temperatures and pressures, its measurement is challenging or not possible and thereby it is usually obtained from simulations based on chemical models or power-law correlations. This work aims to investigate the performance of different models and power-law correla- tions in terms of predicting laminar flame speeds of methane/air at engine conditions. The propagation of spherically expanding laminar flames in a closed chamber was simulated and laminar flame speeds were computed over a broad range of pressures (1-120 atm) and temperatures (30 0-110 0 K) for methane/air mixtures based on seven kinetic models. It was found that at engine conditions, there are notable dis- crepancies among the predictions. GRI Mech. 3.0 and USC Mech. II respectively predict the largest and smallest values at high pressure conditions. This was explained by the difference in CH 3 oxidation and recombination according to reaction pathway analysis. Additionally, laminar flame speeds of methane flames were experimentally determined under engine-relevant conditions. It was shown that the recently developed Foundational Fuel Chemistry Model Version 1.0 model predicts closely the data at high pres- sures and temperatures. Therefore, it was chosen as the reference model for the comparisons. Thirteen published power-law correlations for laminar flame speeds of CH 4 /air were implemented, and their per- formance in predicting the laminar flame speeds at engine conditions was investigated. Most of these correlations have been derived for a narrow range of temperatures and pressures, which are lower than those encountered in engines. A new power-law correlation was derived based on predictions by the Foundational Fuel Chemistry Model Version 1.0. This new correlation is expected to provide reliable pre- dictions at engine conditions for a stoichiometric methane/air mixture and thereby it is recommended to be used in modeling turbulent premixed combustion in spark-ignition engine simulations. Keywords: Laminar flame speed | engine conditions | methane | power-law correlation | propagating spherical flame |
مقاله انگلیسی |
66 |
Study on deep reinforcement learning techniques for building energy consumption forecasting
مطالعه تکنیک های یادگیری تقویتی عمیق برای پیش بینی مصرف انرژی در ساخت-2020 Reliable and accurate building energy consumption prediction is becoming increasingly pivotal in build- ing energy management. Currently, data-driven approach has shown promising performances and gained lots of research attention due to its efficiency and flexibility. As a combination of reinforcement learning and deep learning, deep reinforcement learning (DRL) techniques are expected to solve nonlinear and complex issues. However, very little is known about DRL techniques in forecasting building energy con- sumption. Therefore, this paper presents a case study of an office building using three commonly-used DRL techniques to forecast building energy consumption, namely Asynchronous Advantage Actor-Critic (A3C), Deep Deterministic Policy Gradient (DDPG) and Recurrent Deterministic Policy Gradient (RDPG). The objective is to investigate the potential of DRL techniques in building energy consumption predic- tion field. A comprehensive comparison between DRL models and common supervised models is also provided. The results demonstrate that the proposed DDPG and RDPG models have obvious advantages in forecast- ing building energy consumption compared to common supervised models, while accounting for more computation time for model training. Their prediction performances measured by mean absolute error (MAE) can be improved by 16%-24% for single-step ahead prediction, and 19%-32% for multi-step ahead prediction. The results also indicate that A3C performs poor prediction accuracy and shows much slower convergence speed than DDPG and RDPG. However, A3C is still the most efficient technique among these three DRL methods. The findings are enlightening and the proposed DRL methodologies can be positively extended to other prediction problems, e.g., wind speed prediction and electricity load prediction. Keywords: Energy consumption prediction | Ground source heat pump | Deep reinforcement learning | Asynchronous advantage Actor-Critic | Deep deterministic Policy gradient | Recurrent deterministic Policy gradient |
مقاله انگلیسی |
67 |
Refined composite multivariate multiscale symbolic dynamic entropy and its application to fault diagnosis of rotating machine
آنتروپی پویای نمادین چند متغیره کامپوزیت تصفیه شده و کاربرد آن در تشخیص خطای ماشین چرخشی-2020 Accurate and efficient identification of various fault categories, especially for the big data and multisensory
system, is a challenge in rotating machinery fault diagnosis. For the diagnosis problems with massive
multivariate data, extracting discriminative and stable features with high efficiency is the significant
step. This paper proposes a novel feature extraction method, called Refined Composite multivariate
Multiscale Symbolic Dynamic Entropy (RCmvMSDE), based on the refined composite analysis and multivariate
multiscale symbolic dynamic entropy. Specifically, multivariate multiscale symbolic dynamic
entropy can capture more identification information from multiple sensors with superior computational
efficiency, while refine composite analysis guarantees its stability. The abilities of the proposed method
to measure the complexity of multivariate time series and identify the signals with different components
are discussed based on adequate simulation analysis. Further, to verify the effectiveness of the proposed
method on fault diagnosis tasks, a centrifugal pump dataset under constant speed condition and a ball
bearing dataset under time-varying speed condition are applied. Compared with the existing methods,
the proposed method improves the classification accuracy and F-score to 99.81% and 0.9981, respectively.
Meanwhile, the proposed method saves at least half of the computational time. The result shows that the
proposed method is effective to improve the efficiency and classification accuracy dealing with the massive
multivariate signals. Keywords: Multivariate multiscale symbolic dynamic | entropy | Random forest | Time-varying speed conditions | Fault diagnosis |
مقاله انگلیسی |
68 |
Analysis of energy dissipation and crack evolution law of sandstone under impact load
تجزیه و تحلیل اتلاف انرژی و قانون تکامل ترک ماسه سنگ تحت بار ضربه-2020 Based on the split Hopkinson pressure bar (SHPB) laboratory tests, the dynamic mechanical properties and
failure mode of sandstone are analyzed, and a SHPB numerical model is established by particle flow code (PFC).
The dynamic stress equilibrium, stress wave propagation, stress-strain characteristics and failure mode are
analyzed, respectively, which verifies the effectiveness of the model. Then we studied the impact failure process
form both mesoscopic cracks and energy point of views. The results show that microcracks are activated in large
quantities with the increasing of strain rate. When the crack density reaches a certain degree, the interaction
between the cracks can not be ignored. The failure mode gradually changes from local tension–shear damage
mode to axial splitting failure mode and then to crushing failure mode. During the impact failure process, the
energy is mainly consumed by the generation of the cracks and the friction caused by the slip of the particles,
namely, broken dissipation energy. As the impact load increases, the broken dissipation energy density shows the
high–speed growth and the low–speed growth stage successively with a double exponential growth pattern. The
friction energy increases continually by a certain percentage, which indicates it should be considered during the
analysis of fracturing process. Moreover, the dynamic strength and fragmentation degrees are closely related to
energy dissipation density. Keywords: Rock dynamics | Split hopkinson pressure bar | PFC2D | Crack propagation | Energy transformation |
مقاله انگلیسی |
69 |
Multi-objective stochastic programming energy management for integrated INVELOX turbines in microgrids: A new type of turbines
مدیریت انرژی برنامه نویسی تصادفی چند منظوره برای توربین های یکپارچه INVELOX در میکروگریدها: نوع جدیدی از توربین ها-2020 In this paper, a new type of wind turbine that is called INVELOX has been used. INVELOX has many
advantages such as six times more power generation than previous types, work at low speed, inconsiderable
maintenance and investment costs, and reduce the environmental effects of previous wind
turbines. Moreover, other renewable and nonrenewable generators are used in the energy management
and scheduling of the microgrid. The test case is a microgrid with selling and buying energy capability in
which the cost and pollution are considered as the objective functions. In the following, Uncertainties of
wind speed, solar radiation and electrical-thermal loads are investigated and a multi-objective stochastic
mixed integer linear programming is solved in the first scenario. Then, in the second scenario, the effects
of fuel cost uncertainty on generation units and objective functions have been studied. The Epsilon
constraints method and fuzzy satisfying are utilized to solve the problem and choose the best solution,
respectively. By using of INVELOX turbines, total cost and pollution of the microgrid in both deterministic
and stochastic planning are reduced from 192.68 $ to 97.23 $ and 249.28 $ to 126.38 $, as well 3334.76 Kg
to 3302.7 and 3925.63 to 3910.2 Kg respectively. Keywords: Energy management | INVELOX turbine | Microgrid | Renewable resource | Stochastic programming |
مقاله انگلیسی |
70 |
Automatic human identification from panoramic dental radiographs using the convolutional neural network
شناسایی خودکار انسان از رادیوگرافی دندانپزشکی پانوراما با استفاده از شبکه عصبی کانولوشن-2020 Human identification is an important task in mass disaster and criminal investigations. Although several
automatic dental identification systems have been proposed, accurate and fast identification from
panoramic dental radiographs (PDRs) remains a challenging issue. In this study, an automatic human
identification system (DENT-net) was developed using the customized convolutional neural network
(CNN). The DENT-net was trained on 15,369 PDRs from 6300 individuals. The PDRs were preprocessed by
affine transformation and histogram equalization. The DENT-net took 128 128 7 square patches as
input, including the whole PDR and six details extracted from the PDR. Using the DENT-net, the feature
extraction took around 10 milliseconds per image and the running time for retrieval was 33.03
milliseconds in a 2000-individual database, promising an application on larger databases. The
visualization of CNN showed that the teeth, maxilla, and mandible all contributed to human
identification. The DENT-net achieved Rank-1 accuracy of 85.16% and Rank-5 accuracy of 97.74% for
human identification. The present results demonstrated that human identification can be achieved from
PDRs by CNN with high accuracy and speed. The present system can be used without any special
equipment or knowledge to generate the candidate images. While the final decision should be made by
human specialists in practice. It is expected to aid human identification in mass disaster and criminal
investigation Keywords: Forensic odontology | Human identification | Panoramic dental radiographs | Deep learning | Convolutional neural network |
مقاله انگلیسی |