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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31 |
Deep reinforcement learning and LSTM for optimal renewable energy accommodation in 5G internet of energy with bad data tolerant
یادگیری تقویتی عمیق و LSTM برای استفاده بهینه از انرژی تجدیدپذیر در اینترنت 5G انرژی با تحمل داده های بد-2020 With the high penetration of large scale distributed renewable energy generations, there is a serious curtailment
of wind and solar energy in 5G internet of energy. A reasonable assessment of large scale renewable energy
grid-connected capacities under random scenarios is critical to promote the efficient utilization of renewable
energy and improve the stability of power systems. To assure the authenticity of the data collected by the
terminals and describe data characteristics precisely are crucial problems in assessing the accommodation
capability of renewable energy. To solve these problems, in this paper, we propose an L-DRL algorithm based
on deep reinforcement learning (DRL) to maximize renewable energy accommodation in 5G internet of energy.
LSTM as a bad data tolerant mechanism provides real state value for the solution of accommodation strategy,
which ensures the accurate assessment of renewable energy accommodation capacity. DDPG is used to obtain
optimal renewable energy accommodation strategies in different scenarios. In the numerical results, based on
real meteorological data, we validate the performance of the proposed algorithm. Results show considering
the energy storage system and demand response mechanism can improve the capacity of renewable energy
accommodation in 5G internet of energy. Keywords: 5G internet of energy | Renewable energy accommodation | Deep reinforcement learning | Demand response | LSTM |
مقاله انگلیسی |
32 |
Prediction oof Vessel Trajectories From AIS Data Via Sequence-To-Sequence Recurrent Neural Networks
پیش بینی مسیرهای کشتی از داده های AIS از طریق شبکه های عصبی تکرار شونده به ترتیب-2020 In this paper, we address the problem of predicting vessel
trajectories based on Automatic Identification System (AIS)
data. The goal is to learn the predictive distribution of maritime
traffic patterns using historical data during the training
phase, in order to be able to forecast future target trajectory
samples online on the basis of both the extracted knowledge
and the available observation sequence. We explore neural
sequence-to-sequence models based on the Long Short-Term
Memory (LSTM) encoder-decoder architecture to effectively
capture long-term temporal dependencies of sequential AIS
data and increase the overall predictive power. The experimental
evaluation on a real-world AIS dataset demonstrates
the effectiveness of sequence-to-sequence recurrent neural
networks (RNNs) for vessel trajectory prediction and shows
their potential benefits compared to model-based methods. Index Terms: Vessel trajectory prediction | recurrent neural networks | sequence-to-sequence models | LSTM | AIS |
مقاله انگلیسی |
33 |
Optimization of lightweight task offloading strategy for mobile edge computing based on deep reinforcement learning
بهینه سازی استراتژی بارگیری کار سبک برای محاسبات لبه تلفن همراه مبتنی بر یادگیری تقویت عمیق-2020 With the maturity of 5G technology and the popularity of intelligent terminal devices, the traditional
cloud computing service model cannot deal with the explosive growth of business data quickly.
Therefore, the purpose of mobile edge computing (MEC) is to effectively solve problems such as
latency and network load. In this paper, deep reinforcement learning (DRL) is first proposed to solve
the offloading problem of multiple service nodes for the cluster and multiple dependencies for mobile
tasks in large-scale heterogeneous MEC. Then the paper uses the LSTM network layer and the candidate
network set to improve the DQN algorithm in combination with the actual environment of the MEC.
Finally, the task offloading problem is simulated by using iFogSim and Google Cluster Trace. The
simulation results show that the offloading strategy based on the improved IDRQN algorithm has
better performance in energy consumption, load balancing, latency and average execution time than
other algorithms. Keywords: Mobile edge computing | Task offloading | Deep reinforcement learning | LSTM network | Candidate network |
مقاله انگلیسی |
34 |
Static malware detection and attribution in android byte-code through an end-to-end deep system
شناسایی بدافزارهای استاتیکی و انتساب در بایت کد اندرویدی از طریق یک سیستم عمیق انتها به انتها-2020 Android reflects a revolution in handhelds and mobile devices. It is a virtual machine based, an
open source mobile platform that powers millions of smartphone and devices and even a larger no.
of applications in its ecosystem. Surprisingly in a short lifespan, Android has also seen a colossal
expansion in application malware with 99% of the total malware for smartphones being found in
the Android ecosystem. Subsequently, quite a few techniques have been proposed in the literature
for the analysis and detection of these malicious applications for the Android platform. The increasing
and diversified nature of Android malware has immensely attenuated the usefulness of prevailing
malware detectors, which leaves Android users susceptible to novel malware. Here in this paper,
as a remedy to this problem, we propose an anti-malware system that uses customized learning
models, which are sufficiently deep, and are ’End to End deep learning architectures which detect
and attribute the Android malware via opcodes extracted from application bytecode’. Our results
show that Bidirectional long short-term memory (BiLSTMs) neural networks can be used to detect
static behavior of Android malware beating the state-of-the-art models without using handcrafted
features. For our experiments in our system, we also choose to work with distinct and independent
deep learning models leveraging sequence specialists like recurrent neural networks, Long Short Term
Memory networks and its Bidirectional variation as well as those are more usual neural architectures
like a network of all connected layers(fully connected), deep convnets, Diabolo network (autoencoders)
and generative graphical models like deep belief networks for static malware analysis on Android. To
test our system, we have also augmented a bytecode dataset from three open and independently
maintained state-of-the-art datasets. Our bytecode dataset, which is on an order of magnitude large,
essentially suffice for our experiments. Our results suggests that our proposed system can lead to
better design of malware detectors as we report an accuracy of 0.999 and an F1-score of 0.996 on a
large dataset of more than 1.8 million Android applications. Keywords: End-to-end architecture | Malware analysis | Deep neural networks | Android and big data |
مقاله انگلیسی |
35 |
TBM penetration rate prediction based on the long short-term memory neural network
پیش بینی سرعت نفوذ TBM بر اساس شبکه عصبی حافظه کوتاه مدت-2020 Tunnel boring machines (TBMs) are widely used in tunnel engineering because of their safety and efficiency. The TBM penetration
rate (PR) is crucial, as its real-time prediction can reflect the adaptation of a TBM under current geological conditions and assist the
adjustment of operating parameters. In this study, deep learning technology is applied to TBM performance prediction, and a PR prediction
model based on a long short-term memory (LSTM) neuron network is proposed. To verify the performance of the proposed
model, the machine parameters, rock mass parameters, and geological survey data from the water conveyance tunnel of the Hangzhou
Second Water Source project were collected to form a dataset. Furthermore, 2313 excavation cycles were randomly composed of training
datasets to train the LSTM-based model, and 257 excavation cycles were used as a testing dataset to test the performance. The root mean
square error and the mean absolute error of the proposed model are 4.733 and 3.204, respectively. Compared with Recurrent neuron
network (RNN) based model and traditional time-series prediction model autoregressive integrated moving average with explanation
variables (ARIMAX), the overall performance on proposed model is better. Moreover, in the rapidly increasing period of the PR,
the error of the LSTM-based model prediction curve is significantly smaller than those of the other two models. The prediction results
indicate that the LSTM-based model proposed herein is relatively accurate, thereby providing guidance for the excavation process of
TBMs and offering practical application value. Keywords: TBM performance prediction | Penetration rate | Long short-term memory | Water conveyance tunnel |
مقاله انگلیسی |
36 |
Utilization of a reinforcement learning algorithm for the accurate alignment of a robotic arm in a complete soft fabric shoe tongues automation process
استفاده از الگوریتم یادگیری تقویت کننده برای ترازبندی دقیق بازوی رباتیک در فرایند خودکار سازی نمایش نرم افزاری کفش هایفابریک-2020 As usher in Industry 4.0, there has been much interest in the development and research that combine artificial
intelligence with automation. The control and operation of equipment in a traditional automated shoemaking
production line require a preliminary subjective judgment of relevant manufacturing processes, to determine the
exact procedure and corresponding control settings. However, with the manual control setting, it is difficult to
achieve an accurate quality assessment of an automated process characterized by high uncertainty and intricacy.
It is challenging to replace handicrafts and the versatility of manual product customization with automation
techniques. Hence, the current study has developed an automatic production line with a cyber-physical system
artificial intelligence (CPS-AI) architecture for the complete manufacturing of soft fabric shoe tongues. The
Deep-Q reinforcement learning (RL) method is proposed as a means of achieving better control over the manufacturing
process, while the convolutional and long short-term memory artificial neural network (CNN +
LSTM) is developed to enhance action speed. This technology allows a robotic arm to learn the specific image
feature points of a shoe tongue through repeated training to improve its manufacturing accuracy. For validation,
different parameters of the network architecture are tested, and the test convergence accuracy was found to be as
high as 95.9 %. During its actual implementation, the production line completed 509 finished products, of which
349 products were acceptable due to the anticipated measurement error. This showed that the production line
system was capable of achieving optimum product accuracy and quality with respect to the performance of
repeated computations, parameter updates, and action evaluations. Keywords: Artificial intelligence | Shoemaking automation | Reinforcement learning | Cyber-physical system |
مقاله انگلیسی |
37 |
Surgical Phase Recognition Method with a Sequential Consistency for CAOS-AI Navigation System
روش تشخیص مرحله جراحی با یک سازگاری متوالی برای سیستم ناوبری CAOS-AI-2020 The procedure of orthopedic surgery is quite
complicated, and many kinds of equipment have been used.
Operating room nurses who deliver surgical instruments to surgeon
are supposed to be forced to incur a heavy burden. There are some
studies to recognize surgical phase with convolutional neural
network (CNN) in minimally invasive laparoscopic surgery only.
Previously, we proposed a computer-aided orthopedic surgery
(CAOS)-AI navigation system based on CNN. However, the work
propose a method to improve accuracy of phase recognition by
considering temporal dependency of orthopedic surgery video
acquired from surgeon-wearable video camera. The method
estimates current surgical phase by combining both temporal
dependency and convolutional-long-short term memory network
(CNN-LSTM). Experimental results shows a phase recognition
accuracy of 59.9% by the proposed method applied in unicomapartmenatal
knee arthroplasty (UKA). Keywords: Deep Learning | Computer-aided Orthopaedic Surgery | Operating Room Nurse | Phase Recognition |
مقاله انگلیسی |
38 |
Cryptocurrency forecasting with deep learning chaotic neural networks
پیش بینی cryptocurrency با یادگیری عمیق شبکه های عصبی پر هرج و مرج-2019 We implement deep learning techniques to forecast the price of the three most widely traded digital currencies i.e., Bitcoin, Digital Cash and Ripple. To the best of our knowledge, this is the first work to make use of deep learning in cryptocurrency prediction. The results from testing the existence of non- linearity revealed that the time series of all digital currencies exhibit fractal dynamics, long memory and self-similarity. The predictability of long-short term memory neural network topologies (LSTM) is signif- icantly higher when compared to the generalized regression neural architecture, set forth as our bench- mark system. The latter failed to approximate global nonlinear hidden patterns regardless of the degree of contamination with noise, as they are based on Gaussian kernels suitable only for local approximation of non-stationary signals. Although the computational burden of the LSTM model is higher as opposed to brute force in nonlinear pattern recognition, eventually deep learning was found to be highly efficient in forecasting the inherent chaotic dynamics of cryptocurrency markets. Keywords: Digital currencies | Deep learning | Fractality | Neural networks | Chaos | Forecasting |
مقاله انگلیسی |
39 |
Unsupervised classification of multi-omics data during cardiac remodeling using deep learning
طبقه بندی بدون نظارت شده داده های چند omics در طی بازسازی قلب با استفاده از یادگیری عمیق-2019 Integration of multi-omics in cardiovascular diseases (CVDs) presents high potentials for translational discoveries.
By analyzing abundance levels of heterogeneous molecules over time, we may uncover biological
interactions and networks that were previously unidentifiable. However, to effectively perform integrative
analysis of temporal multi-omics, computational methods must account for the heterogeneity and complexity in
the data. To this end, we performed unsupervised classification of proteins and metabolites in mice during
cardiac remodeling using two innovative deep learning (DL) approaches. First, long short-term memory (LSTM)-
based variational autoencoder (LSTM-VAE) was trained on time-series numeric data. The low-dimensional
embeddings extracted from LSTM-VAE were then used for clustering. Second, deep convolutional embedded
clustering (DCEC) was applied on images of temporal trends. Instead of a two-step procedure, DCEC performes a
joint optimization for image reconstruction and cluster assignment. Additionally, we performed K-means clustering,
partitioning around medoids (PAM), and hierarchical clustering. Pathway enrichment analysis using the
Reactome knowledgebase demonstrated that DL methods yielded higher numbers of significant biological
pathways than conventional clustering algorithms. In particular, DCEC resulted in the highest number of enriched
pathways, suggesting the strength of its unified framework based on visual similarities. Overall, unsupervised
DL is shown to be a promising analytical approach for integrative analysis of temporal multi-omics. Keywords: Cardiovascular | Clustering | Multi-omics Time-series | Unsupervised deep learning | Integrative analysis |
مقاله انگلیسی |
40 |
هوش مصنوعی برای پیش بینی در مدیریت زنجیره تامین: مطالعه موردی میزان مصرف قند سفید در تایلند
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 22 این مقاله یک مدل مناسب برای پیش بینی روند میزان مصرف شکر سفید در تایلند با توجه به نوسانات نرخ مصرف امروزه ارائه می دهد. در این مقاله روی دو نوع مدل اصلی پیش بینی که مدل های رگرسیون و شبکه های عصبی هستند ، تمرکز خواهد شد. علاوه بر این ، عملکرد با استفاده از Root Mean Square Error (RMSE) و مقدار آماری TheilU ارزیابی می شود. پس از پردازش آزمایشات ، نتایج نشان می دهد که شبکه عصبی راجعه با حافظه کوتاه مدت (LSTM) با شرایط ترکیبی بین میزان مصرف موجود و سایر عوامل مرتبط مانند تأمین تولید ، میزان واردات ، صادرات و موجودی کالا بهترین عملکرد را برای پیش بینی فراهم می کند. همچنین تنظیم پارامترهای مدل مسئله مهمی است.
کلمات کلیدی: یادگیری ماشین | اینترنت فیزیکی | پیش بینی تقاضا | شبکه عصبی | رگرسیون |
مقاله ترجمه شده |