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 |
---|---|---|
1 |
تشخیص BECT اسپایک براساس ویژگیهای توالی EEG Novel و الگوریتم های LSTM
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 35 صرع خوشخیم با امواج spinous در منطقه زمانی ۲ ( BECT ) یکی از شایعترین syndromes مبتلا به صرع در کودکان است که به طور جدی رشد سیستم برای کودکان را تهدید میکند . مشخصترین ویژگی ۵ BECT وجود تعداد زیادی از electroencephalogram ۶ ( EEG ) در ناحیه Rolandic در طول دوره interictal است که یک اساس مهم برای کمک به neurologists در BECT diag8 است . با توجه به این مساله , این مقاله یک الگوریتم تشخیص BECT spike را براساس توالی زمانی سری زمانی EEG ثبت میکند و حافظه کوتاهمدت حافظه بلند مدت ( LSTM ) را نشان میدهد . سه ویژگی متوالی دامنه زمانی , که به وضوح ۱۲ را مشخص میکنند , برای نمایش EEG استخراج میشوند . ۱۳ تکنیک نمونهگیری اقلیت ترکیبی ( smote ) برای ۱۴ سخنرانی در مورد مساله عدم تعادل در EEGs بکار گرفته میشود و ۱۵ - ( BiLSTM ) برای تشخیص سیخ آموزشدیده است . این الگوریتم با استفاده از دادههای EEG ۱۵ BECT ثبتشده از ۱۷ بیمار Hospital ثبتشده از ۱۷ بیمارستان کودکان , دانشکده پزشکی University ۱۸ ( CHZU ) , مورد ارزیابی قرار میگیرد . این آزمایش نشان میدهد که الگوریتم پیشنهادی میتواند به طور متوسط 88.54 % F [ 1] , ۹۲.۰۴ درصد حساسیت , و ۲۰ 85.75 درصد را بدست آورد , که به طور کلی از چندین روش تشخیص استاندارد ویژگی استفاده میکند .
عبارات راهنما: BECT | تشخیص اسپایک | حوزه زمان EEG ویژگی توالی | مدل LSTM
|
مقاله ترجمه شده |
2 |
The framework design and empirical study of Chinas marine ecological-economic accounting
طرح چارچوب و مطالعه تجربی از حسابداری اقتصادی زیست محیطی دریایی چین-2021 In recent years, the Chinese government has attached greater importance to marine ecological protection. To
contribute to scientific understanding of the importance of marine ecosystems to human well-being, this paper
analyzes marine ecosystem service and its accounting, and introduces the concept of “quaternary industry” on
the basis of current marine economic accounting framework. Marine ecosystem accounting, marine economic
accounting and marine ecological-economic accounting of coastal areas in China during the time series of
2005–2017 are calculated. The results show that compared with Gross Ocean Product (GOP), the average annual
growth rate of Gross Marine Ecological-Economic Product (GMEEP) stays stable. The proportion of the added
value of quaternary industry in marine ecological economy is relatively large, which is between 46% and 51%.
And the ratio of GMEEP and GOP is around 1.9, suggesting a quite close association between GMEEP and GOP. keywords: حسابداری زیست محیطی زیست محیطی دریایی | خدمات اکوسیستم دریایی | محصول ناخالص اقیانوس | Marine ecological-economic accounting | Marine ecosystem services | Gross ocean product |
مقاله انگلیسی |
3 |
Invariant analysis and conservation laws of time fractional Schrödinger equations
تجزیه و تحلیل و حفاظت قوانین زمان کسری ثابت معادلات شرودینگر-2020 We study the invariance properties (symmetries) and conservation laws of the fractional time
version of the nonlinear Schrödinger equation with power law nonlinearity iyt + y + y|y| = 0,
α n xx for 0 < α < 1, using some recently developed approaches. We will show that the all important
energy conservation due time invariance is lost due to some built in approach that the theory
necessitates Keywords: Symmetries | Conservation laws | Time fractional | Schrodinger equations |
مقاله انگلیسی |
4 |
Effectiveness of implementing the criminal administrative punishment law of drunk driving in China: An interrupted time series analysis, 2004-2017
اثربخشی اجرای قانون مجازات اداری رانندگی در مستی در چین: تجزیه و تحلیل سری زمانی قطع شده ، 2004-2017-2020 In 2011, a more severe drunk driving law was implemented in China, which criminalized driving under the
influence of alcohol for the first time and increased penalties for drunk driving. The present study aimed to assess
effectiveness of the drunk driving law in China in reducing traffic crashes, injuries, and mortality. Data used in
this study was obtained from the Traffic Management Bureau of the Ministry of Public Security of the People’s
Republic of China. An interrupted time series analysis was conducted to analyze annual data from 2004 to 2017,
including the number of road traffic crashes, deaths, and injuries caused by drunk driving in China. The average
annual incidences of crashes, mortality, and injuries have decreased after the promulgation of drunk driving law
in 2011. In the post-intervention period, the increased slope for crashes, mortality and injury rates were, respectively,
-0.140 to -0.006, -0.052 to -0.005 and -0.150 to -0.008, indicating a weaker downward trend of
dependent variables. The more stringent drunk driving law is not as effective as expected. Drunk driving is still a
severe traffic safety problem to be addressed in China. Both legislation and other prevention programs should be
adopted to reduce road traffic injuries caused by drunk driving in China. Keywords: Drunk driving | Interrupted time series analysis | Road traffic law | Injury | Evaluation | China |
مقاله انگلیسی |
5 |
Electricity demand forecasting for decentralised energy management
پیش بینی تقاضای برق برای مدیریت انرژی غیر متمرکز-2020 The world is experiencing a fourth industrial revolution. Rapid development of technologies is advancing smart infrastructure opportunities. Experts observe decarbonisation, digitalisation and decentralisation as the main drivers for change. In electrical power systems a downturn of centralised conventional fossil fuel fired power plants and increased proportion of distributed power generation adds to the already troublesome outlook for op- erators of low-inertia energy systems. In the absence of reliable real-time demand forecasting measures, effective decentralised demand-side energy planning is often problematic. In this work we formulate a simple yet highly effective lumped model for forecasting the rate at which electricity is consumed. The methodology presented focuses on the potential adoption by a regional electricity network operator with inadequate real-time energy data who requires knowledge of the wider aggregated future rate of energy consumption. Thus, contributing to a reduction in the demand of state-owned generation power plants. The forecasting session is constructed initially through analysis of a chronological sequence of discrete observations. Historical demand data shows behaviour that allows the use of dimensionality reduction techniques. Combined with piecewise interpolation an electricity demand forecasting methodology is formulated. Solutions of short-term forecasting problems provide credible predictions for energy demand. Calculations for medium-term forecasts that extend beyond 6-months are also very promising. The forecasting method provides a way to advance a novel decentralised informatics, optimisa- tion and control framework for small island power systems or distributed grid-edge systems as part of an evolving demand response service. Keywords: Demand response | Decentralised | Grid edge | Time series forecasting |
مقاله انگلیسی |
6 |
Policy-based reinforcement learning for time series anomaly detection
یادگیری تقویتی مبتنی بر سیاست برای تشخیص ناهنجاری سری زمانی-2020 Time series anomaly detection has become a crucial and challenging task driven by the rapid increase
of streaming data with the arrival of the Internet of Things. Existing methods are either domain-specific
or require strong assumptions that cannot be met in realistic datasets. Reinforcement learning (RL), as an
incremental self-learning approach, could avoid the two issues well. However, the current investigation is far
from comprehensive. In this paper, we propose a generic policy-based RL framework to address the time series
anomaly detection problem. The policy-based time series anomaly detector (PTAD) is progressively learned
from the interactions with time-series data in the absence of constraints. Experimental results show that it
outperforms the value-based temporal anomaly detector and other state-of-the-art detection methods whether
training and test datasets come from the same source or not. Furthermore, the tradeoff between precision and
recall is well respected by the PTAD, which is beneficial to fulfill various industrial requirements. Keywords: Time series anomaly detection | Reinforcement learning | Policy-based methods |
مقاله انگلیسی |
7 |
Continuous control with Stacked Deep Dynamic Recurrent Reinforcement Learning for portfolio optimization
کنترل مداوم با یادگیری تقویتی مجدد پویا عمیق انباشته برای بهینه سازی نمونه کارها-2020 Recurrent reinforcement learning (RRL) techniques have been used to optimize asset trading systems and have achieved outstanding results. However, the majority of the previous work has been dedicated to sys- tems with discrete action spaces. To address the challenge of continuous action and multi-dimensional state spaces, we propose the so called Stacked Deep Dynamic Recurrent Reinforcement Learning (SDDRRL) architecture to construct a real-time optimal portfolio. The algorithm captures the up-to-date market con- ditions and rebalances the portfolio accordingly. Under this general vision, Sharpe ratio, which is one of the most widely accepted measures of risk-adjusted returns, has been used as a performance metric. Ad- ditionally, the performance of most machine learning algorithms highly depends on their hyperparameter settings. Therefore, we equipped SDDRRL with the ability to find the best possible architecture topology using an automated Gaussian Process ( GP ) with Expected Improvement ( EI ) as an acquisition function. This allows us to select the best architectures that maximizes the total return while respecting the car- dinality constraints. Finally, our system was trained and tested in an online manner for 20 successive rounds with data for ten selected stocks from different sectors of the S&P 500 from January 1st, 2013 to July 31st, 2017. The experiments reveal that the proposed SDDRRL achieves superior performance com- pared to three benchmarks: the rolling horizon Mean-Variance Optimization (MVO) model, the rolling horizon risk parity model, and the uniform buy-and-hold (UBAH) index. Keywords: Reinforcement learning | Policy gradient | Deep learning | Sequential model-based optimization | Financial time series | Portfolio management | Trading systems |
مقاله انگلیسی |
8 |
Reinforcement learning framework for freight demand forecasting to support operational planning decisions
چارچوب یادگیری تقویتی پیش بینی تقاضای حمل بار برای پشتیبانی از تصمیمات برنامه ریزی عملیاتی-2020 Freight forecasting is essential for managing, planning operating and optimizing the use of resources.
Multiple market factors contribute to the highly variable nature of freight flows, which
calls for adaptive and responsive forecasting models. This paper presents a demand forecasting
methodology that supports freight operation planning over short to long term horizons. The
method combines time series models and machine learning algorithms in a Reinforcement
Learning framework applied over a rolling horizon. The objective is to develop an efficient
method that reduces the prediction error by taking full advantage of the traditional time series
models and machine learning models. In a case study applied to container shipment data for a US
intermodal company, the approach succeeded in reducing the forecast error margin. It also allowed
predictions to closely follow recent trends and fluctuations in the market while minimizing
the need for user intervention. The results indicate that the proposed approach is an effective
method to predict freight demand. In addition to clustering and Reinforcement Learning, a
method for converting monthly forecasts to long-term weekly forecasts was developed and tested.
The results suggest that these monthly-to-weekly long-term forecasts outperform the direct long
term forecasts generated through typical time series approaches. Keywords: Freight demand forecasting | Time series | Reinforcement learning | Rolling horizon |
مقاله انگلیسی |
9 |
Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach
پیش بینی پیش بینی پایگاه داده های سری زمانی با استفاده از شبکه های عصبی مکرر در گروه های مشابه سری: یک روش خوشه بندی-2020 With the advent of Big Data, nowadays in many applications databases containing large quantities of sim- ilar time series are available. Forecasting time series in these domains with traditional univariate fore- casting procedures leaves great potentials for producing accurate forecasts untapped. Recurrent neural networks (RNNs), and in particular Long Short Term Memory (LSTM) networks, have proven recently that they are able to outperform state-of-the-art univariate time series forecasting methods in this context, when trained across all available time series. However, if the time series database is heterogeneous, ac- curacy may degenerate, so that on the way towards fully automatic forecasting methods in this space, a notion of similarity between the time series needs to be built into the methods. To this end, we present a prediction model that can be used with different types of RNN models on subgroups of similar time series, which are identified by time series clustering techniques. We assess our proposed methodology using LSTM networks, a widely popular RNN variant, together with various clustering algorithms, such as kMeans, DBScan, Partition Around Medoids (PAM), and Snob. Our method achieves competitive results on benchmarking datasets under competition evaluation procedures. In particular, in terms of mean sMAPE accuracy it consistently outperforms the baseline LSTM model, and outperforms all other methods on the CIF2016 forecasting competition dataset. Keywords: Big data forecasting | RNN | LSTM | Time series clustering | Neural networks |
مقاله انگلیسی |
10 |
The forgotten land use class: Mapping of fallow fields across the Sahel using Sentinel-2
کلاس استفاده از اراضی فراموش شده: نقشه برداری از مزارع مزرعه در ساحل با استفاده از سنتینل-2-2020 Remote sensing-derived cropland products have depicted the location and extent of agricultural lands with an
ever increasing accuracy. However, limited attention has been devoted to distinguishing between actively
cropped fields and fallowed fields within agricultural lands, and in particular so in grass fallow systems of semiarid
areas. In the Sahel, one of the largest dryland regions worldwide, crop-fallow rotation practices are widely
used for soil fertility regeneration. Yet, little is known about the extent of fallow fields since fallow is not
explicitly differentiated within the cropland class in any existing remote sensing-based land use/cover maps,
regardless of the spatial scale. With a 10 m spatial resolution and a 5-day revisit frequency, Sentinel-2 satellite
imagery made it possible to disentangle agricultural land into cropped and fallow fields, facilitated by Google
Earth Engine (GEE) for big data handling. Here we produce the first Sahelian fallow field map at a 10 m resolution
for the baseline year 2017, accomplished by designing a remote sensing driven protocol for generating
reference data for mapping over large areas. Based on the 2015 Copernicus Dynamic Land Cover map at 100 m
resolution, the extent of fallow fields in the cropland class is estimated to be 63% (403,617 km2) for the Sahel in
2017. Similar results are obtained for five contemporary cropland products, with fallow fields occupying
57–62% of the cropland area. Yet, it is noted that the total estimated area coverage depends on the quality of the
different cropland products. The share of cropped fields within the Copernicus cropland area is found to be
higher in the arid regions (200–300 mm rainfall) as compared to the semi-arid regions (300–600 mm rainfall).
The woody cover fraction within cropped and fallow fields is found to have a reversed pattern between arid
(higher woody cover in cropped fields) and semi-arid (higher woody cover in fallow fields) regions. The method
developed, using cloud-based Earth Observation (EO) data and computation on the GEE platform, is expected to
be reproducible for mapping the extent of fallow fields across global croplands. Future applications based on
multi-year time series is expected to improve our understanding of crop-fallow rotation dynamics in grass fallow
systems being key in teasing apart how cropland intensification and expansion affect environmental variables,
such as soil fertility, crop yields and local livelihoods in low-income regions such as the Sahel. The mapping
result can be visualized via a web viewer (https://buwuyou.users.earthengine.app/view/fallowinsahel). Keywords: Fallow fields | Cropland | Satellite image time series | Land use/cover mapping | Sentinel-2 | Drylands | Sahel |
مقاله انگلیسی |