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41 |
Flow resistance law under suspended sediment laden conditions
جریان قانون مقاومت تحت شرایط مملو از رسوب معلق-2020 The uniform flow resistance equation, in the form due to Manning or Darcy-Weisbach, is widely applied to
establish the stage-discharge relationship of a river cross-section. The application of this equation, namely the
slope-area method, allows to indirectly measure the corresponding river discharge by measurements of bed
slope, water level, cross-section area, wetted perimeter and an estimate of channel roughness. In this paper, a
recently deduced flow resistance equation for open channel flow was tested during conditions of suspended
sediment-laden flow. First, the flow resistance equation was determined by dimensional analysis and by applying
the condition of incomplete self-similarity for the flow velocity profile. Then the analysis was developed by the
following steps: (i) for sediment-laden flows characterized by known values of mean diameter and concentration
of suspended sediments, a relationship (Eq. (28)) between the Γ function of the velocity profile, the channel slope
and the Froude number was calibrated by the available measurements; and (ii) a relationship for estimating the Γ
function (Eq. (29)) which also takes into account the mean concentration of suspended particles was also
established. The theoretical flow resistance law (Eq. (26)) coupled with the relationship for estimating the Γ
function (Eq. (28) or Eq. (29)), which is characterized by the applicability of a wide range of flow conditions,
allowed to estimate the Darcy-Weisbach friction factor for flows with suspended-load. The analysis showed that
for large-size mixtures the Darcy-Weisbach friction factor can be accurately estimated neglecting the effect of
mean concentration of suspended sediments while for small-size mixtures the friction factor decreases when the
mean sediment concentration increases. Keywords: Flow resistance | Suspended-load | Dimensional analysis | Self-similarity | Flow velocity profile |
مقاله انگلیسی |
42 |
Aging-aware co-optimization of battery size, depth of discharge, and energy management for plug-in hybrid electric vehicles
بهینه سازی هم افزایی اندازه باتری ، عمق تخلیه و مدیریت انرژی برای وسایل نقلیه الکتریکی هیبریدی پلاگین-2020 Plug-in hybrid electric vehicles (PHEVs) have a large battery pack, and the depth of discharge (DOD) significantly
affects the battery longevity. In this paper, the battery degradation is considered in the co-optimization of
battery size and energy management for PHEVs using convex programming. The impact of DOD on battery
degradation and energy management is also investigated. The cost function consists of fuel consumption, electrical
energy consumption, and equivalent battery life loss. A real-world speed profile collected from the urban
city bus route up to about 70 km is used as an input to evaluate the proposed method. The results suggest that, for
both cases with and without battery degradation, the total cost curve with respect to the preset final state of
charge (SOC) is an upward parabola, where the optimal DOD can be identified, and the optimal battery size and
energy management can be determined. The results also show that, with an initial SOC of 0.9, the proposed
method can reduce the total cost by 3.6 CNY compared to other existing studies with the fixed final SOC.
Moreover, a sensitivity analysis is conducted to explore the effect of battery price and initial SOC on the optimal
DOD and total cost. Keywords: Plug-in hybrid electric bus | Optimal depth of discharge | Convex optimization | Battery aging model | Energy management |
مقاله انگلیسی |
43 |
A novel energy management strategy for the ternary lithium batteries based on the dynamic equivalent circuit modeling and differential Kalman filtering under time-varying conditions
یک استراتژی مدیریت انرژی جدید برای باتری های لیتیوم سه قلو بر اساس مدل سازی مدار معادل پویا و فیلتر کالمن دیفرانسیل تحت شرایط متغیر زمانی-2020 The dynamic model of the ternary lithium battery is a time-varying nonlinear system due to the polarization and
diffusion effects inside the battery in its charge-discharge process. Based on the comprehensive analysis of the
energy management methods, the state of charge is estimated by introducing the differential Kalman filtering
method combined with the dynamic equivalent circuit model considering the nonlinear temperature coefficient.
The model simulates the transient response with high precision which is suitable for its high current and
complicated charging and discharging conditions. In order to better reflect the dynamic characteristics of the
power ternary lithium battery in the step-type charging and discharging conditions, the polarization circuit of the
model is differential and the improved iterate calculation model is obtained. As can be known from the experimental
verifications, the maximize state of charge estimation error is only 0.022 under the time-varying complex
working conditions and the output voltage is monitored simultaneously with the maximum error of 0.08 V and
the average error of 0.04 V. The established model can describe the dynamic battery behavior effectively, which
can estimate its state of charge value with considerably high precision, providing an effective energy management
strategy for the ternary lithium batteries. Keywords: Ternary lithium battery | Dynamic equivalent circuit modeling | Differential Kalman filtering | State of charge estimation | Parameter acquisition | Nonlinear classification |
مقاله انگلیسی |
44 |
Reinforcement learning-based intelligent energy management architecture for hybrid construction machinery
یادگیری تقویتی مبتنی بر معماری هوشمند مدیریت انرژی برای ماشین آلات ساختمانی ترکیبی-2020 Power allocation is of crucial significance to energy management system in the hybrid construction machinery
(HCM). Most of the existing HCM energy management strategies are only formulated based on the predefined
rules, which causes the system unable to adapt to the changeable and complicated working conditions, thus
seriously limiting the energy saving potential of hybrid technology. In this paper, we build a reinforcement
learning-based intelligent energy management architecture for HCM. Given the working conditions and operating
characteristics of HCM, a Q-function updating method combining direct learning and indirect learning is
proposed to enhance the performance and practicability of reinforcement learning. A virtual world model
(VWM) is introduced to approximate the real-world environment and facilitate the identification of data-driven
environment, so as to enhance the real-time performance and adaptability of the architecture. Based on the
characteristics of HCM working conditions, the load cycle is subdivided, and the stationary Markov chain is
employed to yield real-time transfer probability matrices of required power to accelerate the updating of the
environment model. An HCM experiment platform is built, in which the typical signal of working condition is
sampled for simulation. The results indicate that DYNA-Q based architecture outperforms Q-learning and rulebased
strategy (RBS) in terms of adaptivity, real-time performance and optimality. The results also demonstrate
that with the proposed architecture, the working condition of internal combustion engine (ICE) and the chargedischarge
of ultracapacitor are more rational and efficient. Keywords: Hybrid construction machinery | Energy management | Reinforcement learning | Dyna-Q learning | Virtual world model |
مقاله انگلیسی |
45 |
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 |
46 |
Brown rice authenticity evaluation by spark discharge-laser-induced breakdown spectroscopy
ارزیابی اصالت برنج قهوه ای توسط طیف سنجی شکست spark ناشی از spark -2019 Rice is the most consumed food worldwide, therefore its designation of origin (PDO) is very useful. Laserinduced
breakdown spectroscopy (LIBS) is an interesting analytical technique for PDO certification, since it
provides fast multielemental analysis requiring minimal sample treatment. In this work LIBS spectral data from
rice analysis were evaluated for PDO certification of Argentine brown rice. Samples from two PDOs were analyzed
by LIBS coupled to spark discharge. The selection of spectral data was accomplished by extreme gradient
boosting (XGBoost), an algorithm currently used in machine learning, but rarely applied in chemical issues.
Emission lines of C, Ca, Fe, Mg and Na were selected, and the best performance of classification were obtained
using k-nearest neighbor (k-NN) algorithm. The developed method provided 84% of accuracy, 100% of sensitivity
and 78% of specificity in classification of test samples. Furthermore, it is simple, clean and can be easily
applied for rice certification. Keywords: Food authenticity | PDO | Brown rice | SD-LIBS | Pattern recognition |
مقاله انگلیسی |
47 |
Development of machine learning algorithms for prediction of mortality in spinal epidural abscess
توسعه الگوریتم های یادگیری ماشین برای پیش بینی مرگ و میر در آبسه اپیدورال ستون فقرات-2019 BACKGROUND CONTEXT: In-hospital and short-term mortality in patients with spinal epidural
abscess (SEA) remains unacceptably high despite diagnostic and therapeutic advancements.
Forecasting this potentially avoidable consequence at the time of admission could improve patient
management and counseling. Few studies exist to meet this need, and none have explored methodologies
such as machine learning.
PURPOSE: The purpose of this study was to develop machine learning algorithms for prediction
of in-hospital and 90-day postdischarge mortality in SEA.
STUDY DESIGN/SETTING: Retrospective, case-control study at two academic medical centers
and three community hospitals from 1993 to 2016.
PATIENTS SAMPLE: Adult patients with an inpatient admission for radiologically confirmed
diagnosis of SEA.
OUTCOME MEASURES: In-hospital and 90-day postdischarge mortality.
METHODS: Five machine learning algorithms (elastic-net penalized logistic regression, random
forest, stochastic gradient boosting, neural network, and support vector machine) were developed
and assessed by discrimination, calibration, overall performance, and decision curve analysis.
RESULTS: Overall, 1,053 SEA patients were identified in the study, with 134 (12.7%) experiencing
in-hospital or 90-day postdischarge mortality. The stochastic gradient boosting model achieved the best
performance across discrimination, c-statistic=0.89, calibration, and decision curve analysis. The variables
used for prediction of 90-day mortality, ranked by importance, were age, albumin, platelet count,
neutrophil to lymphocyte ratio, hemodialysis, active malignancy, and diabetes. The final algorithm was
incorporated into a web application available here: https://sorg-apps.shinyapps.io/seamortality/.
CONCLUSIONS: Machine learning algorithms show promise on internal validation for prediction
of 90-day mortality in SEA. Future studies are needed to externally validate these algorithms inindependent populations. Keywords: Artificial intelligence | Healthcare | Machine learning | Mortality | Spinal epidural abscess | Spine surgery |
مقاله انگلیسی |
48 |
Detecting adverse drug reactions in discharge summaries of electronic medical records using Readpeer
بررسی عوارض جانبی دارویی در خلاصه تخلیه سوابق پزشکی الکترونیکی با استفاده از Readpeer-2019 Background: Hospital discharge summaries offer a potentially rich resource to enhance pharmacovigilance efforts
to evaluate drug safety in real-world clinical practice. However, it is infeasible for experts to read through
all discharge summaries to find cases of drug-adverse event (AE) relations.
Purpose: The objective of this paper is to develop a natural language processing (NLP) framework to detect drug-
AE relations from unstructured hospital discharge summaries.
Basic procedures: An NLP algorithm was designed using customized dictionaries of drugs, adverse event (AE)
terms, and rules based on trigger phrases, negations, fuzzy logic and word distances to recognize drug, AE terms
and to detect drug-AE relations. Furthermore, a customized annotation tool was developed to facilitate expert
review of discharge summaries from a tertiary hospital in Singapore in 2011.
Main findings: A total of 33 trial sets with 50 to 100 records per set were evaluated (1620 discharge summaries)
by our algorithm and reviewed by pharmacovigilance experts. After every 6 trial sets, drug and AE dictionaries
were updated, and rules were modified to improve the system. Excellent performance was achieved for drug and
AE entity recognition with over 92% precision and recall. On the final 6 sets of discharge summaries (600
records), our algorithm achieved 75% precision and 59% recall for identification of valid drug-AE relations.
Principal conclusions: Adverse drug reactions are a significant contributor to health care costs and utilization.
Our algorithm is not restricted to particular drugs, drug classes or specific medical specialties, which is an
important attribute for a national regulatory authority to carry out comprehensive safety monitoring of drug
products. Drug and AE dictionaries may be updated periodically to ensure that the tool remains relevant for
performing surveillance activities. The development of the algorithm, and the ease of reviewing and correcting
the results of the algorithm as part of an iterative machine learning process, is an important step towards use of
hospital discharge summaries for an active pharmacovigilance program Keywords: Pharmacovigilance | Text mining | Electronic medical records | Expert system | Adverse drug reaction |
مقاله انگلیسی |
49 |
Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention
تکنیک های یادگیری ماشین برای پیش بینی پیش بینی بیمار پس از مداخله کرونر در رحم-2019 OBJECTIVES This study sought to determine whether machine learning can be used to better identify patients at risk
for death or congestive heart failure (CHF) rehospitalization after percutaneous coronary intervention (PCI).
BACKGROUND Contemporary risk models for event prediction after PCI have limited predictive ability. Machine
learning has the potential to identify complex nonlinear patterns within datasets, improving the predictive power of
models.
METHODS We evaluated 11,709 distinct patients who underwent 14,349 PCIs between January 2004 and December
2013 in the Mayo Clinic PCI registry. Fifty-two demographic and clinical parameters known at the time of admission were
used to predict in-hospital mortality and 358 additional variables available at discharge were examined to identify patients
at risk for CHF readmission. For each event, we trained a random forest regression model (i.e., machine learning) to
estimate the time-to-event. Eight-fold cross-validation was used to estimate model performance. We used the predicted
time-to-event as a score, generated a receiver-operating characteristic curve, and calculated the area under the curve
(AUC). Model performance was then compared with a logistic regression model using pairwise comparisons of AUCs and
calculation of net reclassification indices.
RESULTS The predictive algorithm identified a high-risk cohort representing 2% of all patients who had an in-hospital
mortality of 45.5% (95% confidence interval: 43.5% to 47.5%) compared with a risk of 2.1% for the general population
(AUC: 0.925; 95% confidence interval: 0.92 to 0.93). Advancing age, CHF, and shock on presentation were the
leading predictors for the outcome. A high-risk group representing 1% of all patients was identified with 30-day
CHF rehospitalization of 8.1% (95% confidence interval: 6.3% to 10.2%). Random forest regression outperformed
logistic regression for predicting 30-day CHF readmission (AUC: 0.90 vs. 0.85; p ¼ 0.003; net reclassification
improvement: 5.14%) and 180-day cardiovascular death (AUC: 0.88 vs. 0.81; p ¼ 0.02; net reclassification
improvement: 0.02%).
CONCLUSIONS Random forest regression models (machine learning) were more predictive and discriminative than
standard regression methods at identifying patients at risk for 180-day cardiovascular mortality and 30-day CHF rehospitalization,
but not in-hospital mortality. Machine learning was effective at identifying subgroups at high risk for postprocedure
mortality and readmission. (J Am Coll Cardiol Intv 2019;12:1304–11) © 2019 Published by Elsevier on behalf of
the American College of Cardiology Foundation. |
مقاله انگلیسی |
50 |
An intelligent identification method of the edge coherent mode in EAST
یک روش شناسایی هوشمند حالت منسجم لبه در EAST-2019 Edge Coherent Mode (ECM), a newly observed edge plasma behaviour in the Experimental Advanced
Superconducting Tokamak (EAST), exhibits the potential for a new plasma-confinement regime for long-pulse
discharge in future fusion devices. However, due to its intrinsically complex properties, e.g., inconstant shape,
appearing area and occasionally overlapping with the broad spectrum turbulence, the intelligent identification
of ECM turns out to be very challenging. In the present work, we propose an efficient and intelligent method to
identify ECM in EAST by analyzing the row information points of the spectrogram, which is based on a two-step
analysis: firstly, we estimate the difference of information points between adjacent rows as the horizontal feature
of the analyzed spectrogram with ECM appearing. Secondly, the time interval of ECM is estimated as the vertical
feature of the spectrogram. Such a method has been tested for 2000 different spectrograms and the correct rate
of identification is 93.49%. The present work is expected to enhance the efficiency of data analysis in EAST with
huge data base acquired from each discharge. Keywords: Nuclear fusion | Image processing | Pattern recognition | Data retrieval | Edge Coherent Mode |
مقاله انگلیسی |