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نتیجه جستجو - Discharge

تعداد مقالات یافته شده: 106
ردیف عنوان نوع
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
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