دانلود و نمایش مقالات مرتبط با Reliability::صفحه 1
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نتیجه جستجو - Reliability

تعداد مقالات یافته شده: 243
ردیف عنوان نوع
1 ابزار خودسنجی برای سنجش توانمندی‌‌‌‌های فارغ‌التحصیلان نامیبیا: آزمون روایی و پایایی
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 34
بر اساس گزارشات، نامیبیا از جمله کشورهایی است که بالاترین نرخ بیکاری را دارد. در این مقاله، روایی و پایایی ابزار خودسنجی مورد استفاده برای سنجش توانمندی‌‌‌‌های فارغ‌التحصیلان نامیبیا با استفاده از تحلیل عامل اکتشافی (EFA) و تحلیل عامل تاییدی مرتبه دوم (CFA) مورد ارزیابی قرار گرفته‌است. نتایج EFA نشان داد که بیست شاخص را میتوان‌ به پنج عامل، یعنی "مدیریت و انعطاف‌پذیری"، "تخصص و ارتباطات"، "کار گروهی و تفکر انتقادی"، "خویشتن‌داری" و "انگیزه پیشرفت" طبقه‌بندی کرد. نتایج CFA نشان داد که تمام عوامل و شاخص‌ها پایایی بالا و اعتبار خوب ساختاری دارند. دانشجویان و فارغ‌التحصیلان می‌توانند این ابزار خودسنجی معتبر را برای ارزیابی یا تشخیص الگویی از نقاط قوت و ضعف خود به کار گیرند و برآوردی واقعی و عینی از قابلیت استخدام خود داشته باشند، همچنین این ابزار به آن‌ها در افزایش اثربخشی در محل کار کمک می‌کند.
کلمات کلیدی: توانمندی‌‌‌ | قابلیت استخدام | تحلیل عامل تاییدی | روایی ساختار | اعتبار سنجی
مقاله ترجمه شده
2 Calibration of Portable Particulate Matter-Monitoring Device using Web Query and Machine Learning
کالیبراسیون دستگاه کنترل کننده ذرات قابل حمل با استفاده از پرس و جوی وب و یادگیری ماشین-2019
Background: Monitoring and control of PM2.5 are being recognized as key to address health issues attributed to PM2.5. Availability of low-cost PM2.5 sensors made it possible to introduce a number of portable PM2.5 monitors based on light scattering to the consumer market at an affordable price. Accuracy of light scatteringebased PM2.5 monitors significantly depends on the method of calibration. Static calibration curve is used as the most popular calibration method for low-cost PM2.5 sensors particularly because of ease of application. Drawback in this approach is, however, the lack of accuracy. Methods: This study discussed the calibration of a low-cost PM2.5-monitoring device (PMD) to improve the accuracy and reliability for practical use. The proposed method is based on construction of the PM2.5 sensor network using Message Queuing Telemetry Transport (MQTT) protocol and web query of reference measurement data available at government-authorized PM monitoring station (GAMS) in the republic of Korea. Four machine learning (ML) algorithms such as support vector machine, k-nearest neighbors, random forest, and extreme gradient boosting were used as regression models to calibrate the PMD measurements of PM2.5. Performance of each ML algorithm was evaluated using stratified K-fold cross-validation, and a linear regression model was used as a reference. Results: Based on the performance of ML algorithms used, regression of the output of the PMD to PM2.5 concentrations data available from the GAMS through web query was effective. The extreme gradient boosting algorithm showed the best performance with a mean coefficient of determination (R2) of 0.78 and standard error of 5.0 mg/m3, corresponding to 8% increase in R2 and 12% decrease in root mean square error in comparison with the linear regression model. Minimum 100 hours of calibration period was found required to calibrate the PMD to its full capacity. Calibration method proposed poses a limitation on the location of the PMD being in the vicinity of the GAMS. As the number of the PMD participating in the sensor network increases, however, calibrated PMDs can be used as reference devices to nearby PMDs that require calibration, forming a calibration chain through MQTT protocol. Conclusions: Calibration of a low-cost PMD, which is based on construction of PM2.5 sensor network using MQTT protocol and web query of reference measurement data available at a GAMS, significantly improves the accuracy and reliability of a PMD, thereby making practical use of the low-cost PMD possible.
Keywords: Calibration | Machine learning | Monitoring and control | Particulate matter | Web query
مقاله انگلیسی
3 Comparison of banking innovation in low-income countries: A meta-frontier approach
مقایسه نوآوری بانکی در کشورهای کم درآمد: یک رویکرد فرامرزی-2019
Financial innovation is a crucial factor behind many of the improvements in the financial sector that directly affect the economy in a positive way. Financial innovation may also alter financial intermediation and increase reliability and transparency. Research has demonstrated that levels of financial innovation are similar among high-income countries; however, research has shown that financial development differs substantially in low income countries regardless of the economic size, suggesting that financial innovation may also differ. This study evaluated the levels of financial innovation and the determinants of innovation within the low-income countries. In particular, a new two-step meta-frontier approach was constructed to estimate technology gap ratios, and a censored model was built to establish their determinants. The results show that low-income countries do in fact vary greatly in terms of financial innovation. Competition, financial inclusion and banking access constitute major determinants of financial innovation.
Keywords: Financial innovation | Technology gap ratio | Cost efficiency | Stochastic meta-frontier analysis | Low-income countries
مقاله انگلیسی
4 Machine learning based hierarchical classification of frontotemporal dementia and Alzheimers disease
یادگیری ماشین بر اساس طبقه بندی سلسله مراتبی از فراموشی پیشانی گیجگاهی و بیماری آلزایمر-2019
Background: In a clinical setting, an individual subject classification model rather than a group analysis would be more informative. Specifically, the subtlety of cortical atrophy in some frontotemporal dementia (FTD) patients and overlapping patterns of atrophy among three FTD clinical syndromes including behavioral variant FTD (bvFTD), nonfluent/ agrammatic variant primary progressive aphasia (nfvPPA), and semantic variant PPA (svPPA) give rise to the need for classification models at the individual level. In this study, we aimed to classify each individual subject into one of the diagnostic categories in a hierarchical manner by employing a machine learning-based classification method. Methods: We recruited 143 patients with FTD, 50 patients with Alzheimers disease (AD) dementia, and 146 cognitively normal subjects. All subjects underwent a three-dimensional volumetric brain magnetic resonance imaging (MRI) scan, and cortical thickness was measured using FreeSurfer. We applied the Laplace Beltrami operator to reduce noise in the cortical thickness data and to reduce the dimension of the feature vector. Classifiers were constructed by applying both principal component analysis and linear discriminant analysis to the cortical thickness data. For the hierarchical classification, we trained four classifiers using different pairs of groups: Step 1 - CN vs. FTD+AD, Step 2 - FTD vs. AD, Step 3 - bvFTD vs. PPA, Step 4 - svPPA vs. nfvPPA. To evaluate the classification performance for each step, we used a10-fold cross-validation approach, performed 1000 times for reliability. Results: The classification accuracy of the entire hierarchical classification tree was 75.8%, which was higher than that of the non-hierarchical classifier (73.0%). The classification accuracies of steps 1–4 were 86.1%, 90.8%, 86.9%, and 92.1%, respectively. Changes in the right frontotemporal area were critical for discriminating behavioral variant FTD from PPA. The left frontal lobe discriminated nfvPPA from svPPA, while the bilateral anterior temporal regions were critical for identifying svPPA. Conclusions: In the present study, our automated classifier successfully classified FTD clinical subtypes with good to excellent accuracy. Our classifier may help clinicians diagnose FTD subtypes with subtle cortical atrophy and facilitate appropriate specific interventions.
Keywords: Frontotemporal dementia | Classification model | Machine learning
مقاله انگلیسی
5 Comprehensive analysis of machine learning models for prediction of sub-clinical mastitis: Deep Learning and Gradient-Boosted Trees outperform other models
تجزیه و تحلیل جامع مدلهای یادگیری ماشین برای پیش بینی ورم پستان تحت بالینی: یادگیری عمیق و رشد شیب درختان نسبت به سایر مدلها-2019
Sub-clinical bovine mastitis decreases milk quality and production. Moreover, sub-clinical mastitis leads to the use of antibiotics with consequent increased risk of the emergence of antibiotic-resistant bacteria. Therefore, early detection of infected cows is of great importance. The Somatic Cell Count (SCC) day-test used for mastitis surveillance, gives data that fluctuate widely between days, creating questions about its reliability and early prediction power. The recent identification of risk parameters of sub-clinical mastitis based on milking parameters by machine learning models is emerging as a promising new tool to enhance early prediction of mastitis occurance. To develop the optimal approach for early sub-clinical mastitis prediction, we implemented 2 steps: (1) Finding the best statistical models to accurately link patterns of risk factors to sub-clinical mastitis, and (2) Extending this application from the farms tested to new farms (method generalization). Herein, we applied various machine learning-based prediction systems on a big milking dataset to uncover the best predictive models of sub-clinical mastitis. Data from 364,249 milking instances were collected by an electronic automated in-line monitoring system where milk volume, lactose concentration, electrical conductivity (EC), protein concentration, peak flow and milking time for each sample were measured. To provide a platform for the application of the models developed to other farms, the Z transformation approach was employed. Following this, various prediction systems [Deep Learning (DL), Naïve Bayes, Generalized Liner Model, Logistic Regression, Decision Tree, Gradient-Boosted Tree (GBT) and Random Forest] were applied to the non-transformed milking dataset and to a Z-standardized dataset. ROC (Receiver Operating Characteristics Curve), AUC (Area Under The Curve), and high accuracy demonstrated the high sensitivity of GBT and DL in detecting sub-clinical mastitis. GBT was the most accurate model (accuracy of 84.9%) in prediction of sub-clinical bovine mastitis. These data demonstrate how these models could be applied for prediction of subclinical mastitis in multiple bovine herds regardless of the size and sampling techniques.
مقاله انگلیسی
6 Machine learning models accurately predict ozone exposure during wildfire events
دقت پیش بینی مدلهای یادگیری ماشین با قرار گرفتن در معرض ازن در حوادث آتش سوزی-2019
Epidemiologists use prediction models to downscale (i.e., interpolate) air pollution exposure where monitoring data is insufficient. This study compares machine learning prediction models for groundlevel ozone during wildfires, evaluating the predictive accuracy of ten algorithms on the daily 8-hour maximum average ozone during a 2008 wildfire event in northern California. Models were evaluated using a leave-one-location-out cross-validation (LOLO CV) procedure to account for the spatial and temporal dependence of the data and produce more realistic estimates of prediction error. LOLO CV avoids both the well-known overly optimistic bias of k-fold cross-validation on dependent data and the conservative bias of evaluating prediction error over a coarser spatial resolution via leave-k-locations-out CV. Gradient boosting was the most accurate of the ten machine learning algorithms with the lowest LOLO CV estimated root mean square error (0.228) and the highest LOLO CV bR 2 (0.677). Random forest was the second best performing algorithm with an LOLO CV bR 2 of 0.661. The LOLO CV estimates of predictive accuracy were less optimistic than 10-fold CV estimates for all ten models. The difference in estimated accuracy between the 10-fold CV and LOLO CV was greater for more flexible models like gradient boosting and random forest. The order of estimated model accuracy depended on the choice of evaluation metric, indicating that 10-fold CV and LOLO CV may select different models or sets of covariates as optimal, which calls into question the reliability of 10-fold CV for model (or variable) selection. These prediction models are designed for interpolating ozone exposure, and are not suited to inferring the effect of wildfires on ozone or extrapolating to predict ozone in other spatial or temporal domains. This is demonstrated by the inability of the best performing models to accurately predict ozone during 2007 southern California wildfires.
Keywords: Air pollution | Exposure model | Machine learning | Ozone | Wildfire
مقاله انگلیسی
7 Prediction-based protocols for vehicular Ad Hoc Networks: Survey and taxonomy
پروتکل های مبتنی بر پش بینی برای شبکه های تک کاره وسیله ای: بررسی و طبقه بندی-2018
The high mobility of vehicles as a major characteristic of Vehicular Ad Hoc Networks (VANETs) affects vividly the dynamic nature of the networks and results in additional overhead in terms of extra messages and time delay. The future movements of the vehicles are usually predictable. The predictability of the vehicles future movements is a result of the traffic conditions, the urban layout, and the driving requirements to observe the traffic constrains. Hence, predicting these future movements could play a considerable role for both building reliable vehicular communication protocols and solving several issues of intelligent transportation systems. In the literature, numerous prediction-based protocols are presented for VANETs. Therefore, this paper follows the guidelines of systematic literature reviews to provide a premier and unbiased survey of the existing prediction-based protocols and develop novel taxonomies of those protocols based on their main prediction applications and objectives. A discussion on each category of both taxonomies is presented, with a focus on the requirements, constrains, and challenges. Moreover, usage analysis and performance comparisons are investigated in order to derive the suitability of each prediction objective to the various applications. Also, the relevant challenges and open research areas are identified to guide the potential new directions of prediction-based research in VANETs. Throughout this paper, information is provided to developers and researchers to grasp the major contributions and challenges of the predictive protocols in order to pave the way for enhancing their reliability and robustness in VANETs.
keywords: Prediction Applications| Prediction Objectives| Prediction Techniques| Research| VANETs
مقاله انگلیسی
8 Simulation methodology and performance analysis of network coding based transport protocol in wireless big data networks
روش شبیه سازی و تجزیه و تحلیل کارایی پروتکل انتقال مبتنی بر کدگذاری شبکه در شبکه های داده های بزرگ بی سیم-2018
The Multi-Path, Multi-Hop (MPMH) communications have been extensively used in wire less network. It is especially suitable to big data transmissions due to its high throughput. To provide congestion and end-to-end reliability control, two types of transport layer pro tocols have been proposed in the literature: the TCP-based protocols and the rateless cod ing based protocols. However, the former is too conservative to explore the capacity of the MPMH networks, and the latter is too aggressive in filling up the communication capac ity and performs poorly when dealing with congestions. To overcome their drawbacks, a novel network coding scheme, namely, Adjustable Batching Coding (ABC), was proposed by us, which uses redundancy coding to overcome random loss and uses retransmissions and window size shrink to relieve congestion. The stratified congestion control strategy makes the ABC scheme especially suitable for big data transmissions. However, there is no simu lation platform built so far that can accurately test the performance of the network coding based transport protocols. We have built a modular, easy-to-customize simulation system based on an event-based programming method, which can simulate the ABC-based MPMH transport layer behaviors. Using the proposed simulator, the optimal parameters of the protocol can be fine-tuned, and the performance is superior to other transport layer pro tocols under the same settings. Furthermore, the proposed simulation methodology can be easily extended to other variants of MPMH communication systems by adjusting the ABC parameters.
Keywords: Network simulator ، Wireless big data networks ، Multi-path multi-hop communications ، Transport layer ، Network coding
مقاله انگلیسی
9 انواع مدل کسب و کار برای بازیافت مصرف کننده نهایی در چین
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 28 - تعداد صفحات فایل doc فارسی: 31
تلاش برای ساخت یک سیستم EPR برای تجهیزات الکتریکی و الکترونیکی (WEEE) زائد در چین موجب ایجاد ناسازگاری غیر منتظره در عرصه نوآوری در مدل های کسب و کار برای بازیافت زباله های الکترونیکی مصرف کنندگان نهایی و سایر موارد قابل بازیافت در سال های اخیر شده است. این مطالعه با استفاده از تحقیقات عملیاتی برای ارزیابی عملکرد مدل های کسب و کار در حال ظهور برای بازیافت مصرف کننده نهایی در شهر های چین در سال های اخیر انجام شده است. ما سه دسته مدل های در حال ظهور را شناسایی کردیم: (1) برنامه های مبتنی بر جامعه که رفتارهای مربوط به ساماندهی زباله مصرف کنندگان را برای همه ی زباله های خانگی هدف قرار می دهند؛ (2) سیستم های لجستیکی معکوس با دستگاه های فروش اتوماتیک متصل به زنجیره های تجاری سنتی؛ 3) راه حل های اینترنتی ناب برای انجام معاملات بین مصرف کنندگان و بازیافت کنندگان. همه این مدل های کسب و کار ویژگی مشترک دارند که از فن آوری اینترنت استفاده می کنند، که به شدت در چین به عنوان "اینترنت +" توسط سیاست های دولتی و سرمایه سرمایه گذاری ترویج می شود. مدل های مختلف کسب و کار به عنوان پیوند بین شرکت و سطح سیستم و منعکس کننده امکانات متنوع برای تکامل آینده سیستم بازیافت در چین می باشد. ما یک چارچوب ارزیابی کیفی با پنج عنصر شامل راحتی برای مصرف کنندگان، قابلیت ردیابی برای تولید کنندگان، سود دهی برای بازیافت، چند منظوره بودن برای جمع آوری و قابلیت اطمینان برای مردم برای رسیدگی به ارزش های مختلف دنبال شده توسط نقش آفرینان مختلف درگیر در زنجیره بازیافت مطرح می کنیم. نتایج نشان می دهد که معضلات هر مدل کسب و میان تمام مولفه ها متعادل است و چالش دولت در ادغام طرح EPR با سیستم مدیریت زباله شهری را نشان می دهد.
کلمات کلیدی: بازیافت | مسئولیت تولید کننده (EPR) | مدل کسب و کار پایدار | اینترنت +| تجهیزات مربوط به زباله های الکتریکی و الکترونیکی (WEEE) | زباله های الکترونیکی
مقاله ترجمه شده
10 Will aging voting machines cause more voters to experience long waits?
آیا پیرشدگی ماشین های رای گیری باعث خواهد شد تا رای دهنده های بیشتری انتظارات طولانی را تجربه کنند؟-2018
As the majority of voting machines in use today approach or exceed their expected lifetime, an increased number of voting machine failures are expected in upcoming elections. This study examines and quantifies the impact of less reliable voting machines, due to age, on the number of voters waiting longer than 30-min. G/G/s queue approximation and discrete event simulation are used in the analysis. Results show that if reliability measures — mean time between failures, mean time to repair, and availability — are within certain interval ranges, no additional voting machines are needed to ensure that no more than 5% of voters wait for longer than 30 min. However, significantly more voters would have long waits if the reliability of voting machines is poor. Accordingly, less reliable voting machines do not necessarily cause more voters to experience long waits. The proposed closed-form approximation formula and the simulation model are practical tools for election officials to evaluate the impact of less reliable voting machines on voting lines.
keywords: Voting operations |Simulation |Machine failure |Voting lines |Wait time distribution
مقاله انگلیسی
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