Estimation Results using Real Time Data

Top PDF Estimation Results using Real Time Data:

Real-Time Vehicle Emission Estimation Using Traffic Data

Real-Time Vehicle Emission Estimation Using Traffic Data

60 passed the stop-line in, what turning movement they made, and which cycle they passed the stop-line in. For example, in a linkID of 21634: the 2 indicates an approach – Dunbar southbound; the 1 indicates second lane from the right (counts begin at 0); the 634, being greater than 500, indicates a left turn movement; and 634-500=134 indicates the 134 th signal cycle of the dataset. This convention was developed before the real-time emission calculation method, when MOVES runs were required, which only allows a maximum of 5 digits in the linkID. The countyID, zoneID, and roadTypeID are values corresponding with settings chosen for the MOVES run and are held constant in this research. The linkLength values correspond with the distance vehicles travel in the individual “link”. The linkVolume is the number of vehicles traversing the link in the time period of concern. The linkAvgSpeed is the speed of all vehicles in the “link” averaged over the time and distance of concern. It is not used if the Operating Mode Distribution table is provided. The linkDescription is an optional field. The linkAvgGrade, the average grade across the “link”, is held at 0 for simplicity, as it does not affect the methodology developed in this research.
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SAP Makes Big Data Real Real Time. Real Results.

SAP Makes Big Data Real Real Time. Real Results.

Analytics solutions from SAP uniquely enable real- time collective insight by delivering an enterprise business intelligence solution that gives users the power to engage with all their data, on any device, across any platform, while reducing the complexity of the IT landscape and minimizing total cost of ownership. Intuitively mash up, explore, visualize, and present data, both big and small, with agile visualizations. Apply advanced predictive analytics to information and processes across the enterprise to drive real-time understanding of the business, and confidently anticipate what comes next to
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Big Data Technologies for Near-Real-Time Results:

Big Data Technologies for Near-Real-Time Results:

Hadoop Figure 6. Networking upgrade to 10 Gigabit Ethernet: processing-stage speed improvement of approximately 50 percent. 9 Working with this massive data set, the ability to access non-sequential data quickly is a key performance consideration. Therefore, to reduce any existing storage bottleneck, the next upgrade tested was to replace the conventional HDDs with SSDs, taking advantage of their dramatically higher random read times. Building on the previous performance improvement from the processor upgrade, shifting from conventional HDDs to the Intel SSD 520 Series reduced the time to complete the workload by approximately another 80 percent—from about 125 minutes to about 23 minutes, as shown in Figure 5. 7 For those customers interested in combining conventional HDDs and SSDs in the same server, Intel offers Intel® Cache Acceleration Software. This tiered storage model provides some of the performance benefits of SSDs at a lower acquisition cost, but in addition to the performance differences compared to an SSD-only configuration, this approach also sacrifices some of the reliability benefits available from SSDs. Nevertheless, this storage model offers customers another option as they move toward full adoption of SSDs to dramatically reduce the time to get from data to insight.
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Near real-time estimation of water vapour in the troposphere using ground GNSS and the meteorological data

Near real-time estimation of water vapour in the troposphere using ground GNSS and the meteorological data

bias, on the other hand, as shown in this study is more related to discrepancy between NWP data and GPS data (Fig. 15), and according to Table 4 has a small impact on overall solu- tion. Outliers reduction scheme is based on ZTD estimation precision, all observations with larger than a certain thresh- old value was excluded. The other side of this ill-conditioned tomography problem is the condition score here equals to 5 × 10 3 . This is a result of many factors like: ASG-EUPOS network sites location, GPS system only observations, and 100 voxels in one layer with 7 consecutive layers of 1 km.
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Fuel Burn Estimation Using Real Track Data

Fuel Burn Estimation Using Real Track Data

Fuel Burn Estimation Using Real Track Data Gano B. Chatterji * University of California Santa Cruz, Moffett Field, CA, 94035-1000 A procedure for estimating fuel burned based on actual flight track data, and drag and fuel-flow models is described. The procedure consists of estimating aircraft and wind states, lift, drag and thrust. Fuel-flow for jet aircraft is determined in terms of thrust, true airspeed and altitude as prescribed by the Base of Aircraft Data fuel-flow model. This paper provides a theoretical foundation for computing fuel-flow with most of the information derived from actual flight data. The procedure does not require an explicit model of thrust and calibrated airspeed/Mach profile which are typically needed for trajectory synthesis. To validate the fuel computation method, flight test data provided by the Federal Aviation Administration were processed. Results from this method show that fuel consumed can be estimated within 1% of the actual fuel consumed in the flight test. Next, fuel consumption was estimated with simplified lift and thrust models. Results show negligible difference with respect to the full model without simplifications. An iterative takeoff weight estimation procedure is described for estimating fuel consumption, when takeoff weight is unavailable, and for establishing fuel consumption uncertainty bounds. Finally, the suitability of using radar-based position information for fuel estimation is examined. It is shown that fuel usage could be estimated within 5.4% of the actual value using positions reported in the Airline Situation Display to Industry data with simplified models and iterative takeoff weight computation.
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REAL-TIME ATTENDANCE AND ESTIMATION OF PERFORMANCE USING BUSINESS INTELLIGENCE

REAL-TIME ATTENDANCE AND ESTIMATION OF PERFORMANCE USING BUSINESS INTELLIGENCE

Also it takes a lot of time for marketing people to meet the clients and make them fill the survey form or to manually gather the details of the client and then update them in the company database later. Also the client feels tired or frustrated to fill the manual form of his/her details. The application will help in finding out the potential customers for the company. In the marketing department the information gathered by the application will be used to study the growth of product. Also the potential customers identified by the sales team will be provided detailed information about the working of the application and its use in their company. These are the valuable details that are of great value to the company, so they should not be misplaced or lost under any circumstances.
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Real-Time State Estimation in a Flight Simulator Using fNIRS

Real-Time State Estimation in a Flight Simulator Using fNIRS

Working memory is a key executive function for flying an aircraft. This function is particularly critical when pilots have to recall series of air traffic control instructions. However, working memory limitations may jeopardize flight safety. Since the functional near-infrared spectros- copy (fNIRS) method seems promising for assessing working memory load, our objective is to implement an on-line fNIRS-based inference system that integrates two complementary estimators. The first estimator is a real-time state estimation MACD-based algorithm dedi- cated to identifying the pilot’s instantaneous mental state (not-on-task vs. on-task). It does not require a calibration process to perform its estimation. The second estimator is an on- line SVM-based classifier that is able to discriminate task difficulty (low working memory load vs. high working memory load). These two estimators were tested with 19 pilots who were placed in a realistic flight simulator and were asked to recall air traffic control instruc- tions. We found that the estimated pilot’s mental state matched significantly better than chance with the pilot’s real state (62% global accuracy, 58% specificity, and 72% sensitivi- ty). The second estimator, dedicated to assessing single trial working memory loads, led to 80% classification accuracy, 72% specificity, and 89% sensitivity. These two estimators es- tablish reusable blocks for further fNIRS-based passive brain computer
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The estimation of continuous-time systems using discrete data

The estimation of continuous-time systems using discrete data

apparently provide reasonable descriptions of a wide variety of dynamic situations in the real world. However prior evidence may call for a system of greater sophistication. In particular it may well be the case that the rate of change of a process depends not only on its present state but also on its past history. Thus we are led to examine differential equations that involve lagged variables and their derivatives and which are most commonly known as difference- differential equations. Bernoulli (1728) was apparently the first to consider such an equation, in connection with a problem in mechanics.
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Real time motion estimation using a neural architecture implemented on GPUs

Real time motion estimation using a neural architecture implemented on GPUs

As we discussed in the previous experiments, GPU version has low performance in the first iterations of the learning algorithm, where the GPU cannot hide the latencies due to the small number of processing elements. To achieve even bigger acceleration of the GNG algorithm, we propose the use of the CPU in the first iterations of the algorithm, and then start processing data in the GPU only when there is an acceleration regarding CPU, thus achieving a bigger overall acceleration of the algorithm (see Figure 13). To determine the number of neurons necessary to start computing at GPU we have analyzed in detail the execution times for each new insertion, and concluded that each device, depending on its computing power starts being efficient at a different number of neurons. Following several tests, we have determined the threshold at which each device starts accelerating compared to the CPU version. As it can be seen in figure 9, threshold values for different devices are set to 1500, 1700, 2100 for GTX 480, Tesla C2070 and Quadro 2000 models respectively. The hybrid version is proposed as some applications need to operate under time constraints obtaining a solution of a specified quality within certain period of time. In cases when the objective is the disruption of learning due to the application requirements, it is important to insert the maximum number of neurons and perform the maximum number of adjustments to achieve the highest quality in a limited time. The hybrid version ensures a maximum performance in this kind of applications using the computational capabilities of the CPU or the GPU depending on the situation.
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Real Time Elbow Angle Estimation Using Single RGB Camera

Real Time Elbow Angle Estimation Using Single RGB Camera

Estimates elbow angle in degrees with RGB camera and Microsoft Kinect in coronal plane for. 250[r]

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Real-time parameter estimation of Zika outbreaks using model averaging

Real-time parameter estimation of Zika outbreaks using model averaging

three-parameter logistic (3P logistic) [ 7 , 13 , 14 ], five-parameter logistic (5P logistic) [ 15 ], Sigmoid Emax [ 16 ], Gompertz [ 14 , 17 ], and Weibull [ 18 ] mod- els. All these models can be used to fit epidemic data as well. Fitting several models to the same data raises the issue, central in statistical modeling, of model selection. Indeed, a model selection procedure is needed in order to choose the model with the best fit to the data. Often, one is confronted with the problem that several models are performing almost equally well over the range of observed data. Typically, one selects the best- fitting model out of the set of fitted models and ignores the uncertainty due to model selection in estimation and inference. For these reasons, several authors (i.e. Burnham & Anderson [ 19 ], Claeskens & Hjort [ 20 ], Posada [ 21 ], Moon [ 22 ], and Lin [ 23 ]), advocate the use of model averaging (MA) techniques to perform multimodel estimation and inference. MA is a method that takes into account all fitted models for the estimation of the parameters of primary inter- est. It is based upon a weighted average of the param- eter of primary interest obtained from different models, giving largest weights to those models that best fit the data [ 24 ].
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Aircraft Fault Detection Using Real-Time Frequency Response Estimation

Aircraft Fault Detection Using Real-Time Frequency Response Estimation

added complexity. An exponential forgetting method 29 has been attempted in the past, 11 but has displayed poorer performance than a sliding window for frequency response estimation. Selection of the best data- forgetting method for a particular situation is an open problem. This study showed that a real-time modeling method could be used to identify faults in straight and level flight. Aircraft in serious loss-of-control situations can experience nonlinear motions with rapidly-varying flight conditions. In these cases it may not be possible to excite small perturbation maneuvers over 10 s or 20 s and achieve a reasonable frequency response estimate. It would be recommended then to only attempt to excite short period, dutch roll, and roll modes to get a rough idea of time delays and if control surfaces were stuck or damaged. Another possibility is that there could be enough excitation in the aircraft already to estimate reasonable frequency responses, but this case is perhaps better analyzed using techniques that employ averaging.
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Real Time Product Analysis using Data Mining

Real Time Product Analysis using Data Mining

A) Web Crawler B) Scraper A) WEB CRAWLER: Web crawler is one of the main component of the Project. Since the product is price comparison engine, the first thing that is required is to collect large amount of data in terms of products from different E-commerce websites. Manually ,the collection of such large amount of data was not possible. So the best way to get these data is to create a web crawler also known as spider. For crawler to be more effective, it is necessary that the crawler is efficient ,concurrent and multi-threaded. For crawler to be multi-threaded, it is important that the synchronization among the threads are maintained. So use of blocking queue came into picture. The main purpose of the crawler is to crawl different E-commerce websites and to fetch the URLs of the products from these websites. Every E-commerce website can be considered as a graph consisting of several nodes(Links or URLs)as shown in figure 1. The crawler must traverse to all these nodes and fetch these nodes .Once it has fetched the node, that node must be kept in a set of visited nodes so that no two same URLs are fetched. Threads that are created in the thread-pool must be limited so that they do not eat up the entire memory. And each thread that's been started has to be terminated. The Coordinating thread distributes the crawl job to the processing threads. These processing threads fetches the URLs and returns to the Coordinating threads. Thus the fetched URLs that we have in the set visited nodes are given to the scraper for scraping purpose.
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Real-Time Big Data Analytics using Hadoop

Real-Time Big Data Analytics using Hadoop

Amita Jajoo 1 , Kuldeep U. Karpe 2 , Nilesh R. Parade 3 , Pratik D. Powar 4 1,2,3,4 DYPCOE, Akurdi, Pune, India Abstract-- Recent Technology have led to a large volume of data from different areas(ex. medical, aircraft, internet and banking transactions) from last few years. Big Data is the collection of this field’s information. Big Data contains high volume, velocity and high variety of data. For example Data in GB or PB which are in different form i.e. structured, unstructured and semi-structured data and its requires more fast processing or real-time processing. Such Real time data processing is not easy task to do, Because Big Data is large dataset of various kind of data and Hadoop system can only handle the volume and variety of data but for real-time analysis we need to handle volume, variety and the velocity of data. To solve or to achieve the high velocity of data we are using two popular technologies i.e. Apache Kafka, which is message broker system and Apache Storm, which is stream processing engine.
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Real Time Product Analysis using Data Mining

Real Time Product Analysis using Data Mining

Chapter 5. Implementation Details in order to retrieve the accurate results. Now the user is presented with a particular product with different E-commerce websites .This allows the user to compare products based on prices. In this project, the web crawler is required to be very effective and efficient. For this purpose various factors must be taken into consideration such as time, address of the website etc. For creating the crawler, Jsoup libraries are imported which holds specialized functions in URL extraction. Then a function is written in java that filters the unuseful URLs. For deciding which of the URLs are useful or not, web crawler visits all the URLs and the URLs it finds as spam, it filters it. Since different websites have different naming standards therefore it will become difficult for the crawler to fetch and compare different URLs. For this purpose machine learn- ing algorithm is used for URLs comparison. Also to track user behavior and search patterns, algorithms are used that keeps track on users and their behaviors and their searching patterns in order to provide user with better search experience.
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Real Time Data Processing using Spark Streaming

Real Time Data Processing using Spark Streaming

• Received data automatically persisted to HDFS Write Ahead Log to prevent data loss. • set spark.streaming.receiver.writeAheadLog.enable=true in spark conf[r]

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REAL-TIME DATA GENERALISATION AND INTEGRATION USING JAVA

REAL-TIME DATA GENERALISATION AND INTEGRATION USING JAVA

There are several variants of this workflow. One possibility would be to create an SVG file directly in the Java-program. This means that an XSLT transformation is not necessary. A second possibility would be to manipulate the cartographic data in the XSLT transformation (with some Java extensions); which means that there would not be a need for the Java program. The latter approach has successfully been used by Lehto and Kilpeläinen (2000, 2001a, 2001b). However, this approach has certain limitations. Since XSLT transformations only treat one object at a time, it is not possible to implement methods which involve interactions between objects. This type of interaction modelling is often required when creating a real-time map for a service. Some examples where modelling interactions of objects are required:
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A Fast DOA Estimation Based on Space Time Direct Data Domain Approach Using Real Antenna Elements

A Fast DOA Estimation Based on Space Time Direct Data Domain Approach Using Real Antenna Elements

multiple pulses from a coherent radar (the temporal domain) [9]. In this paper, we exploit the characteristic of the D 3 LS that it has good nulling capability even for coherent signals with single snapshot. We add a virtual desired signal to the receiving data. At the same time, the actual desired signals are regarded as interferences. We use the D 3 LS STAP algorithm to get nulls on the direction of the actual desired signals while restricts the mainlobe on the direction of the virtual source. Five antenna configurations are used: Uniform Linear Equal Spaced Array (ULESA), Exponential Spaced Linear Array (ESLA), a Semicircular Array (SCA), Sinusoidal Spaced Array (SSA), and Planar Array. D 3 LS STAP will be applied on the measured voltages from these configurations.
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Transit Demand Estimation And Crowding Prediction Based On Real-Time Transit Data

Transit Demand Estimation And Crowding Prediction Based On Real-Time Transit Data

1.3 Research Approach A transit line runs as a straight-shot line passing through many residential, commercial, and industrial areas along a specific route with very frequent schedules and dozens of passengers waiting each and every time. Overcrowding warrants extra service to keep up with demand. The focus of the current research is primarily on one mode of public transportation: local buses. Many people take buses to go to work, school, commercial venues or local events, and it is among the most popular modes of public transportation including trains, light rails and subways. Busses are particularly useful in urban environments because of their flexibility to navigate the side streets in addition to the main street, provide access to more people or riders in the “remote” areas of a state, city, town or county. The simplicity of a bus also makes the bus a key component of the transit network. Buses nowadays come in all shapes and sizes, from a microbus to very long buses. However, since buses have finite capacities and some shorter in length than others, they are prone to crowding at times. By estimating the total number of passengers waiting at all the stops on a route level and comparing that with current bus capacity, we hope to provide information in terms of, when a bus will be full and an additional bus should be dispatched before crowding occurs.
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Real-time Queue Length Estimation of Signalized Intersections Based on RFID Data

Real-time Queue Length Estimation of Signalized Intersections Based on RFID Data

cycle. Variations of the estimation model are also presented under different traffic volumes, the relationship between queue length and the capacity of the approach is also under consideration. A field experiment using RFID Detector Data was conducted at an intersection in Nanjing. The results of field experiment demonstrate that the proposed models can estimate the real-time queue length with satisfactory accuracy.

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