Imperva offers a complete suite of web application fault tolerance solutions. The first among these is our cloud-based application layer load balancer that can be used for both in-datacenter (local) and cross-datacenter (global) traffic distribution.
The amount of memory required is dependent upon the size of your data set and the access patterns of your application. To improve fault tolerance, once you have a rough idea of the total memory required, divide that memory into enough nodes such that your application can survive the loss of one or two nodes. For example, if your memory requirement is 13GB, you may want to use two cache.m4.large nodes instead of using one cache.m4.xlarge node. It is important that other systems such as databases will not be overloaded if the cache-hit rate is temporarily reduced during failure recovery of one or more of nodes. Please refer to the Amazon ElastiCache User Guide for more details.
Automotive Tolerance Data Free Download
Analysis of Event Data Recorder Data for Vehicle Safety Improvementby Marco P. daSilva, Volpe National Transportation Systems Center (VRTC), Cambridge, MA., April 2008, DOT HS 810 935 Analysis of Event Data Recorder Data for Vehicle Safety ImprovementABSTRACT: The Volpe Center performed a comprehensive engineering analysis of Event Data Recorder (EDR) data supplied by NHTSA to assess its accuracy and usefulness in crash reconstruction and improvement of vehicle safety systems. The Volpe Center gathered and analyzed 2,541 EDR files downloaded from the National Automotive Sampling System (NASS), Special Crash Investigations (SCI), and Crash Injury Research & Engineering Network (CIREN) databases supplied by NHTSA. The analyses focused on EDR file format and potential improvements, assessment of crash types where EDR data exists, review of EDR data for accuracy and completeness, EDR data comparisons with existing crash data, review of pre-crash, crash, and post-crash data for usefulness in better understanding the crash reconstruction, identification of error sources, and determination of methods by which researchers could use the EDR data to improve their crash case information. The results of the engineering analysis show that EDR data can objectively report real-world crash data and therefore be a powerful investigative and research tool, by providing very useful information to crash reconstructionists and vehicle safety researchers. Due to significant limitations however, EDR data should always be used in conjunction with other data sources.
Scope: This recommended practice aims to establish a common format for displaying and presenting crash-related data recorded and stored within certain electronic components currently installed in many light-duty vehicles. This recommended practice pertains only to the post-download format of such data and is not intended to standardize the format of the data stored within any on-board storage unit, or to standardize the method of data recording, storing, or extraction. Historically, crash data recording technology in light-duty vehicles has developed and evolved based on differing technical needs of manufacturers and their customers without industry standards or government regulation. As a result, wide variations currently exist among vehicle manufacturers regarding the scope and extent of recorded data. For this reason, this recommended practice is not intended to standardize or mandate the recording of any specific data element or to specify a minimum data set. Rather, it is intended to be a compilation of data elements and parameters that various manufacturers are currently recording, as well as those elements reasonably predicted to be recorded in the foreseeable future, and to establish a common format for display and presentation of that data so recorded. This version of the recommended practice is limited in application to vehicular data recorded in single frontal-impact events. Provisions for multiple-impact events may be included in the next version. Side-impact and rollover events may be addressed at a later time.
ASTRACT: Knowledge from real-world crashes is important in the design of a crashworthy road transportation system. Such design must be based on the human injury tolerance limits. Links between impact severity and injury outcome are important and could be used in order to achieve such tolerance limits. Traditionally impact severity has been calculated with retrospective reconstruction technique, although recently, injury risk functions have been presented where impact severity has been measured with crash pulse recorders. The aims of this paper were to present injury risk functions, with special reference to neck injuries, calculated with crash recorder and paired comparison technique, and to propose a way of combining the two methods. By combining comprehensive statistical material with in depth crash recorder information, injury risk functions for injuries to different body regions were established. Risk functions for AIS1 neck injuries both in frontal and rear-end impacts have also been established. It was found that the data from the crash pulse recorder generated risk functions could be used to validate and calibrate risk functions based on the matched-paired technique. Moreover, it was found that the shape of the injury risk curves differed significantly for injuries to different body regions. It was also found that the neck injury risk differed significantly for frontal and rear-end impacts. It is concluded, that adding new techniques to the existing techniques based on reconstruction can further refine generating risk functions. The injury risks found are important for the understanding of injury tolerance limits for injuries to different body regions, but also for the understanding of injury mechanisms for different injury types.
ASTRACT: In this paper we describe a preliminary version of a frontal impact crash sensing algorithm capable of continuously predicting the severity of a crash in real time. This kind of algorithms could be used to control an air bag system with a variable output inflator, which supplies a variable amount of gas into the air bag on demand. The algorithm consists of two parts linked in series. The first part categorizes the class of an event. The second part predicts the severity of the crash using a function of the occupant free flight displacement and time. Linear regression and neural network analyses were performed separately to determine the coefficients for the severity function of each crash mode. The algorithm was implemented in Simulink and validated with test data. While both analyses achieved reasonably good correlation between the severity of each event and its corresponding severity function, the neural network analysis generally provided a better correlation.
ABSTRACT: Vetronix manufactures and sells tools to download EDR data from many vehicles. To answer questions related to their tools and applications, this is being provided on the EDR Web site. The United States Government does not endorse products or manufacturers. See: www.vetronix.com
ABSTRACT: The National Highway Traffic Safety Administration acquires detailed engineering information on new and rapidly changing technologies in real-world crashes utilizing the National Automotive Sampling System Crashworthiness Data System (NASS CDS), Special Crash Investigations (SCI) and Crash Injury Research and Engineering Network (CIREN) programs. The data are used by NHTSA, the automotive industry, and consumer groups to evaluate the performance of motor vehicles in crashes. Currently, the primary metric used to represent crash severity in NHTSA programs in the delta in velocity (delta-v). The principle source for the delta-v estimates in the NHTSA programs is a computer algorithm. The reconstruction computer program has a number of limitations. As a result, only about 38 percent of the NASS cases have reported DV. Beginning with its 1994 model year vehicles, General Motors began producing a fleet of vehicles that recorded the DV. With the assistance of GM, SCI began collecting the DV from these vehicles' Event Data Recorders (EDR) on crashes of special interest to the Agency. In early 2000, a commercially available tool to read the output from General Motors vehicles' event data recorders became publicly available. NHTSA has implemented 50 of these units into their field data collection. In 2000, NHTSA and Ford Motor Company initiated a collaborative effort to perform case-by-case evaluation of the real world performance of Ford's advanced occupant protection technologies. Particularly noteworthy is the technical analysis of the information relating to occupant status, severity assessment and deployment control in researching crashes with advanced occupant protection systems. NHTSA is expanding its databases to allow event data to be stored. For the 2000 data collection year, variables were added to NASS to identify if a vehicle is equipped with an on board recorder and, if data was downloaded. Additionally, an open-format field was provided for recording the data collected. Future enhancement will include the automation of all EDR output. This paper will present information from NHTSA's NASS and SCI data collection programs concerning crash investigations of vehicles equipped with event data recorders. The focus of the paper will be to provide information on specific findings from the event data recorder compared to the physical evidence and computer reconstruction models. (Source: Augustus Chidester; John Hinch; Thomas A. Roston; National Highway Traffic Safety Administration, United States of America; Paper Number 247)FULL DOCUMENT
ABSTRACT: In March of 2000, Vetronix Corporation unveiled a Crash Data Retrieval (CDR) system that allows users to download data from certain GM vehicles subjected to a crash event involving the deployment or near deployment of an air bag. The recording of crash event data is a by-product of the introduction of air bags and the need to measure or sense the severity of a crash by automobile manufacturers. GM has been using a sensing and diagnostic module (SDM) to measure crash severity since 1994 and started recording pre-crash data, such as vehicle speed, engine rpm, throttle position, and brake status with some 1999 model year vehicles. This paper reviews the evolution of automatic recoding devices in transportation, including the automatic EDR. The recording and retrieval of data in the GM and Vetronix system are examined with particular attention on using the data for accident reconstruction purposes. Twelve low-speed tests investigate the current threshold and sensitivity for recording data, while five case studies investigate the usefulness and limitations of the recorded data. (Source: Joe T. Correia, Ken A. Iliadis, Ed S. McCarron, Mario A. Smolej, Hastings, Boulding, Correia Consulting Engineers)FULL DOCUMENT 2ff7e9595c
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