The concept of minimum ignition energy (MIE) has traditionally formed the basis for studying ignition hazards of fuels. However, the viewpoint of ignition as a statistical phenomenon appears to be more consistent with the inherent variability in engineering test data. We have developed a very low-energy capacitive spark ignition system to produce short sparks with fixed lengths of 1 to 2 mm, an...

Training deep networks is a time-consuming process, with networks for object recognition often requiring multiple days to train. For this reason, leveraging the resources of a cluster to speed up training is an important area of work. However, widely-popular batch-processing computational frameworks like MapReduce and Spark were not designed to support the asynchronous and communication-intensi...

We benchmarked Apache Spark with a popular parallel machine learning training application, Distributed Stochastic Gradient Descent for Matrix Factorization [5] and compared the Spark implementation with alternative approaches for communicating model parameters, such as scheduled pipelining using POSIX socket or MPI, and distributed shared memory (e.g. parameter server [13]). We found that Spark...

A survey of the development of the spark chamber from the spark counter is given. An effort has been made to clarify the early history of the device. Some unpublished recent Russian work on spark chambers is summarized. A consistent terminology for the various times relevant to spark chamber operation is suggested.

In this article, the electrode erosion and the gap growth of spark plugs were measured on of a CNG fueled engine after durability tests. Then, the required ignition voltage and the spark quality were investigated by a spark plug test rig. Considering results of measurements, the gap growth increased as the running time increased. Thus, the gap growth of spark plugs with 200 and 280 hours runnin...

The role of components in an Electrostatic Precipitator (EP) for a spark and both the phenomenon and mechanism of a spark are elaborated and then the type of spark is classified by the condition of shortage between the discharge and collecting electrodes. The secondary voltage and current waveforms of a spark are discussed in order to address the severe effect of the secondary and primary curre...

The aim of this project is to introduce Spark Clouds, which integrate spark lines into a tag cloud to convey trends between multiple tag clouds. Spark Clouds ability is to show trends compares favorably to the alternative visualizations. In the Existing System, Tag clouds are used to display the relative tag frequency, popularity, or importance by font size. They serve as a visual summary of do...

Decision tree is one of the most widely used classification methods. For massive data processing, MapReduce is a good choice. Whereas, MapReduce is not suitable for iterative algorithms. The programming model of Spark is proposed as a memory-based framework that is fit for iterative algorithms and interactive data mining. In this paper, C4.5 is implemented on both MapReduce and Spark. The resul...

Apache Spark is a popular framework for writing large scale data processing applications. Our long term goal is to develop automatic tools for reasoning about Spark programs. This is challenging because Spark programs combine database-like relational algebraic operations and aggregate operations, corresponding to (nested) loops, with User Defined Functions (UDFs). In this paper, we present a no...

Spark is a popular framework for writing large scale data processing applications. Our goal is to develop tools for reasoning about Spark programs. This is challenging because Spark programs combine database-like relational algebraic operations and aggregate operations with User Defined Functions (UDF s). We present the first technique for verifying the equivalence of Spark programs. We model S...