Influence of coke structure on coke quality using image analysis method

Abstract The quality of coke affects the performance of the blast furnace, factors affecting coke quality include coal properties, coal charge granulometry and carbonization conditions. The coke properties include the size analysis, cold strength (Micum Indices-M40, M10) and hot strength (Coke Reactivity Index-CRI, Coke Strength after Reaction-CSR) properties and structural properties such as coke structure and texture. Structural properties comprise the porosity, pore-cell wall thickness and pore sizes, while textures consist of the carbon forms in the coke. In present work, advanced method such as image analysis method was used to interpret coke microstructure. Conventional methods such as determination of coke porosity by measurement of real and apparent density and mercury porosimetry have a number of limitations. Coke size, magnification, number of image frames captured, process of pellet preparations and coke properties such as M40, M10, CRI and CSR (low, medium and high values) were taken as variables for experimental purposes. The coke structure parameters such as porosity, length, perimeter, breadth, roundness, pore-wall thickness and pore size distribution of the pores were determined by image analysis method. This method provided average porosity in addition to pore-wall thickness and pore-size distribution. The pore wall thickness measurement by image analysis method provided significant correlations with M40, CRI and CSR values. This explained the usability of image analysis for coke structure measurement

Competitive analysis of aggregate max in windowed streaming ⋆

Abstract. We consider the problem of maintaining a fixed number k of items observed over a data stream, so as to optimize the maximum value over a fixed number n of recent observations. Unlike previous approaches, we use the competitive analysis framework and compare the performance of the online streaming algorithm against an optimal adversary that knows the entire sequence in advance. We consider the problem of maximizing the aggregate max, i.e., the sum of the values of the largest items in the algorithm’s memory over the entire sequence. For this problem, we prove an asymptotically tight competitive ratio, achieved by a simple heuristic, called partition-greedy, that performs stream updates efficiently and has almost optimal performance. In contrast, we prove that the problem of maximizing, for every time t, the value maintained by the online algorithm in memory, is considerably harder: in particular, we show a tight competitive ratio that depends on the maximum value of the stream. We further prove negative results for the closely related problem of maintaining the aggregate minimum and for the generalized version of the aggregate max problem in which every item comes with an individual window.

SNEE: a query processor for wireless sensor networks

A wireless sensor network (WSN) can be construed as an intelligent, large-scale device for observing and measuring properties of the physical world. In recent years, the database research community has championed the view that if we construe a WSN as a database (i.e., if a significant aspect of its intelligent behavior is that it can execute declaratively-expressed queries), then one can achieve a significant reduction in the cost of engineering the software that implements a data collection program for the WSN while still achieving, through query optimization, very favorable cost:benefit ratios. This paper describes a query processing framework for WSNs that meets many desiderata associated with the view of WSN as databases. The framework is presented in the form of compiler/optimizer, called SNEE, for a continuous declarative query language over sensed data streams, called SNEEql. SNEEql can be shown to meet the expressiveness requirements of a large class of applications. SNEE can be shown to generate effective and efficient query evaluation plans. More specifically, the paper describes the following contributions: (1) a user-level syntax and physical algebra for SNEEql, an expressive continuous query language over WSNs; (2) example concrete algorithms for physical algebraic operators defined in such a way that the task of deriving memory, time and energy analytical cost-estimation models (CEMs) for them becomes straightforward by reduction to a structural traversal of the pseudocode; (3) CEMs for the concrete algorithms alluded to; (4) an architecture for the optimization of SNEEql queries, called SNEE, building on well-established distributed query processing components where possible, but making enhancements or refinements where necessary to accommodate the WSN context; (5) algorithms that instantiate the components in the SNEE architecture, thereby supporting integrated query planning that includes routing, placement and timing; and (6) an empirical performance evaluation of the resulting framework