Compaction Characteristics of Fly Ash and Pond Ash

This study is based on compaction characteristics of Fly ash and pond ash. In thermal power plants, there are three kinds of ash formed named as (a) pond ash, (b) fly ash, and (c) bottom ash. Fly ash is one of the products of coal combustion, consisting of the fine particles that are determined out of the boiler with the flue gasses. The ash falls to the bottom of the boiler is called bottom ashes. In existing coal plants, generally,fly ash is captured by electrostatic precipitators and other clarified particles equipment before reaching the chimney. Pond ash is the by-product of thermoelectric power plants, which is recognized by means of an unused material and disposal is an important environmental issue and also needs a lot of removal regions. Several factors influence the dry density of Fly ash and Pond ash such as specific gravity, moisture content, compaction energy, layer thickness and mold area. The difference of the OMC and MDD of Fly Ash (collected from NTPC kanhia, Odisha) according to the standard proctor compaction energy is 0.90 – 1.59 gm/cc and 18 - 27%, respectively. This difference of the OMC and MDD of Pond ash as per standard proctor compaction energy at the level of 0.856 – 1.248 gm/cc and 33 - 46%, respectively. The study was that variation in these factors influencing the dry density of fly ash and ash pond significantly and to determine the Geotechnical properties of pond ash and fly ash

Energy Cost and Gait Efficiency of Below-Knee Amputee and Normal Subject with Similar Physical Parameters & Quality of Life: A Comparative Case Study

The study focused on the comparative analysis of energy cost and gait efficiency between a below knee (BK) amputee and a reference subject (without amputation). It also attempted to indicate the specific feature responsible for a controlled gait with optimum energy cost for BK amputees. Selection criteria of the subjects were similar physical parameters and quality of life studied with WHOQOL-100 quality of life assessment. A Cosmed® k4 b2 Respiratory Analyzer system was used for the measurement of Oxygen Uptake (VO2), Energy Expenditure per minute (EE) and Heart Rate (HR). Gait efficiency (p < 0.0002) was found higher for BK amputee than normal subject. The therapeutic activities and mainly walking rhythm contributed to improve the mobility & balance. This ensures the optimum time & co-ordination of movements and hence improves the gait efficiency for the BK amputee. Comparison with control group was performed to validate the data

Shape Classification Via Contour Matching Using the Perpendicular Distance Functions

We developed a novel shape descriptor for object recognition, matching, registration and analysis of two-dimensional (2-D) binary shape silhouettes. In this method, we compute the perpendicular distance from each point on the object contour to the line passing through the fixed point. The fixed point is the centre of gravity of a shape. As a geometrically invariant feature, we measure the perpendicular distance function for each line that satisfies the centre of gravity of an object and one of the points on the shape contour. In the matching stage, we used principal component analysis concerning the moments of the perpendicular distance function. This method gives an excellent discriminative power, which is demonstrated by excellent retrieval performance that has been experimented on several shape benchmarks, including Kimia silhouettes, MPEG7 data set

Robust Stability of Neural-Network Controlled Nonlinear Systems with Parametric Variability

Stability certification and identification of the stabilizable operating region of a system are two important concerns to ensure its operational safety/security and robustness. With the advent of machine-learning tools, these issues are specially important for systems with machine-learned components in the feedback loop. Here we develop a theory for stability and stabilizability of a class of neural-network controlled nonlinear systems, where the equilibria can drift when parametric changes occur. A Lyapunov based convex stability certificate is developed and is further used to devise an estimate for a local Lipschitz upper bound for a neural-network (NN) controller and a corresponding operating domain on the state space, containing an initialization set from where the closed-loop (CL) local asymptotic stability of each system in the class is guaranteed under the same controller, while the system trajectories remain confined to the operating domain. For computing such a robust stabilizing NN controller, a stability guaranteed training (SGT) algorithm is also proposed. The effectiveness of the proposed framework is demonstrated using illustrative examples.Comment: 15 pages, 7 figure