Generalized Method for Gun Propellant Formulation Design

Determination of propellant formulation by ballistic requirement is an important area of research in recent times. In this study, a theoretical method for the design of gun propellant formulation using primary data of ingredients and necessary thermochemical properties of the resultant propellant was established. The employed method is based on a mathematical model of thermochemical properties of the propellant by optimizing the heat of explosion of the propellant using the fmincon tool in MATLAB. A graphical user interface (GUI) based code was generated and developed for the formulation design of solid gun propellants. The designed code was verified by available data in the literature. Such code will be useful to the researchers working in the area of high energy materials for the design of unknown propellant compositions. Further, it can be extended to redesign the existing propellant formulation in order to enhance the ballistic performance.     

发电机定子绝缘剩余寿命评估参量的选择

为选择发电机定子绝缘剩余寿命评估的特征量,测量了运行23年的发电机定子线棒局部放电、tanδ和击穿电压等参量,分析了非破坏性参量与击穿电压的关系,并根据绝缘老化机理和过程,得出了tanδ、Δtanδ、S+k,S-k, Asy等5个特征参量。分析发现第一主成分中上述参量的比重较大,证明它们能较好预测绝缘的老化状态。     

Estimation of Critical Inductance for DC Separately Excited Motor Fed by Load-Dependent Chopper

ABSTRACT

This paper describes a method to estimate the value of a critical inductance for a dc separately excited motor fed by load dependent chopper to eliminate the discontinuous conduction of armature current. A trapezoid approximation has been used to estimate the value of the commutation time of the chopper. To verify the suitability of the method, the performance equations corresponding to different intervals of a chopping cycle are solved numerically on a digital computer. The value of critical inductance has also been obtained using the method developed for rectangular wave chopper. It is found that the proposed method predicts more accurate value of the critical inductance than that of a rectangular wave chopper. It is also seen that a much larger value of the inductance is needed to eliminate the discontinuous conduction in dc separately excited motor fed by load dependent chopper than using rectangular wave chopper.     

Gradient Boundary Detection for Time Series Snapshot Construction in Sensor Networks

In many applications of sensor networks, the sink needs to keep track of the history of sensed data of a monitored region for scientific analysis or supporting historical queries. We call these historical data a time series of value distributions or snapshots. Obviously, to build the time series snapshots by requiring all of the sensors to transmit their data to the sink periodically is not energy efficient. In this paper, we introduce the idea of gradient boundary and propose the gradient boundary detection (GBD) algorithm to construct these time series snapshots of a monitored region. In GBD, a monitored region is partitioned into a set of subregions and all sensed data in one subregion are within a predefined value range, namely, the gradient interval. Sensors located on the boundaries of the subregions are required to transmit the data to the sink and, then, the sink recovers all subregions to construct snapshots of the monitored area. In this process, only the boundary sensors transmit their data and, therefore, energy consumption is greatly reduced. The simulation results show that GBD is able to build snapshots with a comparable accuracy and has up to 40 percent energy savings compared with the existing approaches for large gradient intervals.     

Hypothalamic gamma-aminobutyric acid neurons project to the neocortex

Three groups of gamma-aminobutyric acid--containing neurons were found in the mammillary region of the posterior hypothalamus. The groups correspond to the tuberal, caudal, and postmammillary caudal magnocellular nuclei. Many cells in these nuclei were retrogradely labeled with fast blue after the injection of this fluorescent dye into the neocortex. Immunohistochemical experiments showed that these same neurons also contained the gamma-aminobutyric acid-synthesizing enzyme glutamate decarboxylase. These results provide morphological evidence for a gamma-aminobutyric acid pathway arising in magnocellular neurons of the posterior hypothalamus and innervating the neocortex.     

On coherence, random-self-reducibility, and self-correction

We study three types of self‐reducibility that are motivated by the theory of program verification. A set A is random‐self‐reducible if one can determine whether an input x is in A by making random queries to an A‐oracle. The distribution of each query may depend only on the length of x. A set B is self‐correctable over a distribution if one can convert a program that is correct on most of the probability mass of to a probabilistic program that is correct everywhere. A set C is coherent if one can determine whether an input x is in C by asking questions to an oracle for C–{x}.?We first show that adaptive coherence is more powerful than nonadaptive coherence, even if the nonadaptive querier is nonuniform. Blum et al.(1993) showed that every random‐self‐reducible function is self‐correctable. It is unknown, however, whether self‐correctability implies random‐self‐reducibility. We show, assuming a reasonable complexity‐theoretic hypothesis, that certain hard, sparse, tally sets exist, and that there is a self‐correctable function which is not random‐self‐reducible. For easily samplable distributions, however, we show that constructing a self‐correctable function that is not random‐self‐reducible is as hard as proving that P is different from PP. Received: 14 June, 1996     

Social group optimization for global optimization of multimodal functions and data clustering problems

Neural Computing and Applications - Cost and physical constraints in the engineering applied problems obligate finding the best results that global optimization algorithms cannot realize. For...     

Extreme learning machine and bayesian optimization-driven intelligent framework for IoMT cyber-attack detection

The Journal of Supercomputing - The Internet of Medical Things (IoMT) is a bionetwork of allied medical devices, sensors, wearable biosensor devices, etc. It is gradually reforming the healthcare...