Structural and electrical properties of mixed oxides of manganese and vanadium: A new semiconductor oxide thermistor material

Binary oxides of manganese and vanadium have been synthesized by solid state sintering, in which the mass ratio of the individual components Mn₂O₃ and VO₂ have been varied from 90:10 to 5:95. The bulk ceramic samples were characterized by X-ray diffraction and scanning electron microscopy with energy dispersive X-ray analysis. The initial compositions either rich in Mn₂O₃ or in equi-proportion by mass with VO₂ yield β-Mn₂V₂O₇ or a new crystalline form of Mn₂V₂O₇, with unit cell parameters: a = 7.73091 Å, b = 6.640788 Å, c = 6.70779 Å α = γ = 90° and β = 98.7086° which is designated as γ-Mn₂V₂O₇. The compositions, richer in VO₂ produce MnV₂O₆ co-existing with V₂O₅ the proportion of which increases with increase in VO₂. The surface microanalysis shows a spherical-granular morphology in Mn₂V₂O₇ structure and plate/rod-like structures co-existing with granular morphology in case of MnV₂O₆ together with V₂O₅. The electrical parameters of the negative temperature coefficient thermistors were determined. Depending on the constituent oxide composition, the NTC thermistors showed room temperature resistivity in the range of 6.52 × 10² to 6.1 × 10⁶ Ω-cm. The thermistor constant and activation energy are in the range of 0.12–0.458 eV and 1393–4801 K, respectively.     

A genetic algorithm-based artificial neural network model for the optimization of machining processes

Artificial intelligent tools like genetic algorithm, artificial neural network (ANN) and fuzzy logic are found to be extremely useful in modeling reliable processes in the field of computer integrated manufacturing (for example, selecting optimal parameters during process planning, design and implementing the adaptive control systems). When knowledge about the relationship among the various parameters of manufacturing are found to be lacking, ANNs are used as process models, because they can handle strong nonlinearities, a large number of parameters and missing information. When the dependencies between parameters become noninvertible, the input and output configurations used in ANN strongly influence the accuracy. However, running of a neural network is found to be time consuming. If genetic algorithm-based ANNs are used to construct models, it can provide more accurate results in less time. This article proposes a genetic algorithm-based ANN model for the turning process in manufacturing Industry. This model is found to be a time-saving model that satisfies all the accuracy requirements.     

NTRU参数选择方法的研究

NTRU算法是一种基于环的公开密钥体制,与RSA和ECC等典型的加密算法相比,在安全性和速度方面具有明显的优势。分析目前比较成熟的攻击NTRU方法,从安全的角度,根据不同的应用场合,给出NTRU加密参数选择方法,减少选择NTRU参数的盲目性,达到提高算法的执行速度、减少占用系统资源的目的。     

参数未知耦合时滞不同复杂网络的广义同步

针对具有不同动力学节点和不同拓扑结构的两个复杂网络,研究其广义同步问题,并考虑具有耦合时滞和参数未知的情况.基于Babalat引理,并利用Lyapunov稳定性方法,获得广义同步判据,设计有效的自适应控制器,实现混沌系统参数已知和参数未知两种情况下的广义同步.仿真验证了算法的有效性.     

基于Isight与Tosca的抗侧滚扭杆装置优化分析

基于Isight和Tosca软件对抗侧滚扭杆装置轻量化设计问题进行优化分析.采用Isight软件集成Abaqus软件对扭杆轴进行结构参数优化分析;采用Tosca软件集成Abaqus软件和fe-safe软件对扭转臂进行结构拓扑优化分析,研究扭杆轴与扭转臂的轻量化设计方法.     

灰色DNGM(1,1)预测模型及其优化

传统的灰色GM(1,1)预测模型是针对近似齐次指数序列建立的预测模型。为了拓广灰色预测模型的适用范围,建立了近似非齐次指数序列的灰色DNGM(1,1)预测模型。研究了这种灰色预测模型的性质,证明了这种模型都具有线性不变性,也能够完全拟合非齐次指数序列。考虑到初值条件对灰色模型的影响,对该模型进行了参数优化。数据仿真和实例分析表明,灰色DNGM(1,1)预测模型具有较高的预测精度。     

三角形迷宫流道滴灌灌水器结构参数及水力特性研究

以滴灌灌水器三角形迷宫流道结构为研究单元,利用均匀设计方法,结合计算流体动力学软件Fluent6.2对流道内部流体的流动进行了数值模拟,通过多元回归计算,获得流道各结构参数与流量系数和流态指数的量化关系。分析这一量化关系表明,流道转角与流量成负相关,与流态指数成正相关;流道宽与流量成正相关,与流态指数成负相关;齿高与流量成负相关,与流态指数成正相关。同时分析了灌水器内部流道的水力性能和流场特性,为三角形迷宫流道滴灌灌水器的研发提供了相应的理论依据。     

Integrating spectral indices with environmental parameters for estimating heavy metal concentrations in rice using a dynamic fuzzy neural-network model

A generalized dynamic fuzzy neural network (GDFNN) was created to estimate heavy metal concentrations in rice by integrating spectral indices and environmental parameters. Hyperspectral data, environmental parameters, and heavy metal content were collected from field experiments with different levels of heavy metal pollution (Cu and Cd). Input variables used in the GDFNN model were derived from 10 variables acquired by gray relational analysis. The assessment models for Cd and Cu concentration employed five and six input variables, respectively. The results showed that the GDFNN for estimating Cu and Cd concentrations in rice performed well at prediction with a compact network structure using the training, validation, and testing sets (for Cu, fuzzy rules=9, R² greater than 0.75, and RMSE less than 2.5; for Cd, fuzzy rules=9, R² greater than 0.75, and RMSE less than 1.0). The final GDFNN model was then compared with a back-propagation (BP) neural network model, adaptive-network-based fuzzy interference systems (ANFIS), and a regression model. The accuracies of GDFNN model prediction were usually slightly better than those of the other three models. This demonstrates that the GDFNN model is more suitable for predicting heavy metal concentrations in rice.     

Rational method for 3D manufacturing tolerancing synthesis based on the TTRS approach “R3DMTSyn”

During the production of new part in an industrial environment, it results in a high percentage of scrap if manufacturing planning is not carried out properly. One of the major factors responsible for this phenomenon is tolerance synthesis. In this paper, we deal with tolerance synthesis, and especially tolerance type identified after transfer. An algorithmic Rational method for 3D Manufacturing Tolerancing Synthesis (R3DMTSyn), which is based on the use of the Technologically and Topologically Related Surface (TTRS) rules, is developed. The TTRS concept helps to generate only the necessary manufacturing specification needed to guarantee the respect of the functional specification studied.The manufacturing project is modeled by a graphical representation called the SPIDER GRAPH. With the SPIDER GRAPH, all active surfaces can be detected (machined and positioning surfaces), so it is possible to identify the location of the functional surfaces used in each functional specification. The construction or the determination of the tolerancing torsor, belonging to each active surface, contributes to the selection of the adequate case of associations. A semantic study is already done to identify all possible combinations or associations needed to locate surfaces during each phases.Finally, referring to the developed TTRS_Cars_Process and in every phase, one or more manufacturing specifications are generated until finishing the treatment of all surfaces (surface belonging to the functional condition, or intermediate surfaces).     

Segment inertial parameters of Korean adults estimated from three-dimensional body laser scan data

In this study, body segment parameters of Korean adults were estimated using the three-dimensional (3D) body scan data from the SizeKorea database. Mass-inertial parameters and segment dimensions of 40 male subjects and 40 female subjects (18-59 years old) were estimated using a 16-segment model under the assumption that each segment has a constant density distribution after reconstructing the scan data. Therefore, several sets of linear regression functions based on gender, stature, and weight were established, which provided a convenient method for estimating body segment parameters of Korean adults. The obtained mass ratios of body segments were compared with those for Russians reported by Zatsiorsky and Seluyanov (1983) and for those for Chinese and Germans reported by Shan and Bohn (2003), in which the same 16-segment model was used. In addition, the results were compared with the Korean data results reported by [Jung, 1993] and [Lim, 1994], and Park et al. (1999). These comparisons revealed that Asians have larger head mass ratios and smaller lower limb mass ratios than Caucasians, while the differences in the head mass ratios between males and females from the same ethnic groups were not significant. Females were found to have larger mass ratios for the lower torso and smaller mass ratios for the upper torso, middle torso, upper arm, forearm, foot, and hand, as compared to males from the same ethnic groups. In addition, the data obtained by different researchers were compared, thereby showing high reproducibility of our method.

Relevance to industry

The obtained segment parameters can be used to define digital human models and applied to the fields of ergonomics and biomechanics, such as for workspace design, furniture design, vehicle interior design, and human movement analysis.