硅压阻式压力传感器的高精度补偿算法及其实现

硅压阻式压力传感器广泛应用于汽车、医疗、航空航天、环保等领域。随着科学技术的发展,各领域对压力测量精度的要求越来越高。但由于半导体材料的固有特性,硅压阻式压力传感器普遍存在零点随温度漂移、灵敏度随温度变化和非线性等问题。为了提高硅压阻式压力传感器测量精度、降低输出误差,对该传感器的几种常用补偿算法进行了对比分析和研究,提出了一种基于最小二乘法的曲面拟合高精度补偿算法。该补偿算法能有效消除硅压阻式压力传感器零点漂移、灵敏度漂移和非线性误差,提高该传感器的输出精度。试验结果表明,在-40^+80℃温度范围内,硅压阻式压力传感器经该补偿算法计算后,测量精度得以大幅度提高,输出误差小于0.01%F·S。     

一种压阻式压力传感器误差修正的软件方法

针对硅压阻式压力传感器的误差特性,设计了一种对其非线性及温度变化所引起的误差进行补偿的软件算法。该算法根据实验确定的修正系数,直接由微处理器对传感器信号进行数字化的修正,已在一定的温度和压力范围内实现0.1%的测量精度,可直接应用于硅压阻式压力传感器的设计。     

基于DBN的硅压阻式压力传感器高精度温度补偿模型的研究

硅压阻式压力传感器因对温度具有敏感性,工作时受环境温度的影响会产生温度漂移现象,降低了测量精度,为提升压力传感器的检测精度,提出了一种基于深度信念网络(Deep Belief Network,DBN)的高精度温度补偿模型.研究了压阻式压力传感器的工作原理和温度补偿的数学模型,利用深度学习强大的数据表征能力,设计了区间定位的温度补偿模型构建算法,建立并优化DBN模型的网络结构,将DBN温度补偿模型对实验数据进行训练拟合,结果表明:温度补偿后的满量程相对误差由原来的7.013×10-3提升至8.240×10-5,验证了所提出的方法能具有较好的稳定性和温度补偿效果,较大幅度地提升了传感器的检测精度.     

基于最小二乘支持向量机的硅压阻式传感器温度补偿

针对硅压阻式传感器灵敏度和零点温度漂移大、硬件补偿电路效果不佳的问题,提出最小二乘支持向量机方法对其温度漂移进行补偿。首先分析了经硬件补偿后的硅压阻式传感器的温度漂移特性,在整个检测范围内选取均匀分布的温度、压力数据作为模型输入,经预处理后对输出数值进行训练,并运用网格搜索法和交叉确认法优化模型的惩罚因子和正则化参数,建立了传感器温度补偿模型。实验结果表明,基于最小二乘支持向量机的温度补偿算法在0~100℃温度范围内把传感器输出综合精度从3.2%FS提高到0.25%FS,进一步提高了传感器的精度和温度使用范围,具有较高的实用价值。     

基于自主传感器信号调理芯片温度补偿的软件设计

利用现代信号调理技术,以自主设计的信号调理芯片为核心,采用C#设计开发了硅压阻式传感器的智能误差补偿校准软件,实现了对核心补偿芯片的可视化操作与控制,解决了传统的硬件电路对压力传感器进行温度补偿的缺点。在多个温度点进行校准获取补偿曲线,得到零点及温度漂移补偿数据,解决了硅压阻式传感器一致性差、温度漂移和非线性等问题。系统运行结果表明:通过使用补偿软件,采用高精度温度补偿算法的传感器输出精度有了明显提高,在-55℃～125℃的温度范围内输出的信号与压力成良好的线性关系,压力参数测量精度达到了0.6%以内。     

多路硅压阻式压力传感器温度补偿系统的设计与实现

基于MAX1457实现了一种多路硅压阻式压力传感器温度补偿系统.MAX1457是一种专用传感器信号调理芯片.此芯片集成化程度较高,可以补偿硅压阻式压力传感器的温度误差和非线性误差.经调理后的综合误差不超过0.1%.在分析该芯片补偿原理的基础上,设计并实现了一种多路补偿系统.     

基于DE优化BP的压阻式压力传感器的温度补偿研究

压阻式压力传感器存在温度漂移的问题,因此需要对该传感器进行温度补偿。为此,首先从标定实验中获取被测压力值、压阻式压力传感器的输出电压值以及环境温度值、温度传感器的输出电压值,然后用差分进化算法（DE）优化的BP神经网络算法从该标定数据中得到补偿后的数据。实验结果表明,所提出的温度补偿方法对压阻式压力传感器进行温度补偿后,其灵敏度温度系数提高了一个数量级,相对误差也得到很大的改善,因而其具有显著的理论意义和应用价值。     

基于FOA- LSSVM算法的扩散硅压阻式压力传感器温度补偿

扩散硅压阻式压力传感器具有精度高、灵敏度高、动态响应快等优点,但是存在严重的温度漂移现象,因此必须对其进行温度补偿。针对扩散硅压阻式压力传感器的温度漂移现象,设计了一种基于果蝇算法优化最小二乘支持向量机(FOA-LSSVM)算法的温度补偿模型。首先,运用MPX10扩散硅压阻式压力传感器和LM35温度传感器,进行压力和温度的二维标定试验。然后,利用果蝇优化算法(FOA)自动寻优的优点,解决了最小二乘支持向量机(LSSVM)手动选取参数的问题,从而提高了算法的效率和补偿精度。试验证明,运用FOA-LSSVM算法对扩散硅压阻式压力进行温度补偿,零位温度系数(α_0)和灵敏度温度系数(α_s)均提高了一个数量级,达到了对该传感器温度补偿的目的。     

基于分段融合的压阻式传感器温度补偿方法

针对硅压阻式压力传感器在工程应用中受环境温度和压力的影响产生漂移,影响测量精度等问题.提出一种基于粒子群优化RBF神经网络与最小二乘法融合的温度补偿模型.使用粒子群算法对常规RBF神经网络的权值和阙值进行优化,提高神经网络的泛化性能和训练效率,增强传感器非线性段温度补偿的效果;使用最小二乘法对线性段进行温度补偿,提高整体模型的补偿效率.以飞思卡尔24 PC型压力传感器进行补偿实验,结果表明:对比优化前的神经网络和最小二乘方法,利用本文方法进行温度补偿,耗时短,总体误差低于其他两种方法.传感器在整个温度区间和压力测试点下的输出基本不受影响,补偿效果明显,数据精度符合课题实验的要求.     

消除硅压阻式压力传感器温度误差的方案探讨

本文通过分析硅压阻式压力传感器的特性,提出了通过微处理对其温度误差进行补偿的两种方法.其中第二种方法是在测量压力的同时测量与温度有关的量,并以此对传感器进行温度补偿,从而使这类传感器有可能应用于温度变化较频繁的场合.     

基于视觉的象棋棋盘识别

基于视觉的象棋棋盘识别是象棋机器人软件的重要组成部分,其核心工作是棋盘图像二值化和棋子识别。针对棋盘全局二值化存在的问题,提出了基于相邻像素灰度差阈值的棋盘图像二值化方法;针对棋子文字方向任意的现象,提出了３种基于统计特征的棋子文字识别方法。经实验验证,这些方法处理速度快、效果理想。     

基于衍生多边形的混合坐标

将多边形三角化,利用三角网格将三角形衍生为点多边形、边多边形和面多边形,再根据已有 的重心坐标提出基于衍生多边形的混合坐标。首先在三角网格内根据初始多边形内部一点所在的三角形得到衍 生多边形,然后使用调和坐标、局部重心坐标、迭代坐标中任意一种计算衍生多边形的顶点关于初始多边形顶 点的重心坐标,再使用迭代坐标计算初始多边形内部一点关于衍生多边形顶点的重心坐标,最后使用合适的混 合函数计算该点关于初始多边形顶点的重心坐标。通过实例证明,这种新的重心坐标具有良好的局部性、非负 性和光滑性。     

Personalized app recommendation based on app permissions

With the development of science and technology, the popularity of smart phones has made exponential growth in mobile phone application market. How to help users to select applications they prefer has become a hot topic in recommendation algorithm. As traditional recommendation algorithms are based on popularity and download, they inadvertently fail to recommend the desirable applications. At the same time, many users tend to pay more attention to permissions of those applications, because of some privacy and security reasons. There are few recommendation algorithms which take account of apps’ permissions, functionalities and users’ interests altogether. Some of them only consider permissions while neglecting the users’ interests, others just perform linear combination of apps’ permissions, functionalities and users’ interests to implement top-N recommendation. In this paper, we devise a recommendation method based on both permissions and functionalities. After demonstrating the correlation of apps’ permissions and users’ interests, we design an app risk score calculating method ARSM based on app-permission bipartite graph model. Furthermore, we propose a novel matrix factorization algorithm MFPF based on users’ interests, apps’ permissions and functionalities to handle personalized app recommendation. We compare our work with some of the state-of-the-art recommendation algorithms, and the results indicate that our work can improve the recommendation accuracy remarkably.     

Social recommendation based on trust and influence in SNS environments

The development of social media provides convenience to people’s lives. People’s social relationship and influence on each other is an important factor in a variety of social activities. It is obviously important for the recommendation, while social relationship and user influence are rarely taken into account in traditional recommendation algorithms. In this paper, we propose a new approach to personalized recommendation on social media in order to make use of such a kind of information, and introduce and define a set of new measures to evaluate trust and influence based on users’ social relationship and rating information. We develop a social recommendation algorithm based on modeling of users’ social trust and influence combined with collaborative filtering. The optimal linear relation between them will be reached by the proposed method, because the importance of users’ social trust and influence varies with the data. Our experimental results show that the proposed algorithm outperforms traditional recommendation in terms of recommendation accuracy and stability.

     

Verification of Galois field based circuits by formal reasoning based on computational algebraic geometry

Algebraic error correcting codes (ECC) are widely used to implement reliability features in modern servers and systems and pose a formidable verification challenge. We present a novel methodology and techniques for provably correct design of ECC logics. The methodology is comprised of a design specification method that directly exposes the ECC algorithm’s underlying math to a verification layer, encapsulated in a tool “BLUEVERI”, which establishes the correctness of the design conclusively by using an apparatus of computational algebraic geometry (Buchberger’s algorithm for Gröbner basis construction). We present results from its application to example circuits to demonstrate the effectiveness of the approach. The methodology has been successfully applied to prove correctness of large error correcting circuits on IBM’s POWER systems to protect memory storage and processor to memory communication, as well as a host of smaller error correcting circuits.     

基于多专家区间数的多属性群决策方法

针对区间数的多专家多属性决策问题,提出了一种基于非线性规划模型的群决策方法。该方法建立如下准则:在不同对象和属性下,当某专家的估计值与所有专家估计值的均值越靠近时,则其专家权重就越大;反之就越小。基于该准则利用区间距离公式和规划模型解决了专家权重难以确定的问题。结合集成算子理论,利用区间数算术平均算子将决策矩阵集成为综合决策矩阵,再利用属性权重将其集成为综合属性值,通过二维可能度建立比较可能度矩阵,然后利用排序向量法进行排序。最后通过实例分析验证了该方法的可行性和合理性。     

An anonymous e-rental protocol based on ID-based cryptography and NFC

Most e-rental services require customers to register sensitive information, which gives malicious service providers a good opportunity to launch social engineering attacks, or to use data mining techniques collecting and analyzing customers’ information or rental preferences. Therefore, we propose an anonymous e-rental protocol based on ID-based cryptography and near field communication technology, with particular focus on vehicle rentals. Our contributions include: (1) Anonymity. Users’ real identity is hidden from the rental service providers. (2) Unlinkability. Rental service providers cannot find the relation between two rental records. (3) Traceability. As full anonymity is not always desirable, traceability allows disclosure of a malicious user’s identity, whereas other users’ privacy remains unviolated. Rental service providers can request TTP to reveal users’ identity with a legal warrant. (4) Flexibility. Users choose their preferred service providers and vehicles. (5) Anonymous payment. Rental service providers cannot associate users’ identity with the financial transactions.     

Advanced computational reasoning based on the NKRL conceptual model

After having recalled some well-known shortcomings linked with the Semantic Web approach to the creation of (application oriented) systems of “rules” – e.g., limited expressiveness, adoption of an Open World Assumption (OWA) paradigm, absence of variables in the original definition of OWL – this paper examines the technical solutions successfully used for implementing advanced reasoning systems according to the NKRL’s methodology. NKRL (Narrative Knowledge Representation Language) is a conceptual meta-model and a Computer Science environment expressly created to deal, in an ‘intelligent’ and complete way, with complex and content-rich non-fictional ‘narrative’ data sources. These last include corporate memory documents, news stories, normative and legal texts, medical records, surveillance videos, actuality photos for newspapers and magazines, etc. In this context, we will expound first the need for distinguishing between “plain/static” and “structured/dynamic” knowledge and for introducing appropriate (and different) knowledge representation structures for these two types of knowledge. In a structured/dynamic context, we will then show how the introduction of “functional roles” – associated with the possibility of making use of n-ary structures – allows us to build up highly ‘expressive’ rules whose “atoms” can directly represent complex situations, actions, etc. without being restricted to the use of binary clauses. In an NKRL context, “functional roles” are primitive symbols interpreted as “relations” – like “subject”, “object”, “source”, “beneficiary”, etc. – that link a semantic predicate with its arguments within an n-ary conceptual formula. Functional roles contrast then with the “semantic roles” that are equated to ordinary concepts like “student”, to be inserted into the “non-sortal” (no direct instances) branch of a traditional ontology.     

A hybrid controller based on the egocentric perceptual principle

In this paper we extend the control methodology based on Extended Markov Tracking (EMT) by providing the control algorithm with capabilities to calibrate and even partially reconstruct the environment’s model. This enables us to resolve the problem of performance deterioration due to model incoherence, a problem faced in all model-based control methods. The new algorithm, Ensemble Actions EMT (EA-EMT), utilises the initial environment model as a library of state transition functions and applies a variation of prediction with experts to assemble and calibrate a revised model. By so doing, this is the first hybrid control algorithm that enables on-line adaptation within the egocentric control framework which dictates the control of an agent’s perceptions, rather than an agent’s environment state. In our experiments, we performed a range of tests with increasing model incoherence induced by three types of exogenous environment perturbations: catastrophic—the environment becomes completely inconsistent with the model, deviating—some aspect of the environment behaviour diverges compared to that specified in the model, and periodic—the environment alternates between several possible divergences. The results show that EA-EMT resolved model incoherence and significantly outperformed its EMT predecessor by up to 95%.