无线传感器网络节点自身定位算法研究

无线传感器网络中节点自身位置信息是实现其应用的最重要因素之一.针对节点自身定位的研究很多,但很大一部分都集中在算法的改进和新算法的开发上,很少给出纵向直观的仿真效果比较.研究选取无需测距算法中4种典型的算法,给出它们在不同参数下的仿真定位效果图,对于工程人员根据实际选取最优算法有很好的借鉴作用.在此基础上,利用曲线拟合...     

光纤Bragg光栅电流传感器的温度补偿研究

在基于超磁致伸缩材料Tb0.30Dy0 70Fe1.95的光纤Bragg光栅(FBG)大电流传感器的基础上,通过对传感头进行设计,提出了一种温度补偿的方案,解决了FBG的温度-应变交叉敏感问题,提高了传感器的精度.实验结果表明:在0～90℃的温度变化范围内,该传感器的Bragg波长差与电流变化具有较好的线性度,不受温度...     

巨磁阻抗磁传感器研究进展

巨磁阻抗效应是指材料的交流阻抗在外加直流磁场的作用下发生显著变化的现象,利用该效应研制的巨磁阻抗磁传感器具有灵敏度高、体积小和功耗低等特点,具有巨大的应用前景.分析了巨磁阻抗磁传感器的研究现状,重点介绍了磁场传感器、电流传感器和生物传感器的工作原理和特性,并指出了其目前存在的问题和发展趋势.     

旋光效应在微位移测量中的实验研究

微位移测量原理有许多种,利用偏振光的旋光效应是一种高效、简洁、精确的新方法.旋光效应是指单色线偏振光在沿光轴方向通过石英晶体时,透射出来的光的振动面会产生旋转的物理现象,其旋转角度与晶体厚度有关.用激光器、起偏器、石英晶体、千分尺等设计了一个微位移测试系统,测试表明:其测量精度能达到0.1 μm,测量范围可达300 μ...     

Single-machine scheduling with sum-of-logarithm-processing-times-based learning considerations

Scheduling with learning effects has attracted growing attention of the scheduling research community. A recent survey classifies the learning models in scheduling into two types, namely position-based learning and sum-of-processing-times-based learning. However, the actual processing time of a given job drops to zero precipitously as the number of jobs increases in the first model and when the normal job processing times are large in the second model. Motivated by this observation, we propose a new learning model where the actual job processing time is a function of the sum of the logarithm of the processing times of the jobs already processed. The use of the logarithm function is to model the phenomenon that learning as a human activity is subject to the law of diminishing return. Under the proposed learning model, we show that the scheduling problems to minimize the makespan and total completion time can be solved in polynomial time. We further show that the problems to minimize the maximum lateness, maximum tardiness, weighted sum of completion times and total tardiness have polynomial-time solutions under some agreeable conditions on the problem parameters.     

Magnetoelectric transducer with high quality factor for wireless power receiving

To achieve strong power coupling, a resonance-type magnetoelectric (ME) transducer with high quality factor is developed to achieve strong ME coupling. The ME transducer employs a type of iron–nickel-based ferromagnetic alloy with constant elasticity and piezoelectric Pb(Zr,Ti)O₃ (PZT8) material. The dynamic magnetomechanical behavior of the ferromagnetic alloy is investigated. The result indicates that the strain coefficient of the ferromagnetic alloy at resonance achieves 557.07 nm/A due to the high effective mechanical quality factor of the alloy. The transducer is designed to operate as a half-wavelength, longitudinal resonator. The dynamic performance of the transducer is evaluated by measuring its electrical and vibrational characteristics. The results reveal that (1) the resonance of the transducer occurs at the frequency of 26.9336 kHz with a strain coefficient of 314.74 nm/A, an effective mechanical quality factor of 1600; (2) the ME voltage coefficient achieves 30.07 V/Oe (i.e., 375.875 V/cm Oe) at resonance; (3) the ME output power density at optimal load resistance of 25 kΩ achieves 0.956 mW/cm³ under 0.3 Oe root-mean-square AC magnetic field. The performances indicate that the transducer is promising for ME energy conversion application.     

Modeling Default Data Via an Interactive Hidden Markov Model

In this paper, we first introduce the use of an interactive hidden Markov model (IHMM) for modeling and analyzing default data in a sector. Under the IHMM, transitions of the hidden risk states of the sector depend on the observed number of bonds in the sector that default in the current time period. This incorporates the feedback effect of the number of defaults on the transitions of the hidden risk states. This feature seems to be more realistic and does not enjoy by the traditional HMMs. We then develop a “dynamic” version of the binomial expansion technique (BET) modulated by the IHMM for modeling the occurrence of defaults of bonds issued by firms in the same sector. Under the BET modulated by the IHMM, the number of bonds defaulting in each time period follows a Markov-modulated binomial distribution with the probability of defaulting of each bond depending on the states of the IHMM, which represent the hidden risk states of the sector. Efficient method will be presented for estimating the model parameters in the BET modulated by the IHMM. We shall compare the hidden risk state process extracted from the IHMM-modulated BET with that extracted from the BET modulated by HMM in order to illustrate the significance of the feedback effect using real data. We shall also present the estimation results for the BET modulated by the IHMM and compare them with those for the BET modulated by the HMM.     

应用使能检测单元的抗辐射数字像素图像传感器

介绍了一种经过抗辐射设计加固的 CMOS 数字像素图像传感器,并提出了一种可以抵抗单粒子效应的使能检测单元。这个使能检测单元通过将信号传输给三个移位寄存器并判断寄存器输出是否一致来判断和屏蔽单粒子效应。实验结果表明：芯片的最大信噪比和动态范围分别是54.15 dB 和56.10 dB,使能检测单元可以屏蔽单粒子效应。     

面向属性效应控制的等均值约束支持向量回归机

针对现有的属性效应控制方法无法有效控制非线性回归建模的属性效应问题, 基于间隔最大化和结构风险最小化原则, 通过向SVR 目标学习准则中施加等均值条件约束, 提出等均值支持向量回归机(EM-SVR). 所提出的方法具有较好的泛化能力, 同时继承了EM-LS 的良好性能. 实验结果验证了所提出方法的有效性.

     

通用稀疏多核学习

针对L1范数多核学习方法产生核权重的稀疏解时可能会导致有用信息的丢失和泛化性能退化,Lp范数多核学习方法产生核权重的非稀疏解时会产生很多冗余信息并对噪声敏感,提出了一种通用稀疏多核学习方法。该算法是基于L1范数和Lp范数(p>1) 混合的网状正则化多核学习方法,不仅能灵活的调整稀疏性,而且鼓励核权重的组效应,L1范数和Lp范数多核学习方法可以认为是该方法的特例。该方法引进的混合约束为非线性约束,故对此约束采用二阶泰勒展开式近似,并使用半无限规划来求解该优化问题。实验结果表明,改进后的方法在动态调整稀疏性的前提下能获得较好的分类性能,同时也支持组效应,从而验证了改进后的方法是有效可行的。