求多项式方程全部实根的混合差分进化算法

针对多项式方程求实根问题,提出了一种混合差分进化算法.在该算法中,先对标准差分进化算法进行了一些改进,对计算种群个体的适应度并排序,利用二分之一规则选取个体,并引入自适应变异算子和进化策略重组算子,用改进的差分进化算法对种群进行优化,然后引入模拟退火算法和小生境技术对生成的新个体进一步优化.通过典型算例的数值仿真表明,文中提出的算法克服了标准差分进化算法易陷入局部极优等缺点,可以求任意高次多项式方程的全部实根,而且求解效率高,是一种求解多项式方程全部实根的有效算法.     

有限域GF（2^m）幂运算的一种新方法

在有限域GF（2^m）引进了开平方运算,描述了有限域GF（2^m）上利用开平方求幂的一种新方法。与经典的平方一乘求幂算法相比,在只增加少量预计算的情况下,新的方法所需GF（2^m）上的乘法运算少33％。     

A Ramsey theorem in Boyer-Moore logic

We use the Boyer-Moore Prover, Nqthm, to verify the Paris-Harrington version of Ramsey's theorem. The proof we verify is a modification of the one given by Ketonen and Solovay. The theorem is not provable in Peano Arithmetic, and one key step in the proof requires ₀ induction.Author supported by NSF Grant DMS-9100665.     

A variable-precision square root implementation for field programmable gate arrays

Applications requiring variable-precision arithmetic often rely on software implementations because custom hardware is either unavailable or too costly to build. By using the flexibility of the Xilinx XC4010 field programmable gate arrays, we present a hardware implementation of square root that is easily tailored to any desired precision. Our design consists of three types of modules: a control logic module, a data path module to extend the precision in 4-bit increments, and an interface module to span multiple chips. Our data path design avoids the common problem of large fan-out delay in the critical path. Cycle time is independent of precision, and operation latency can be independent of interchip communication delays.Notation S_j square root digit of weight 2^–j - S _j {–1, 0, 1} - S[j] computed square root value as of stepj - S _j ^s sign bit in the representation ofS _j in sign and magnitude form - S _j ^m magnitude bit in the representation ofS _j in sign and magnitude form - w[j] residual at stepj in two's complement carry-save representation - a sum vector in the carry-save representation of 2w[j] - b carry vector in the carry-save representation of 2w[j] - a _i bit of weight 2^–i in the sum vector,a - b_i bit of weight 2^–i in the carry vector,b - T[j]=–S[j – 1]s_j – s _j ² 2^–(j+1) T _i bit of weight 2^–i inT     

用于高能物理实验电子读出芯片的低噪声锁相环芯片设计

基于TSMC 180 nm工艺设计并流片测试了一款用于高能物理实验的电子读出系统的低噪声、低功耗锁相环芯片。该芯片主要由鉴频鉴相器、电荷泵、环路滤波器、压控振荡器和分频器等子模块组成,在锁相环电荷泵模块中,使用共源共栅电流镜结构精准镜像电流以减小电流失配和用运放钳位电压进一步减小相位噪声。测试结果表明,该锁相环芯片在1.8 V电源电压、输入50 MHz参考时钟条件下,可稳定输出200 MHz的差分时钟信号,时钟均方根抖动为2.26 ps(0.45 mUI),相位噪声在1 MHz频偏处为-105.83 dBc/Hz。芯片整体功耗实测为23.4 mW,锁相环核心功耗为2.02 mW。     

基于Conv1d-LSTM模型的能源分配预测

能源分配问题往往与其所在区域环境有关,能源分配的预测可以通过当地环境因素数据来推测之后对该区域的能源分配数值,最大程度上分配好能源. LSTM网络预测短期效果良好,但预测较长时期的数据会导致误差积累,速度慢且准确性差; Informer是近期新提出的能源预测算法模型,速度快但在该任务上预测能力不够.本文提出Conv1d-LSTM模型,预测结果优于上述两个模型,具有更低的平均绝对误差和均方根误差.     

Implementation and characterization of flash-based hardware security primitives for cryptographic key generation

Hardware security primitives, also known as physical unclonable functions (PUFs), perform innovative roles to extract the randomness unique to specific hardware. This paper proposes a novel hardware security primitive using a commercial off-the-shelf flash memory chip that is an intrinsic part of most commercial Internet of Things (IoT) devices. First, we define a hardware security source model to describe a hardware-based fixed random bit generator for use in security applications, such as cryptographic key generation. Then, we propose a hardware security primitive with flash memory by exploiting the variability of tunneling electrons in the floating gate. In accordance with the requirements for robustness against the environment, timing variations, and random errors, we developed an adaptive extraction algorithm for the flash PUF. Experimental results show that the proposed flash PUF successfully generates a fixed random response, where the uniqueness is 49.1%, steadiness is 3.8%, uniformity is 50.2%, and min-entropy per bit is 0.87. Thus, our approach can be applied to security applications with reliability and satisfy high-entropy requirements, such as cryptographic key generation for IoT devices.     

基于 SST-SCKF 的运动目标超宽带定位算法研究

使用超宽带(UWB)进行定位过程中,卡尔曼滤波是一种常见的降噪方法,但由于对非线性系统滤波性能差,且定位目标运动轨迹易超出基站布局区域以及受到异常噪声干扰,会影响定位系统的准确性和稳定性。针对这一问题,提出一种对称强跟踪(SST)平方根容积卡尔曼(SCKF)算法,通过引入对称时变渐消因子调节各协方差矩阵,实现改变误差协方差矩阵中多重衰落因子矩阵的工作方式,进而调整滤波增益,计算复杂度虽略有增加,但增强定位模型的适应性与鲁棒性。仿真验证表明,在异常噪声干扰下,改进后的算法(SST-SCKF)相较于SCKF/多重渐消因子的SCKF(ST-ASCKF)算法可有效提高定位准确度,且定位轨迹较于单渐消因子的SCKF算法(STSCKF)更为平滑;利用SST-SCKF算法设计基于UWB技术的定位方案,通过动态模拟实验表明,本文提出的SST-SCKF算法较之SCKF/STSCKF/ST-ASCKF滤波性能更优,为复杂环境噪声下人员UWB定位提供更好的降噪,使定位更为精准。     

基于集成经验模态分解和最小二乘双支持向量回归机的风速预测算法研究

为有效解决风电大规模并网过程中面临的并网难和弃风等问题,实现可再生能源大规模平滑并网并保证大电网的安全稳定运行,采用集成经验模态分解(ensemble empirical mode decomposition,EEMD)和最小二乘双支持向量回归机(least square twin support vector regression,LSTSVR)算法进行风电场风速预测。分别介绍了LSTSVR、EEMD及自适应变异粒子群算法原理。给出基于EEMD和LSTSVR的风速预测流程,以安徽女儿岭风电场测风声雷达30、70 m处风速采样数据为例,开展基于EEMD和LSTSVR的风速预测算法验证,预测结果误差分析表明:基于EEMD+LSTSVR+自适应变异粒子群算法可以实现风电场风速的高精度预测。     

Application of ANFIS Model for Thailand’s Electric Vehicle Consumption

Generally, road transport is a major energy-consuming sector. Fuel consumption of each vehicle is an important factor that affects the overall energy consumption, driving behavior and vehicle characteristic are the main factors affecting the change of vehicle fuel consumption. It is difficult to analyze the influence of fuel consumption with multiple and complex factors. The Adaptive Neuro-Fuzzy Inference System (ANFIS) approach was employed to develop a vehicle fuel consumption model based on multivariate input. The ANFIS network was constructed by various experiments based on the ANFIS Parameter setting. The performance of the ANFIS network was validated using Root Mean Square Error (RMSE) and Mean Average Error (MAE) which related to the setting of ANFIS parameters. The experimental results indicated that the training data sample, number, and type of membership functions are the most important factor affecting the performance of the ANFIS network. However, the number of epochs does not necessarily significantly improve the system performance, too many the number of epochs setting may not provide the best results and lead to excessive responding time. The results also demonstrate that three factors, consisted of the engine size, driving speed, and the number of passengers, are important factors that influence the change of vehicle fuel consumption. The selected ANFIS models with minimum error can be properly and efficiently used to predict vehicle fuel consumption for Thailand’s road transport sector.