采用工作量证明共识机制的区块链中挖矿攻击者间的“鲶鱼效应”

近年来,采用工作量证明共识机制(Proof of Work,PoW)的区块链被广泛地应用于以比特币为代表的数字加密货币中.自私挖矿攻击(Selfish mining)等挖矿攻击(Mining attack)策略威胁了采用工作量证明共识机制的区块链的安全性.在自私挖矿攻击策略被提出之后,研究者们进一步优化了单个攻击者的挖矿攻击策略.在前人工作的基础上,本文提出了新颖的两阶段挖矿攻击模型,该模型包含拥有单攻击者的传统自私挖矿系统与拥有两个攻击者的多攻击者系统.本文的模型同时提供了理论分析与仿真量化分析,并将两个攻击者区分为内部攻击者与外部攻击者.通过引入内部攻击者与外部攻击者的概念,本文指出传统自私挖矿系统转化为多攻击者系统的条件.本文进一步揭示了在多攻击者系统中两个攻击者将产生竞争并面临着“矿工困境”问题.攻击者间的竞争可被总结为“鲶鱼效应”:外部攻击者的出现导致内部攻击者的相对收益下降至多67.4%,因此内部攻击者需要优化攻击策略.本文提出了名为部分主动发布策略的全新挖矿攻击策略,相较于自私挖矿策略,该策略是半诚实的攻击策略.在特定场景下,部分主动发布策略可以提高攻击者的相对收益并破解攻击者面临的“矿工困境”问题.     

基于手工特征提取与结果融合的CNN音频隐写分析算法

随着互联网技术的快速发展,出现了基于IP的语音传输技术,给人们带来方便的同时也带来了许多安全隐患,如不法分子利用VoIP压缩域语音传输协议传送秘密信息.因此,针对基于G.729A编码的基音隐写算法和互补邻居顶点的量化索引调制音频隐写算法,本文提出了一种基于手工特征提取与结果融合的卷积神经网络音频隐写分析算法.通过将手工提取特征与卷积神经网络相结合,可以实现在VoIP压缩域同时对基于基音的隐写算法和互补邻居顶点的量化索引调制音频隐写算法进行有效检测.实验结果表明,在同时对基音隐写算法和互补邻居顶点的量化索引调制音频隐写算法进行检测时,本文所提出的基于手工特征提取与结果融合的卷积神经网络音频隐写分析算法的检测准确率可以达到86.2％(嵌入率为100％、音频样本时长为0.1s).与现有隐写分析算法相比,在音频时长较短时,本文所提算法取得了优异的检测结果.     

Optimization of the grapes reception process

Neural Computing and Applications - Grapes reception is a key process in wine production. The harvest days are extremely challenging days in managing the reception of the grapes, as the winery...     

基于非线性PID的柔性两轮机器人运动控制

柔性两轮机器人是一种不稳定、非线性、强耦合系统。该系统的突出特点是在机器人的腰部装有柔性的机体结构,能够更好地模拟人和动物的生物动力学特性,具有更好的仿生性质,同时,系统的控制难度显著增大,为使机器人能够平衡直立运动,且具有较强的鲁棒性,提出了非线性PD的姿态平衡控制方法,实现了机器人的姿态平衡,并同时设计了PID航向差动控制结构驱动左右轮电机,使机器人能够完成直线行进、自旋、环绕等多种运动平衡模式。实验结果表明,机器人具有优良的平衡能力和机动性能,从而验证了方法的有效性。     

Identifying the criterion spontaneously minimized during the take-off phase of a sub-maximal long jump through optimal synthesis

Optimal synthesis of human movement or the prediction of the kinematics of a new movement require not only that the multi-body system be modeled but also that a performance criterion is specified. For sub-maximal movements the selection of a suitable performance criterion, able to generate realistic dynamic behavior is difficult. A two-dimensional simulation model of the take-off phase of a sub-maximal long jump was developed to study the effect of criterion choice on the realism of simulated movements. A parametric optimization technique was employed to obtain solutions to the constrained equations of motion. Seven different criteria were evaluated, by comparing simulated movements with an actual performance, to identify the criterion which most closely approximated that spontaneously minimized by the athlete. Synthesis of the take-off phase of a sub-maximal long jump was found to be sensitive to the chosen criterion, with a criterion based on minimizing joint intersegmental forces found to perform well.     

Enterprise architecture,IT effectiveness and the mediating role of IT alignment in US hospitals

Despite the possible benefits of implementing healthcare information technologies, successful implementation of effective healthcare information technology is constrained by cultural and regulatory concerns and technical obstacles encountered when establishing or upgrading an organisation's enterprise infrastructure. In this paper, we advance Ross' four‐stage model of enterprise architecture maturity as a valuable IT resource for helping healthcare organisations sustain a competitive advantage. We use partial least squares (PLS) structural equation modelling to analyse survey data from 164 US hospitals at different stages of EA maturity. Our results provide evidence that enterprise architecture maturity directly influences the effectiveness of hospitals' IT resources for achieving strategic goals. Further, enterprise architecture maturity indirectly influences the effectiveness of IT resources when IT alignment is incorporated as a mediating variable. We discuss the implications of our findings for research and practice and suggest opportunities for future research.     

遗传算法在智能车控制器参数寻优中的应用

研究智能车控制器优化设计问题,由于控制器参数不准确,引起控制误差.为解决上述问题,根据智能车的运动学模型,设计出智能车轨迹跟踪控制律.采用遗传算法对控制律参数寻优,利用逆推(Backstepping)方法,在搜索空间里获得全局最优解.最后利用提出的遗传算法对控制律参数优化方法,通过智能车对圆周轨迹的跟踪仿真,证明了智能车在遗传算法对该控制律参数寻优下,能够实现对给定轨迹的全局渐近跟踪,获得了较好的控制效果.验证了遗传算法对智能车控制器参数寻优的可行性和有效性.     

基于EMD-WPT-SVD和指数加权平均的MOA阻性电流去干扰方法

金属氧化物避雷器(metal oxide arrester,MOA)阻性电流测量受环境干扰、高频噪声干扰和脉冲干扰等因素的影响,阻性电流实测信号必然含有多种强干扰成分.针对传统阻性电流去干扰方法在多种并发干扰情形下难以适用的难题,提出了一种基于EMD-WPT-SVD和指数加权平均的MOA阻性电流去干扰方法.首先,依据阻性电流中周期干扰的基波频率、采样频率和特征频率是否被间谐波浸没的特点,选取最佳分解层数和最优小波基;其次,对MOA阻性电流进行经验模态分解(empirical mode decomposition,EMD),将含有特征频率的模态分量再使用小波包变换(wavelet packet transform,WPT),使每个子信号最多含有一个特征频率;再采用奇异值分解(singular value decomposition,SVD)准确提取所有子信号中的周期分量;最后,从原信号中准确消除阻性电流受到的周期干扰,然后通过指数加权平均算法得到能真实反映MOA工况的阻性电流变化趋势.将模拟信号和工程实测数据与多种经典方法进行比较,结果证明:所提方法能消除阻性电流的多种干扰,得到能真实反映MOA实时工况的阻性电流,其结果满足实际工程测量的要求.