Bed morphology, flow structure, and sediment transport at the outlet of Lake Huron and in the upper St. Clair River

An integrated multibeam echo sounder and acoustic Doppler current profiler field survey was conducted in July 2008 to investigate the morphodynamics of the St. Clair River at the outlet of Lake Huron. The principal morphological features of the upper St. Clair River included flow-transverse bedforms that appear weakly mobile, erosive bedforms in cohesive muds, thin non-cohesive veneers of weakly mobile sediment that cover an underlying cohesive (till or glacio-lacustrine) surface, and vegetation that covers the bed. The flow was characterized by acceleration as the banks constrict from Lake Huron into the St. Clair River, an approximately 1500-m long region of flow separation downstream from the Blue Water Bridge, and secondary flow connected to: i) channel curvature; ii) forcing of the flow by local bed topography, and iii) flow wakes in the lee side of ship wrecks. Nearshore, sand-sized, sediment from Lake Huron was capable of being transported into, and principally along, the banks of the upper St. Clair River by the measured flow. A comparison of bathymetric surveys conducted in 2007 and 2008 identifies that the gravel bed does undergo slow downstream movement, but that this movement does not appear to be generated by the mean flow, and could possibly be caused by ship-propeller-induced turbulence. The study results suggest that the measured mean flow and dredging within the channel have not produced major scour of the upper St. Clair River and that the recent fall in the level of Lake Huron is unlikely to have been caused by these mechanisms.     

超差特征量指标集约束下PID调节器的满意优化

针对一类具有目的域的控制系统,提出将一组由滞留时间、超差时间以及随机穿越周期时间等构成的超差特征量指标纳入满意PID调节器的目标函数集中进行研究。将PID调节器的参数设计转化为局部状态反馈问题,推导出带状目的域下超差特征量指标的解析表达式,并分析了其与稳定裕度、动态误差系数以及稳态输出方差指标之间的相容性;当上述指标相容时,给出了一种利用双群体机制下的差分进化算法和内点法相结合对PID调节器参数进行优化求解的策略;数值算例表明了所提方法的有效性。     

Fault tolerance in cellular automata at high fault rates

A commonly used model for fault-tolerant computation is that of cellular automata. The essential difficulty of fault-tolerant computation is present in the special case of simply remembering a bit in the presence of faults, and that is the case we treat in this paper. We are concerned with the degree (the number of neighboring cells on which the state transition function depends) needed to achieve fault tolerance when the fault rate is high (nearly 1/2). We consider both the traditional transient fault model (where faults occur independently in time and space) and a recently introduced combined fault model which also includes manufacturing faults (which occur independently in space, but which affect cells for all time). We also consider both a purely probabilistic fault model (in which the states of cells are perturbed at exactly the fault rate) and an adversarial model (in which the occurrence of a fault gives control of the state to an omniscient adversary). We show that there are cellular automata that can tolerate a fault rate 1/2−ξ (with ξ>0) with degree O((1/ξ²)log(1/ξ)), even with adversarial combined faults. The simplest such automata are based on infinite regular trees, but our results also apply to other structures (such as hyperbolic tessellations) that contain infinite regular trees. We also obtain a lower bound of Ω(1/ξ²), even with only purely probabilistic transient faults.     

Quantifying requirements volatility effects

In an organization operating in the bancassurance sector we identified a low-risk IT subportfolio of 84 IT projects comprising together 16,500 function points, each project varying in size and duration, for which we were able to quantify its requirements volatility. This representative portfolio stems from a much larger portfolio of IT projects. We calculated the volatility from the function point countings that were available to us. These figures were aggregated into a requirements volatility benchmark. We found that maximum requirements volatility rates depend on size and duration, which refutes currently known industrial averages. For instance, a monthly growth rate of 5% is considered a critical failure factor, but in our low-risk portfolio we found more than 21% of successful projects with a volatility larger than 5%. We proposed a mathematical model taking size and duration into account that provides a maximum healthy volatility rate that is more in line with the reality of low-risk IT portfolios. Based on the model, we proposed a tolerance factor expressing the maximal volatility tolerance for a project or portfolio. For a low-risk portfolio its empirically found tolerance is apparently acceptable, and values exceeding this tolerance are used to trigger IT decision makers. We derived two volatility ratios from this model, the π-ratio and the ρ-ratio. These ratios express how close the volatility of a project has approached the danger zone when requirements volatility reaches a critical failure rate. The volatility data of a governmental IT portfolio were juxtaposed to our bancassurance benchmark, immediately exposing a problematic project, which was corroborated by its actual failure. When function points are less common, e.g. in the embedded industry, we used daily source code size measures and illustrated how to govern the volatility of a software product line of a hardware manufacturer. With the three real-world portfolios we illustrated that our results serve the purpose of an early warning system for projects that are bound to fail due to excessive volatility. Moreover, we developed essential requirements volatility metrics that belong on an IT governance dashboard and presented such a volatility dashboard.     

A new approach based on a discrete hidden Markov model using the Rocchio algorithm for the diagnosis of heart valve diseases

Abstract: Application of the Doppler ultrasound technique in the diagnosis of heart diseases has been increasing in the last decade since it is non‐invasive, practicable and reliable. In this study, a new approach based on the discrete hidden Markov model (DHMM) is proposed for the diagnosis of heart valve disorders. For the calculation of hidden Markov model (HMM) parameters according to the maximum likelihood approach, HMM parameters belonging to each class are calculated by using training samples that only belong to their own classes. In order to calculate the parameters of DHMMs, not only training samples of the related class but also training samples of other classes are included in the calculation. Therefore HMM parameters that reflect a class's characteristics are more represented than other class parameters. For this aim, the approach was to use a hybrid method by adapting the Rocchio algorithm. The proposed system was used in the classification of the Doppler signals obtained from aortic and mitral heart valves of 215 subjects. The performance of this classification approach was compared with the classification performances in previous studies which used the same data set and the efficiency of the new approach was tested. The total classification accuracy of the proposed approach (95.12%) is higher than the total accuracy rate of standard DHMM (94.31%), continuous HMM (93.5%) and support vector machine (92.67%) classifiers employed in our previous studies and comparable with the performance levels of classifications using artificial neural networks (95.12%) and fuzzy‐C‐means/CHMM (95.12%).     

Feature extraction by autoregressive spectral analysis using maximum likelihood estimation: internal carotid arterial Doppler signals

Abstract: In this study, Doppler signals recorded from the internal carotid artery (ICA) of 97 subjects were processed by personal computer using classical and model-based methods. Fast Fourier transform (classical method) and autoregressive (model-based method) methods were selected for processing the ICA Doppler signals. The parameters in the autoregressive method were found by using maximum likelihood estimation. The Doppler power spectra of the ICA Doppler signals were obtained by using these spectral analysis techniques. The variations in the shape of the Doppler spectra as a function of time were presented in the form of sonograms in order to obtain medical information. These Doppler spectra and sonograms were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of stenosis and occlusion in the ICA. Reliable information on haemodynamic alterations in the ICA can be obtained by evaluation of these sonograms.     

地空大容量传输自适应均衡技术

地空信道远距离、低仰角、高速信息传输时,信道引入的多径将不可避免,多径引起的符号间的干扰和衰落将变得十分严重,传统的自适应均衡技术多基于解调器正常工作,动态适应性较差,均衡效果有限。本文详细介绍了采用均衡器与解调器联合设计原理,并结合多谱勒校正技术,能够满足在地空低莱斯因子信道下,解调器一均衡器校正多谱勒频偏的同时均衡动态多径,达到平稳传输效果。     

分点:无结构对等网络的拓扑关键点

虽然对等网络中的结点在功能上互相平等,但其中某些结点对于覆盖网拓扑却有重要意义.提出了分点概念来描述对等网络的拓扑关键点,这些结点的失效很可能导致覆盖网的分割.设计了一套简单、有效、分布式的分点检测和避免方法.模拟实验的结果表明,该方法可以优化覆盖网拓扑结构,并显著地提高无结构对等网络在动态环境下的容错性.     

基于新型协调量化容差关系的不完备数据填补方法

提出一种基于新型协调量化容差关系的不完备数据补齐算法。首先求出属性值不完备对象的容差类,结合属性的重要度,将容差类里面的对象容差关系量化,再根据决策规则独立原则,在尽可能保持系统协调性的前提下对缺失属性值进行补齐。大量实验证明了本算法的有效性。     

多普勒频率补偿算法在GPS信号捕获中的应用

在GPS卫星高速运动和地面GPS接收机的移动中,由于卫星和接收机之间的径向速度引起了载波多普勒频移、C/A码的多普勒频移和数据码的多普勒频移,多普勒频移会对GPS接收机的信号捕获产生影响,使其准确度下降。针对上述情况,在常用的相干积分结合非相干积分的GPS信号捕获方案中引入一种多普勒频率补偿算法,并对此算法进行相应的仿真。结果表明该设计方案是可行的,引入的补偿算法能够有效消除多普勒频移对GPS信号捕获的影响,能够实现对GPS卫星信号的多普勒和码相的准确捕获。