水库淤积初步平衡问题

水库淤积平衡问题对于确定淤积危害程度、可持续利用标准等都非常重要。本文通过分析大量已建、在建和规划水库淤积情况,对水库淤积初步平衡后淤积变化趋势与设计淤积标准等问题进行了讨论和商榷。研究表明,水库淤积初步平衡后,兴利库容淤积发展仍然很快,库尾河床与水位抬升仍在继续,并未最终稳定。水库淤积平衡应该更加注重兴利库容淤积情况和库尾河床与水位抬升趋势,仅仅关注总淤积量变化情况是不够的。从区域长远利益考虑,大江大河上的水库设计淤积年限还应大大加长。     

Convergence analysis of hybrid bi-directional associative memory neural networks with discrete delays

In this paper, the dynamical characteristics of hybrid bi-directional associative memory (BAM) neural networks with constant transmission delays are investigated. Without assuming the symmetry of synaptic connection weights and the monotonicity and differentiability of activation functions, the Lyapunov functionals are constructed and Halanay-type inequalities are respectively employed to derive the delay-independent sufficient conditions under which the networks converge exponentially to the equilibria associated with temporally uniform external inputs. Some examples are given to illustrate that the results are less conservative and less restrictive than the previously known results.     

Flame inertia into lean region in stratified hydrogen mixture

In hydrogen engines, the direct-injection stratified charge system has the ability to realize a more lean operation than homogeneous charge systems. Flame propagation into the lean region in the stratified hydrogen mixture was investigated in this study. Hydrogen was injected into a lean hydrogen–air premixed mixture charge of the equivalence ratio of 0.074–0.257. This stratified hydrogen mixture was ignited by an electric spark. The flame generated in the hydrogen jet propagated into the lean premixed mixture charge as if it had “inertia”, even though the stoichiometry of the premixed mixture charge was less than the lower flammability limit. The amount of burned hydrogen in the case of a stratified mixture was greater than that in the case of the homogeneous mixture. The flame seems to be supported from behind by the burned gas in the rich region until the decay of this support.     

车联网基于信任度的RSU识别及资源分配算法

在车联网中,由于部分路侧单元（RSU）的位置比较偏远,无法得到网络管理员的及时维护,可能遭到攻击,变为不可信任的路侧单元,降低了车辆的通信质量和网络能效.为了有效地识别RSU的可靠性,提出了一种基于信任度的路侧单元识别及资源分配算法,将路侧单元识别、传输模式的选择和功率分配联合建模为网络能效的优化问题,并分成3个子优化问题,分别求解.首先,采用基于信任度的RSU识别算法,识别出可信任和不可信任的路侧单元;其次,优化车辆资源块配置和链路选择;最后,通过次梯度算法优化传输功率,最大化网络能效.理论分析和仿真结果表明,所提算法具备较低的复杂度,可以达到较高的路侧单元识别准确度,并有效地提升网络总能效.     

Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems

Recommender systems apply data mining and machine learning techniques for filtering unseen information and can predict whether a user would like a given item. This paper focuses on gray-sheep users problem responsible for the increased error rate in collaborative filtering based recommender systems. This paper makes the following contributions: we show that (1) the presence of gray-sheep users can affect the performance – accuracy and coverage – of the collaborative filtering based algorithms, depending on the data sparsity and distribution; (2) gray-sheep users can be identified using clustering algorithms in offline fashion, where the similarity threshold to isolate these users from the rest of community can be found empirically. We propose various improved centroid selection approaches and distance measures for the K-means clustering algorithm; (3) content-based profile of gray-sheep users can be used for making accurate recommendations. We offer a hybrid recommendation algorithm to make reliable recommendations for gray-sheep users. To the best of our knowledge, this is the first attempt to propose a formal solution for gray-sheep users problem. By extensive experimental results on two different datasets (MovieLens and community of movie fans in the FilmTrust website), we showed that the proposed approach reduces the recommendation error rate for the gray-sheep users while maintaining reasonable computational performance.     

A weighted multivariate Fuzzy C-Means method in interval-valued scientific production data

Clustering is the process of organizing objects into groups whose members are similar in some way. Most of the clustering methods involve numeric data only. However, this representation may not be adequate to model complex information which may be: histogram, distributions, intervals. To deal with these types of data, Symbolic Data Analysis (SDA) was developed. In multivariate data analysis, it is common some variables be more or less relevant than others and less relevant variables can mask the cluster structure. This work proposes a clustering method based on fuzzy approach that produces weighted multivariate memberships for interval-valued data. These memberships can change at each iteration of the algorithm and they are different from one variable to another and from one cluster to another. Furthermore, there is a different relevance weight associated to each variable that may also be different from one cluster to another. The advantage of this method is that it is robust to ambiguous cluster membership assignment since weights represent how important the different variables are to the clusters. Experiments are performed with synthetic data sets to compare the performance of the proposed method against other methods already established by the clustering literature. Also, an application with interval-valued scientific production data is presented in this work. Clustering quality results have shown that the proposed method offers higher accuracy when variables have different variabilities.     

An effective node-selection scheme for the energy efficiency of solar-powered WSNs in a stream environment

Previous research on scheduling and solar power issues of wireless sensor networks (WSNs) assumes that the sensors are deployed in a general environment. While monitoring the stream environment, sensors are attached to the stream side to collect the sensed data and transmit the data back to the sink. The stream environment can be scaled in several similar environments. This type of geographic limitation not only exists in a stream environment but also on streets, roads, and trails. This study presents an effective node-selection scheme to enhance the efficiency of saving power and coverage of solar-powered WSNs in a stream environment. Analysis of the sensor deployment in the stream environment permits sensors to be classified into different segments, and then allows the selection of active nodes for building inter-stream connections, inter-segment connections, and intra-segment connections. Based on these connections, the number of active nodes and transmitted packets is minimized. Simulation results show that this scheme can significantly increase the energy efficiency and maintain the monitoring area in solar-powered WSNs.     

高帧频激光光斑采集系统设计

随着半导体技术的发展,COMS图像传感器的灵敏度、分辨率动态范围不断提高,采用高速COMS图像传感器替代传统的CCD图像传感器作为激光通信中APT系统的激光光斑探测单元是APT发展的一种趋势。提出了一种基于高速LUPA-300 COMS图像传感器、DSP和FPGA的通用型高帧频光斑采集系统。FPGA内部生成的异步FIFO作为图像传感器与DSP之间的数据缓冲器,简化了系统结构布局,提高了系统稳定性。经过硬件电路设计、调试、软件部分调试等工作,最终实现了每秒钟1 000帧128×128图像的数据采集,可用于高速激光光斑的采集。     

双模移动终端主动探测系统的DSP设计与实现

针对移动终端用户身份识别的技术需求,设计了一种基于GSM和CDMA双模制式下基带信号处理的主动探测系统。为了满足主动探测系统的实时处理需求,采用高性能的DSP芯片TMS320C6416作为核心处理器,结合其他器件设计硬件系统,完成基带信号处理板中DSP芯片硬件接口的设计。经现场测试结果验证,系统能够实现对开机状态下GSM和CDMA移动用户身份信息的快速捕获,进而表明本设计可满足系统实时性和可靠性的要求。     

Automatic classification and prediction models for early Parkinson’s disease diagnosis from SPECT imaging

Early and accurate diagnosis of Parkinson’s disease (PD) is important for early management, proper prognostication and for initiating neuroprotective therapies once they become available. Recent neuroimaging techniques such as dopaminergic imaging using single photon emission computed tomography (SPECT) with ¹²³I-Ioflupane (DaTSCAN) have shown to detect even early stages of the disease. In this paper, we use the striatal binding ratio (SBR) values that are calculated from the ¹²³I-Ioflupane SPECT scans (as obtained from the Parkinson’s progression markers initiative (PPMI) database) for developing automatic classification and prediction/prognostic models for early PD. We used support vector machine (SVM) and logistic regression in the model building process. We observe that the SVM classifier with RBF kernel produced a high accuracy of more than 96% in classifying subjects into early PD and healthy normal; and the logistic model for estimating the risk of PD also produced high degree of fitting with statistical significance indicating its usefulness in PD risk estimation. Hence, we infer that such models have the potential to aid the clinicians in the PD diagnostic process.