二滩水电站低励限制与失磁保护配合关系研究

二滩水电站发变组保护系统改造后,为满足规范所要求的低励限制能优先于失磁保护动作,提出将失磁保护的机端低电压判据、定子侧静稳极限阻抗圆判据和转子侧电压判据与低励限制曲线绘制在同一P-Q坐标系中,以验证保护装置失磁保护与低励限制的配合关系.计算结果验证了失磁保护整定值的正确性,同时证明了失磁保护动作区与低励限制之间存在一定的稳定裕度,满足低励限制优先于失磁保护动作的关系.     

水资源定价方法与实践研究Ⅱ:海河流域水价探析

以水资源价值内涵分析为基础,对水资源定价理论与实践进行了研究,提出水资源供给价格应包括水资源费、供给成本和水环境补偿税;水资源需求价格通过产业用水经济价值确定;供给价格和需求价格共同作用决定水资源市场价格;在非市场化体系中通过影子价格评价水资源价值;依据水价构成及平衡关系,确定了水资源费的定量评价方法;利用投入产出分析技术构建简化的CGE模型,结合多种评价方法对水资源影子价格、水经济价值、水资源费、供给成本和水环境补偿税分别进行了定量评价,为水价形成机制提供了理论基础,为水资源定价标准提供了方法和实践支撑。研究成果可对实行最严格的水资源管理制度、完善水价定价机制、促进利用价格杠杆调控水资源分配提供参考。     

甘肃省农业抗旱能力评估分析研究

根据现状农业抗旱能力形成要素,选择了水利工程建设、经济实力支持、农业生产水平以及抗旱组织管理等4个代表性指标作为评价因子,并分流域确定了指标权重;采用目标评价方法确定代表性指标构成要素,形成评价指标体系并确定评价层权重;提出农业抗旱能力评价等级,依据评价指标体系计算结果,分市(州)评价了现状甘肃农业的抗旱能力,为甘肃省农业实现高产稳产提供更合理的依据。     

实时响应物联网中基于查询的数据转发方案

在许多基于传感器网络技术的物联网应用中,用户需要快速的查询响应,比如智能交通物联网应用中,行驶在路上的司机即时查询附近的空停车位信息.如何为此类物联网设计一种符合传感器网络特性(如能量有效等)的快速数据转发方案是一项重要的挑战性工作.已有的传感器网络实时数据转发协议大都因未解决好转发断路带来的额外开销、孤立节点处理耗时、难以适应网络拓扑动态变化等关键性问题而未取得理想的实时性效果.为此,该文提出一种新的基于查询的快速数据转发方案,利用查询消息为每个传感器节点建立最快速的数据转发路径(有向无环图),此外文中给出的综合路径代价模型可以均衡网络能量和减少网络拥塞延时,最后设计了贪婪的分布式数据转发算法及其改进算法,并用仿真实验验证了该方案的有效性和高效性.     

基于MAX263的带通跟踪滤波设计

介绍了MAX263滤波芯片性能,分析了其软件设计方法及其工作原理,结合锁相环频率跟踪原理设计了针对转子不平衡量电信号的带通跟踪滤波器,实验证明该方法滤波效果十分明显,能够有效滤除干扰信号,且电路简单,具有很高的应用价值。     

基于TMS320C6678的语种识别并行算法设计与实现

针对语种识别在实际应用中的实时性需求,在分析语种识别算法原理和多核DSP任务并行的基础上,设计了一种基于TMS320C6678的语种识别算法并行实现方法。该方法实现了任务级并行流水和核间高效通信。实验结果表明,该方法可靠、有效,并且使该系统至少能够完成132路的实时识别任务。     

Research on multi-robot open architecture of an intelligent CNC system based on parameter-driven technology

This paper presents a multi-robot open architecture of an intelligent computer numerical control (CNC) system based on parameter-driven technology that has been developed for flexible and high-efficiency manipulation. An open architecture control system capable of distributed processing of decision-making and extraction of task information provides a premise for intelligent control and flexible operation. Intelligent detection with database feedback based on real-time assignment of tasks is proposed to achieve dynamic modification of the processing trajectory. In the context of flexible task control, a multi-robot architecture with collision-free path planning and a novel programming approach based on parameter-driven technology are developed. The proposed CNC system has been successfully implemented and demonstrated on an H-beam steel-cutting task that requires flexible and accurate machining.     

Designing fast LTL model checking algorithms for many-core GPUs

Recent technological developments made various many-core hardware platforms widely accessible. These massively parallel architectures have been used to significantly accelerate many computation demanding tasks. In this paper, we show how the algorithms for LTL model checking can be redesigned in order to accelerate LTL model checking on many-core GPU platforms. Our detailed experimental evaluation demonstrates that using the NVIDIA CUDA technology results in a significant speedup of the verification process. Together with state space generation based on shared hash-table and DFS exploration, our CUDA accelerated model checker is the fastest among state-of-the-art shared memory model checking tools.     

An enhanced Support Vector Machine classification framework by using Euclidean distance function for text document categorization

This paper presents the implementation of a new text document classification framework that uses the Support Vector Machine (SVM) approach in the training phase and the Euclidean distance function in the classification phase, coined as Euclidean-SVM. The SVM constructs a classifier by generating a decision surface, namely the optimal separating hyper-plane, to partition different categories of data points in the vector space. The concept of the optimal separating hyper-plane can be generalized for the non-linearly separable cases by introducing kernel functions to map the data points from the input space into a high dimensional feature space so that they could be separated by a linear hyper-plane. This characteristic causes the implementation of different kernel functions to have a high impact on the classification accuracy of the SVM. Other than the kernel functions, the value of soft margin parameter, C is another critical component in determining the performance of the SVM classifier. Hence, one of the critical problems of the conventional SVM classification framework is the necessity of determining the appropriate kernel function and the appropriate value of parameter C for different datasets of varying characteristics, in order to guarantee high accuracy of the classifier. In this paper, we introduce a distance measurement technique, using the Euclidean distance function to replace the optimal separating hyper-plane as the classification decision making function in the SVM. In our approach, the support vectors for each category are identified from the training data points during training phase using the SVM. In the classification phase, when a new data point is mapped into the original vector space, the average distances between the new data point and the support vectors from different categories are measured using the Euclidean distance function. The classification decision is made based on the category of support vectors which has the lowest average distance with the new data point, and this makes the classification decision irrespective of the efficacy of hyper-plane formed by applying the particular kernel function and soft margin parameter. We tested our proposed framework using several text datasets. The experimental results show that this approach makes the accuracy of the Euclidean-SVM text classifier to have a low impact on the implementation of kernel functions and soft margin parameter C.     

Clump splitting via bottleneck detection and shape classification

Under-segmentation of an image with multiple objects is a common problem in image segmentation algorithms. This paper presents a novel approach for splitting clumps formed by multiple objects due to under-segmentation. The proposed algorithm includes three steps: (1) decide whether to split a candidate connected component by application-specific shape classification; (2) find a pair of points for clump splitting and (3) join the pair of selected points. In the first step, a shape classifier is applied to determine whether a connected component should be split. In the second step, a pair of points for splitting is detected using a bottleneck rule, under the assumption that the desired objects have roughly a convex shape. In the third step, the selected splitting points from step two are joined by finding the optimal splitting line between them, based on minimizing an image energy. The shape classifier is built offline via various shape features and a support vector machine. Steps two and three are application-independent. The performance of this method is evaluated using images from various applications. Experimental results show that the proposed approach outperforms the state-of-the-art algorithms for the clump splitting problem.