遮挡条件下多运动车辆跟踪研究

针对SURF算法能够提取到的图像特征点较少的问题,基于保持亮度特性的双直方图均衡算法,通过重构SURF尺度空间提取图像特征。将这种方法与卡尔曼滤波相结合进行目标跟踪,用特征点的中心作为跟踪点;通过卡尔曼滤波预测出运动目标的位置,判断遮挡是否发生;最后,应用该方法进行目标特征向量匹配。实验结果表明,该算法对发生旋转、缩放以及遮挡的多运动目标都可进行稳定跟踪,其跟踪速度比R-SURF算法提高20%;在跟踪速度相当的情况下,跟踪精度要高于卡尔曼滤波跟踪算法。     

链路服务质量限制下多输入多输出媒体接入控制的建模与分析

针对SCMA协议没有同时考虑链路服务质量(QoS)和多输入多输出(MIMO)流的信道接入调度策略的不足,提出一种SCMA/QA协议。协议充分考虑了每个链路中不同流的信道状态,建立了一个基于流的信道状态离散马尔可夫链模型,并融合考虑了每条链路的QoS需求,采用修改的RTS/CTS进行链路QoS的信息交换,通过链路QoS权重作为链路选择的主要因素,将MIMO下基于QoS的链路调度问题建模为一个最优化问题,并在卡罗需-库恩-塔克(KKT)条件下得到最优链路以及通信的流数目。最后,以吞吐量为QoS指标进行了数值分析,结果表明在相同网络环境下,SCMA/QA比SCMA和QCSCMA能更好地提高系统的吞吐量。     

Modeling of optimal control for urban freeway corridor under incident conditions

Traffic control and management are effective measures to solve the problem of traffic congestion. The optimal control model for freeway corridor is developed under incident conditions, which is in the form of minimization of the sum of the square of the difference between traffic demand and capacity at each intersection and on the freeway bottleneck section. The model optimizes control parameters of phase splits at arterial intersections, off-ramp diversion rates at upstream off-ramps and on-ramp diversion rates at downstream on ramps. Finally, the objective function is discussed and it is showed that the optimal control model is simple and practical.     

Modeling of optimal control for urban freeway corridor under incident conditions

Traffic control and management are effective measures to solve the problem of traffic congestion. The optimal control model for freeway corridor is developed under incident conditions, which is in the form of minimization of the sum of the square of the difference between traffic demand and capacity at each intersection and on the freeway bottleneck section. The model optimizes control parameters of phase splits at arterial intersections, off-ramp diversion rates at upstream off-ramps and on-ramp diversion rates at downstream on ramps. Finally, the objective function is discussed and it is showed that the optimal control model is simple and practical.     

Wastewater treatment plant design and operation under multiple conflicting objective functions

Wastewater treatment plant design and operation involve multiple objective functions, which are often in conflict with each other. Traditional optimization tools convert all objective functions to a single objective optimization problem (usually minimization of a total cost function by using weights for the objective functions), hiding the interdependencies between different objective functions. We present an interactive approach that is able to handle multiple objective functions simultaneously. As an illustration of our approach, we consider a case study of plant-wide operational optimization where we apply an interactive optimization tool. In this tool, a commercial wastewater treatment simulation software is combined with an interactive multiobjective optimization software, providing an entirely new approach in wastewater treatment. We compare our approach to a traditional approach by solving the case study also as a single objective optimization problem to demonstrate the advantages of interactive multiobjective optimization in wastewater treatment plant design and operation.     

Study on an intelligent fault-tolerant technique for multiple satellite configured navigation under highly dynamic conditions

The probability of pseudo-range information being faulty will increase when there is increased noise in the receiver due to highly dynamic conditions. In this paper, an intelligent fault-tolerant federated Kalman filter (IFFKF) is presented to solve the problem of invalid position information as a result of the uncertainty in the category and quantity of failed satellites in a multiple configured satellite system. At the same time, a corresponding intelligent fault-tolerant system structure based on a cell-...     

Modeling data with multiple time dimensions

A large class of problems in time series analysis can be represented by a set of overlapping time series with different starting times. These time series may be treated as different probes of the same underlying process. Such probes may follow a characteristic lifecycle as a function of the time since the series began. They may also be subject to environmental shocks according to calendar time. In addition, the calibration of each probe may be unknown such that each series may show a different magnitude of response to the underlying lifecycles and environmental impacts.This paper describes an approach to analyzing these multiple time series as a single set such that the underlying lifecycles and calendar-based shocks may be measured. Simultaneously, the individual calibrations of the time series are also measured. This technique is referred to as dual-time dynamics, and it applies to many important business problems. Applications to tree ring analysis, the SETI@home project, and retail loan portfolio forecasting are provided. Other areas of possible application include digital media services, insurance, human resource management, health care, and biological systems to name a few.     

On the equivalence of minimum compliance and stress-constrained minimum weight design of trusses under multiple loading conditions

This article is devoted to topology optimization of trusses under multiple loading conditions. Compliance minimization with material volume constraint and stress-constrained minimum weight problem are considered. In the case of a single loading condition, it has been shown that the two problems have the same optimal topology. The possibility of extending this result for problems involving multiple loading conditions is examined in the present work. First, the compliance minimization problem is formulated as a multicriterion optimization problem, where the conflicting criteria are the compliances of the different loading conditions. Then, the optimal topologies of the stress-constrained minimum weight problem and the multicriterion compliance minimization problem for a simple test example are compared. The results verify that when multiple loading conditions are involved, the stress-constrained minimum weight topology cannot be obtained in general by solving the compliance minimization problem.     

基于隐性记忆的非平稳时变污水处理过程多目标运行优化

针对污水处理过程中运行能耗大和水质超标严重等问题,基于隐性记忆策略,提出一种适用于无规律且非平稳时变过程的多目标运行优化方法.首先,采用集成即时学习在线建模算法建立运行指标模型,实现时变工况下运行优化目标的准确描述;然后,提出基于隐性记忆的多目标优化算法,通过充分考虑复杂时变工况,挖掘埋藏在历史优化信息中的结构化知识,引导进化搜索过程,并结合平均距离聚类指标进行均匀随机局部搜索,提高算法收敛性、多样性和分布性;最后,利用基于集成即时学习的智能决策方法,获取操作变量溶解氧浓度和硝态氮浓度的优化设定值并进行跟踪控制.数据实验表明:所提出方法能够实现污水处理过程的稳定运行,在满足操作限制的情况下,提高污染物去除性能,降低运行成本.     

Modeling mental navigation in scenes with multiple objects

Various lines of evidence indicate that animals process spatial information regarding object locations differently from spatial information regarding environmental boundaries or landmarks. Following Wang and Spelke's (2002) observation that spatial updating of egocentric representations appears to lie at the heart of many navigational tasks in many species, including humans, we postulate a neural circuit that can support this computation in parietal cortex, assuming that egocentric representations of multiple objects can be maintained in prefrontal cortex in spatial working memory (not simulated here). Our method is a generalization of an earlier model by Droulez and Berthoz (1991), with extensions to support observer rotation. We can thereby simulate perspective transformation of working memory representations of object coordinates based on an egomotion signal presumed to be generated via mental navigation. This biologically plausible transformation would allow a subject to recall the locations of previously viewed objects from novel viewpoints reached via imagined, discontinuous, or disoriented displacement. Finally, we discuss how this model can account for a wide range of experimental findings regarding memory for object locations, and we present several predictions made by the model.     

Video distribution under multiple constraints

We consider the optimization problem of providing a set of video streams to a set of clients, where each stream has costs in m possible measures (such as communication bandwidth, processing bandwidth, etc.), and each client has its own utility function for each stream. We assume that the server has a budget cap on each of the m cost measures; each client has an upper bound on the utility that can be derived from it, and potentially also upper bounds in each of the m cost measures. The task is to choose which streams the server will provide, and out of this set, which streams each client will receive. The goal is to maximize the overall utility subject to the budget constraints. We give an efficient approximation algorithm with approximation factor of O(m) with respect to the optimal possible utility for any input, assuming that clients have only a bound on their maximal utility. If, in addition, each client has at most m_c capacity constraints, then the approximation factor increases by another factor of O(m_clogn), where n is the input length. We also consider the special case of “small” streams, namely where each stream has cost of at most O(1/logn) fraction of the budget cap, in each measure. For this case we present an algorithm whose approximation ratio is O(logn).     

Coalition-hierarchical game under uncertainty conditions

An optimization problem in a coalition-hierarchical game under uncertainty conditions is formulated. In the game, information assumptions are that the player of the high hierarchical level (controlling Center) and each low-level coalition estimates uncertainty in its own way. The Center constructs its strategy from the maximum condition for its own payoff function and its minimum in uncertainty. The relationships between coalitions are built upon the guaranteeing absolute active equilibrium understood in the sense of providing the players with guaranteed payoff under the actual uncertainty. The guaranteed uncertainty is obtained with the help of Slater principle. The total equilibrium in the game is called CH-equilibrium. For a quadratic game version, sufficient optimality conditions are obtained. A numerical procedure for solving the game is described and an example is given.     

Modeling and analysis of multiple impacts in multibody systems under unilateral and bilateral constrains based on linear projection operators

Multibody System Dynamics - This paper presents a unifying dynamics formulation for nonsmooth multibody systems (MBSs) subject to changing topology and multiple impacts based on a linear projection...     

A new hybrid fuzzy-goal programming scheme for multi-objective topological optimization of static and dynamic structures under multiple loading conditions

This work presents a hybrid fuzzy-goal multi-objective programming scheme for topological optimization of continuum structures, in which both static and dynamic loadings are considered. The proposed methodology fortopological optimization first employs a fuzzy-goal programming scheme at the top level for multi-objective problems with static and dynamic objectives. For the static objective with multi-stiffness cases in the fuzzy-goal formulation, a hybrid approach, involving a hierarchical sequence approach or a hierarchical sequence approach coupled with a compromise programming method, is especially suggested for the statically loaded multi-stiffness structure at the sublevel. Concerning dynamic optimization problems of freevibration cases, nonstructural mass, oscillation of the objective function, and repeated eigenvalues are also discussed. Solid Isotropic Material with Penalization density–stiffness interpolation scheme is used to indicate the dependence ofmaterial modulus upon regularized element densities. The globally convergent version of the method of moving asymptotes and the sequential linear programming method areboth employed as optimizers. Several applications have been applied to demonstrate the validation of the presented methodologies.     

Detection algorithm for color dynamic images by multiple surveillance cameras under low luminance conditions based on fuzzy corresponding map

An objects detection algorithm for color dynamic images from two cameras is proposed for a surveillance system under low illumination. It provides automatic calculation of a fuzzy corresponding map and color similarity for lower luminance conditions, which detects little chromatic regions in CCD camera images under lower illumination and presents regions with a possibility of occlusion situation. Experimental detection results for two dynamic images from real surveillance cameras in a downtown area in Japan under low luminance conditions show that the proposed algorithm has 15% improved accuracy compared with the independent detection algorithm in the same false alarm rate, which occlusion regions are correctly presented. Moreover, implementability for severe surveillance situation is discussed. The proposed algorithm is being considered for use in a low cost surveillance system at a relatively poor security downtown (shopping mall) area in Japan.     

Modeling the impact of observation conditions on localization systems

In various applications, sensor fusion has demonstrated success as means to enhance a system performance in perceiving its environment. By combing observations of different sensors, the system is able to achieve improved sensing accuracy, and potentially, expanded sensing capabilities. However, the observation conditions in the surrounding of any multi-sensor system have a considerable impact on the performance of the system. This impact can be hard to mitigate if the observation conditions are stochastic in nature. Therefore, for any sensor fusion strategy to achieve reliable and robust performance it must possess a capability to assess the quality of the observation conditions in its surrounding, and ultimately, the quality of its decisions, as a function of these conditions. One typical application where the impact of the observation conditions can cause sever deterioration of the sensing performance is vehicle localization. It is typical in this application that location measurements obtained from multiple sensors (e.g., GPS, Vision, Inertial, etc.) are combined together to compute accurate vehicle location. However, such improved accuracy can only be attained under nominal observation conditions. Therefore, real-time awareness of the observation conditions around the vehicle position is pivotal for the multi-sensor system to achieve effective fusion performance.In this paper, a Markovian model is proposed to capture the impact of observation conditions on a sensor’s localization performance and to consequently determine a reliability index with respect to the localization accuracy claimed by the sensor.The proposed model is implemented on two localization techniques: single-sensor localization and multi-sensor localization. A number of experiments are conducted to determine the different levels of localization accuracy that can be achieved by each technique under a wide range of observation conditions. The proposed reliability model is tested in a variety of real-life and simulated observation conditions scenarios. It is evident from the experimental results that the proposed model is able to estimate the reliability of location estimates produced by either one of the localization techniques. The paper discusses how such reliability model can benefit multi-sensor systems.     

Modeling user multiple interests by an improved GCS approach

User interest profile is the crucial component of most personalized recommender systems. The diversity and time-dependent evolving nature of user interests are creating difficulties in constructing and maintaining a sound user profile. This paper presents a simple but effective model, by using improved growing cell structures (GCS), to address this problem. The GCS is a kind of self-organizing map neural network with changeable network structure. By virtue of the clustering and structure adaptation capability of GCS, the proposed model maps the problem of learning and keeping track of user interests into a clustering and cluster-maintaining problem. Each cluster found by GCS represents an interest category of a user and the cluster maintaining, including cluster addition and deletion, corresponds to the addition of user's new interests and the removal of user's old interests. The proposed model has been validated by a set of experiments performed on a benchmark dataset. Results from experiments show that our model provides reasonable performance and high adaptability for learning user multiple interests and their changes.