基于拥塞预测的NoC自适应仲裁方法*

传统用于总线系统或互联网的仲裁方法已不能很好地适应NoC应用环境。围绕NoC系统性能的关键影响因素——拥塞状态,提出了一种基于全局和本地拥塞预测的仲裁策略(GLCA),以改善NoC网络延迟。实验结果表明,相对于RR方法,新仲裁算法使得网络平均包延迟和平均吞吐量最大分别可改善20.5%和8%,并且在不同负载条件下都保持了其优势。综合结果显示, GLCA与RR方法相比,路由器仅在组合逻辑上有少许增加(25.7%)。     

利用多维统计方法预测网络拥塞状态

为在具有优先级调度网络环境下较准确地预估平均队列长度和队列等待时间这两个拥塞控制核心指标值,通过综合帕雷托分布、泊松随机过程和平均加权三种统计方法,设计一个包括数据到达过程、数据离开过程、数据优先级调度的计算模型,同时利用矩阵方法推导曲线性参数计算方程。通过仿真,实验结果与模型计算结果比较,两者偏差度小,验证了模型能准确预测网络状态。     

A hybrid framework combining data-driven and model-based methods for system remaining useful life prediction

Remaining useful life prediction is one of the key requirements in prognostics and health management. While a system or component exhibits degradation during its life cycle, there are various methods to predict its future performance and assess the time frame until it does no longer perform its desired functionality. The proposed data-driven and model-based hybrid/fusion prognostics framework interfaces a classical Bayesian model-based prognostics approach, namely particle filter, with two data-driven methods in purpose of improving the prediction accuracy. The first data-driven method establishes the measurement model (inferring the measurements from the internal system state) to account for situations where the internal system state is not accessible through direct measurements. The second data-driven method extrapolates the measurements beyond the range of actually available measurements to feed them back to the model-based method which further updates the particles and their weights during the long-term prediction phase. By leveraging the strengths of the data-driven and model-based methods, the proposed fusion prognostics framework can bridge the gap between data-driven prognostics and model-based prognostics when both abundant historical data and knowledge of the physical degradation process are available. The proposed framework was successfully applied on lithium-ion battery remaining useful life prediction and achieved a significantly better accuracy compared to the classical particle filter approach.     

Real-time tracking using trust-region methods

Optimization methods based on iterative schemes can be divided into two classes: line-search methods and trust-region methods. While line-search techniques are commonly found in various vision applications, not much attention is paid to trust-region ones. Motivated by the fact that line-search methods can be considered as special cases of trust-region methods, we propose to establish a trust-region framework for real-time tracking. Our approach is characterized by three key contributions. First, since a trust-region tracking system is more effective, it often yields better performances than the outcomes of other trackers that rely on iterative optimization to perform tracking, e.g., a line-search-based mean-shift tracker. Second, we have formulated a representation model that uses two coupled weighting schemes derived from the covariance ellipse to integrate an object's color probability distribution and edge density information. As a result, the system can address rotation and nonuniform scaling in a continuous space, rather than working on some presumably possible discrete values of rotation angle and scale. Third, the framework is very flexible in that a variety of distance functions can be adapted easily. Experimental results and comparative studies are provided to demonstrate the efficiency of the proposed method.     

改进的非线性离散系统自适应数据驱动控制

基于同步扰动随机近似的算法, 控制器选取为一个函数逼近器, 并在这里被确定为神经网络. 控制算法中使用了自适应的参数估计, 明显改善了控制性能, 同时也给出了相应的收敛性分析. 最后, 新型的控制算法被应用到了解决非线性离散系统的跟踪控制问题中, 并通过仿真比较结果, 充分验证了这种自适应数据驱动控制策略的可行性和有效性.     

The identification and adaptive prediction of urban sewer flows

The input raw material to a waste-water treatment plant exhibits large, and generally poorly quantified, variations with time. In particular, rainfall run-off can cause gross overloading of the treatment processes of the plant. For a proper operational control of the plant, and hence the quality of the receiving river's water, it would be extremely useful to have advance (short-term) estimates of the effluent flow from the sewer network, i.e. the influent to the plant. This paper studies the feasibility of using an on-line adaptive predictor in such a capacity. The procedure is divided into two steps : (i) the parameters of a multiple input/single output time-series model are recursively estimated at each time-step by the method of least squares ; (ii) a forecast of the plant influent flow is then made on the basis of the newly updated prediction model. Results are presented for data from a treatment plant in Stockholm, Sweden. These demonstrate the adaptability of the predictor to unknown changes in the process dynamics when no information is assumed to be available for rainfall events occurring over the urban land surface,     

一种节点权重自适应调整的DTN拥塞控制

由于部署、位置等原因,DTN中各均质节点在通信中的角色和作用存在差异,采取保管传输通信模式,热点区域的节点会因缓存过早耗尽而导致网络拥塞。提出了一种考虑节点自身作用的路由算法ARBR,每个节点根据网络负载状况,自主调整自身权重参数,各节点仅接收优先级不低于自身的节点数据,从而有效利用网络局部连通性,对网络中关键节点的流量进行抑制。仿真结果表明,ARBR缓解了瓶颈节点拥塞,利用随机连接的通信机会,有效提高了网络吞吐率。     

Time-series prediction using adaptive neuro-fuzzy networks

In this paper, we propose an Adaptive Neuro-Fuzzy Network (ANFN) to deal with forecasting problems. The ANFN model is inherently a modified Takagi–Sugeno–Kang-type fuzzy-rule-based model possessing a neural network's learning ability. We propose a hybrid learning algorithm which combines the Genetic Algorithm (GA) and the Least-Squares Estimate (LSE) method to construct the ANFN model. The GA is used to tune membership functions at the precondition part of fuzzy rules, while the LSE method is used to tune parameters at the consequent part of fuzzy rules. Simulations demonstrate that the proposed ANFN model has a good predictive capability.     

Optimal behaviour prediction using a primitive-based data-driven model-free iterative learning control approach

This paper suggests an optimal behaviour prediction mechanism for Multi Input-Multi Output control systems in a hierarchical control system structure, using previously learned solutions to simple tasks called primitives. The optimality of the behaviour is formulated as a reference trajectory tracking problem. The primitives are stored in a library of pairs of reference input/controlled output signals. The reference input primitives are optimized at the higher hierarchical level in a model-free iterative learning control (MFILC) framework without using knowledge of the controlled process. Learning of the reference input primitives is performed in a reduced subspace using radial basis functions for approximations. The convergence of the MFILC learning scheme is achieved via a Virtual Reference Feedback Tuning design of the feedback controllers in the lower level feedback control loops. The new complex trajectories to be tracked are decomposed into the output primitives regarded as basis functions. Next, the optimal reference input fed to the control system in order to track the desired new trajectory is then recomposed from the reference input primitives. The efficiency of this approach is demonstrated on a case study concerning the control of a two-axis positioning mechanism, and the experimental validation is offered.     

A hybrid data-driven and mechanism-basedmethod for vehicle trajectory prediction

Ensuring the safe and efficient operation of self-driving vehicles relies heavily on accurately predicting their future trajectories.Existing approaches commonly employ an encoder–decoder neural network structure to enhance information extraction duringthe encoding phase. However, these methods often neglect the inclusion of road rule constraints during trajectory formulationin the decoding phase. This paper proposes a novel method that combines neural networks and rule-based constraints in thedecoder stage to improve trajectory prediction accuracy while ensuring compliance with vehicle kinematics and road rules.The approach separates vehicle trajectories into lateral and longitudinal routes and utilizes conditional variational autoencoder(CVAE) to capture trajectory uncertainty. The evaluation results demonstrate a reduction of 32.4% and 27.6% in the averagedisplacement error (ADE) for predicting the top five and top ten trajectories, respectively, compared to the baseline method.     

Real-time stereo on GPGPU using progressive multi-resolution adaptive windows

We introduce a new GPGPU-based real-time dense stereo matching algorithm. The algorithm is based on a progressive multi-resolution pipeline which includes background modeling and dense matching with adaptive windows. For applications in which only moving objects are of interest, this approach effectively reduces the overall computation cost quite significantly, and preserves the high definition details. Running on an off-the-shelf commodity graphics card, our implementation achieves a 36 fps stereo matching on 1024 × 768 stereo video with a fine 256 pixel disparity range. This is effectively same as 7200 M disparity evaluations per second. For scenes where the static background assumption holds, our approach outperforms all published alternative algorithms in terms of the speed performance, by a large margin. We envision a number of potential applications such as real-time motion capture, as well as tracking, recognition and identification of moving objects in multi-camera networks.     

An adaptive congestion control mechanism for intelligent broadband networks

The provision of novel multimedia services in broadband networks through intelligent network (IN) technology is a promising solution that guarantees fast deployment of new services and minimum changes to already operating networks. In this architecture, the broadband service control point (B-SCP) is the main actor in the processing of complex IN service requests. This centralized approach which is reflected in the multiplicity of tasks undertaken by the B-SCP in a broadband IN architecture, can easily lead to network performance degradation. Therefore, the presence of a congestion control mechanism operating at the level of calls is considered essential in order to protect the network nodes from overload conditions and to attain high levels of network performance. In this paper, we propose an adaptive congestion control mechanism (ACCM) which is able to guarantee the desirable quality of service under overload conditions. The proposed mechanism gradually reduces the message transmission rate from the source node to the destination node when overflow conditions have been detected, taking as feedback the buffer capacity state of the destination. Results, obtained by simulation show that the proposed ACCM improves the network performance by maximising the number of established calls and reducing the number of rejected calls.     

Real-time prediction of order flowtimes using support vector regression

In a make-to-order production system, a due date must be assigned to new orders that arrive dynamically, which requires predicting the order flowtime in real-time. This study develops a support vector regression model for real-time flowtime prediction in multi-resource, multi-product systems. Several combinations of kernel and loss functions are examined, and results indicate that the linear kernel and the ε$\epsilon$-insensitive loss function yield the best generalization performance. The prediction error of the support vector regression model for three different multi-resource systems of varying complexity is compared to that of classic time series models (exponential smoothing and moving average) and to a feedforward artificial neural network. Results show that the support vector regression model has lower flowtime prediction error and is more robust. More accurately predicting flowtime using support vector regression will improve due-date performance and reduce expenses in make-to-order production environments.     

Real-time face tracking with instability using a feature-based adaptive model

International Journal of Control, Automation and Systems - Unstable object tracking usually happens in hand-held video sequences that consist of the movements of both tracked object and camera. The...     

传感器网络中一种实时的自适应时隙调度算法

无线传感器网络节点数目众多,MAC协议为节点分配工作时隙面临能量利用不高、节点延时较长等方面的难题。目前基于时隙调度的MAC协议一般采用等长的时隙大小,不能适应数据流量变化大的网络且忽略与网络层的融合,没有利用路由层信息来减低时隙分配算法性能代价。提出一种基于路由转发树的时隙调度算法（ATSA）,网络采用簇结构,在簇内构造一棵路由转发树,根据路由转发树形成的路径信息对节点实时获取节点每轮需要发送的数据量大小,根据节点的数据量大小来分配节点每轮需要的时隙,然后由簇头据此动态地为成员节点分配时隙,降低时隙划分的能量和时间代价,减少空闲侦听时间,避免串音。仿真表明,该算法有效地提高了网络能量利用效率,延长了网络生存周期,降低数据包的延时。     

Real-time structured light coding for adaptive patterns

Coded structured light is a technique that allows the 3D reconstruction of poorly or non-textured scene areas. With the codes uniquely associated with visual primitives of the projected pattern, the correspondence problem is quickly solved by means of local information only, with robustness against disturbances like high surface curvatures, partial occlusions, out-of-field of view or out-of-focus. Real-time 3D reconstruction with one shot is possible with pseudo-random arrays, where the encoding is done in a single pattern using spatial neighbourhood. To correct more mismatched visual primitives and to get patterns globally more robust, a higher Hamming distance between all the used codewords should be suited. Recent works in the structured light field have shown a growing interest for adaptive patterns. These can account for geometrical or spectral specificities of the scene to provide better features matching and reconstructions. Up till today, such patterns cannot benefit from the robustness offered by spatial neighbourhood coding with a minimal Hamming distance constraint, because the existing algorithms for such a class of coding are designed with an offline coding only. In this article, we show that due to two new contributions, a mixed exploration/exploitation search behaviour and a O(n ²) to ～O(n) complexity reduction using the epipolar constraint, the real-time coding of patterns having similar properties than those coded offline can be achieved. This allows to design a complete closed-loop processing pipeline for adaptive patterns.     

Noise prediction for a turbulent jet using different hybrid methods

The acoustic field of a cold single stream jet at Mach number 0.9 and Reynolds number 3600 is determined via computational aeroacoustics (CAA) methods. The jet computation of the acoustical field is performed by two hybrid approaches using a large-eddy simulation (LES) for the flow field and various systems of equations for the acoustical field to construct a robust, efficient, and reliable LES/CAA solver. The acoustic equations are the Ffowcs Williams-Hawkings equation (FWH) in the frequency domain and the acoustic perturbation equations (APE). The pronounced impact of the data windowing and the radial and streamwise extension of the integration surface on the directivity of the FWH solution is discussed at length. The comparison with available experimental and numerical results at similar flow conditions based on the noise characteristics in the near field shows the solution of the APE system to match the results of the direct LES more accurately than the FWH approach. The APE solution is less susceptible to the size of the source term region than the FWH approach to the location of the source surface. In conjunction with the APE formulation the LES domain can be chosen smaller than for the FWH ansatz resulting in less computational cost for the jet flow. The dominant source term in the APE system for cold jet noise is shown to be the Lamb vector.     

Real-time transient stability status prediction using cost-sensitive extreme learning machine

Real-time transient stability status prediction (RTSSP) is very important to maintain the safety and stability of electrical power systems, where any unstable contingency will be likely to cause large-scale blackout. Most of machine learning methods used for RTSSP attempt to attain a low classification error, which implies that the misclassification costs of different categories are the same. However, misclassifying an unstable case as stable one usually leads to much higher costs than misclassifying a stable case as unstable one. In this paper, a new RTSSP method based on cost-sensitive extreme learning machine (CELM) is proposed, which recognizes the RTSSP as a cost-sensitive classification problem. The CELM is constructed pursuing the minimum misclassification costs, and its detailed implementation procedures for RSSTP are also researched in this work. The proposed method is implemented on the New England 39-bus electrical power system. Compared with three cost-blind methods (ELM, SVM and DT) and two cost-sensitive methods (cost-sensitive DT, cost-sensitive SVM), the simulation results have proved that the lower total misclassification costs and false dismissal rate with low computational complexity can be achieved by the proposed method, which meets the demands for the computation speed and the reliability of RTSSP.