Predicting fingerprint biometrics performance from a small gallery

Predicting the performance of a biometrics is an important problem in a real-world application. In this paper, we present a binomial model to predict both the fingerprint verification and identification performance. The match and non-match scores are computed, using the number of corresponding triangles as the match metric, between the query and gallery fingerprints. The triangles are formed using the minutiae features. The match score and non-match score in a binomial prediction model are used to predict the performance on large (relative to the size of the gallery) populations from a small gallery. We apply the model to the entire NIST-4 database and show the results for both the fingerprint verification and the identification.     

Finger surface as a biometric identifier

We present a novel approach for personal identification and identity verification which utilizes 3D finger surface features as a biometric identifier. Using 3D range images of the hand, a surface representation for the index, middle, and ring finger is calculated and used for comparison to determine subject similarity. We use the curvature based shape index to represent the fingers’ surface. Gallery and probe shape index signatures are compared using the normalized correlation coefficient to compute a match score. A large unique database of hand images supports the research. We use data sets obtained over time to examine the performance of each individual finger surface as a biometric identifier as well as the performance obtained when combining them. Both identification and verification experiments are conducted. In addition, probe and gallery sets sizes are increased to further improve recognition performance in our experiments. Our approach yields good results for a first-of-its-kind biometric technique, indicating that this approach warrants further research.     

Modified Optimal Prediction and its Application to a Particle-Method Problem

The paper is concerned with system reduction by statistical methods and, in particular, by the optimal prediction method introduced in (Chorin, A.J., Hald, O.H., Kupferman, R., Optimal prediction with memory, Phys. D 166:239–257, 2002). The optimal prediction method deals with problems that possess large and small scales and uses the conditional expectation to model the influence of the small scales on the large ones. In the current paper, we develop a different variant of the optimal prediction method as well as introduce and compare several approximations of this method. We apply the original and modified optimal prediction methods to a system of ODEs obtained from a particle method discretization of a hyperbolic PDE and demonstrate their performance in a number of numerical experiments.     

基于大数据的电力企业资产组维修策略优化研究

汇总过去若干年的电力设备故障数据,运用大数据分析方法,把故障预测技术引入到预防性维修的实践中,提出一种基于大数据的预防性维修策略。首先,根据由状态检测信息得到剩余寿命的预测结果,以预防性维修时的剩余寿命为阀值制定预防性维修策略。然后,根据更新过程理论,建立以电力设备的预防性维修阀值和预测间隔期为优化变量,综合考虑电力设备维修成本、客户满意度、电量销售、停电损失、维修时机选择等约束条件呢,以电力设备平均维修费用最小和电力设备可用度最大为优化目标的预防性维修优化模型。采用人群搜索算法进行优化求解,得到系统最佳的预防性维修阀值和维修预测间隔期。最后,通过引入算例,对所建模型优化仿真求解,得到电力设备最佳的预测周期,在保证电力设备可用度的同时,使电力设备的平均维修费用最小,验证了所建模型的可行性和有效性,从而提高电力企业的整体效益。     

Prediction Error Based Performance Monitoring,Degradation Diagnosis and Remedies in Offset‐Free MPC: Theory and Applications

We present in this paper a control performance monitoring method for linear offset‐free model predictive control (MPC) algorithms, in which the prediction error sequence is used to detect whether the internal model and the observer are correct or not. When the prediction error is a white noise signal, revealed by the Ljung‐Box test, optimal performance is detected. Otherwise, we use a closed‐loop subspace identification approach to reveal the order of a minimal realization of the system from the deterministic input to the prediction error. When such order is zero, we prove that the model is correct and the source of suboptimal performance is an incorrect observer. In such cases, we suggest an optimization method to recalculate the correct augmented state estimator. If, instead, such order is greater than zero we prove that the model is incorrect, and re‐identification is suggested. A variant for (large‐scale) block‐structured systems is presented, in which diagnosis and corrections are performed separately in each block. Two examples of different complexity are presented to highlight effectiveness and scalability of the method.     

基于特征迁移学习的综合能源系统小样本日前电力负荷预测

基于历史数据和深度学习的负荷预测已广泛应用于以电能为中心的综合能源系统中以提高预测精度,然而,当区域中出现新用户时,其历史负荷数据往往极少,此时,深度学习难以适用.针对此,本文提出基于负荷特征提取和迁移学习的预测机制.首先,依据源域用户历史负荷数据,融合聚类算法和门控循环单元网络构建源域数据的特征提取和分类模型;然后,...     

Segmentation,paging and optimal page sizes in virtual memory

On the basis of empirical data two topics concerning virtual memory systems are discussed: determining an optimal page size and performance of segmentation as compared to paging. Several production programs have been executed (on a simulator) both in a segmented system and in a paged system with various page sizes; the memory management was based on the working set policy. The memory usage and the fault rates were recorded, and the lifetime functions and space-time integrals were evaluated. The observations are explained using a new model of program behaviour which is a refinement of the phase-transition model. The results show that there is no globally optimal page size. Two characteristic types of programs are observed: the first requires a small page size and a large window size, the second requires a large page size and a small window size. Segmentation and paging are compared with respect to their usage of various resources. In the sense of the space-time integral, segmentation usually outperforms paging; if the mean segment size is large, the difference is remarkable. Several commonly used assumptions about the effects of page size on program behaviour are validated; some of them are found inaccurate or even wrong.     

Detection of Mesangial hypercellularity of MEST-C score in immunoglobulin A-nephropathy using deep convolutional neural network

Immunoglobulin A (IgA)-nephropathy (IgAN) is one of the major reasons for renal failure. It provides vital clues to estimate the stage and the proliferation rate of end-stage kidney disease. IgA stage can be estimated with the help of MEST-C score. The manual estimation of MEST-C score from whole slide kidney images is a very tedious and difficult task. This study uses some Convolutional neural networks (CNNs) related models to detect mesangial hypercellularity (M score) in MEST-C. CNN learns the features directly from image data without the requirement of analytical data. CNN is trained efficiently when image data size is large enough for a particular class. In the case of smaller data size, transfer learning can be used efficiently in which CNN is pre-trained on some general images and then on subject images. Since the data set size is small, time spent in collecting large data set is saved. The training time of transfer learning is also reduced because the model is already pre-trained. This research work aims at the detection of mesangial hypercellularity from biopsy images with small data size by utilizing the transfer learning. The dataset used in this research work consists of 138 individual glomerulus (× 20 magnification digital biopsy) images of IgA patients received from All India Institute of Medical Science, Delhi. Here, machine learning (k-nearest neighbour (KNN) and support vector machine (SVM)) classifiers are compared to transfer learning CNN methods. The deep extracted image features are used by machine learning classifiers. The different evaluation parameters have been used for comparing the predictions of basic classifiers to the deep learning model. The research work concludes that the transfer learning deep CNN method can improve the detection of mesangial hypercellularity as compare to KNN, SVM methods when using the small data set. This model could help the pathologists to understand the stages of kidney failure.

     

Centralized and decentralized control schemes for Gauss-Poisson processes

Gauss-Poisson processes are defined as jump processes with jump times according to a Poisson process and Gaussian jump size. Filtering and prediction recursive schemes are obtained and used in the derivation of optimal control schemes. Dynamic programming sufficient conditions are given for both centralized and delayed information sharing decentralized schemes. For the linear quadratic model, we derive explicit solutions for the optimal control.     

A reliable multi-objective control strategy for batch processes based on bootstrap aggregated neural network models

This paper presents a reliable multi-objective optimal control method for batch processes based on bootstrap aggregated neural networks. In order to overcome the difficulty in developing detailed mechanistic models, bootstrap aggregated neural networks are used to model batch processes. Apart from being able to offer enhanced model prediction accuracy, bootstrap aggregated neural networks can also provide prediction confidence bounds indicating the reliability of the corresponding model predictions. In addition to the process operation objectives, the reliability of model prediction is incorporated in multi-objective optimisation in order to improve the reliability of the obtained optimal control policy. The standard error of the individual neural network predictions is taken as the indication of model prediction reliability. The additional objective of enhancing model prediction reliability forces the calculated optimal control policies to be within the regions where the model predictions are reliable. By such a means, the resulting control policies are reliable. The proposed method is demonstrated on a simulated fed-batch reactor and a simulated batch polymerisation process. It is shown that by incorporating model prediction reliability in the optimisation criteria, reliable control policy is obtained.     

Structural model updating and prediction variability using Pareto optimal models

A multi-objective identification method for structural model updating based on modal residuals is presented. The method results in multiple Pareto optimal structural models that are consistent with the experimentally measured modal data and the modal residuals used to measure the discrepancies between the measured and model predicted modal characteristics. These Pareto optimal models are due to uncertainties arising from model and measurement errors. The relation between the multi-objective identification method and the conventional single-objective weighted modal residuals method for model updating is investigated. Using this relation, an optimally weighted modal residuals method is also proposed to rationally select the most preferred model among the alternative multiple Pareto optimal models for further use in structural model prediction studies. Computational issues related to the reliable solution of the resulting multi-objective and single optimization problems are addressed. The model updating methods are compared and their effectiveness is demonstrated using experimental results obtained from a three-story laboratory structure tested at a reference and a mass modified configuration. The variability of the Pareto optimal models and their associated response prediction variability are explored using two structural model classes, a simple 3-DOF model class and a higher fidelity 546-DOF finite element model class. It is demonstrated that the Pareto optimal structural models and the corresponding response and reliability predictions may vary considerably, depending on the fidelity of the model class and the size of measurement errors.     

On the order of test goals in specification-based testing

Model-based testing techniques often select test cases according to test goals such as coverage criteria or mutation adequacy. Complex criteria and large models lead to large test suites, and a test case created for one coverage item usually covers several other items as well. This can be problematic if testing is expensive and resources are limited. Therefore, test case generation can be optimized in order to avoid unnecessary test cases and minimize the test generation and execution costs. Because of this optimization the order in which test goals are selected is expected to have an impact on both the performance of the test case generation and the size of resulting test suites, although finding the optimal order is not feasible in general. In this paper we report on experiments to determine the effects of the order in which test goals are selected on performance and the size of resulting test suites, and evaluate different heuristics to select test goals such that the time required to generate test suites as well as their size are minimized. The test case generation approach used for experimentation uses model checkers, and experimentation shows that good results can be achieved with any random ordering, but some improvement is still possible with simple heuristics.     

Structured prediction with reinforcement learning

We formalize the problem of Structured Prediction as a Reinforcement Learning task. We first define a Structured Prediction Markov Decision Process (SP-MDP), an instantiation of Markov Decision Processes for Structured Prediction and show that learning an optimal policy for this SP-MDP is equivalent to minimizing the empirical loss. This link between the supervised learning formulation of structured prediction and reinforcement learning (RL) allows us to use approximate RL methods for learning the policy. The proposed model makes weak assumptions both on the nature of the Structured Prediction problem and on the supervision process. It does not make any assumption on the decomposition of loss functions, on data encoding, or on the availability of optimal policies for training. It then allows us to cope with a large range of structured prediction problems. Besides, it scales well and can be used for solving both complex and large-scale real-world problems. We describe two series of experiments. The first one provides an analysis of RL on classical sequence prediction benchmarks and compares our approach with state-of-the-art SP algorithms. The second one introduces a tree transformation problem where most previous models fail. This is a complex instance of the general labeled tree mapping problem. We show that RL exploration is effective and leads to successful results on this challenging task. This is a clear confirmation that RL could be used for large size and complex structured prediction problems.     

基于ACO-LSSVM的网络流量预测

为了提高了网络流量的预测精度,提出一种蚁群算法（ACO）优化最小二乘支持向量机（LSSVM）参数的网络流量预测算法（ACO-LSSVM）。将LSSVM算法参数作为蚂蚁的位置向量,采用动态随机抽取的方法来确定目标个体引导蚁群进行全局搜索,并在最优蚂蚁邻域内进行小步长局部搜索,找到算法的最优参数,建立了基于ACO-LSSVM的网络流量预测模型。仿真结果表明,相对其他网络流量预测算法,ACO-LSSVM算法提高了网络流量预测精度,更能准确地描述网络流量变化规律。     

基于Pareto解集分段预测策略的动态多目标进化算法

针对现有的动态多目标优化算法种群收敛速度慢、多样性难以保持等问题,提出了一种基于Pareto解集分段预测策略的动态多目标进化算法BPDMOP。当检测到环境变化时,对前一时刻进化得到的Pareto最优解根据任一子目标函数进行排序,并按照该子目标的大小均分为3段,分别计算出每一段Pareto解集中心点的移动方向;对每一段Pareto子集进行系统抽样得到Pareto前沿面的特征点,利用线性模型分段预测下一代种群;根据优化问题的难易程度,自适应地在预测的种群周围产生随机个体来增加种群的多样性。通过对3类标准测试函数的实验表明了该算法能够有效求解动态多目标优化问题。     

基于连续型深度置信神经网络的软件可靠性预测

为了提高软件可靠性智能预测的精度,采用连续型深度置信神经网络算法用于软件可靠性预测。首先提取影响软件可靠性的核心要素样本,并获取样本要素的关键特征;然后建立连续型深度置信神经网络(Deep Belief Network,DBN)的软件可靠性预测模型,输入待预测样本,通过多个受限波尔兹曼机(Restricted Boltzmann Machine,RBM)层的预处理训练,以及多次反向微调迭代获取DBN权重等参数,直到达到最大RBM层数和最大反向微调迭代次数;最后获得稳定的软件可靠性预测模型。实验结果证明,通过合理设置DBN隐藏层节点数和学习速率,可以获得良好的软件可靠性预测准确率和标准差。与常用的软件可靠性预测算法相比,所提算法的预测准确度高且标准差小,在软件可靠性预测方面的适用度较高。     

Fusion of EEG response and sentiment analysis of products review to predict customer satisfaction

This paper proposes a novel multimodal framework for rating prediction of consumer products by fusing different data sources, namely physiological signals, global reviews obtained separately for the product and its brand. The reviews posted by global viewers are retrieved and processed using Natural Language Processing (NLP) technique to compute compound score considered as global rating. Also, electroencephalogram (EEG) signals of the participants were recorded simultaneously while watching different products on computer’s screen. From EEG, valence scores in terms of product rating are obtained using self-report towards each viewed product for acquiring local rating. A higher valence score corresponds to intrinsic attractiveness of the participant towards a product. Random forest based regression techniques is used to model EEG data to build a rating prediction framework considered as local rating. Furthermore, Artificial Bee Colony (ABC) based optimization algorithm is used to boost the overall performance of the framework by fusing global and local ratings. EEG dataset of 40 participants including 25 male and 15 female is recorded while viewing 42 different products available on e-commerce website. Experiment results are encouraging and suggest that the proposed ABC optimization approach can achieve lower Root Mean Square Error (RMSE) in rating prediction as compared to individual unimodal schemes.     

Semi-empirical Multiprocessor Performance Predictions

This paper presents a multiprocessor performance prediction methodology supported by experimental measurements, which predicts the execution time of large application programs on large parallel architectures based on a small set of sample data. We propose a graph model to describe application program behavior. In order to precisely abstract an architecture model for the prediction, important and implicit architecture parameters are obtained by experiments. We focus on performance predictions of application programs in shared-memory and data-parallel architectures. Real world applications are implemented using the shared-memory model on the KSR-1 and using the data-parallel model on the CM-5 for performance measurements and prediction validation. We show that experimental measurements provide strong support for performance predictions on multiprocessors with implicit communications and complex memory systems, such as shared-memory and data-parallel systems, while analytical techniques partially applied in the prediction significantly reduce computer simulation and measurement time.     

Application of interval predictor model into robust model predictive control

Here we apply interval prediction model into robust model predictive control (MPC) strategy. After introducing the family of models and some basic information, we present the computational results for the construction of interval predictor model, whose regression parameters are included in a sphere parameter set. Given a size measure to scale the average amplitude of the predictor interval, one optimal model that minimises a size measure is efficiently computed by solving a linear programming problem. We apply the active set approach to solve the linear programming problem and based on these optimisation variables, the predictor interval of the considered model with sphere parameter set can be constructed. As for a fixed non-negative number from the size measure, we propose a better choice by using the optimality conditions. In order to apply interval prediction model into robust MPC, two strategies are proposed to analyse a min-max optimisation problem. After input and output variables are regarded as decision variables, a standard quadratic optimisation is obtained and its dual form is derived, then Gauss–Seidel algorithm is proposed to solve the dual problem and convergence of Gauss–Seidel algorithm is provided too. Finally two simulation examples confirm the theoretical results.