动力学效应的动力定位船舶模型在线辨识算法

由于船舶模型的高度非线性以及外界干扰力、推进器推力的无法测量性,导致它们的在线辨识和估计显得十分困难.本文提出一种以动力学效应为基础,应用无味卡尔曼滤波器（unscented Kalman filters,UKF）进行动力定位船舶动力学模型、外界载荷以及推进器推力在线辨识的算法.此算法能够在动力定位过程中不断求解船舶模型和其受到的载荷力,使得拥有这些参数的船舶模型和载荷所反映出的动力学效应不断逼近传感器检测到的运动反馈.基于此原理,用这些参数作为名义上的船舶模型、外界力、推进器推力就能够完成高效、自适应的定位控制.通过控制仿真,证明了此算法的有效性和正确性.     

AAOP-based dynamically reconfigurable monitoring system

A key aspect of resource management is efficient and effective deployment of available resources whenever needed. The issue typically covers two areas: monitoring of resources used by software systems and managing the consumption of resources. A key aspect of each monitoring system is its reconfigurability – the ability of a system to limit the number of resources monitored at a given time to those that are really necessary at any particular moment. The authors of this article propose a fully dynamic and reconfigurable monitoring system based on the concept of Adaptable Aspect-Oriented Programming (AAOP) in which a set of AOP aspects is used to run an application in a manner specified by the adaptability strategy. The model can be used to implement systems that are able to monitor an application and its execution environment and perform actions such as changing the current set of resource management constraints applied to an application if the application/environment conditions change. Any aspect that implements a predefined interface may be used by the AAOP-based monitoring system as a source of information. The system utilizes the concept of dynamic AOP, meaning that the aspects (which are sources of information) may be dynamically enabled/disabled.     

Refining imprecise data by integrity constraints

Uncertain data in databases were originally denoted as null values, which represent the meaning of ‘values unknown at present.” Null values were generalized into partial values, which correspond to a set of possible values, to provide a more powerful notion. In this paper, we derive some properties to refine partial values into more informative ones. In some cases, they can even be refined into definite values. Such a refinement is possible when there exist range constraint on attribute domains, or referential integrities, functional dependencies, or multivalued dependencies among attributes.

Our work actually eliminates redundant elements in a partial value. By this process, we not only provide a more concise and informative answer to users, but also speedup the computation of queries issued afterward. Besides, it reduces the communication cost when imprecise data are requested to be transmitted from one site to another site in a distributed environment.     

Applying models of imprecise probabilities in the mathematical theory of criteria importance

We show that if preferences can be defined with an additive utility function then decision making models based on the theory of criteria importance can be defined with imprecise probabilities. With this idea, we analyze new approaches to decision making in the theory of criteria importance.     

Online monitoring of fermentation processes

In biotechnology, few online measurements of biological variables are available. This fact hampers effective monitoring and control in the fermentation field. A novel approach to this is the use of a computer to combine outputs of online sensors for physical and chemical parameters to estimate directly otherwise unaccessible biological quantities like biomass. Here, a sensor configuration is presented for the online computation of the oxygen uptake rate. The equations that have to be implemented on the digital machine are derived and a case study on the fermentation of the antibiotic Gramicidine-S is performed and discussed.The experiments indicate that for a major part of the growth and fermentation period, the computer is suitable for the evaluation of the biomass based on oxygen uptake estimates. Consequently, the process can be monitored online.     

Obtaining linguistic fuzzy rule-based regression models from imprecise data with multiobjective genetic algorithms

Backfitting of fuzzy rules is an Iterative Rule Learning technique for obtaining the knowledge base of a fuzzy rule-based system in regression problems. It consists in fitting one fuzzy rule to the data, and replacing the whole training set by the residual of the approximation. The obtained rule is added to the knowledge base, and the process is repeated until the residual is zero, or near zero. Such a design has been extended to imprecise data for which the observation error is small. Nevertheless, when this error is moderate or high, the learning can stop early. In this kind of algorithms, the specificity of the residual might decrease when a new rule is added. There may happen that the residual grows so wide that it covers the value zero for all points (thus the algorithm stops), but we have not yet extracted all the information available in the dataset. Focusing on this problem, this paper is about datasets with medium to high discrepancies between the observed and the actual values of the variables, such as those containing missing values and coarsely discretized data. We will show that the quality of the iterative learning degrades in this kind of problems, because it does not make full use of all the available information. As an alternative to sequentially obtaining rules, we propose a new multiobjective Genetic Cooperative Competitive Learning (GCCL) algorithm. In our approach, each individual in the population codifies one rule, which competes in the population in terms of maximum coverage and fitting, while the individuals in the population cooperate to form the knowledge base.

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Building and using application utility models to dynamically choose thread counts

On today’s multiprocessor systems, simultaneously executing multi-threaded applications contend for cache space and CPU time. This contention can be managed by changing application thread count. In this paper, we describe a technique to configure thread count using utility models. A utility model predicts application performance given its thread count and other workload thread counts. Built offline with linear regression, utility models are used online by a system policy to dynamically configure applications’ thread counts. We present a policy which uses the models to maximize throughput while maintaining QoS. Our approach improves system throughput by 6 % and meets QoS 22 % more often than the best evaluated traditional policy.     

Online learning with hidden markov models

We present an online version of the expectation-maximization (EM) algorithm for hidden Markov models (HMMs). The sufficient statistics required for parameters estimation is computed recursively with time, that is, in an online way instead of using the batch forward-backward procedure. This computational scheme is generalized to the case where the model parameters can change with time by introducing a discount factor into the recurrence relations. The resulting algorithm is equivalent to the batch EM algorithm, for appropriate discount factor and scheduling of parameters update. On the other hand, the online algorithm is able to deal with dynamic environments, i.e., when the statistics of the observed data is changing with time. The implications of the online algorithm for probabilistic modeling in neuroscience are briefly discussed.     

Online inventory analysis models and system

The purpose of this paper is to report an approach to the development of an online inventory analysis and control system. Attention has been focused on the model aspects of the development process. Inventory Analysis and Control System 1 (IACS 1) is a series of inventory analysis models which is designed to provide management and functional personnel with the analytical tools for inventory decisions on purchasing, production and material control. IACS 1 contains self-explanatory procedures from the selection of a model and definition of the data input to execution of the program and the interpretation of the output result.     

Crack‐free rendering of dynamically tesselated B‐rep models

We propose a versatile pipeline to render B‐Rep models interactively, precisely and without rendering‐related artifacts such as cracks. Our rendering method is based on dynamic surface evaluation using both tesselation and ray‐casting, and direct GPU surface trimming. An initial rendering of the scene is performed using dynamic tesselation. The algorithm we propose reliably detects then fills up cracks in the rendered image. Crack detection works in image space, using depth information, while crack‐filling is either achieved in image space using a simple classification process, or performed in object space through selective ray‐casting. The crack filling method can be dynamically changed at runtime. Our image space crack filling approach has a limited runtime cost and enables high quality, real‐time navigation. Our higher quality, object space approach results in a rendering of similar quality than full‐scene ray‐casting, but is 2 to 6 times faster, can be used during navigation and provides accurate, reliable rendering. Integration of our work with existing tesselation‐based rendering engines is straightforward.     

Online monitoring: security or social control?

Imagine your big brother habitually following you around "for your own good" and snooping into everything that you, a mature adult, choose to do. Now imagine discovering that he couldn't do so if you wore blue socks rather than brown. Wouldn't you switch to blue socks as a matter of principle? Online monitoring today presents a similar situation. You can indeed modify your online conduct to preserve your privacy almost as easily as you can change socks.     

Calibration of imprecise and inaccurate numerical models considering fidelity and robustness: a multi-objective optimization-based approach

Traditionally, model calibration is formulated as a single objective problem, where fidelity to measurements is maximized by adjusting model parameters. In such a formulation however, the model with best fidelity merely represents an optimum compromise between various forms of errors and uncertainties and thus, multiple calibrated models can be found to demonstrate comparable fidelity producing non-unique solutions. To alleviate this problem, the authors formulate model calibration as a multi-objective problem with two distinct objectives: fidelity and robustness. Herein, robustness is defined as the maximum allowable uncertainty in calibrating model parameters with which the model continues to yield acceptable agreement with measurements. The proposed approach is demonstrated through the calibration of a finite element model of a steel moment resisting frame.     

Online scheduling and placement of hardware tasks with multiple variants on dynamically reconfigurable field-programmable gate arrays

Hardware task scheduling and placement at runtime plays a crucial role in achieving better system performance by exploring dynamically reconfigurable Field-Programmable Gate Arrays (FPGAs). Although a number of online algorithms have been proposed in the literature, no strategy has been engaged in efficient usage of reconfigurable resources by orchestrating multiple hardware versions of tasks. By exploring this flexibility, on one hand, the algorithms can be potentially stronger in performance; however, on the other hand, they can suffer much more runtime overhead in selecting dynamically the best suitable variant on-the-fly based on its runtime conditions imposed by its runtime constraints. In this work, we propose a fast efficient online task scheduling and placement algorithm by incorporating multiple selectable hardware implementations for each hardware request; the selections reflect trade-offs between the required reconfigurable resources and the task runtime performance. Experimental studies conclusively reveal the superiority of the proposed algorithm in terms of not only scheduling and placement quality but also faster runtime decisions over rigid approaches.     

Online multi-label dependency topic models for text classification

Multi-label text classification is an increasingly important field as large amounts of text data are available and extracting relevant information is important in many application contexts. Probabilistic generative models are the basis of a number of popular text mining methods such as Naive Bayes or Latent Dirichlet Allocation. However, Bayesian models for multi-label text classification often are overly complicated to account for label dependencies and skewed label frequencies while at the same time preventing overfitting. To solve this problem we employ the same technique that contributed to the success of deep learning in recent years: greedy layer-wise training. Applying this technique in the supervised setting prevents overfitting and leads to better classification accuracy. The intuition behind this approach is to learn the labels first and subsequently add a more abstract layer to represent dependencies among the labels. This allows using a relatively simple hierarchical topic model which can easily be adapted to the online setting. We show that our method successfully models dependencies online for large-scale multi-label datasets with many labels and improves over the baseline method not modeling dependencies. The same strategy, layer-wise greedy training, also makes the batch variant competitive with existing more complex multi-label topic models.     

Online end-use energy disaggregation via jump linear models

This paper presents two iterative algorithms for non-intrusive appliance load monitoring, which aims to decompose the aggregate power consumption only measured at the household level into the contributions of the individual electric appliances. The approaches are based on modelling the total power consumption as a combination of jump linear sub-models, each of them describing the behaviour of the individual appliance. Dynamic-programming and multi-model Kalman filtering techniques are used to reconstruct the power consumptions at the single-appliance level from the aggregate power in an iterative way.     

An integro-differential generalization and dynamically consistent discretizations of some hyperbolic models with nonlinear damping

This note presents a weak generalization of a time-delayed partial differential equation which, in turn, generalizes the well-known Burger–Fisher and Burgers–Huxley models. In this work, we provide a full discretization which is consistent with the integro-differential equation under consideration. The main analytical result of this note establishes that the discrete temporal rate of change of the discretization yields a consistent approximation to the differential form of the integro-differential equation investigated. Some numerical examples are provided in order to assess the efficiency and effectiveness of our methodology.     

基于GLRGMM的间歇过程在线监控策略

针对间歇过程的非线性和动态性,提出了全局—局部正则化高斯混合模型 （GLRGMM）算法。首先引入邻域保持嵌入算法提取局部流形结构,通过寻求一种低维投影对非线性过程进行全局结构保持,同时最大限度地保留局部流形特征;然后通过对高斯混合模型引入正则项来在线监控更新高斯模型,获取非线性数据流形结构,解决数据动态性问题;最后集成全局—局部监控指标实现在线监控。通过青霉素发酵过程进行了验证,结果表明所提算法比DPCA、GLNPE具有更好的在线监控效果。     

矿用高压电缆绝缘在线监测方法

为了实时监测矿用高压电缆的绝缘状态,提出了一种基于广域测量系统(WAMS)的矿用高压电缆绝缘在线监测方法。该方法利用WAMS实时同步获取电缆线路首末两端的电压和电流,通过建立电缆线路对地等效电路模型推导出电缆的绝缘电阻、等值电容,从而动态监测电缆的绝缘状态。仿真结果表明,该方法测量精度满足矿用高压电缆绝缘在线监测要求,且随着电缆绝缘劣化程度增加,该方法测量误差逐渐减小。     

基于回归预测的在线水质监测系统

保护水资源是环境保护的重中之重。利用React、Antd等前端框架和Spring、Spring MVC、Mybatis等服务端框架,设计实现在线水质监测系统,为用户提供监测数据的获取、存储、分析,以及数据可视化和更深层次的预测服务,同时为管理者和决策者提供更加精准科学的数据参考。     

基于物联网技术的设施农业在线监控系统

运用物联网技术、嵌入式技术和云服务器技术设计一套基于物联网技术的设施农业在线监控系统,该系统能实时监测农业环境参数信息和作物生长状况,实现对分散在各地的温室环境进行状态监测、设备远程智能控制及实时的在线数据、图像视频查询与信息服务。从而使温室的环境适宜作物生长,提高资源利用率。