Speaker recognition utilizing distributed DCT-II based Mel frequency cepstral coefficients and fuzzy vector quantization

In this paper, a new and novel Automatic Speaker Recognition (ASR) system is presented. The new ASR system includes novel feature extraction and vector classification steps utilizing distributed Discrete Cosine Transform (DCT-II) based Mel Frequency Cepstral Coefficients (MFCC) and Fuzzy Vector Quantization (FVQ). The ASR algorithm utilizes an approach based on MFCC to identify dynamic features that are used for Speaker Recognition (SR). A series of experiments were performed utilizing three different feature extraction methods: (1) conventional MFCC; (2) Delta-Delta MFCC (DDMFCC); and (3) DCT-II based DDMFCC. The experiments were then expanded to include four classifiers: (1) FVQ; (2) K-means Vector Quantization (VQ); (3) Linde, Buzo and Gray VQ; and (4) Gaussian Mixed Model (GMM). The combination of DCT-II based MFCC, DMFCC and DDMFCC with FVQ was found to have the lowest Equal Error Rate for the VQ based classifiers. The results found were an improvement over previously reported non-GMM methods and approached the results achieved for the computationally expensive GMM based method. Speaker verification tests carried out highlighted the overall performance improvement for the new ASR system. The National Institute of Standards and Technology Speaker Recognition Evaluation corpora was used to provide speaker source data for the experiments.     

Modeling and testing object-oriented distributed systems with linear-time temporal logic

We present a framework for constructing formal models of object-oriented distributed systems and a property language to express behavioral constraints in such models. Most of the existing models have their origin in specific mathematical notations and/or concepts. In contrast, we have developed our model such that it accounts for a large set of phenomena associated with industrial implementations of object-oriented distributed systems. The model that we propose, while closer to industrial concerns and practice, still has the powerful features of formal approaches. It also offers the possibility to automatically check at service run-time that the final service implementation has not violated and is not violating properties expressed at the abstraction level of our model. In our model, which relies on event-based behavioral abstraction, we use linear-time temporal logic as the underlying formalism for the specification of properties. We introduce two novel operators which are especially useful for object-oriented systems and which provide a number of advantages over the well-known temporal logic operators. A recent decision of one of our industrial partners to adopt our proposal into one of their development platforms can be seen as a strong evidence of the relevance of our work and as a promising step towards a better understanding between the academic formal methods community and industry. Copyright © 2001 John Wiley & Sons, Ltd.     

摄动开普勒问题形式化建模与验证

摄动开普勒问题广泛应用于卫星轨道摄动分析,然而卫星轨道摄动分析数学模型的错误将导致灾难性后果.传统的建模与分析方法涉及到矢量代数、旋量代数、复数、四元数等多种不同的代数系统,在各个代数系统相互转换过程中极易引入错误.几何代数方法将多种代数系统统一到相同代数结构中,弥补了传统分析方法的不足.但是基于几何代数的摄动开普勒问题数学模型的正确性并没有通过严格的形式化验证.本文采用高阶逻辑来描述该问题的属性和规范,以公认的逻辑公理和推理规则为基础构建其形式化模型并进行验证,从而最大程度确保数学模型的正确性和分析方法的可靠性.     

Formalizing Default Reasoning

Fuzzy set systems can be used to solve the problem with uncertain knowledge,and default logic can be used to solve the problem with incomplete knowledge,in some sense.In this paper,based on interval-valued fuzzy sets we introduce a method of inference which combines approximate reasoning an default ogic,and give the procedure of transforming monotonic reasoning into default reasoning.     

Vivid: A framework for heterogeneous problem solving

We introduce Vivid, a domain-independent framework for mechanized heterogeneous reasoning that combines diagrammatic and symbolic representation and inference. The framework is presented in the form of a family of denotational proof languages (DPLs). We present novel formal structures, called named system states, that are specifically designed for modeling potentially underdetermined diagrams. These structures allow us to deal with incomplete information, a pervasive feature of heterogeneous problem solving. We introduce a notion of attribute interpretations that enables us to interpret first-order relational signatures into named system states, and develop a formal semantic framework based on 3-valued logic. We extend the assumption-base semantics of DPLs to accommodate diagrammatic reasoning by introducing general inference mechanisms for the valid extraction of information from diagrams, and for the incorporation of sentential information into diagrams. A rigorous big-step operational semantics is given, on the basis of which we prove that the framework is sound. We present examples of particular instances of Vivid in order to solve a series of problems, and discuss related work.     

Automated test‐data generation for exception conditions

This paper presents a technique for automatically generating test‐data to test exceptions. The approach is based on the application of a dynamic global optimization based search for the required test‐data. The authors' work has focused on test‐data generation for safety‐critical systems. Such systems must be free from anomalous and uncontrolled behaviour. Typically, it is easier to prove the absence of any exceptions than proving that the exception handling is safe. A process for integrating automated testing with exception freeness proofs is presented as a way forward for tackling the special needs of safety critical systems. The results of a number of simple case‐studies are presented and show the technique to be effective. The major result shows the application of the technique to a commercial aircraft engine controller system as part of a proof of exception freeness. This illustrates how automated testing can be effectively integrated into a formal safety‐critical process to reduce costs and add value. Copyright © 2000 John Wiley & Sons, Ltd.     

An executable formal semantics for UML-RT

We propose a formal semantics for UML-RT, a UML profile for real-time and embedded systems. The formal semantics is given by mapping UML-RT models into a language called kiltera, a real-time extension of the \(\pi \)-calculus. Previous attempts to formalize the semantics of UML-RT have fallen short by considering only a very small subset of the language and providing fundamentally incomplete semantics based on incorrect assumptions, such as a one-to-one correspondence between “capsules” and threads. Our semantics is novel in several ways: (1) it deals with both state machine diagrams and capsule diagrams; (2) it deals with aspects of UML-RT that have not been formalized before, such as thread allocation, service provision points, and service access points; (3) it supports an action language; and (4) the translation has been implemented in the form of a transformation from UML-RT models created with IBM’s RSA-RTE tool, into kiltera code. To our knowledge, this is the most comprehensive formal semantics for UML-RT to date.     

A polymorphic service management scheme based on virtual object for ubiquitous computing environment

For the ubiquitous computing environment, an important assumption is that all the components in any specific environment are connected with each other. With this assumption, we introduce an effective scheme to provide a personalized service based on Virtual Personal World (VPW). Virtual Personal World (VPW) which is a model focused on service continuity with specially designed components. Previous ubiquitous frameworks have been concerned with the location where a user is provided any specific service. However those questions above are not the most important problems anymore in VPW. It concentrates on the point whether the services are successive or not, wherever a user goes to any place. Services are not regarded as a sum of functions which is embedded on any objects in any certain place. We conceptually define a resource management scheme based on a unified form of the object which participates in service provision, so called virtual object (VO). Thus the service can be described as the sum of functions of VOs. With our resource management scheme, users can utilize their required object as VO wherever it is located. And also, for better utilization of VPW service, we introduce a novel form of profiles and service provision scheme based on the polymorphism. Our simulation result shows that the Ratio of VPW pure service time is 0.15 % higher than conventional location based service. And also the possibility that users can meet adequate service he wants raised 29 % in our proposed VPW environment.     

On the use of interval mathematics in fuzzy expert systems

Fuzzy expert systems attempt to model the cognitive processes of human experts. They currently accomplish this by capturing knowledge in the form of linguistic propositions. Real-world problems dictate the need to include mathematical knowledge as well. Pattern matching is a critical part of the inference procedure in expert systems. Matches are made between data clauses, premise clauses, and conclusion clauses, forming an inference chain. Preprocessing the clauses may generate intervals of real numbers which are compared in the fuzzy matching algorithm. These same intervals may be used in arithmetic expressions. the purpose of this article is to devise a method for incorporating arithmetic expressions into inference process of Fuzzy Expert Systems. Interval arithmetic is used to evaluate these expressions. Logical relations between intervals are analyzed using probability theory. © 1994 John Wiley & Sons, Inc.     

AutoSyn: A new approach to automated synthesis of composite web services with correctness guarantee

How to compose existing web services automatically and to guarantee the correctness of the design(e.g. freeness of deadlock and unspecified reception,and temporal constraints) is an important and challenging problem in web services. Most existing approaches require a detailed specification of the desired behaviors of a composite service beforehand and then perform certain formal verification to guarantee the correctness of the design,which makes the composition process both complex and time-consuming. In th...     

Fuzzy Self-Adaptation of Mission-Critical Software Under Uncertainty

Mission-critical software(MCS) must provide continuous,online services to ensure the successful accomplishment of critical missions.Self-adaptation is particularly desirable for assuring the quality of service(QoS) and availability of MCS under uncertainty.Few techniques have insofar addressed the issue of MCS self-adaptation,and most existing approaches to software self-adaptation fail to take into account uncertainty in the self-adaptation loop.To tackle this problem,we propose a fuzzy control based approach,i.e.,Software Fuzzy Self-Adaptation(SFSA),with a view to deal with the challenge of MCS self-adaptation under uncertainty.First,we present the SFSA conceptual framework,consisting of sensing,deciding and acting stages,and establish the formal model of SFSA to lay a rigorous and mathematical foundation of our approach.Second,we develop a novel SFSA implementation technology as well as its supporting tool,i.e.,the SFSA toolkit,to automate the realization process of SFSA.Finally,we demonstrate the effectiveness of our approach through the development of an adaptive MCS application in process control systems.Validation experiments show that the fuzzy control based approach proposed in this work is effective and with low overheads.     

Flood flow forecasting using ANN,ANFIS and regression models

Flood prediction is an important for the design, planning and management of water resources systems. This study presents the use of artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), multiple linear regression (MLR) and multiple nonlinear regression (MNLR) for forecasting maximum daily flow at the outlet of the Khosrow Shirin watershed, located in the Fars Province of Iran. Precipitation data from four meteorological stations were used to develop a multilayer perceptron topology model. Input vectors for simulations included the original precipitation data, an area-weighted average precipitation and antecedent flows with one- and two-day time lags. Performances of the models were evaluated with the RMSE and the R ². The results showed that the area-weighted precipitation as an input to ANNs and MNLR and the spatially distributed precipitation input to ANFIS and MLR lead to more accurate predictions (e.g., in ANNs up to 2.0 m³ s^?1 reduction in RMSE). Overall, the MNLR was shown to be superior (R ² = 0.81 and RMSE = 0.145 m³ s^?1) to ANNs, ANFIS and MLR for prediction of maximum daily flow. Furthermore, models including antecedent flow with one- and two-day time lags significantly improve flow prediction. We conclude that nonlinear regression can be applied as a simple method for predicting the maximum daily flow.     

模糊推理的函数变换观点

基于函数论的立场 ,指出模糊推理过程是一个函数变换过程 ,模糊规则蕴涵了一个从函数空间到函数空间的映射 ,现存的种种模糊推理方法都是对这种映射的估计 ,进而指出插值和回归的方法都适用于这种估计。系统地提出了用回归的方法处理模糊推理的思想 ,并结合线性回归模型进行了示范 ,证明了基于线性回归模型的模糊推理系统 (FIS)同样是一个万能函数逼近器。     

A calibration approach based on Takagi–Sugeno fuzzy inference system for digital electronic compasses

Recently, the development of industrial processes brought on the outbreak of technologically complex systems. This development generated the necessity of research relative to the mathematical techniques that have the capacity to deal with project complexities and validation. Fuzzy models have been receiving particular attention in the area of nonlinear systems identification and analysis due to it is capacity to approximate nonlinear behavior and deal with uncertainty. A fuzzy rule-based model suitable for the approximation of many systems and functions is the Takagi–Sugeno (TS) fuzzy model. TS fuzzy models are nonlinear systems described by a set of if then rules which gives local linear representations of an underlying system. Such models can approximate a wide class of nonlinear systems. In this paper a performance analysis of a system based on TS fuzzy inference system for the calibration of electronic compass devices is considered. The contribution of the evaluated TS fuzzy inference system is to reduce the error obtained in data acquisition from a digital electronic compass. For the reliable operation of the TS fuzzy inference system, adequate error measurements must be taken. The error noise must be filtered before the application of the TS fuzzy inference system. The proposed method demonstrated an effectiveness of 57% at reducing the total error based on considered tests.     

A strategy for service realization in service-oriented design

Recently service orientation is becoming a mainstream approach for building large scale software systems. A key requirement in service-oriented design is the de- pendability of service. It stipulates that the behavior of services is controllable. Formal models can aid in constructing software in a dependable manner. They can describe ser- vices precisely and compose them together consistently. This paper presents a strategy for service realization based on a novel layered formal model. When a service is provided or required via internet, the semantic consistency becomes critical issue. Our architecture tackles the issue by proposing a novel scheme: defining contract of a service to keep semantic information. Although service-oriented design is our prime target, the contract models, to a large extent, are independent of the framework of design and therefore ap- plicable to large scale software design in general.     

Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning

This paper presents a dynamic fuzzy Q-learning (DFQL) method that is capable of tuning fuzzy inference systems (FIS) online. A novel online self-organizing learning algorithm is developed so that structure and parameters identification are accomplished automatically and simultaneously based only on Q-learning. Self-organizing fuzzy inference is introduced to calculate actions and Q-functions so as to enable us to deal with continuous-valued states and actions. Fuzzy rules provide a natural mean of incorporating the bias components for rapid reinforcement learning. Experimental results and comparative studies with the fuzzy Q-learning (FQL) and continuous-action Q-learning in the wall-following task of mobile robots demonstrate that the proposed DFQL method is superior.     

因果关系,贝叶斯网络与认知图

因果关系在预测和推理中具有重要的作用.贝叶斯网络已被用于构建诊断和决策系 统.近年来模糊认知图得到了重视.模糊认知图为结构性知识与因果推理提供了又一个理论 框架.本文简单介绍贝叶斯网络与认知图及其推理方法在智能系统中的应用.     

嵌入式软件建模、实现与验证：研究与进展

随着计算机硬件设备计算能力的迅速提高,嵌入式系统中软件的规模和复杂度的急剧增大,软件可靠性在嵌入式系统中的重要性占据了统治地位。本文首先概要介绍了嵌入式软件不同于传统商业软件、科学计算软件的物理性、实时性、领域性等重要特征,以及由此带来的困难和挑战。然后重点介绍目前在解决嵌入式软件系统开发过程中的问题时所采取的建模思想、实现技术和验证方法。最后对嵌入式软件及其相关技术的发展进行了展望。     

可信系统性质的分类和形式化研究综述

计算机系统被应用于各种重要领域,这些系统的失效可能会带来重大灾难.不同应用领域的系统对于可信性具有不同的要求,如何建立高质量的可信计算机系统,是这些领域共同面临的巨大挑战.近年来,具有严格数学基础的形式化方法已经被公认为开发高可靠软硬件系统的有效方法.目标是对形式化方法在不同系统的应用进行不同维度的分类,以更好地支撑可信软硬件系统的设计.首先从系统的特征出发,考虑6种系统特征:顺序系统、反应式系统、并发与通信系统、实时系统、概率随机系统以及混成系统.同时,这些系统又运行在众多应用场景,分别具有各自的需求.考虑4种应用场景:硬件系统、通信协议、信息流以及人工智能系统.对于以上的每个类别,介绍和总结其形式建模、性质描述以及验证方法与工具.这将允许形式化方法的使用者对不同的系统和应用场景,能够更准确地选择恰当的建模、验证技术与工具,帮助设计人员开发更加可靠的系统.