基于智能终端的移动MIS基础平台设计与研究

在综述目前广泛研究的智能终端模式的基础上,提出了一种新的基于智能终端和面向服务体系结构的移动MIS基础平台。该平台基于智能客户端技术和元数据业务模型,可以实现移动公办与后台管理信息系统的结合。详细阐述了移动MIS基础平台的设计、实现及其优点。     

苏州市景观水体表观污染类型识别及特征指标筛选

为解决表观污染成因的问题,以苏州市区河网为主要研究对象,采集796个数据样本,对景观水体的表观污染类型进行划分界定,并通过对数据样本的统计分析,探究水体表观污染类型和水质指标之间的关系。结果表明:氧化还原电位、溶解氧、叶绿素a、高锰酸盐指数和浊度对不同污染类型的水体具有很好的区分度,可作为主要特征指标;景观水体可分为有机主导型、无机主导型、营养主导型和混合型4种表观污染类型;不同表观污染水体判别的优先次序为有机主导型、无机主导型、营养主导型、混合型。     

Analysis of oceanaut operating performance using an integrated Bayesian network aided by the fuzzy logic theory

Because the oceanaut plays a significant role in safety and capability during manned deep-diving scientific tasks, preventing oceanaut performance decline is of paramount importance. However, the factors responsible for oceanaut performance are almost entirely unexplored. To address the preceding issues, a quantitative method of fuzzy integrated Bayesian network (FIBN) was modeled within the limits of oceanaut operating procedures. To quantify the probabilities of the influencing factors, the probability of each node in the FIBN was calculated using integrated expert judgement, fuzzy logic theory, and Bayesian network. By considering a total of 28 factors related to oceanaut performance in the “Jiaolong” manned submersible, this study found that difficult sampling, long sampling times, cabin equipment failure, oceanaut physical decline, and declining decision-making ability are important factors that affect oceanaut performance. The FIBN proposed in our study fused the qualitative and quantitative methods and can be developed into a versatile tool for analysis of comprehensive systems that contain both static and dynamic factors.Relevance to industryThe results provide a powerful basis for the design of manned submersible and assignment of tasks to oceanauts, while the fuzzy integrated Bayesian network (FIBN) method proposed can be effectively applied to various quantitative assessment fields which direct researchers to deal with analysis problems of complex systems.     

CLP(ℜ) and some electrical engineering problems

The Constraint Logic Programming Scheme defines a class of languages designed for programming with constraints using a logic programming approach. These languages are soundly based on a unified framework of formal semantics. In particular, as an instance of this scheme with real arithmetic constraints, the CLP() language facilitates and encourages a concise and declarative style of programming for problems involving a mix of numeric and non-numeric computation.In this paper we illustrate the practical applicability of CLP() with examples of programs to solve electrical engineering problems. This field is particularly rich in problems that are complex and largely numeric, enabling us to demonstrate a number of the unique features of CLP(). A detailed look at some of the more important programming techniques highlights the ability of CLP() to support well-known, powerful techniques from constraint programming. Our thesis is that CLP() is an embodiment of these techniques in a language that is more general, elegant and versatile than the earlier languages, and yet is practical.An earlier version of this paper appeared in the proceedings of the 4th International Conference on Logic Programming, Melbourne, May 1987. Much of this work was carried out while the authors were at Monash University, Melbourne, Australia.     

Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization

Certain tasks, such as formal program development and theorem proving, fundamentally rely upon the manipulation of higher-order objects such as functions and predicates. Computing tools intended to assist in performing these tasks are at present inadequate in both the amount of knowledge they contain (i.e., the level of support they provide) and in their ability to learn (i.e., their capacity to enhance that support over time). The application of a relevant machine learning technique—explanation-based generalization (EBG)—has thus far been limited to first-order problem representations. We extend EBG to generalize higher-order values, thereby enabling its application to higher-order problem encodings.Logic programming provides a uniform framework in which all aspects of explanation-based generalization and learning may be defined and carried out. First-order Horn logics (e.g., Prolog) are not, however, well suited to higher-order applications. Instead, we employ Prolog, a higher-order logic programming language, as our basic framework for realizing higher-order EBG. In order to capture the distinction between domain theory and training instance upon which EBG relies, we extend Prolog with the necessity operator of modal logic. We develop a meta-interpreter realizing EBG for the extended language, Prolog, and provide examples of higher-order EBG.     

A Universal Method of Scientific Inquiry

A paradigm of scientific discovery is defined within a first-order logical framework. Within this paradigm, the concept of successful scientific inquiry is formalized and investigated. Among other results, it is shown that a simple method of scientific inquiry is universal in the sense that it leads to success on every problem for which success is in principle possible.     

Modal logic as metalogic

The goal of this paper is to show how modal logic may be conceived as recording the derived rules of a logical system in the system itself. This conception of modal logic was propounded by Dana Scott in the early seventies. Here, similar ideas are pursued in a context less classical than Scott's.First a family of propositional logical systems is considered, which is obtained by gradually adding structural rules to a variant of the nonassociative Lambek calculus. In this family one finds systems that correspond to the associative Lambek calculus, linear logic, relevant logics, BCK logic and intuitionistic logic. Above these basic systems, sequent systems parallel to the basic systems are constructed, which formalize various notions of derived rules for the basic systems. The deduction theorem is provable for the basic systems if, and only if, they are at least as strong as systems corresponding to linear logic, or BCK logic, depending on the language, and their deductive metalogic is not stronger than they are.However, though we do not always have the deduction theorem, we may always obtain a modal analogue of the deduction theorem for conservative modal extensions of the basic systems. Modal postulates which are necessary and sufficient for that are postulates of S4 plus modal postulates which mimic structural rules. For example, the modal postulates which Girard has recently considered in linear logic are necessary and sufficient for the modal analogue of the deduction theorem.All this may lead towards results about functional completeness in categories. When functional completeness, which is analogous to the deduction theorem, fails, we may perhaps envisage a modal analogue of functional completeness in a modal category, of which our original category is a full subcategory.     

The quantifier structure of sentences that characterize nondeterministic time complexity

For every nondeterministic Turing machineM of time complexityT(n), there is a second-order sentence of a very restricted form, whose set of finite models encodes the set of strings recognized byM. Specifically, has a relational symbol which is interpreted as addition restricted to finite segments of the natural numbers, and a prefix consisting of existentially quantified unary second-order variables followed by a universal-existential first-order part. Here, every input stringx is encoded by a model of sizeT(|x|). Using a closely related encoding of strings as models where the size of the model is the length of the string, a consequence is that ifT(n)=n ^d, then there is a sentence with a similar prefix but whose second-order variables ared-ary and whose finite models encode the strings accepted byM. Potential applications to low-level complexity are discussed.     

神经网络PID在PLC系统控制中的应用研究

针对传统的PID算法由于难以给出精确的数学模型,使得系统参数设定困难,同时系统控制效果上存在一定的缺陷,造成系统安全性和可靠性降低,系统控制质量不高。为了解决传统的PID算法所带来的问题,提出了基于模糊神经网络的PID算法,将PID算法、模糊控制算法以及神经网络算法相结合,形成了一种智能控制算法。将算法应用在PLC控制系统中,实验表明算法有效的实现了PID参数的自整定,并且提高了控制质量,具有一定的实际应用推广价值。