A study on the Web intelligence

This paper surveys important aspects of Web Intelligence (WI). WI explores the fundamental roles as well as practical impacts of Artificial Intelligence (AI) and advanced Information Technology (IT) on the next generation of Web - related products, systens, and activities. As a direction for scientific research and devlopment,WI can be extremely beneficial for the field of Artificial Intelligence in Education (AIED). This paper covers these issues only very briefly. It focuses more on other issues in WI, such as intelligent Web services, and semantic web, and proposes how to use them as basis for tackling new and challenging research problems in AIED.     

Parameterized Algorithmics for Computational Social Choice：Nine Research Challenges

Computational Social Choice is an interdisciplinary research area involving Economics, Political Science,and Social Science on the one side, and Mathematics and Computer Science（including Artificial Intelligence and Multiagent Systems） on the other side. Typical computational problems studied in this field include the vulnerability of voting procedures against attacks, or preference aggregation in multi-agent systems. Parameterized Algorithmics is a subfield of Theoretical Computer Science seeking to exploit meaningful problem-specific parameters in order to identify tractable special cases of in general computationally hard problems. In this paper, we propose nine of our favorite research challenges concerning the parameterized complexity of problems appearing in this context. This work is dedicated to Jianer Chen, one of the strongest problem solvers in the history of parameterized algorithmics,on the occasion of his 60 th birthday.     

环境智能

“Ambient”这个词很有趣，它是Ambience的形容词，Ambience的中文翻译成”氛围”或者是“背景”，那么真正在英文Ambient Intelligence这个词又是怎么出来的呢？人工智能（Artificial Intelligence）主要指机器人跟计算机上自动化的代理人程序，当计算机微型化之后，人们希望它能在日常生活中，在人的生活背景中处理很多事情，等到人有需要的时候，整个背景就会来对人的需要做出反应。你可以想象计算机在你生活中一直持续在运行，它平常不会引起你的注意，但是当它觉得你需要信息时，它才会去反应你的需要，这就是“Ambient Intelligence”。     

Application of Artificial Neural Network in Active Vibration Control of Diesel Engine

Artificial Neural Network （ANN） is applied to diesel two-stage vibration isolating system and an AVC （Active Vibration Control） system is developed. Beth identifier and controller are constructed by three-layer BP neural network. Besides computer simulation, experiment research is carried out on both analog bench and diesel bench. The results of simulation and experiment show a diminished response of vibration.     

人工智能的发展综述

人工智能(Artificial Intelligence)自1956年正式问世以来的五十年间已经取得了长足的进展,由于其应用的极其广泛性及存在的巨大研究开发潜力,吸引了越来越多的科技工作者投入人工智能的研究中去。许多发达国家的高科技计划的重要内容是计算机技术,而尤以人工智能为其基本重要组成部分。人工智能成为国际公认的当代高技术的核心部分之一。本文通过人工智能的发展历史研究,旨在     

The Algorithm for Rule-base Refinement on Fuzzy Set

In the course of running an artificial intelligent system many redundant rules are often produced. To refine the knowledge base, viz. to remove the redundant rules, can accelerate the reasoning and shrink the rule base. The purpose of the paper is to present the thinking on the topic and design the algorithm to remove the redundant rules from the rule base. The ＂abstraction＂ of ＂state variable＂, redundant rules and the least rule base are discussed in the paper. The algorithm on refining knowledge base is also presented.     

人工智能技术在电力系统中的应用探讨

人工智能(Artificial Intelligence,简称AI)已在电力系统的应用中获得了健康的发展,在较为成熟的技术如专家系统实用化的同时,进行多种智能技术的研究和探索。随着我国电力建设和电力市场竞争机制的引入不确定性因素和运行复杂性的增加,人工智能在电力系统中的应用前景将更加广阔。本文简述了人工智能理论的基本特点和实现方法,结合专家系统、神经网络、模糊理论、遗传算法的特点具体对国内外电力系统中的应用研究情况进行综述。     

管理决策与人工智能

本文试图用人工智能(Artificial Intelligence)技术对制定非程序化决策的方法问题进行一些探讨。     

Computational Intelligence and Its Encoding Mechanism

The origin and characteristics of computational intelligence, and several typical computational intelligence algorithms such as genetic algorithm and DNA computing are described, and the influence of evolution strategies and convergence properties on the encoding mechanism is discussed. A novel genetic algorithm based on degressive carry number encoding is then proposed. This algorithm uses degressive carry number encoding in the evolutionary process instead of commonly used fixed carry number. Finally a novel encoding mechanism and a new algorithm are proposed, which combine modem computational intelligence with the traditional Chinese methodology.     

Intelligent Decision Support System for Bank Loans Risk Classification

Intelligent Decision Support System (IDSS) for Bank Loans Risk Classification (BLRC),based on the way of integration Artificial Neural Network (ANN) and Expert System (ES), is proposed. According to the feature of BLRC, the key financial and non-financial factors are analyzed. Meanwhile, ES and Model Base (MB) which contain ANN are designed . The general framework, interaction and integration of the system are given. In addition, how the system realizes BLRC is elucidated in detail.     

人工智能技术在城市智能交通系统中的应用

目前,多类基于人工智能技术应用于交通环境的不同领域。为此,对应用于智能交通系统(Intelligent Transportation Systems,ITS)的多类人工智能(Artificial Intelligence,AI)技术进行分析。具体而言,将AI技术在智能交通系统的应用划分三个主要领域:1)车辆控制;2)交通控制和预测;3)道路安全和事故预测。同时,考虑四项AI技术:人工神经网络(Artificial Neural Networks,ANNs)、遗传算法(Genetic Algorithms,GAs)、模糊逻辑(Fuzzy Logic,FL)、专家系统(Expert Systems,ESs)。研究分析表明,不同的AI技术的结合,能够有效地管理、分析交通领域的海量数据。     

A Machine Learning Approach to Automated Negotiation

Automated negotiation between two competitive agents is analyzed, and a multi-issue negotiation model based on machine learning, time belief, offer belief and state-action pair expected Q value is developed. Unlike the widely used approaches such as game theory approach, heuristic approach and argumentation approach, This paper uses a machine learning method to compute agents‘ average Q values in each negotiation stage. The delayed reward is used to generate agents‘ offer and counteroffer of every issue. The effect of time and discount rate on negotiation outcome is analyzed. Theory analysis and experimental data show this negotiation model is practical.     

Fuzzy-Expert Diagnostics for Detecting and Locating Internal Faults in Three Phase Induction Motors

Internal faults in three phase induction motors can result in serious performance degradation and eventual system failures if not properly detected and treated in time. Artificial intelligence techniques, the core of soft-computing, have numerous advantages over conventional fault diagnostic approaches; therefore, a soft-computing system was developed to detect and diagnose electric motor faults. The fault diagnostic system for three-phase induction motors samples the fault symptoms and then uses a fuzzy-expert forward inference model to identify the fault. This paper describes how to define the membership functions and fuzzy sets based on the fault symptoms and how to construct the hierarchical fuzzy inference nets with the propagation of probabilities concerning the uncertainty of faults. The designed hierarchical fuzzy inference nets efficiently detect and diagnose the fault type and exact location in a three phase induction motor. The validity and effectiveness of this approach is clearly shown from obtained testing results.     

Hand Gesture Recognition Based on Improved FRNN

The trained Gaussian mixture model is used to make skincolour segmentation for the input image sequences. The hand gesture region is extracted, and the relative normalization images are obtained by interpolation operation. To solve the proem of hand gesture recognition, Fuzzy-Rough based nearest neighbour（RNN） algorithm is applied for classification. For avoiding the costly compute, an improved nearest neighbour classification algorithm based on fuzzy-rough set theory （FRNNC） is proposed. The algorithm employs the represented cluster points instead of the whole training samples, and takes the hand gesture data＇s fuzziness and the roughness into account, so the campute spending is decreased and the recognition rate is increased. The 30 gestures in Chinese sign language alphabet are used for approving the effectiveness of the proposed algorithm. The recognition rate is 94.96%, which is better than that of KNN （K nearest neighbor）and Fuzzy- KNN （Fuzzy K nearest neighbor）.     

Embryo implantation: A time for recalling and forwarding

The success of embryo implantation is a critical step towards further embryo development and pregnancy outcome. The observations and investigations on embryo implantation have been over a century. A huge body of knowledge has been accumulated in anatomy, histology, ultrastructure and hormonal regulation; as well as recently in depth information about molecular signaling pathways got from studies of genomic wide gene screening and specific gene deletion. The knowledge from basic research has also substantially helped to initiate and improve the Artificial Reproductive Technology （ART） in clinical applications. Now we＇ve known that the normal embryo implantation involves the embryo＇s development into an implantation-competent blastocyst and the synchronized transformation of uteri into a receptive stage. The interdependent relationship between the blastocyst and uterus involves complicated hormonal regulation and local paracrine, juxtacrine interactions. In this paper, we review some important historical findings regarding uterine receptivity and blastocyst activation, as well as some less discussed topics such as embryo spacing, embryo orientation. Further understandings on detailed mechanisms during the process of embryo implantation will help cure women infertility as well as develop new generation of non-steroids contraceptives.     

Application of Rough Set Theory in Fault Diagnostic Rules Acquisition

Rough set theory is a new mathematical tool to deal with vagneness and uncertainty. But original rough sets theory only generates deterministic rules and deals with data sets in which there is no noise. The variable precision rough set model （VPRSM） is presented to handle uncertain and noisy information. A method based on VPRSM is proposed to apply to fault diagnosis feature extraction and rules acquisition for industrial applications. An example for fault diagnosis of rotary machinery is given to show that the method is very effective.     

Mobile-Customer Identity Recognition

By utilizing artificial intelligence and pattern rec ognition techniques, we propose an integrated mobile-customer identity recognition approach in this paper, based on cus tomer＇s behavior characteristics extracted from the customer information database. To verify the effectiveness of this approach, a test has been run on the dataset consisting of 1 000 customers in 3 consecutive months.The result is compared with the real dataset in the fourth month consisting of 162 customers, which has been set as the customers for recognition. The high correct rate of the test （96.30%）, together with 1. 87% of the judge-by-mistake rate and 7.82% of the leaving-out rate, demonstrates the effectiveness of this approach.     

Nonlinear Spline Kernel-based Partial Least Squares Regression Method and Its Application

Inspired by the traditional Wold＇s nonlinear PLS algorithm comprises of NIPALS approach and a spline inner function model, a novel nonlinear partial least squares algorithm based on spline kernel （named SK-PLS ） is proposed for nonlinear modeling in the presence of multicollinearity. Based on the inner-product kernel spanned by the spline basis functions with infinite number of nodes, this method firstly maps the input data into a highdimensional feature space, and then calculates a linear PLS model with reformed NIPALS procedure in the feature space and gives a unified framework of traditional PLS ＂kernel＂ algorithms in consequence. The linear PLS in the feature Space corresponds to a nonlinear PLS in the original input （primal） space. The good approximating property of spline kernel function enhances the generalization ability of the novel model, and two numerical experiments are given to illustrate the feasibility of the proposed method.     

SYNTHESIS AND LIGHTFASTNESS PROPERTIES OF DYES CONTAINING HINDERED PIPERIDINE RESIDUES AS INTERNAL STABILIZING GROUPS

The representative azo and anthraquinone dyes containing an insulated hindered piperidinegroup were synthesized.The effect of these pendent groups on lightfastness properties of the dyeswas studied,both in ethyl acetate solution and on cellulose acetate and polyester films.The differ-ent trend of fading rates of azo dyes and anthraquinone dyes in solution and on substrates suggestsdifferent fading mechanisms operating in each case.