仿人智能橡胶制品的精密硫化控制

分析了橡胶制品硫化成型的对象特性，选择了硫化成型过程控制策略，研究了控制算法及其工程应用。研究结果表明，选择仿人智能控制作为硫化成型过程的控制策略时，可实现速度快、精度高的控制，且具有极强的鲁棒性。     

地表水质监测模型中的几种人工智能方法

重点对地表水质预测模型的几种人工智能方法进行了评述,具体说明了BP神经网络水质预测模型、地表水COD灰色预测模型、时间数列预测方法及WASP5模型系统等方法的原理和特点.并提出地表水质预测模型WASP5与GIS系统集成的趋势,建立水质自动监测系统是我国水质现状和社会发展的需要,并分析了发展水质自动检测系统的方法及可行性.     

Acquiring logistics process intelligence: Methodology and an application for a Chinese bulk port

The processes of logistics service providers are considered as highly human-centric, flexible and complex. Deviations from the standard operating procedures as described in the designed process models, are not uncommon and may result in significant uncertainties. Acquiring insight in the dynamics of the actual logistics processes can effectively assist in mitigating the uncovered risks and creating strategic advantages, which are the result of uncertainties with respectively a negative and a positive impact on the organizational objectives.In this paper a comprehensive methodology for applying process mining in logistics is presented, covering the event log extraction and preprocessing as well as the execution of exploratory, performance and conformance analyses. The applicability of the presented methodology and roadmap is demonstrated with a case study at an important Chinese port that specializes in bulk cargo.     

On reactive routing protocols in ZigBee wireless sensor networks

ZigBee is the primary standard solution for wireless sensor networks, implementing the Ad hoc On‐Demand Distance Vector (AODV) protocol in the network layer and supported by the standard IEEE 802.15.4. This study is focused on mesh topologies and the critical problems encountered when AODV is executed in conjunction with the Carrier Sense Multiple Access with Collision Avoidance protocol. These problems are mainly related to the packet overhead required to carry out route creation. To perform preliminary experiments to be able to implement AODV in a real network, a new metric is proposed herein. This metric uses fuzzy logic to help in the decision‐making process. The objective of the fuzzy routine is to determine, during the route‐discovery process, the best node to forward request/reply packets, with the aim of reducing packet overhead and energy consumption. Moreover, minor changes are also added to the discovery procedure of AODV to improve the performance of the route‐creation process. This intelligent version of AODV has provided promising experimental results, greatly reducing the number of packets required, with the consequent energy saving while selecting the best nodes to be part of the routes.     

Recommending suitable learning scenarios according to learners’ preferences: An improved swarm based approach

The paper presents a new approach for recommending suitable learning paths for different learners groups. Selection of the learning path is considered as recommendations to choosing and combining the sequences of learning objects (LOs) according to learners’ preferences. Learning path can be selected by applying artificial intelligence techniques, e.g. a swarm intelligence model. If we modify and/or change some LOs in the learning path, we should rearrange the alignment of new and old LOs and reallocate pheromones to achieve effective learning recommendations. To solve this problem, a new method based on the ant colony optimisation algorithm and adaptation of the solution to the changing optimum is proposed. A simulation process with a dynamic change of learning paths when new LOs are inserted was chosen to verify the method proposed. The paper contributes with the following new developments: (1) an approach of dynamic learning paths selection based on swarm intelligence, and (2) a modified ant colony optimisation algorithm for learning paths selection. The elaborated approach effectively assist learners by helping them to reach most suitable LOs according to their preferences, and tutors – by helping them to monitor, refine, and improve e-learning modules and courses according to the learners’ behaviour.     

A UNIFIED FRAMEWORK OF TARGETED MARKETING USING CUSTOMER PREFERENCES

One of the fundamental tasks of targeted marketing is to elicit associations between customers and products. Based on the results from information retrieval and utility theory, this article proposes a unified framework of targeted marketing. The customer judgments of products are formally described by preference relations and the connections of customers and products are quantitatively measured by market value functions. Two marketing strategies, known as the customer‐oriented and product‐oriented marketing strategies, are investigated. Four marketing models are introduced and examined. They represent, respectively, the relationships between a group of customers and a group of products, between a group of customers and a single product, between a single customer and a group of products, and between a single customer and a single product. Linear and bilinear market value functions are suggested and studied. The required parameters of a market value function can be estimated by exploring three types of information, namely, customer profiles, product profiles, and transaction data. Experiments on a real‐world data set are performed to demonstrate the effectiveness of the proposed framework.     

Multiple targets enclosure by robotic swarm

Target enclosure by autonomous robots is useful for many practical applications, for example, surveillance of disaster sites. Scalability is important for autonomous robots because a larger group is more robust against breakdown, accidents, and failure. However, since the traditional models have discussed only the cases in which minimum number of robots enclose a single target, there has been no study on the utilization of the redundant number of robots. In this paper, to achieve a highly scalable target enclosure model about the number of target to enclose, we introduce swarm based task assignment capability to Takayama’s enclosure model. The original model discussed only single target environment but it is well suited for applying to the environments with multiple targets. We show the robots can enclose the targets without predefined position assignment by analytic discussion based on switched systems and a series of computer simulations. As a consequence of this property, the proposed robots can change their target according to the criterion about robot density while they enclose multiple targets.     

A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation

Metaheuristic optimization algorithms have become a popular choice for solving complex problems which are otherwise difficult to solve by traditional methods. However, these methods have the problem of the parameter adaptation and many researchers have proposed modifications using fuzzy logic to solve this problem and obtain better results than the original methods. In this study a comprehensive review is made of the optimization techniques in which fuzzy logic is used to dynamically adapt some important parameters in these methods. In this paper, the survey mainly covers the optimization methods of Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Ant Colony Optimization (ACO), which in the last years have been used with fuzzy logic to improve the performance of the optimization methods.     

基于混沌免疫粒子群优化和广义回归神经网络的回采工作面瓦斯涌出量预测模型

为提高回采工作面绝对瓦斯涌出量预测的精度和效率,提出了将混沌免疫粒子群优化(CIPSO)算法与广义回归神经网络(GRNN)相耦合的绝对瓦斯涌出量预测模型。该方法采用CIPSO对GRNN的光滑因子进行动态优化调整,减少了人为因素对GRNN网络输出结果的影响,并采用优化后的网络建立瓦斯涌出量预测模型。通过对某煤矿瓦斯涌出量数据的仿真实验结果表明:基于CIPSO-GRNN的回采工作面绝对瓦斯涌出量模型比BP神经网络、Elman网络预测模型具有更好的预测精度和收敛速度,证明了该方法的有效性和可行性。     

股票投资短期预测的多模匹配识别算法

从股票投资预测的技术发展方向来看,不同人工智能学习算法之间的组合学习日益得到关注。基于组合学习的思想,提出了股票投资短期预测的多模匹配识别算法（ MPMA）。算法通过迭代计算数据采样频率、聚类分组、模式匹配将股价预测和涨跌预测纳入到一个统一的学习框架中,建立起不同人工智能学习算法之间的组合学习模型。实验结果表明,所提算法具有较好的预报和泛化能力。