Research on network design problem in integrated three-layer logistics systems

选址—路径问题是物流系统中的一个组合优化问题,启发式方法一般采用两阶段法将其分解为选址分派和车辆路径问题来顺序求解,但这两个阶段间的信息无法有效传递,因而往往不能得到集成问题的优化解。设计了具有能力约束的三级物流网络选址—路径问题模型,采用遗传算法整体求解该问题,避免了顺序求解带来的问题;设计了采用整数编码的三级染色体编码结构,采用禁忌搜索算法对交叉和变异操作作了改进,提高了算法的搜索效率,能够更适合集成问题的求解;最后通过算例分析,验证了本算法求解小规模选址路径问题的有效性

Mining Urban Moving Trajectory Patterns Based on Multi-scale Space Partition and Road Network Modeling

针对城市移动轨迹模式挖掘问题展开研究,提出移动全局模式与移动过程模式相结合的挖掘方法,即通过移动轨迹的起始位置点–终点位置点(Origin-destination,OD点)与移动过程序列分别进行移动全局模式与过程模式的发现.在移动全局模式发现中,提出了弹性多尺度空间划分方法,避免了硬性等尺度网格划分对密集区域边缘的破坏,同时增强了密集区域与稀疏区域的区分能力.在移动过程模式发现中,提出了基于移动轨迹的路网拓扑关系模型构建方法,通过路网关键位置点的探测抽取拓扑关系模型.最后基于空间划分集合与路网拓扑模型对原始移动轨迹数据进行序列数据转换与频繁模式挖掘.通过深圳市出租车历史GPS轨迹数据的实验结果..

Trajectory Pattern Mining Based on Ｒoad Network Detection

针对现有的移动受限轨迹离散化方法效率低、不直观、易丢失移动模式等问题,提出了一种先进行路网结构探测,再基于道路匹配对轨迹进行离散化的方法.算法首先基于数学形态学理论从轨迹中提取出路网结构,然后将轨迹点匹配到路网中的网格中,以网格序列来表示连续的轨迹,最后使用最大频繁序列模式挖掘方法从中挖掘出轨迹模式.实验结果表明,该算法能够快速有效地对轨迹进行离散化,且能比其它算法挖掘出更多更细致的轨迹模式

Smart Manufacturing Space

在基于泛在信息的智能制造时代，信息技术的大量涌现，以从未有过的能力，不断满足市场对制造业变革的技术需求，极大地冲击和促进制造业从传统模式向未曾有过的模式转变，使其逐步进入新的、不断发展的信息环境与制造模式之中，这种新的信息环境与制造模式组成了智能制造空间，它的发展与变化将影响智能制造的发展．文章详细阐述了在制造系统结构、设计与制造技术、人机关系方面正在发生的巨大变革，并且指出以“分散与集中相统一的制造系统、虚实结合的设计与制造手段、人机共融的生产方式”为特征的智能制造空间将快速形成，重塑需求牵引和技术驱动下的智能制造发展模式和技术体系

Mining Frequent Patterns from Uncertain Spatiotemporal Moving Trajectory Database

针对不确定移动轨迹ε-邻域的空间分布特征,提出一种基于网格分割面积的不确定轨迹近邻网格概率匹配方法,将原始不确定移动轨迹数据转换为以网格单元表示的概率序列数据,通过对经典序列模式挖掘算法Prefix Span的相关改进,设计并实现了适应于严格时间间隔约束条件下的移动概率序列模式挖掘算法UTFP-Prefix Span.合成数据的测试实验仿真结果表明,本文所提出的方法较基于距离的概率转换方法在挖掘结果、可扩展性等方面具有更好的性能

A Novel Complex Event Mining Network for RFID-Enable Supply Chain Information Security

This paper presents a novel Complex Event Mining Network (CEMN) and defines the fundamentals of radio-frequency identification (RFID)-enabled supply chain event management and discusses how an Complex Event Processing (CEP) can be used to resolve the underlying architecture challenges and complexities of integrating real-time decision support into the supply chain. The proposed CEP architecture is a distributed Event Processing Networks (EPNs) capable middleware infrastructure which enables automatic and real-time routing, caching, filtering, aggregation and processing of RFID events. It provides a global platform for distributed execution and management of RFID-enabled supply chain data. It enables a federated control over supply chain nodes deployed in many different organizations, respecting diverse security requirements while supporting centralized deployment and management of processes and rules. Finally, a distributed complex event defection algorithm based on Master-workers pattern is proposed to detect complex events and trigger correlation actions. The results showed that our proposed approach has more robust and scaleable in large-scale RFID applications

Multi-objective Artificial Bee Colony Algorithm with Information Learning for Model Optimization of Extreme Learning Machine

As an important branch of neural networks, extreme learning machine with single-hiddenlayer feedforward have been a effective tool for regression and classification applications. However, it is difficult for ELMs to strike a balance between testing accuracy and generalization due to the random input weights and hidden biases. In this paper, a novel multi-objective optimization method of ELM based on swarm intelligence behavior is proposed to obtain good generalization ability and high testing accuracy simultaneously. The multi-objective optimization algorithm is used to select optimal input weights by minimizing this testing error and the norm of output weight. In order to improve optimal performance, an information learning method is introduced to multi-objective artificial bee colony algorithm. Experiments on four UCI data sets are conducted, and original ELM, ELM with nondominated sorting genetic algorithm and the proposed algorithm are compared. The results show that the proposed algorithm can generally obtain better generalization performance and higher accuracy with more compact network than original ELM and ELM with nondominated sorting genetic algorithm simultaneously

Multi-criteria radio frequency identification (RFID) tag data integration and conversion method

本发明提供一种多标准RFID标签数据集成与转换方法，所述多协议处理器主要包括以下组件：RFID数据收发器，输入缓冲区，RFID标准转换接口，消息产生器，流数据处理引擎，解析器，输出缓冲区；具体连接关系为：所述RFID数据收发器输出端与输入缓冲区通信连接，信号输入端与输出缓冲区通信连接；所述RFID标准转换接口输入端与输入缓冲区通信连接，输出端与输出缓冲区连接；所述RFID标准转换接口输出端还与消息产生器连接，输入端还与解析器连接；所述流数据处理引擎输入端接收消息产生器的信息，输出端与解析器连接。本发明有效解决RFID多协议标准的标准化转化问题，满足了现有RFID应用系统中兼容多标准RFID标签数据的需求

Multi-criteria radio frequency identification (RFID) tag data integration and conversion method

本发明提供一种多标准RFID标签数据集成与转换方法，所述多协议处理器主要包括以下组件：RFID数据收发器，输入缓冲区，RFID标准转换接口，消息产生器，流数据处理引擎，解析器，输出缓冲区；具体连接关系为：所述RFID数据收发器输出端与输入缓冲区通信连接，信号输入端与输出缓冲区通信连接；所述RFID标准转换接口输入端与输入缓冲区通信连接，输出端与输出缓冲区连接；所述RFID标准转换接口输出端还与消息产生器连接，输入端还与解析器连接；所述流数据处理引擎输入端接收消息产生器的信息，输出端与解析器连接。本发明有效解决RFID多协议标准的标准化转化问题，满足了现有RFID应用系统中兼容多标准RFID标签数据的需求

Moving destination prediction using sparse dataset: A mobility gradient descent approach

Moving destination prediction offers an important category of location-based applications and provides essential intelligence to business and governments. In existing studies, a common approach to destination prediction is to match the given query trajectory with massive recorded trajectories by similarity calculation. Unfortunately, due to privacy concerns, budget constraints, and many other factors, in most circumstances, we can only obtain a sparse trajectory dataset. In sparse dataset, the available moving trajectories are far from enough to cover all possible query trajectories; thus the predictability of the matching-based approach will decrease remarkably. Toward destination prediction with sparse dataset, instead of searching similar trajectories over the sparse records, we alternatively examine the changes of distances from sampling locations to final destination on query trajectory. The underlying idea is intuitive: It is directly motivated by travel purpose, people always get closer to the final destination during the movement. By borrowing the conception of gradient descent in optimization theory, we propose a novel moving destination prediction approach, namely MGDPre. Building upon the mobility gradient descent, MGDPre only investigates the behavior characteristics of query trajectory itself without matching historical trajectories, and thus is applicable for sparse dataset. We evaluate our approach based on extensive experiments, using GPS trajectories generated by a sample of taxis over a 10-day period in Shenzhen city, China. The results demonstrate that the effectiveness, efficiency, and scalability of our approach outperform state-of-the-art baseline methods