Formulation and analysis of dynamic supply chain of backfill in construction waste management using agent-based modeling

Backfill is the excavated material from earthworks, which constitutes over 50% of the construction wastes in Hong Kong. This paper considers a supply chain that consists of construction sites, landfills and commercial sources in which operators seek cooperation to maximize backfill reuse and improve waste recovery efficiency. Unlike the ordinary material supply chain in manufacturing industries, the supply chain for backfill involves many dynamic processes, which increases the complexity of analyzing and solving the logistic issue. Therefore, this study attempts to identify an appropriate methodology to analyze the dynamic supply chain, for facilitating the backfill reuse. A centralized optimization model and a distributed agent-based model are proposed and implemented in comparing their performances. The centralized optimization model can obtain a global optimum but requires sharing of complete information from all supply chain entities, resulting in barriers for implementation. In addition, whenever the backfill supply chain changes, the centralized optimization model needs to reconfigure the network structure and recompute the optimum. The distributed agent-based model focuses on task distribution and cooperation between business entities in the backfill supply chain. In the agent-based model, decision making and communication between construction sites, landfills, and commercial sources are emulated by a number of autonomous agents. They perform together through a negotiation algorithm for optimizing the supply chain configuration that reduces the backfill shipment cost. A comparative study indicates that the agent-based model is more capable of studying the dynamic backfill supply chain due to its decentralization of optimization and fast reaction to unexpected disturbances.     

Dynamic multi-commodity capacitated facility location: a mathematical modeling framework for strategic supply chain planning

In this paper, we focus on the strategic design of supply chain networks. We propose a mathematical modeling framework that captures many practical aspects of network design problems simultaneously but which have not received adequate attention in the literature. The aspects considered include: dynamic planning horizon, generic supply chain network structure, external supply of materials, inventory opportunities for goods, distribution of commodities, facility configuration, availability of capital for investments, and storage limitations. Moreover, network configuration decisions concerning the gradual relocation of facilities over the planning horizon are considered. To cope with fluctuating demands, capacity expansion and reduction scenarios are also analyzed as well as modular capacity shifts. The relation of the proposed modeling framework with existing models is discussed. For problems of reasonable size, we report on our computational experience with standard mathematical programming software. In particular, useful insights on the impact of various factors on network design decisions are provided.     

Hybrid client-server architecture and control techniques for collaborative product development using haptic interfaces

In this paper, a collaborative product development and prototyping framework is proposed by using distributed haptic interfaces along with deformable objects modeling. Collaborative Virtual Environment (CVE) is a promising technique for industrial product development and virtual prototyping. Network control problems such as network traffic and network delay in communication have greatly limited collaborative virtual environment applications. The problems become more difficult when high-update-rate haptic interfaces and computation intensive deformable objects modeling are integrated into CVEs for intuitive manipulation and enhanced realism. A hybrid network architecture is proposed to balance the computational burden of haptic rendering and deformable object simulation. Adaptive artificial time compensation is used to reduce the time discrepancy between the server and the client. Interpolation and extrapolation approaches are used to synchronize graphic and haptic data transmitted over the network. The proposed techniques can be used for collaborative product development, virtual assembly, remote product simulation and other collaborative virtual environments where both haptic interfaces and deformable object models are involved.     

供应链系统异构知识集成方法研究

供应链系统流动着大量的异构信息和知识．为了有效地利用现有知识对供应链进行优化管理和运作．供应链不仅要实现信息系统的集成而且需要实现知识集成。本文运用领域本体论(Ontology)、多Agent等技术研究供应链系统异构知识集成的方法．应用资源描述框架模式(RDFS)作为信息和知识的核心转换技术．实现异构知识的转换和集成,为供应链系统异构知识集成提供一个切实可行的技术解决方案。该方案可以保证全程信息数据和知识的正确性、及时性和完整性,进一步促使信息流为决策人员提供及时、可靠和科学的有效决策支持。     

基于代理的信息安全网络隔离装置的研究与实现

介绍一种在电力系统信息内外网之间的基于代理的信息安全网络隔离装置的实现方案.该方案采用专用软硬件技术实现了在安全网络隔离情况下的信息安全交换,同时内嵌的SQL安全引擎可以有效地保证传输信息的安全.针对电力信息内外网的业务需求,结合电力系统网络和系统特征,给出了一个既能保证各网络之间系统的安全隔离交换,又能保证内部信息网络不受来自外界黑客攻击的隔离解决方案.     

COMPONENT-BASED MODELS AND SIMULATIONS FOR SUPPORTING VALID MULTI-AGENT SYSTEM SIMULATIONS

The more simulation becomes an established tool in the design of multi-agent systems, the more urgent the question becomes how valid the induced properties and behavior patterns are. Answering this question depends on the validity of the models, the correctness of the simulators, and the simulations. In all of these aspects, reuse and a declarative representation plays a crucial role. With James II, we developed a customizable framework for modeling and simulation. Its component-based architecture supports a reuse of models, simulators, and experimental settings. The benefits of this architecture for agent-based modeling and simulation will be illuminated by an excerpt of a simulation study about trading strategies for mobile ad hoc networks.     

An agent-based framework for collaborative negotiation in the global manufacturing supply chain network

This paper applies the multi-agent system paradigm to collaborative negotiation in a global manufacturing supply chain network. Multi-agent computational environments are suitable for dealing with a broad class of coordination and negotiation issues involving multiple autonomous or semiautonomous problem solving agents. An agent-based multi-contract negotiation system is proposed for global manufacturing supply chain coordination. Also reported is a case study of mobile phone global manufacturing supply chain management.     

基于SOA的分布式服务供应链信息共享平台研究与实践*

以SOA理论体系为出发点,提出了基于企业服务总线（enterprise service bus,ESB）的分布式服务供应链信息共享平台的体系结构,并分析了该信息共享平台建设的生命周期。进一步采用案例分析的研究方法,以企业信息系统设计项目与IBM高校合作项目为例子,探讨了分布式服务供应链信息共享平台的服务建模过程,并设计和开发了一个分布式服务供应链信息共享平台。     

User authentication and undeniable billing support for agent-based roaming service in WLAN/cellular integrated mobile networks

In this paper, a framework of authentication and undeniable billing support for an agent-based roaming service in WLAN/cellular networks interworking networks is proposed. This framework circumvents the requirement of peer-to-peer roaming agreements to provide seamless roaming service between WLAN hotspots and cellular networks operated by independent wireless network service providers. Within the framework, an adaptive authentication and an event-tracking scheme have been developed, which allow the application of undeniable billing service to cellular network even when it still uses a traditional authentication scheme. The proposed modified dual directional hash chain (MDDHC) based billing support mechanism features mutual non-repudiation. Security analysis and overhead evaluation demonstrate that the proposed framework is secure and efficient.     

基于多Agent技术的生化网络仿真平台的实现

介绍了基于多agent技术、针对于生化网络的仿真框架，在分析Java相关技术特性的基础上，给出了框架的Java实现，并针对细菌趋药性网络进行了仿真实验，结果表明基于多agent的仿真平台具有良好的特性，在生化网络仿真中具有广泛的应用前景。     

动态连续时间网络表示学习

随着时间的推移,网络会随着节点和连边的变化不断发展。针对传统网络表示学习算法不能正确处理动态网络的问题,提出一种基于随机游走的动态连续时间网络表示学习算法（DCTNE）。通过定义一个灵活的节点时序邻居概念,设计一个有偏的随机游走过程。根据时间信息,有效地探索节点的不同时序邻居并建模不同邻居的影响,学习网络表示。实验证明了DCTNE动态网络时序信息的有效性。在链接预测任务上,DCTNE的AUC值与其他算法相比最高获得了50%的增益;在节点分类任务上,DCTNE相较于其他算法在效果上有明显提升。结果表明,对网络中时间依赖关系进行建模有助于后续的网络分析任务。     

A framework using cluster-based hybrid network architecture for collaborative virtual surgery

Research on collaborative virtual environments (CVEs) opens the opportunity for simulating the cooperative work in surgical operations. It is however a challenging task to implement a high performance collaborative surgical simulation system because of the difficulty in maintaining state consistency with minimum network latencies, especially when sophisticated deformable models and haptics are involved. In this paper, an integrated framework using cluster-based hybrid network architecture is proposed to support collaborative virtual surgery. Multicast transmission is employed to transmit updated information among participants in order to reduce network latencies, while system consistency is maintained by an administrative server. Reliable multicast is implemented using distributed message acknowledgment based on cluster cooperation and sliding window technique. The robustness of the framework is guaranteed by the failure detection chain which enables smooth transition when participants join and leave the collaboration, including normal and involuntary leaving. Communication overhead is further reduced by implementing a number of management approaches such as computational policies and collaborative mechanisms. The feasibility of the proposed framework is demonstrated by successfully extending an existing standalone orthopedic surgery trainer into a collaborative simulation system. A series of experiments have been conducted to evaluate the system performance. The results demonstrate that the proposed framework is capable of supporting collaborative surgical simulation.     

Agent-based architecture for context-aware and personalized event recommendation

In order to offer context-aware and personalized information, intelligent processing techniques are necessary. Different initiatives considering many contexts have been proposed, but users preferences need to be learned to offer contextualized and personalized services, products or information. Therefore, this paper proposes an agent-based architecture for context-aware and personalized event recommendation based on ontology and the spreading algorithm. The use of ontology allows to define the domain knowledge model, while the spreading activation algorithm learns user patterns by discovering user interests. The proposed agent-based architecture was validated with the modeling and implementation of eAgora? application, which was illustrated at the pervasive university context.     

基于跨通道交叉融合和跨模块连接的轻量级卷积神经网络

针对传统卷积神经网络参数量过多、计算复杂度高的问题,提出了基于跨通道交叉融合和跨模块连接的轻量级卷积神经网络架构C-Net。首先,提出了跨通道交叉融合的方法,它在一定程度上克服了分组卷积中各分组之间存在缺乏信息流动的问题,简单高效地实现了不同分组之间的信息通信;其次,提出了一种跨模块连接的方法,它克服了传统轻量级架构中各基本构建块之间彼此独立的缺点,实现了同一阶段内具有相同分辨率特征映射的不同模块之间的信息融合,从而增强了特征提取能力;最后,基于提出的两种方法设计了一种新型的轻量级卷积神经网络架构C-Net。C-Net在Food_101数据集上的准确率为69.41%,在Caltech_256数据集上的准确率为63.93%。实验结果表明,与目前先进的轻量级卷积神经网络模型相比,C-Net降低了存储开销和计算复杂度。在Cifar_10数据集上的消融实验验证了所提出的两种方法的有效性。     

基于跨通道交叉融合和跨模块连接的轻量级卷积神经网络

针对传统卷积神经网络参数量过多、计算复杂度高的问题，提出了基于跨通道交叉融合和跨模块连接的轻量级卷积神经网络架构C-Net。首先，提出了跨通道交叉融合的方法，它在一定程度上克服了分组卷积中各分组之间存在缺乏信息流动的问题，简单高效地实现了不同分组之间的信息通信；其次，提出了一种跨模块连接的方法，它克服了传统轻量级架构中各基本构建块之间彼此独立的缺点，实现了同一阶段内具有相同分辨率特征映射的不同模块之间的信息融合，从而增强了特征提取能力；最后，基于提出的两种方法设计了一种新型的轻量级卷积神经网络架构C-Net。C-Net在Food_101数据集上的准确率为69.41%，在Caltech_256数据集上的准确率为63.93%。实验结果表明，与目前先进的轻量级卷积神经网络模型相比，C-Net降低了存储开销和计算复杂度。在Cifar_10数据集上的消融实验验证了所提出的两种方法的有效性。     

可任意灵活接入的虚实网络融合仿真方法

针对虚实网络融合仿真技术存在构建仿真链路不够灵活的问题,提出了一种可任意灵活接入的虚实网络融合仿真方法.分析了虚实网络融合仿真系统的体系架构,主要包括接入端设备和虚实融合仿真平台,并描述了该平台的关键技术和功能实现;通过平台的灵活接口将接入端设备任意灵活接入到虚实网络,并分析其实验原理,完成不同链路间通信的任意切换;通过构建虚实网络拓扑实例,采用灵活接入的方法分别进行不同链路间的PING命令实验与WEB应用访问.实验分析结果证明了方法的可行性和有效性.     

Two-phase simulation-based location-allocation optimization of biomass storage distribution

This study presents a two-phase simulation-based framework for finding the optimal locations of biomass storage facilities that is a very critical link on the biomass supply chain, which can help to solve biorefinery concerns (e.g. steady supply, uniform feedstock properties, stable feedstock costs, and low transportation cost). The proposed framework consists of two simulation phases: (1) crop yield estimation using a process-based model such as Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) and (2) biomass transportation cost estimation using agent-based simulation (ABS) such as AnyLogic® with geographic information system (GIS). The OptQuest^® in AnyLogic is used as an optimization engine to find the best locations of biomass storage facilities based on evaluation results given by the two-phase simulation framework. In addition, network partitioning and integer linear programming techniques are used to mitigate computation demand of the optimization problem. Since the proposed hybrid simulation approach utilizes realistic biofuel feedstock production and considers dynamics of supply chain activities, it is able to provide reliable locations of biomass storage facilities for operational excellence of a biomass supply chain.     

Speeding up backpropagation using multiobjective evolutionary algorithms

The use of backpropagation for training artificial neural networks (ANNs) is usually associated with a long training process. The user needs to experiment with a number of network architectures; with larger networks, more computational cost in terms of training time is required. The objective of this letter is to present an optimization algorithm, comprising a multiobjective evolutionary algorithm and a gradient-based local search. In the rest of the letter, this is referred to as the memetic Pareto artificial neural network algorithm for training ANNs. The evolutionary approach is used to train the network and simultaneously optimize its architecture. The result is a set of networks, with each network in the set attempting to optimize both the training error and the architecture. We also present a self-adaptive version with lower computational cost. We show empirically that the proposed method is capable of reducing the training time compared to gradient-based techniques.     

多网融合下异构分层的智能化供水系统研究

在多网融合的过程中,网络和服务覆盖的范围向更多的行业延伸。探讨供水行业的智能化供水系统,特别是系统的网络架构、远程预付费、IC卡预付费、自动抄表功能的设计。针对目前用水计量与信息环境的复杂特点,提出在多网融合环境下按异构分层模式来构建系统。该系统采用多线程的集中轮询异步模式实现智能水表远程预付费功能,应用虚拟串口技术解决分布式远程设备控制以实现 IC 卡表预付费功能,具有可调时间的自动抄表及水费自动结算、水损分析等功能,应用表明了该系统的有效性与适用性。