A new grey forecasting model based on BP neural network and Markov chain

A new grey forecasting model based on BP neural network and Markov chain was proposed. In order to combine the grey forecasting model with neural network, an important theorem that the grey differential equation is equivalent to the time response model, was proved by analyzing the features of grey forecasting model（GM（1,1））. Based on this, the differential equation parameters were included in the network when the BP neural network was constructed, and the neural network was trained by extracting samples from grey system＇s known data. When BP network was converged, the whitened grey differential equation parameters were extracted and then the grey neural network forecasting model （GNNM（1,1）） was built. In order to reduce stochastic phenomenon in GNNM（1,1）, the state transition probability between two states was defined and the Markov transition matrix was established by building the residual sequences between grey forecasting and actual value. Thus, the new grey forecasting model（MNNGM（1,1）） was proposed by combining Markov chain with GNNM（1,1）. Based on the above discussion, three different approaches were put forward for forecasting China electricity demands. By comparing GM（1, 1） and GNNM（1,1） with the proposed model, the results indicate that the absolute mean error of MNNGM（1,1） is about 0.4 times of GNNM（1,1） and 0.2 times of GM（I, 1）, and the mean square error of MNNGM（1,1） is about 0.25 times of GNNM（1,1） and 0.1 times of GM（1,1）.

Stability of the Konjac Glucomannan Topological Chain Based on Quantum Spin Model

In this paper we investigated the stability of konjac glucomnnan（KGM） chain hydrogen networks based on the quantum spin model. Dissipative particle dynamics method was applied in the structure simulation of KGM. The results reveled that acetyl residues of KGM were bonded with water molecules in aqueous solutions. Increasing the hydrogen bond formation decreases the energy in acetyl system. The expect-valuation of the thermal state with respect to the Hamiltonian is negative. Hence, the total energy of konjac glucomnnan chain with the acetyl groups decreases, which indicates the increasing stability of konjac glucomnnan chain. Our approach could provide a new insight into the investigation on the stability of konjac glucomnnan chain.

A Chain Routing Algorithm Based on Traffic Prediction in Wireless Sensor Networks

As a representative of chain-based protocol in Wireless Sensor Networks (WSNs), EEPB is an elegant solution on energy efficiency. However, in the latter part of the operation of the network, there is still a big problem: reserving energy of the node frequently presents the incapacity of directly communicating with the base station, at the same time capacity of data acquisition and transmission as normal nodes. If these nodes were selected as LEADER nodes, that will accelerate the death process and unevenness of energy consumption distribution among nodes.This paper proposed a chain routing algorithm based ontraffic prediction model (CRTP).The novel algorithmdesigns a threshold judgment method through introducing the traffic prediction model in the process of election of LEADER node. The process can be dynamically adjusted according to the flow forecasting. Therefore, this algorithm lets the energy consumption tend-ing to keep at same level. Simulation results show that CRTP has superior performance over EEPB in terms of balanced network energy consumption and the prolonged network life.

Literature Review on Network Design Problems in Closed Loop and Reverse Supply Chains

The productivity of an organization is very much affected by non-value adding activity like logistics, which moves the resources from suppliers to factory, raw materials/semi-finished items within the factory and finished goods from factory to customers via a designated distribution channel called as forward logistics. In some cases, parts of the products such as automobiles, computers, cameras, mobile phones, washing machines, refrigerators, garments, footwear and empty glass bottles of beverages, etc. will be brought back to the factories as a product recovery strategy through reverse logistics network which is integrated in a sustainable closed loop supply chain network. So, it is highly essential to optimize the movement of the items in the reverse logistics network. This paper gives a comprehensive review of literature of the design of networks for the reverse logistics as well as for the reverse logistics coupled with forward logistics. The contributions of the researchers are classified into nine categories based on the methods used to design the logistics network.

Development and Comparison of Hybrid Genetic Algorithms for Network Design Problem in Closed Loop Supply Chain

This paper presents four different hybrid genetic algorithms for network design problem in closed loop supply chain. They are compared using a complete factorial experiment with two factors, viz. problem size and algorithm. Based on the significance of the factor “algorithm”, the best algorithm is identified using Duncan’s multiple range test. Then it is compared with a mathematical model in terms of total cost. It is found that the best hybrid genetic algorithm identified gives results on par with the mathematical model in statistical terms. So, the best algorithm out of four algorithm proposed in this paper is proved to be superior to all other algorithms for all sizes of problems and its performance is equal to that of the mathematical model for small size and medium size problems.

DTMC-based Modeling and Analysis of Obstacle Ad hoc Networks

One of the main characteristics of Ad hoc networks is node mobility, which results in constantly changing in network topologies. Consequently, the ability to forecast the future status of mobility nodes plays a key role in QOS routing. We propose a random mobility model based on discretetime Markov chain, called ODM. ODM provides a mathematical framework for calculating some parameters to show the future status of mobility nodes, for instance, the state transition probability matrix of nodes, the probability that an edge is valid, the average number of valid-edges and the probability of a request packet found a valid route. Furthermore, ODM can account for obstacle environment. The state transition probability matrix of nodes can quantify the impact of obstacles. Several theorems are given and proved by using the ODM. Simulation results show that the calculated value can forecast the future status of mobility nodes.

Application Research on Two-Layer Threat Prediction Model Based on Event Graph

Advanced Persistent Threat(APT)is now the most common network assault.However,the existing threat analysis models cannot simultaneously predict the macro-development trend and micro-propagation path of APT attacks.They cannot provide rapid and accurate early warning and decision responses to the present system state because they are inadequate at deducing the risk evolution rules of network threats.To address the above problems,firstly,this paper constructs the multi-source threat element analysis ontology(MTEAO)by integrating multi-source network security knowledge bases.Subsequently,based on MTEAO,we propose a two-layer threat prediction model(TL-TPM)that combines the knowledge graph and the event graph.The macro-layer of TL-TPM is based on the knowledge graph to derive the propagation path of threats among devices and to correlate threat elements for threat warning and decision-making;The micro-layer ingeniously maps the attack graph onto the event graph and derives the evolution path of attack techniques based on the event graph to improve the explainability of the evolution of threat events.The experiment’s results demonstrate that TL-TPM can completely depict the threat development trend,and the early warning results are more precise and scientific,offering knowledge and guidance for active defense.

多周期农产品废弃物供应链网络均衡模型构建

目的研究农产品废弃物可持续利用问题,既实现供应链整体利润最优,又减轻对环境的污染。方法在随机需求的背景下,考虑生产合作社对零售商的不同激励度,分析生产合作社、零售商、需求市场的最优状态,利用变分不等式建立相应的多周期供应链网络均衡模型。结果从多周期角度,实现了农产品废弃物供应链网络均衡利益最优化,可为合作社与零售商在较长时间达到一种均衡状态提供有效解决方案。结论既实现了多周期农产品废弃物的持续利用,又促进了循环经济的发展,同时还实现了供应链网络均衡、供应链整体利润最优和减轻环境污染等目标。

基于跨链技术的食品供需网信息共享激励模式构建

随着食品种类多元化和消费者需求的提升,食品生产流程更加复杂,供应链之间交流越来越多,相关企业、食品供应链和行业机构之间的交互形成了复杂的食品供需网结构。食品供需网成员之间信息共享有利于企业之间充分合作实现长期发展,但仍有许多企业共享信息意识较弱,无法进行有效信息沟通。区块链跨链技术的应用和发展,为食品供需网信息共享提供了新思路。根据食品供需网复杂性,引入区块链技术和声誉激励机制,将声誉进行量化管理,结合区块链智能合约技术实现声誉激励机制自动化。进一步引入区块链跨链技术,打破不同行业、地区区块链之间的交易壁垒,构建基于区块链跨链技术的食品供需网信息共享声誉激励模式,以猪肉供需网为例阐述跨链信息共享声誉激励流程。该激励模式不仅保障共享信息安全性与准确性,同时保护企业隐私,最大程度保护交易方自身利益,破解信息无法共享的障碍,激发食品供需网企业参与信息共享积极性。不仅实现食品供需网内部异构区块链之间的信息交流和价值互通,还为食品供需网价值信息交流互通的发展提供新思路。

基于环境中移动运输代理的传感器网络建模

针对大型稀疏传感器网络中的数据获取,本文提出了一种利用环境中普遍存在的移动代理来连接稀疏传感器的网络体系结构和一种2-维网格随机游走分析模型;提出的传感器网络模型由3个抽象层构成,即由无线传感器构成的底层、由各种运输代理构成的中间层和由接入点/中央存储库构成的顶层。具体实现原理是位于中间层的移动运输代理从底层分布的无线传感器收集数据并缓冲数据,然后经过游走运输,最后将从底层的无线传感器收集的数据交付到顶层必要的接入点进行必要的存储和处理,从而实现整个传感器网络的数据获取;理论分析和仿真实验结果表明,提出的基于移动运输代理的传感器网络模型不仅具有较好的鲁棒性和可扩展性,而且相比于基站网络模型和Ad-hoc网络模型,在传感器功率消耗、数据成功率和基础设施投入成本方面有明显的优势。

攻防实战视角下医院进攻性网络安全防御体系的构建与应用

目的为解决医疗结构网络安全工作无法量化、被动防御体系、专业技术人员短缺的现状,探索构建医院进攻性网络安全防御体系。方法通过搭建态势感知平台,联合互联网暴露面管理、开放端口管理、渗透测试、网络区域隔离、终端威胁检测与响应、安全培训等技术措施,构建医院进攻性网络安全防御体系。结果通过构建医院进攻性网络安全防御体系,医院网络安全事件主动发现数量和自动拦截数量均显著高于实施前(P<0.05),且平台具备良好的处理性能和稳定性。结论医院进攻性网络安全防御体系可改善目前医疗行业网络安全被动防御的现状,显著增强医院的网络安全防护能力。

多主体协同视角下供应链网络韧性评价与提升策略

提升供应链网络韧性对于我国经济的稳定发展至关重要。本研究基于复杂网络与级联失效理论,综合考虑节点过载和欠载两种失效机制,构建多主体协同视角下的供应链网络韧性评价与提升策略模型。通过数值仿真构建算例供应链网络,借助模拟实验分析该网络在随机攻击下的级联失效过程和恢复过程,得到算例供应链网络韧性值。在此基础上,分别面向级联失效过程中的可调参数α和β和恢复过程中的恢复策略,提出该算例供应链网络的韧性提升策略:通过调整节点负载分布的均匀程度使其达到中等水平而改变α、通过改变所投入的成本而增大β和优先恢复具有高特征向量中心性的失效节点,验证了该模型的可行性。本文所构建的模型为有效评估和增强供应链网络的韧性水平提供了有益的参考和借鉴,助力国家经济社会可持续稳健发展。

基于报童模型的供应链网络均衡决策研究

在不确定性需求下构建了基于报童模型的两层供应链网络,供应链网络是由原料供应商、产品生产商和需求市场构成。针对不确定性需求引入报童模型到供应链网络均衡模型中,用于供应链网络中厂商的产量和价格决策。模型构建了基于报童模型的变分不等式来表达供应链网络在不确定性需求下的均衡条件。最后,通过数值案例给出了需求均匀分布下,方差对于产品价格、原料供应商产品流量的影响分析。

安徽省生鲜农产品冷链物流需求预测研究

生鲜农产品等冷链产品市场需求快速增长,冷链物流的供给无法满足人们的需求给生鲜农产品带来新的挑战.安徽省作为一个农产品丰富的地区,生鲜农产品的供应对于满足市场需求至关重要.收集了2001~2022年生鲜农产品产量数据,采用反向传播神经网络(Back Propagation Neural Network,BP神经网络)、长短时记忆(long short-term memory,LSTM)、粒子群算法优化的长短期记忆神经网络(Particle Swarm Optimization-Long Short-Term Memory,PSO-LSTM)三种模型进行训练和验证,通过三种模型的对比分析,三种模型相对误差分别为0.13%、0.06%、0.02%.结果表明,PSO-LSTM模型预测精度最高,拟合效果最好,能够有效预测未来四年安徽省生鲜农产品冷链物流需求,以应对不断增长的冷链物流需求压力.

基于历史数据的水库事故链演化概率研究

水库一旦溃决将造成严重的生命财产损失,溃坝过程具有典型的链式特征,辨识溃坝事故链典型模式与演化特征有利于工程防灾减灾工作的开展。土石坝是我国水库大坝的主要坝型之一,以土石坝为研究对象,针对水库溃坝事件致灾因素复杂、不确定性强、量化分析手段单一等问题,通过深入挖掘历史溃坝数据中的潜在风险事件和破坏模式,建立了土石坝溃坝事故链网络模型;基于溃坝年代特征差异,利用风险事件共现矩阵定量分析了典型溃坝案例中风险事件间的关联度及影响度;提出了量化分析土石坝溃决事故链演化概率的计算方法,并将演化结果与现有研究成果进行对比分析,一定程度验证了所提方法的有效性和可行性。研究成果为加强水库大坝风险防控提供了重要的技术支撑。

基于APT活动全生命周期的攻击与检测综述

从攻击方法和检测方法两方面展开,首先综述高级持续威胁(APT)攻击的定义与特点,总结相关攻击模型的研究发展,在此基础上给出更一般性的APT全生命周期模型,并划分4个阶段,信息收集阶段、入侵实施阶段、内网攻击阶段和数据渗出阶段,对每一个阶段,重点调研近5年的研究论文,归纳总结各阶段的攻击与检测技术,并给出分析。最后,结合APT攻防技术相互博弈、快速发展的趋势,指出了当前攻防双方面临的挑战和未来研究的发展方向。

大数据视域下网络健康信息可信模型构建

[研究目的]将网络健康信息可信性的研究聚焦于健康数据层面,建构健康数据技术信任与网络用户感知信任过程,分析健康数据时间、空间及内容三维架构在支撑网络健康信息可信性的作用价值及方式,并将健康数据与网络用户作为整体信任进行分析。为后续健康数据溯源、数据空间及数据关联等技术应用于健康信息可信保障提供引导。健康数据三维架构的各维度表征要素也可能是提升健康信息可信性的一种新思路。[研究方法]以信息链、信任理论、理性行动理论等为基础,建构健康数据技术信任和用户感知信任过程,在对可信要素分析基础上,提出支撑网络健康信息可信的健康数据三维架构。在诠释与剖析健康数据时间、空间及内容三维架构作用方式、价值及各维度表征要素后,将网络用户与健康数据作为整体信任进行网络健康信息可信模型的建构。[研究结论]网络健康信息可信模型分别由网络用户感知层信任、健康数据技术层信任、健康数据三维架构作用及保障模块组成;健康数据技术层信任通过健康数据三维架构作用,通过影响健康信息感知质量、健康信息来源及可读性对用户感知可信产生影响;网络健康信息可信的健康数据三维架构中的24个表征要素能够为相关网站健康信息可信性保障方案的制定提供参考。

价格波动对战略性关键矿产资源产业链安全的影响——以铁、锰、铬为例

战略性关键矿产资源产业链安全事关国家长远发展大计,事关国计民生和国家安全,从复杂系统的视角分析价格波动对产业链的传导机制,对于维护我国产业链安全,避免经济领域风险积聚具有重要意义。本文选取了与战略性关键金属矿产铁、锰、铬产业链相关的30个工业行业出厂价格指数(PPI)代表产业链上、中、下游的行业价格,构建滞后1—4期的复杂网络模型,同时测算价格波动溢出指数度量不同行业的溢出效应。结果表明:(1)价格波动可以在产业链不同环节之间进行传导,价格之间存在依存关系;(2)产业链不同环节价格波动的溢出效应不同,行业间价格波动的影响存在差异性,且随着时间的推移,上中游产业的溢出效应不会消失,还会逐渐积累;(3)价格波动明显增加了产业链关联企业特别是中下游企业的成本管控压力。基于此,建议:(1)建立战略性关键矿产资源产业链风险评价、预警机制和风险处置方案,增强风险防控能力,特别是在行业价格发生较大波动时,长短期结合对重点行业实施动态监管,进一步提升产业链的安全性;(2)提升矿产资源保障能力,同时,密切关注下游产业成本积压问题,有效应对产业链各环节价格波动对产业链安全带来的冲击,进一步增强产业链的稳定性;(3)深化矿产资源国际合作以及价格波动对产业链传导路径和机制的研究,降低矿产品供应来源地集中可能引发的风险,进一步提升产业链的竞争力。

需求正态分布条件下基于报童模型的供应链网络均衡模型

针对不确定性需求引入报童模型到供应链网络均衡模型中,用于供应链网络中厂商的产量和价格决策。考虑到在实际生活中正态分布的更为常见性和重要性,在需求正态分布条件下运用博弈理论、网络均衡理论、报童模型以及变分不等式等方法建立了需求正态分布条件下基于报童模型的供应链网络均衡模型,研究了当需求服从正态分布条件下供应链与供应链之间的竞争行为和竞争结果。

基于遗传算法的农产品冷链物流网络布局优化模型求解研究

随着经济全球化和农产品市场需求的增加,农产品冷链物流成为整个农产品供应链中的重要环节。完善的农产品冷链物流网络可以提高物流运输效率,保证农产品质量,加强整个供应链上企业之间的信息共享。然而,农产品冷链物流的复杂性和需求多变性成为了优化农产品冷链物流网络布局时需要考虑的难点。在此背景下,遗传算法的自适应性、全局性、并行性成为了优化农产品冷链物流网络布局的有效工具。因此,文章构建了农产品冷链物流网络布局优化模型,并基于遗传算法对模型进行了求解,以期为相关人员提供参考。