基于改进克隆选择算法的统一混沌系统同步控制与参数辨识

提出一种改进的克隆选择算法用于解决混沌系统的参数辨识问题.该算法利用抗体的高频变异和受体编辑两种机制有效平衡算法的全局探索与局部开发，并引入向精英抗体学习策略进一步提高算法的收敛质量.对10个优化问题的实验表明：所提出算法在求解精度、收敛速度以及稳定性方面具有更好的性能.以参数未知统一混沌系统的同步控制为研究对象，合理设计同步控制器，并对同步系统的稳定性进行理论分析.通过对同步比例因子的设置，实现统一混沌系统的完全同步、反同步、投影同步等多种同步方式.仿真实验结果表明该方法能够实现对未知系统参数的精确辨识以及驱动-响应系统的有效同步控制，验证了所提方法的可行性与有效性.     

An Isotonic Regression Problem for Infinite-Dimensional Parameters

We consider an infinite-dimensional isotonic regression problem which is an extension of the suitably revised classical isotonic regression problem. Given p-summable data, for p finite and at least one, there exists an optimal estimator to our problem. For p greater than one, this estimator is unique and is the limit in the p-norm of the sequence of unique estimators in canonical finite-dimensional truncations of our problem. However, for p equal to one, our problem, as well as the finite-dimensional truncations, admit multiple optimal estimators in general. In this case, the sequence of optimal estimator sets to the truncations converges to the optimal estimator set of the infinite problem in the sense of Kuratowski. Moreover, the selection of natural best optimal estimators to the truncations converges in the 1-norm to an optimal estimator of the infinite problem.     

Diffraction mode selection in planar Bragg resonators of optical and microwave wavelength ranges

Using the coupled waves approach complemented by the time-domain quasi-optical approximation, we solve a 2D diffraction problem which allows to evaluate the eigenfrequencies, quality factors and spatial structures of eigenmodes in planar Bragg resonators with a finite length and width of the corrugated area. We find the values of the Fresnel parameter determined by the geometrical dimensions of the system which allows for efficient transverse mode discrimination due to the larger diffraction losses of the modes with higher transverse numbers.     

基于风险-均值分析的供应商选择与订单分配集成优化模型与求解

针对不确定环境下具有不同供应合约的供应商选择与订单分配问题,本文构建了基于风险-均值分析的模糊两阶段多周期集成优化模型。与传统的该问题研究并未充分考虑供应商选择与订单分配两阶段决策的交互影响不同,在该模型中,第一阶段供应商选择的评价目标依赖于后期实际运营中的订单分配决策;并考虑未来需求和实际运营成本的不确定性,引入在险价值和期望值两种决策准则对供应商选择方案的绩效进行评价。提出了该模型的分析求解方法,在险价值得以精确评估,期望值被控制在确定的误差范围内,并可以达到足够的精度要求。     

Injection attack detection using machine learning for smart IoT applications

Smart cities are a rapidly growing IoT application. These smart cities mainly rely on wireless sensors to connect their different components (smart devices) together. Smart cities rely on the integration of IoT and 5G technologies, and this has created a demand for a massive IoT network of connected devices. The data traffic coming from indoor wireless networks (e.g., smart homes, smart hospitals, smart factories , or smart school buildings) contributes to over 80% of the total data traffic of the current IoT network. As smart cities and their applications grow, security and privacy challenges have become a major concern for billions of IoT smart devices. One reason for this could be the oversight of handling security issues of IoT devices by their manufacturers, which enables attackers to exploit the vulnerabilities in these devices by performing different types of attacks, e.g., DDoS and injection attacks. Intrusion detection is one way to detect and mitigate the risk of such attacks. In this paper, an intrusion detection method was proposed to detect injection attacks in IoT applications (e.g. smart cities). In this method, two types of feature selection techniques (constant removal and recursive feature elimination) were used and tested by a number of machine learning classifiers (i.e., SVM, Random Forest, and Decision Tree). The T-Test was conducted to evaluate the quality of this proposed feature selection method. Using the public dataset, AWID, the evaluation results showed that the decision tree classifier can be used to detect injection attacks with an accuracy of 99% using only 8 features, which were selected using the proposed feature selection method. Also, the comparison with the most related work showed the advantages of the proposed intrusion detection method.     

绿色产品零售企业与市场消费者策略选择演化分析

线上线下结合营销模式有效缓解了流通渠道不畅和营销方式单一的问题,进而促进绿色产品消费。在相关假设的基础上,通过构建绿色产品零售企业和市场消费者的演化博弈模型,重现博弈主体的动态演进过程。研究结果表明,在线上线下结合营销模式中,绿色产品零售企业与消费者演化博弈系统存在两个可能的均衡状态,分别是O{维持传统营销策略,使用传统渠道购买产品}、C{建立线上营销策略,使用线上渠道购买产品}。演化博弈系统的演进受到9个相关因素的干扰,这些因素通过影响博弈主体的收益、成本及潜在的风险损失等发生作用。在理论推导的基础上,以被调研的A绿色产品零售企业为样本进行数值模拟,验证理论推导的正确性,给出上述影响因素的作用强度。     

度量射影的连续选择

本文讨论了集值映射的弱连续选择并应用于度量射影．设Y是Banach空间X的子空间且Y是可分的,在相差一个第一纲集的情况下,于弱拓扑下,支撑于Y上的度量射影是下半连续的并有连续选择．     

Steiner选择和算子的集值扩张(Ⅱ)——对逼近理论的应用

在(Ⅰ)的基础上,得出对集值函数逼近理论的某些应用:Korovkin型定理,一种将经典逼近算子扩张到集值族的方法,以及Jackson估算.     

应用CARS和SPA算法对草莓SSC含量NIR光谱预测模型中变量及样本筛选

采用光谱技术对水果进行定量或定性分析,如何获得一个简单、有效的校正模型对后续模型的应用和维护都非常关键。以草莓内部品质近红外光谱预测为例,从关键变量和特征样本优选两方面进行研究。采用竞争性自适应重加权CARS算法对光谱变量进行初次选择,随后采用连续投影算法SPA对校正集样本进行优选,获得98个特征样本,针对优选后的变量/样本子集利用SPA算法作二次关键变量提取,获得25个关键变量。为了验证CARS算法的性能,蒙特卡罗无信息变量消除MC-UVE和连续投影算法SPA用于比较研究。CARS算法在消除无信息变量的同时可以对共线性信息进行去除。同样,为了评估SPA算法在特征样本选择中的性能,经典的Kennard-Stone算法也用于比较分析。SPA算法能够用于校正集特征样本的优选。针对最终优选后的变量/样本(25/98)子集建立PLS和MLR模型对草莓内部可溶性固形物含量SSC含量进行定量预测。结果表明,两个模型利用原始变量/样本的0.59%/65.33%的信息均能够获得比基于原始变量/样本所建模型更好的性能,且MLR模型比PLS模型性能略优,r2_pre,RMSEP和RPD分别为0.909 7,0.348 4和3.327 8。     

Minimax kernels for density estimation with biased data

This paper considers the asymptotic properties of two kernel estimates % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXafv3ySLgzGmvETj2BSbqefm0B1jxALjhiov2D% aebbfv3ySLgzGueE0jxyaibaiGc9yrFr0xXdbba91rFfpec8Eeeu0x% Xdbba9frFj0-OqFfea0dXdd9vqaq-JfrVkFHe9pgea0dXdar-Jb9hs% 0dXdbPYxe9vr0-vr0-vqpWqaaeaabiGaciaacaqabeaadaqaaqGaaO% qaaGqaciqa-zgagaacamaaBaaaleaadaWgaaadbaGaa8NBaaqabaaa% leqaaaaa!3E82!\[\tilde f_{_n }\]and % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXafv3ySLgzGmvETj2BSbqefm0B1jxALjhiov2D% aebbfv3ySLgzGueE0jxyaibaiGc9yrFr0xXdbba91rFfpec8Eeeu0x% Xdbba9frFj0-OqFfea0dXdd9vqaq-JfrVkFHe9pgea0dXdar-Jb9hs% 0dXdbPYxe9vr0-vr0-vqpWqaaeaabiGaciaacaqabeaadaqaaqGaaO% qaaGqaciqa-zgagaqcamaaBaaaleaadaWgaaadbaGaa8NBaaqabaaa% leqaaaaa!3E83!\[\hat f_{_n }\], which have been proposed by Bhattacharyya et al. (1988, Comm. Statist. Theory Methods, A17, 3629–3644) and Jones (1991, Biometrika, 78, 511–519), respectively, for estimating the underlying density f at a point under a general selection biased model. The asymptotic optimality of % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXafv3ySLgzGmvETj2BSbqefm0B1jxALjhiov2D% aebbfv3ySLgzGueE0jxyaibaiGc9yrFr0xXdbba91rFfpec8Eeeu0x% Xdbba9frFj0-OqFfea0dXdd9vqaq-JfrVkFHe9pgea0dXdar-Jb9hs% 0dXdbPYxe9vr0-vr0-vqpWqaaeaabiGaciaacaqabeaadaqaaqGaaO% qaaGqaciqa-zgagaqcamaaBaaaleaadaWgaaadbaGaa8NBaaqabaaa% leqaaaaa!3E83!\[\hat f_{_n }\]and % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXafv3ySLgzGmvETj2BSbqefm0B1jxALjhiov2D% aebbfv3ySLgzGueE0jxyaibaiGc9yrFr0xXdbba91rFfpec8Eeeu0x% Xdbba9frFj0-OqFfea0dXdd9vqaq-JfrVkFHe9pgea0dXdar-Jb9hs% 0dXdbPYxe9vr0-vr0-vqpWqaaeaabiGaciaacaqabeaadaqaaqGaaO% qaaGqaciqa-zgagaacamaaBaaaleaadaWgaaadbaGaa8NBaaqabaaa% leqaaaaa!3E82!\[\tilde f_{_n }\]is measured by the corresponding asymptotic minimax mean squared errors under a compactly supported Lipschitz continuous family of the underlying densities. It is shown that, in general, % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXafv3ySLgzGmvETj2BSbqefm0B1jxALjhiov2D% aebbfv3ySLgzGueE0jxyaibaiGc9yrFr0xXdbba91rFfpec8Eeeu0x% Xdbba9frFj0-OqFfea0dXdd9vqaq-JfrVkFHe9pgea0dXdar-Jb9hs% 0dXdbPYxe9vr0-vr0-vqpWqaaeaabiGaciaacaqabeaadaqaaqGaaO% qaaGqaciqa-zgagaqcamaaBaaaleaadaWgaaadbaGaa8NBaaqabaaa% leqaaaaa!3E83!\[\hat f_{_n }\]is a superior local estimate than % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXafv3ySLgzGmvETj2BSbqefm0B1jxALjhiov2D% aebbfv3ySLgzGueE0jxyaibaiGc9yrFr0xXdbba91rFfpec8Eeeu0x% Xdbba9frFj0-OqFfea0dXdd9vqaq-JfrVkFHe9pgea0dXdar-Jb9hs% 0dXdbPYxe9vr0-vr0-vqpWqaaeaabiGaciaacaqabeaadaqaaqGaaO% qaaGqaciqa-zgagaacamaaBaaaleaadaWgaaadbaGaa8NBaaqabaaa% leqaaaaa!3E82!\[\tilde f_{_n }\]in the sense that the asymptotic minimax risk of % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXafv3ySLgzGmvETj2BSbqefm0B1jxALjhiov2D% aebbfv3ySLgzGueE0jxyaibaiGc9yrFr0xXdbba91rFfpec8Eeeu0x% Xdbba9frFj0-OqFfea0dXdd9vqaq-JfrVkFHe9pgea0dXdar-Jb9hs% 0dXdbPYxe9vr0-vr0-vqpWqaaeaabiGaciaacaqabeaadaqaaqGaaO% qaaGqaciqa-zgagaqcamaaBaaaleaadaWgaaadbaGaa8NBaaqabaaa% leqaaaaa!3E83!\[\hat f_{_n }\]is lower than that of % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXafv3ySLgzGmvETj2BSbqefm0B1jxALjhiov2D% aebbfv3ySLgzGueE0jxyaibaiGc9yrFr0xXdbba91rFfpec8Eeeu0x% Xdbba9frFj0-OqFfea0dXdd9vqaq-JfrVkFHe9pgea0dXdar-Jb9hs% 0dXdbPYxe9vr0-vr0-vqpWqaaeaabiGaciaacaqabeaadaqaaqGaaO% qaaGqaciqa-zgagaacamaaBaaaleaadaWgaaadbaGaa8NBaaqabaaa% leqaaaaa!3E82!\[\tilde f_{_n }\]. The minimax kernels and bandwidths of % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXafv3ySLgzGmvETj2BSbqefm0B1jxALjhiov2D% aebbfv3ySLgzGueE0jxyaibaiGc9yrFr0xXdbba91rFfpec8Eeeu0x% Xdbba9frFj0-OqFfea0dXdd9vqaq-JfrVkFHe9pgea0dXdar-Jb9hs% 0dXdbPYxe9vr0-vr0-vqpWqaaeaabiGaciaacaqabeaadaqaaqGaaO% qaaGqaciqa-zgagaqcamaaBaaaleaadaWgaaadbaGaa8NBaaqabaaa% leqaaaaa!3E83!\[\hat f_{_n }\]are computed explicity and shown to have simple forms and depend on the weight functions of the model.