单向通信拓扑领航者动态未知线性多智能体系统的协同迭代学习控制（英文）

This paper considers the cooperative tracking of linear multi-agent systems with a dynamic leader whose input information is unavailable to any followers. Cooperative iterative learning controllers, based on the relative state information of neighboring agents, are proposed for tracking the dynamic leader over directed communication topologies. Stability and convergence of the proposed controllers are established using Lyapunov-Krasovskii functionals. Furthermore, this result is extended to the output feedback case where only the output information of each agent can be obtained. A local observer is constructed to estimate the unmeasurable states. Then, cooperative iterative learning controllers, based on the relative observed states of neighboring agents,are devised. For both cases, it is shown that the multi-agent systems whose communication topologies contain a spanning tree can reach synchronization with the dynamic leader, and meanwhile identify the unknown input of the dynamic leader using distributed iterative learning laws. An illustrative example is provided to verify the proposed control schemes.     

主旋翼升力和机身姿态受限的模型直升机非线性控制（英文）

A nonlinear control is proposed for trajectory tracking of a 6-DOF model-scaled helicopter with constraints on main rotor thrust and fuselage attitude. In the procedure of control design, the mathematical model of helicopter is simplified into three subsystems: altitude subsystem, longitudinal-lateral subsystem and attitude subsystem. The proposed control is developed by combining the sub-controls for the corresponding subsystems. The sub-controls for altitude subsystem and longitudinal-lateral subsystem are designed with hyperbolic tangent functions to satisfy the constraints; the sub-control for attitude subsystem is based on backstepping technique such that fuselage attitude tracks the virtual control for longitudinallateral subsystem. It is proved theoretically that tracking errors are ultimately bounded, and control constraints are satisfied.Performances of the proposed controller are demonstrated by simulation results.     

基于有限时间跟踪微分器的迭代学习控制

针对迭代学习控制（Iterative learning control,ILC）中的初始状态问题,提出了采用有限时间跟踪微分器安排过渡过程方法,根据迭代学习控制中期望轨迹已知的特点,设计了其参数有明显物理意义并且调节方便的有限时间跟踪微分器. 在此基础上,针对一类具有不确定性的非线性时变系统的迭代学习控制问题,提出了具有对不确定项进行估计的迭代学习控制算法,并应用类Lyapunov方法给出了相关定理证明. 仿真结果表明所提出的方法是有效的.     

Characteristics of epoxy resin cured with in situ polymerized curing agent

In order to improve the heat resistance of a cured epoxy resin together with reducing the viscosity of the resin composition, an epoxy resin was cured with a curing agent formed from the radical copolymerization of vinyl monomers during the cure process of the epoxy resin. N-phenylmaleimide and p-acetoxystyrene were used as vinyl monomers of the curing agent. The epoxy resin was cured by the insertion reaction of the ester group of the in situ polymerized curing agent and the epoxy group of the epoxy resin. In the cure system of the epoxy and the phenol resins, reduction of the viscosity of the resin composition was achieved by replacing some or all of the phenol resin with these monomers. When all phenol resins were replaced by these monomers, the viscosity of resin composition (0.01 Pa s at 70 °C) decreased by about 1/2000 compared with that of the system with only phenol resin (21 Pa s at 70 °C). The glass transition temperature (T_g) of the cured resin with no phenols was 174 °C, an improvement of 17 °C compared with that of the system cured with only phenol resin. The flexural strength of the new resins remained unchanged.     

混杂系统的一致输入输出对状态稳定性

混杂系统的输入输出对状态稳定性是混杂控制系统领域极富挑战性的课题之一. 为了观测混杂系统的状态,本文提出了一类混杂系统的一致输入输出对状态稳定的充分条件,分析了混杂系统的一致输入输出对状态稳定性、光滑Lyapunov函数存在性和状态模估计器存在性三者之间的关系. 借助状态模估计器将混杂系统化为受扰动系统,获得了受扰动系统一致输入输出对状态稳定性的结果,并进一步证明了混杂系统的一致输入输出对状态稳定性.     

Sm~（3＋），Eu~（3＋），Tb~（3＋）等离子在低熔点玻璃中的光谱性质及

Ｓｍ￣（３＋），Ｅｕ￣（３＋），Ｔｂ￣（３＋）等离子在低熔点玻璃中的光谱性质及可见荧光的研究姜妍彦，王承遇，王继红（大连轻工业学院１１６００１）ＳｔｕｄｙｏｎＳｐｅｃｔｒａＰｒｏｐｅｒｔｉｅｓａｎｄＶｉｓｉｂｌｅＦｌｕｏｒｅｓｃｅｎｃｅｏｆＳｍ￣（３...     

Effect of extraction systems on the quality of virgin olive oil

Research has been carried out to ascertai the effects of different processing systems on olive oil quality. Tests were performed in industrial oil mills that were equipped with both pressure and centrifugation systems. Results show that oils extracted from good-quality olives do not differ in free fatty acids, peroxide value, ultraviolet absorption and organoleptic properties. Polyphenols ando-diphenols contents and induction times are higher in oils obtained from good-quality olives by the pressure system because it does not require addition of water to the olive paste. The centrifugation system requires the addition of warm water to the olive paste and helps to obtain oils with a lower content of natural antioxidants. Oils obtained from poorquality or from ripe olives in continuous centrifugal plants are lower in free fatty acids than those obtained by the pressure system. Dr. Mario Solinas is deceased—May 23, 1993.     

A knowledge and collaboration-based CBR process to improve network performance-related support activities

In a context characterized by a growing demand for networked services, users of advanced applications sometimes face network performance troubles that may actually prevent them from completing their tasks. Therefore, providing assistance for user communities that have difficulties using the network has been identified as one of the major issues of performance-related support activities. Despite the advances network management has made over the last years, there is a lack of guidance services to provide users with information that goes beyond merely presenting network properties. In this light, the research community has been highlighting the importance of User-Perceived Quality (UPQ) scores during the evaluation of network services for network applications, such as Quality of Experience (QoE) and Mean Opinion Score (MOS). However, despite their potential to assist end-users to deal with network performance troubles, only few types of network applications have well established UPQ scores. Besides that, they are defined through experiments essentially conducted in laboratory, rather than actual usage. This paper thus presents a knowledge and Collaboration-based Network Users’ Support (CNUS) Case-Based Reasoning (CBR) Process that predicts UPQ scores to assist users by focusing on the collaboration among them through the sharing of their experiences in using network applications. It builds (i) a knowledge base that includes not only information about network performance problems, but also applications’ characteristics, (ii) a case base that contains users’ opinions, and (iii) a user database that stores users’ profiles. By processing them, CNUS benefits users through the indication of the degree of satisfaction they may achieve based on the general opinion from members of their communities in similar contexts. In order to evaluate the suitability of CNUS, a CBR system was built and validated through an experimental study conducted in laboratory with a multi-agent system that simulated scenarios where users request for assistance. The simulation was supported by an ontology of network services and applications and reputation scheme implemented through the PageRank algorithm. The results of the study pointed to the effectiveness of CNUS, and its resilience to users’ collusive and incoherent behaviors. Besides that, they showed the influence of the knowledge about network characteristics, users’ profiles and application features on computer-based support activities.     

Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems

Recommender systems apply data mining and machine learning techniques for filtering unseen information and can predict whether a user would like a given item. This paper focuses on gray-sheep users problem responsible for the increased error rate in collaborative filtering based recommender systems. This paper makes the following contributions: we show that (1) the presence of gray-sheep users can affect the performance – accuracy and coverage – of the collaborative filtering based algorithms, depending on the data sparsity and distribution; (2) gray-sheep users can be identified using clustering algorithms in offline fashion, where the similarity threshold to isolate these users from the rest of community can be found empirically. We propose various improved centroid selection approaches and distance measures for the K-means clustering algorithm; (3) content-based profile of gray-sheep users can be used for making accurate recommendations. We offer a hybrid recommendation algorithm to make reliable recommendations for gray-sheep users. To the best of our knowledge, this is the first attempt to propose a formal solution for gray-sheep users problem. By extensive experimental results on two different datasets (MovieLens and community of movie fans in the FilmTrust website), we showed that the proposed approach reduces the recommendation error rate for the gray-sheep users while maintaining reasonable computational performance.