现代大学制度:从制度链、制度网到制度体系——以普林斯顿大学人才培养制度为例

有学者认为,政府要解决的问题大致可以分为两大类:一类是定型化问题,即常规性问题.其解决以设定一定的程序,制定标准化的解决方法为特征.其主要应对措施就是法律及次于法律的规章制度.一类是非定型化问题,即新出现的尚未被理解的问题(包括非常重要的问题).解决这类问题很难以一定的程序或标准化的方法进行,其主要解决之道就是政策与战略.

抗战初期四川省教育视导网制度之建立

教育视导是教育行政管理工作的一个重要组成部分,清末建立了近代意义上的中国教育视导制度。民国成立后,四川省视导制度有一定发展。1938年,四川省兼采浙江省教育辅导制度与山东、安徽等省教育视导制度之精华,结合省情创立了教育视导网制度。此后省教育厅制定视导法规,选拔优秀的视导人员,不断充实和完善视导制度,使得抗战初期教育视导制度对于全省教育的发展起到了一定作用。

新中国早期宣传工作:做法·成效·启示——基于建国初期宣传网制度的考察

宣传网制度作为建国初期党在宣传工作领域的一项重要制度安排,通过依托党的各级组织和发动群众广泛参与宣传工作,迅速传播了主流意识形态,短期内形成覆盖全国的宣传网络,有力捍卫新生的人民政权,其经验和做法对于做好新时代宣传思想工作仍然具有指导和借鉴意义。

保监会召六险企起草人身险网销制度

近日，保监会寿险部召集六险企的闭门会议，讨论了《关于召开人身险网销业务监管制度起草工作启动会的通知》。据有关人士透露，参加会议的6家公司包括太保寿险、平安人寿、泰康人寿、光大永明、国华人寿、弘康人寿。会议透露，年内将制定并出台关于人身险网销业务监管的规范性文件。

建立健全防止利益冲突制度研究

公职人员的利益冲突是产生腐败的重要根源。当前,腐败面临的窘境已凸显在世界各国政府面前,各国开始探索防止利益冲突问题以打破社会发展的瓶颈。从学术角度来看,既要从公共权力和私人权利视角对防止利益冲突制度进行理论解读,又要挖掘防止利益冲突制度的核心要件。从实践角度来分析,既要建立健全防止利益冲突的制度支持网,搞好系统配套工程,又要落实防止利益冲突的各项举措。归根结底,建立健全防止利益冲突制度势不可挡,实现防止利益冲突的制度化与法制化,建立防止利益冲突过程中的政府、市场与社会合作共担责任机制迫在眉睫。

Intrusion detection model based on deep belief nets

This paper focuses on the intrusion classification of huge amounts of data in a network intrusion detection system. An intrusion detection model based on deep belief nets （DBN） is proposed to conduct intrusion detection,and the principles regarding DBN are discussed.The DBN is composed of a multiple unsupervised restricted Boltzmann machine （RBM） and a supervised back propagation （BP）network.First,the DBN in the proposed model is pre-trained in a fast and greedy way,and each RBM is trained by the contrastive divergence algorithm.Secondly,the whole network is fine-tuned by the supervised BP algorithm,which is employed for classifying the low-dimensional features of the intrusion data generated by the last RBM layer simultaneously.The experimental results on the KDD CUP 1999 dataset demonstrate that the DBN using the RBM network with three or more layers outperforms the self-organizing maps （SOM）and neural network （NN）in intrusion classification.Therefore,the DBN is an efficient approach for intrusion detection in high-dimensional space.

Wavelet Network Based MQAM Digital Communication Adaptive Equalizers

A novel wavelet network based adaptive equalizer (WNBAE) is presented and the structure and stochastic gradient learning algorithm is given. The proposed WNBAE has better performance than that of the conventional linear transversal equalizer based on the LMS and the RLS algorithms, as well as that of the decision feedback equalizer based on the RLS algorithm, especially for MQAM digital communication reception systems over the nonlinear channels. In addition, it outperforms the BP neural network based adaptive equalizer slightly. However, it has a slow convergence rate and a high computational complexity. Several simulations are performed to evaluate the behavior of the WNBAE.

Modeling and Robustness of Knowledge Network in Supply Chain

The growth and evolution of the knowledge network in supply chain can be characterized by dynamic growth clustering and non-homogeneous degree distribution.The networks with the above characteristics are also known as scale-free networks.In this paper,the knowledge network model in supply chain is established,in which the preferential attachment mechanism based on the node strength is adopted to simulate the growth and evolution of the network.The nodes in the network have a certain preference in the choice of a knowledge partner.On the basis of the network model,the robustness of the three network models based on different preferential attachment strategies is investigated.The robustness is also referred to as tolerances when the nodes are subjected to random destruction and malicious damage.The simulation results of this study show that the improved network has higher connectivity and stability.

A Grey Wolf Optimization-Based Tilt Tri-rotor UAV Altitude Control in Transition Mode

To solve the problem of altitude control of a tilt tri-rotor unmanned aerial vehicle(UAV)in the transition mode,this study presents a grey wolf optimization(GWO)based neural network adaptive control scheme for a tilt trirotor UAV in the transition mode.Firstly,the nonlinear model of the tilt tri-rotor UAV is established.Secondly,the tilt tri-rotor UAV altitude controller and attitude controller are designed by a neural network adaptive control method,and the GWO algorithm is adopted to optimize the parameters of the neural network and the controllers.Thirdly,two altitude control strategies are designed in the transition mode.Finally,comparative simulations are carried out to demonstrate the effectiveness and robustness of the proposed control scheme.

Application of PID Controller Based on BP Neural Network in Export Steam’s Temperature Control System

By combining the Back-Propagation （BP） neural network with conventional proportional Integral Derivative （PID） controller, a new temperature control strategy of the export steam in supercritical electric power plant is put forward. This scheme can effectively overcome the large time delay, inertia of the export steam and the influencee of object in varying operational parameters. Thus excellent control quality is obtaitud. The present paper describes the development and application of neural network based controller to control the temperature of the boiler＇s export steam. Through simulation in various situations, it validates that the control quality of this control system is apparently superior to the conventional PID control system.

Determining heating pipe temperature in greenhouse using proportional integral plus feedforward control and radial basic function neural-networks

Proportional integral plus feedforward (PI+FF) control was proposed for identifying the pipe temperature in hot water heating greenhouse. To get satisfying control result, ten coefficients must be adjusted properly. The data for training and testing the radial basic function (RBF) neural-networks model of greenhouse were collected in a 1028 m2 multi-span glasshouse. Based on this model, a method of coefficients adjustment is described in this article.

Optimal congestion control algorithm for ad hoc networks: Penalty function-based approach

In this paper, based on the inherent characteristic of the contention relation between flows in ad hoc networks, we introduce the notion of the link＇s interference set, extend the utility maximization problem representing congestion control in wireline networks to ad hoc networks, apply the penalty function approach and the subgradient method to solve this problem, and propose the congestion control algorithm Penalty function-based Optical Congestion Control （POCC） which is implemented in NS2- simulator. Specifically, each link transmits periodically the information on its congestion state to its interference set; the set ; the sermon at each source adjusts the transmission rate based on the optimal tradeoffbetween the utility value and the congestion level which the interference set of the links that this session goes though suffers from. MATLAB-based simulation results showed that POCC can approach the globally optimal solution. The NS2-based simulation results showed that POCC outperforms default TCP and ATCP to achieve efficient and fair resource allocation in ad hoc networks.

Multi-spacecraft Intelligent Orbit Phasing Control Considering Collision Avoidance

This paper proposes an intelligent low-thrust orbit phasing control method for multiple spacecraft by simultaneously considering fuel optimization and collision avoidance. Firstly,the minimum-fuel orbit phasing control database is generated by the indirect method associated with the homotopy technique. Then,a deep network representing the minimum-fuel solution is trained. To avoid collision for multiple spacecraft,an artificial potential function is introduced in the collision-avoidance controller. Finally,an intelligent orbit phasing control method by combining the minimum-fuel neural network controller and the collision-avoidance controller is proposed. Numerical results show that the proposed intelligent orbit phasing control is valid for the multi-satellite constellation initialization without collision.

A NOVEL CALL ADMISSION CONTROL SCHEME IN CELLULAR/WLAN INTEGRATION AND PERFORMANCE ANALYSIS

In order to achieve the Quality of Service(QoS) provisioning and efficient resource utili-zation in cellular network and Wireless Local Area Network(WLAN) integration,an Integrated Ser-vice-Based Call Admission Control(ISB-CAC) scheme is proposed in this paper.The integrated network is modeled by using multi-dimensional Markov chains.The numerical analysis is presented to evaluate the important performance measures such as the blocking probability of originating calls,the dropping probability,and the average transfer time,etc.The steady-state probabilities of the multi-dimensional Markov chains are obtained by using an iterative approach,and the CAC pa-rameters are optimally designed.The analytical model is validated by the computer simulation.It is shown that compared with the conventional WLAN-First Call Admission Control(WF-CAC) scheme,the proposed ISB-CAC scheme not only provides better QoS for mobile users but also utilizes the bandwidth resources more efficiently.

Brief Analysis on the Right of Privacy in a Perspective of Law and Economic

The development of the technology of network had been of great convenience to get access to information. However, it was undeniable that a bad phenomenon had become even more and more serious that others＇ information would be obtained by illegal means, and the research on the protection of privacy had increasingly aroused people＇s concern. The right of privacy not only had the usually outside personality characteristics, but also had a certain characteristics of property. This paper had attempted to study the right of privacy from the perspective of law and economics by interpreting the property in the right of privacy, in order to perfect the protection system of privacy in our country.

A Signal Recognition Algorithm Based on Compressive Sensing and Improved Residual Network at Airport Terminal Area

It is particular important to identify the pattern of communication signal quickly and accurately at the airport terminal area with the increasing number of radio equipments.A signal modulation pattern recognition method based on compressive sensing and improved residual network is proposed in this work.Firstly,the compressive sensing method is introduced in the signal preprocessing process to discard the redundant components for sampled signals.And the compressed measurement signals are taken as the input of the network.Furthermore,based on a scaled exponential linear units activation function,the residual unit and the residual network are constructed in this work to solve the problem of long training time and indistinguishable sample similar characteristics.Finally,the global residual is introduced into the training network to guarantee the convergence of the network.Simulation results show that the proposed method has higher recognition efficiency and accuracy compared with the state-of-the-art deep learning methods.

Temperature and Humidity Control System Identification Based on Neural Network in Heating and Drying System

Artificial neural network has unique advantages for massively parallel processing, distributed storage capacity and self-learning ability. The paper mainly constructs neural network identifier and neural network controller for system identification and control on temperature and hmnidity of heating and drying system of materials. And the paper introduces the structure and principles of neural network, and focuses on analyzing learning algorithm, training algorithm and limitation of the most widely applied multi-layer feed-forward neural network （ BP network） , based on which the paper proposes introducing momentum to improve BP network.

Trajectory Control of Scale-Free Dynamical Networks with Exogenous Disturbances

In this paper, the trajectory control of multi-agent dynamical systems with exogenous disturbances is studied. Suppose multiple agents composing of a scale-free network topology, the performance of rejecting disturbances for the low degree node and high degree node is analyzed. Firstly, the consensus of multi-agent systems without disturbances is studied by designing a pinning control strategy on a part of agents, where this pinning control can bring multiple agents＇ states to an expected consensus track. Then, the influence of the disturbances is considered by developing disturbance observers, and disturbance observers based control （DOBC） are developed for disturbances generated by an exogenous system to estimate the disturbances. Asymptotical consensus of the multi-agent systems with disturbances under the composite controller can be achieved for scale-free network topology. Finally, by analyzing examples of multi-agent systems with scale-free network topology and exogenous disturbances, the verities of the results are proved. Under the DOBC with the designed parameters, the trajectory convergence of multi-agent systems is researched by pinning two class of the nodes. We have found that it has more stronger robustness to exogenous disturbances for the high degree node pinned than that of the low degree node pinned.

A QoS-Aware Packet Scheduling Mechanism in Cognitive Radio Networks for Smart Grid Applications

Cognitive radio sensor network is applied to facilitate network monitoring and management, and achieves high spectrum efficiencies in smart grid. However, the conventional traffic scheduling mechanisms are hard to provide guaranteed quality of service for the secondary users. It is because that they ignore the influence of diverse transition requirements in heterogeneous traffi c. Therefore, a novel Qo S-aware packet scheduling mechanism is proposed to improve transmission quality for secondary users. In this mechanism, a Qo S-based prioritization model is established to address data classification firstly. And then, channel quality and the effect of channel switch are integrated into priority-based packet scheduling mechanism. At last, the simulation is implemented with MATLAB and OPNET. The results show that the proposed scheduling mechanism improves the transmission quality of high-priority secondary users and increase the whole system utilization by 10%.