二三维一体化GIS Server在城市信息模型(CIM)中的应用

城市信息模型(CIM)是新型智慧城市的数字基础设施和城市数字化转型的关键抓手,而无缝集成二维地理信息、三维模型和建筑信息模型(BIM)等实现二三维一体化是建设城市信息模型(CIM)基础平台中的关键技术。本文在分析研究CIM平台关键技术基础上提出了一套跨平台、自主化、界面化、B/S网络化的二三维一体化GIS Server解决方案,重点介绍该系统的总体框架及关键技术,并展示了其在城市信息模型(CIM)中的应用成效,为CIM基础平台的建设提供借鉴。

A Multi-Interface Remote Monitoring and Control System Architecture Based on Embedded Server

This paper presents a multi-interface embedded server architecture for remote real-time monitoring system and distributed monitoring applications. In the scheme,an embedded microprocessor( LPC3250 from NXP) is chosen as the CPU of the embedded server with a linux operation system( OS) environment. The embedded server provides multiple interfaces for supporting various application scenarios. The whole network is based on local area network and adopts the Browser / Server( B / S) model. The monitoring and control node is as a browser endpoint and the remote node with an embedded server is as a server endpoint. Users can easily acquire various sensors information through writing Internet protocol address of remote node on the computer browser. Compared with client / server( C / S) mode,B / S model needs less maintain and can be applicable to large user group. In addition,a simple network management protocol( SNMP) is used for management of devices in Internet protocol( IP) networks. The results of the demonstration experiment show that the proposed system gives good support to manage the network from different user terminals and allows the users to better interact with the ambient environment.

基于Multi-Agent的无人机集群体系自主作战系统设计

针对无人集群自主作战体系设计中的关键问题,提出基于Multi-Agent的无人集群自主作战系统设计方法。建立无人集群各节点的Agent模型及其推演规则;对于仿真系统模块化和通用化的需求,设计系统互操作式接口和无人集群自主作战的交互关系;开展无人集群系统仿真推演验证。仿真结果表明,所提设计方案不仅能够有效开展并完成自主作战网络生成-集群演化-效能评估的全过程动态演示验证,而且能够通过重复随机试验进一步评估无人集群的协同作战效能,最后总结了集群协同作战的策略和经验。

AN M/G/1 RETRIAL QUEUE WITH SECOND MULTI-OPTIONAL SERVICE,FEEDBACK AND UNRELIABLE SERVER

An M/G/1 retrial queue with two-phase service and feedback is studied in this paper, where the server is subject to starting failures and breakdowns during service. Primary customers get in the system according to a Poisson process, and they will receive service immediately if the server is available upon arrival. Otherwise, they will enter a retrial orbit and are queued in the orbit in accordance with a first-come-first-served （FCFS） discipline. Customers are allowed to balk and renege at particular times. All customers demand the first “essential” service, whereas only some of them demand the second “multi-optional” service. It is assumed that the retrial time, service time and repair time of the server are all arbitrarily distributed. The necessary and sufficient condition for the system stability is derived. Using a supplementary variable method, the steady-state solutions for some queueing and reliability measures of the system are obtained.

Application Server Aging Model and Multi-Level Rejuvenation Strategy Using Semi-Markov Process

Aiming at the characteristic of the dependency between the application components and the application server platform, a rejuvenation strategy with two different levels of rejuvenation granularities is put forward in this paper including the application component reiuvenation and the application server system rejuvenation. The availability and maintenance cost functions are obtained by means of establishing the application server aging model and the boundary condition of the optimal rejuvenation time is analyzed. Theory analysis indicates that the two-level rejuvenation strategy is superior to the traditional single level one. Finally, evaluation experiments are carried out and numerical result shows that compared with the traditional rejuvenation policy, the rejuvenation strategy proposed in this paper can further increase availability of the application server and reduce maintenance cost.

An efficient hash-based authenticated key agreement scheme for multi-server architecture resilient to key compromise impersonation

During the past decade,rapid advances in wireless communication technologies have made it possible for users to access desired services using hand-held devices.Service providers have hosted multiple servers to ensure seamless online services to end-users.To ensure the security of this online communication,researchers have proposed several multi-server authentication schemes incorporating various cryptographic primitives.Due to the low power and computational capacities of mobile devices,the hash-based multi-server authenticated key agreement schemes with offline Registration Server(RS)are the most efficient choice.Recently,Kumar-Om presented such a scheme and proved its security against all renowned attacks.However,we find that their scheme bears an incorrect login phase,and is unsafe to the trace attack,the Session-Specific Temporary Information Attack(SSTIA),and the Key Compromise Impersonation Attack(KCIA).In fact,all of the existing multi-server authentication schemes(hash-based with offline RS)do not withstand KCLA.To deal with this situation,we propose an improved hash-based multi-server authentication scheme(with offline RS).We analyze the security of the proposed scheme under the random oracle model and use the t4Automated Validation of Internet Security Protocols and Applications''(AVISPA)tool.The comparative analysis of communication overhead and computational complexity metrics shows the efficiency of the proposed scheme.

Weaknesses of a Dynamic ID Based Remote User Authentication Protocol for Multi-Server Environment

Currently, smart card based remote user authentication schemes have been widely adopted due to their low cost and convenient portability. With the purpose of using various different internet services with single registration and to protect the users from being tracked, various dynamic ID based multi-server authentication protocols have been proposed. Recently, Li et al. proposed an efficient and secure dynamic ID based authentication protocol using smart cards. They claimed that their protocol provides strong security. In this paper, we have demonstrated that Li et al.’s protocol is vulnerable to replay attack, denial of service attack, smart card lost attack, eavesdropping attack and server spoofing attacks.

联合物理层与MAC层的multi-TRP上行重叠传输处理机制

针对非理想回程下现有协议难以有效处理多传输接收节点(multi-TRP)场景中多定时提前(multi-TA)导致的严重上行链路(UL)重叠传输问题,联合改进物理层复用技术和介质访问控制(MAC)令牌桶技术,提出了一种新型的UL重叠传输处理机制。该新型机制通过改进物理层重叠信道识别流程、复用要求及复用规则,将物理层复用信息与重叠信息反馈至MAC层,并对MAC层令牌桶技术进行优化。通过仿真实验对所提机制与现有协议机制进行对比,结果表明,在逻辑时隙不可重叠和可重叠2种情形下,物理上行控制信道(PUCCH)实际复用数量性能平均提升了57.58%和49.40%,物理上行共享信道(PUSCH)实际可用资源数量性能平均提升了12.09%和26.03%;优先级最高逻辑信道实际占用资源数量性能平均提升了33.33%和45.48%。

基于Multi-Agent的水电站变压器故障诊断系统

为了精准、快速完成水电站变压器的故障诊断,设计基于Multi-Agent的水电站变压器故障诊断系统。变压器状态监控agent将检测到的变压器故障信息发送给系统管理agent,系统管理agent通过通信agent将变压器故障信息发送给变压器故障诊断agent,变压器故障诊断agent利用小波变换方法提取变压器故障特征,并将其作为IFOA-SVM模型输入,完成变压器故障分类后,获取变压器故障诊断结果,该结果通过通信agent显示给用户。实验表明,该系统可有效诊断变压器故障诊断,诊断成功率受系统故障信息丢失率的影响较小,诊断耗时、耗能小,并具有较高故障诊断成功率。

Block-based logical hierarchical cluster for distributed multimedia architecture on demand server

A structure of logical hierarchical cluster for the distributed multimedia on demand server is proposed. The architecture is mainly composed of the network topology and the resource management of all server nodes. Instead of the physical network hierarchy or the independent management hierarchy, the nodes are organized into a logically hieraxchical cluster according to the multimedia block they caches in the midderware layer. The process of a member joining/leaving or the structure adjustment cooperatively implemented by all members is concerned with decentralized maintenance of the logical cluster hierarchy. As the root of each logically hierarchical cluster is randomly mapped into the system, the logical structure of a multimedia block is dynamically expanded across some regions by the two replication policies in different load state respectively. The local load diversion is applied to fine-tune the load of nodes within a local region but belongs to different logical hierarchies. Guaranteed by the dynamic expansion of a logical structure and the load diversion of a local region, the users always select a closest idle node from the logical hierarchy under the condition of topology integration with resource management.

SQL Server性能优化的技术分析

随着客户端/服务器技术和多层架构的进步,系统效率问题日益引发广泛讨论。由于缺乏数据库管理系统(DBMS)提供的优化方法和工具,以及基于微软技术的数据库管理员和应用程序开发人员的知识,无法对设计和服务执行系统进行优化。文章回顾了提高SQL Server实例性能时需要考虑的目标,并阐述了用于优化查询的技术。

基于Windows Server Failover Cluster的Apache故障转移集群应用实践

在当前互联网时代,Web应用程序的高可用性和可靠性是确保业务持续运行的关键因素。针对Apache Web服务器,介绍了利用Windows Server 2019 Failover Cluster构建Apache故障转移集群的实践方案。通过配置两个节点的故障转移集群,当某个节点发生故障时,Apache服务将自动在另一个节点上重新启动,从而最大程度地减少系统中断的时间,提高Web应用的可用性。详细阐述了集群搭建的具体步骤、配置要点、集群验证方法以及运行时的监控与维护方法,为构建高可用Apache Web服务器提供一种实用的解决方案。

On One Model of Multichannel Queuing System with Unreliable Repairable Servers and Input Memory

The work deals with the development of analytical model of multichannel technical queuing system with unreliable servers and input memory where server failure flows and incoming request flows comply with Poissonian laws, while the flows of failed facilities repairs and flows of incoming requests comply with exponential laws of probability distribution. Random process of system change-over is a Markovian process with continuous time and discrete states. Relations binding basic parameters and output characteristics of the system indicated are obtained as probabilities of system staying in the given moment in one of the possible states. The proposed model is the most generalized compared to some models known in literature which could be considered as special cases of the considered model.

Solving the Multi Observer 3D Visual Area Coverage Scheduling Problem by Decomposition

This paper presents two solution methodologies for the Visual Area Coverage Scheduling problem. The objective is to schedule a number of dynamic observers over a given 3D terrain such that the total visual area covered (viewed) over a planning horizon is maximal. This problem is a more complicated extension of the Set Covering Problem, known to be Np-Hard. We present two decomposition based heuristic methods each containing three stages. The first methodology finds a set of area covering points, and then partitions them into routes (cover first, partition second). The second methodology partitions the area into a region for each observer, and then finds the best covering points and routes (partition first, cover second). In each, a last stage determines dwell (view) times so as to maximize the visible coverage smoothly over the terrain. Comparative tests were made for the two methods on real terrains for several scenarios. When comparing the best solutions of both methods the CF-PS method was slightly better. However, because of the increased computation time we suggest that the PF-CS method with a fine terrain approximation be used. This method is faster as partitioning the terrain into separate regions for each observer results in smaller coverage and routing problems. A sensitivity analysis of the number of observation points to the total number of terrain points covered depicted the classical notion of decreasing returns to scale, increasing in a convex manner as the number of observation points was increased. The best method achieved 100 percent coverage of the terrain by using only 2.7 percent of its points as observation points. Experts stated that the computer based solutions can save precious time and help plan observation missions with satisfying results.

Remote Multimedia Monitoring System Based on Embedded Web Server for Networked Manufacturing

A new-style remote monitoring system is proposed, which is based on enterprises' embedded web servers and can be widely used in enterprises' networked manufacturing systems. The principle and characteristics of remote monitoring system based on embedded web server are analyzed. Such a kind of system for networked manufacturing is designed, and it proves efficient and feasible in promoting communication among enterprises, improving designing and scheduling, decreasing facility failure and reducing product cost.

Multi Location Field Evaluation of BC1F2 Sorghum Populations for Striga Resistance in Niger

In Niger, a landlocked country, sorghum is the second staple food cultivated over the country by smallholder farmer. The crop is important for human and animal consumption. Despite its importance, the crop is affected by biotic and abiotic constraints. Among those constraints, striga has a high impact on yield. In fact, to survive, farmers are growing their local preferred sorghum varieties wish is highly sensible to the weed. Striga management is a challenge that requires a permanent solution. In addition, the development of high-yielding Striga resistant genotypes will be appreciated by farmers. The development of striga resistance will be based on the breeding population performances under farmer’s diverse environmental conditions adaptation. The main objective of this study is to evaluate two breeding populations for striga resistance in two different environments at Boulke and Dibissou in Tahoua region, to identify the early and high-yielding striga tolerant genotypes under natural infestation.

Leader-following formation control of multi-agent networks based on distributed observers

To investigate the leader-following formation control, in this paper we present the design problem of control protocols and distributed observers under which the agents can achieve and maintain the desired formation from any initial states, while the velocity converges to that of the virtual leader whose velocity cannot be measured by agents in real time. The two cases of switching topologies without communication delay and fixed topology with time-varying communication delay are both considered for multi-agent networks. By using the Lyapunov stability theory, the issue of stability is analysed for multi-agent systems with switching topologies. Then, by considering the time-varying communication delay, the sufficient condition is proposed for the multi-agent systems with fixed topology. Finally, two numerical examples are given to illustrate the effectiveness of the proposed leader-following formation control protocols.

A Robust Approach for Multi Classification-Based Intrusion Detection through Stacking Deep Learning Models

Intrusion detection is a predominant task that monitors and protects the network infrastructure.Therefore,many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and detection.In particular,the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)is an extensively used benchmark dataset for evaluating intrusion detection systems(IDSs)as it incorporates various network traffic attacks.It is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models,but the performance of these models often decreases when evaluated on new attacks.This has led to the utilization of deep learning techniques,which have showcased significant potential for processing large datasets and therefore improving detection accuracy.For that reason,this paper focuses on the role of stacking deep learning models,including convolution neural network(CNN)and deep neural network(DNN)for improving the intrusion detection rate of the NSL-KDD dataset.Each base model is trained on the NSL-KDD dataset to extract significant features.Once the base models have been trained,the stacking process proceeds to the second stage,where a simple meta-model has been trained on the predictions generated from the proposed base models.The combination of the predictions allows the meta-model to distinguish different classes of attacks and increase the detection rate.Our experimental evaluations using the NSL-KDD dataset have shown the efficacy of stacking deep learning models for intrusion detection.The performance of the ensemble of base models,combined with the meta-model,exceeds the performance of individual models.Our stacking model has attained an accuracy of 99%and an average F1-score of 93%for the multi-classification scenario.Besides,the training time of the proposed ensemble model is lower than the training time of benchmark techniques,demonstrating its efficiency and robustness.

Performance Modeling and Approximate Analysis of Multiserver Multiqueue Systems with Poisson and Self-similar Arrivals

The problem of state space explosion is still ran outstanding challenge in Markovian performance analysis for multiserver multiqueue (MSMQ) systems. The system behavior of a MSMQ system is described using stochastic high-level Petri net (SHLPN) models, and an approximate performance analysis technique is proposed based on decomposition and refinement methods as well as iteration technique. A real MSMQ system, Web-server cluster, is investigated. The performance of an integrated scheme of request dispatching and scheduling is analyzed with both Poisson and self-similar request arrivals. The study shows that the approximate analysis technique significantly reduces the complexity of the model solution and is also efficient for accuracy of numerical results.

Improving Multiple Sclerosis Disease Prediction Using Hybrid Deep Learning Model

Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis(MS),a chronic autoimmune neurological condition.It disrupts signals between the brain and body,causing symptoms including tiredness,muscle weakness,and difficulty with memory and balance.Traditional methods for detecting MS are less precise and time-consuming,which is a major gap in addressing this problem.This gap has motivated the investigation of new methods to improve MS detection consistency and accuracy.This paper proposed a novel approach named FAD consisting of Deep Neural Network(DNN)fused with an Artificial Neural Network(ANN)to detect MS with more efficiency and accuracy,utilizing regularization and combat over-fitting.We use gene expression data for MS research in the GEO GSE17048 dataset.The dataset is preprocessed by performing encoding,standardization using min-max-scaler,and feature selection using Recursive Feature Elimination with Cross-Validation(RFECV)to optimize and refine the dataset.Meanwhile,for experimenting with the dataset,another deep-learning hybrid model is integrated with different ML models,including Random Forest(RF),Gradient Boosting(GB),XGBoost(XGB),K-Nearest Neighbors(KNN)and Decision Tree(DT).Results reveal that FAD performed exceptionally well on the dataset,which was evident with an accuracy of 96.55%and an F1-score of 96.71%.The use of the proposed FAD approach helps in achieving remarkable results with better accuracy than previous studies.