计算机本科专业软件实习工厂的构建研究

本文针对目前中国计算机专业本科教育中生产实习受客观因素所限,造成学生实验动手和科技创新能力不足的问题,提出利用软件实习工厂进行生产实习的方法,将生产实习投入到一个实际项目的开发与建设上,真实模拟软件公司的生产过程,并探讨其建设的总体设计思想和结构模型。     

Enabling On-Demand Conformal Zn-Ion Batteries on Non-Developable Surfaces

Conventional power sources encounter difficulties in achieving structural unitization with complex-shaped electronic devices because of their fixed form factors. Here, it is realized that an on-demand conformal Zn-ion battery (ZIB) on non-developable surfaces uses direct ink writing (DIW)-based nonplanar 3D printing. First, ZIB component (manganese oxide-based cathode, Zn powder-based anode, and UV-curable gel composite electrolyte) inks are designed to regulate their colloidal interactions to fulfill the rheological requirements of nonplanar 3D printing, and establish bi-percolating ion/electron conduction pathways, thereby enabling geometrical synchronization with non-developable surfaces, and ensuring reliable electrochemical performance. The ZIB component inks are conformally printed on arbitrary curvilinear substrates to produce embodied ZIBs that can be seamlessly integrated with complicated 3D objects (including human ears). The conformal ZIB exhibits a high fill factor (i.e., areal coverage of cells on underlying substrates, ≈100%) that ensures high volumetric energy density (50.5 mWh cm_cell⁻³), which exceeds those of previously-reported shape-adaptable power sources.     

Network Intrusion Detection in Internet of Blended Environment Using Ensemble of Heterogeneous Autoencoders (E-HAE)

Contemporary attackers, mainly motivated by financial gain, consistently devise sophisticated penetration techniques to access important information or data. The growing use of Internet of Things (IoT) technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation, as it facilitates multiple new attack vectors to emerge effortlessly. As such, existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems. To address this problem, we designed a blended threat detection approach, considering the possible impact and dimensionality of new attack surfaces due to the aforementioned convergence. We collectively refer to the convergence of different technology sectors as the internet of blended environment. The proposed approach encompasses an ensemble of heterogeneous probabilistic autoencoders that leverage the corresponding advantages of a convolutional variational autoencoder and long short-term memory variational autoencoder. An extensive experimental analysis conducted on the TON_IoT dataset demonstrated 96.02% detection accuracy. Furthermore, performance of the proposed approach was compared with various single model (autoencoder)-based network intrusion detection approaches: autoencoder, variational autoencoder, convolutional variational autoencoder, and long short-term memory variational autoencoder. The proposed model outperformed all compared models, demonstrating F1-score improvements of 4.99%, 2.25%, 1.92%, and 3.69%, respectively.     

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL) can be employed to identify anonymous intrusions. Therefore, the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection (HGSODL-ID) model for the IIoT environment. The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format. The HGSO algorithm is employed for Feature Selection (HGSO-FS) to reduce the curse of dimensionality. Moreover, Sparrow Search Optimization (SSO) is utilized with a Graph Convolutional Network (GCN) to classify and identify intrusions in the network. Finally, the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model. The proposed HGSODL-ID model was experimentally validated using a benchmark dataset, and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.     

Genetic algorithm-based partial charging schedule of rechargeable sensor networks

The use of rechargeable sensors is a promising solution for wireless sensor networks. On this type of network, mobile charging vehicles (MC) are used for charging sensors using wireless energy transfer (WET) technology. In on-demand charging, a sensor transmits a charging request to the service station, and the MC visits the sensor to transfer energy. The key disadvantages of utilizing MC-based WET are its high energy expenditure rate due to mobility, long service time, and slow charging rate. Because of these reasons, sensors deplete their energy and become dead before the MC reaches the requesting nodes to recharge. We have adapted a genetic algorithm-based partial charging scheme to serve the charging requests. Our objective is to improve the survival ratio of the network. Using comprehensive simulations, we analyze the performance of our proposed method and compare it to two other existing approaches. The simulation results demonstrate that our proposed algorithm improves the survival ratio by up to 20 % by developing a dynamic energy threshold function for transmitting charging requests from the sensors and a partial charging schedule using a genetic algorithm.     

基于无线传感器网络的工厂安防系统设计

工厂具有占地面积大、功能区域复杂、人员流动大的特点.因此对于对安防技术有较高的要求;无线传感器网络是具有感知能力、计算能力、无线通讯能力的传感器节点组成的智能网络,可以很好地监测参数变化,实现复杂的大规模环境监测和目标追踪任务,由此设计一套基于WSN的身份识别的智能安防管理系统;对整个系统的安全要求、设计方法、功能实现、程序流程、软硬件实现方法进行了分析研究,结果表明由无线传感器网络构成的系统可以高效的实现工厂安防任务.     

Design and realization of ad-hoc VoIP with embedded p-SIP server

In this paper we design and implement the pseudo session initiation protocol (p-SIP) server embedded in each mobile node to provide the ad-hoc voice over Internet protocol (VoIP) services. The implemented p-SIP server, being compatible with common VoIP user agents, integrates the standard SIP protocol with SIP presence to handle SIP signaling and discovery mechanism in the ad-hoc VoIP networks. The ad-hoc VoIP signaling and voice traffic performances are analyzed using E-model R rating value for up to six hops in the implemented test-bed. We also conduct the interference experiments to imitate the practical ad-hoc VoIP environment. The analyzed results demonstrate the realization of ad-hoc VoIP services by using p-SIP server.     

基于EtherCAT协议现场级实时以太网控制系统研究

针对工业现场级控制对实时性要求越来越高的问题,提出了采用新一代工业以太网协议标准—EtherCAT协议构建现场级控制系统的方案。为了验证方案的可行性,以海洋环境监测应用为背景设计了多路浪高数据采集与控制系统,并在实验室条件下对包括主站和从站的完整系统进行了测试。实验结果表明,该类系统能够实现响应时间为微秒级的实时数据监控,为现场级以太网高实时性监控的实现提供了可行的解决途径。     

基于社区云的按需个性化流程构建方法研究

针对传统流程模式在互联网应用的局限性,为提高Web服务发现组合能力,提出了社区云的概念,用社区云组织管理Web服务,可以屏蔽服务多样性和多变性带来的复杂性。研究了服务的抽取和社区云服务池的构建方法以及相应的组织和管理机制。在此基础上,构建了一种适用于个性化按需流程生成的云计算支撑平台框架,给出了各个部分的作用及相互间的关系。通过该平台自动生成个性化按需流程,并验证了流程的正确性。     

飞轮通用测试软件平台的设计与实现

针对原有飞轮测试设备复用性差、维护成本高、难以保证多套产品同步自动测试的问题,设计开发了针对多型号飞轮的通用自动化测试系统;通过深入分析,提炼出多个产品在测试过程中功能的一致性和接口的差异性,采用分层架构、抽象工厂的设计模式以及配置文件的方法,解决了测试软件的通用性问题;提出了基于测试流程表的自动测试解决方案,能够适应多种测试流程的要求,扩展性强;采用混合多线程并行处理架构,解决了资源冲突,完善了多台飞轮的并行测试;该测试软件已在多型号飞轮的批产任务中使用,稳定可靠、适应性强,提高了设备使用率三倍以上,大大提升了飞轮批产测试效率。