Efficient Deep Learning Framework for Fire Detection in Complex Surveillance Environment

To prevent economic, social, and ecological damage, fire detection and management at an early stage are significant yet challenging. Although computationally complex networks have been developed, attention has been largely focused on improving accuracy, rather than focusing on real-time fire detection. Hence, in this study, the authors present an efficient fire detection framework termed E-FireNet for real-time detection in a complex surveillance environment. The proposed model architecture is inspired by the VGG16 network, with significant modifications including the entire removal of Block-5 and tweaking of the convolutional layers of Block-4. This results in higher performance with a reduced number of parameters and inference time. Moreover, smaller convolutional kernels are utilized, which are particularly designed to obtain the optimal details from input images, with numerous channels to assist in feature discrimination. In E-FireNet, three steps are involved: preprocessing of collected data, detection of fires using the proposed technique, and, if there is a fire, alarms are generated and transmitted to law enforcement, healthcare, and management departments. Moreover, E-FireNet achieves 0.98 accuracy, 1 precision, 0.99 recall, and 0.99 F1-score. A comprehensive investigation of various Convolutional Neural Network (CNN) models is conducted using the newly created Fire Surveillance SV-Fire dataset. The empirical results and comparison of numerous parameters establish that the proposed model shows convincing performance in terms of accuracy, model size, and execution time.     

Drone-based AI/IoT Framework for Monitoring,Tracking and Fighting Pandemics

Since World Health Organization (WHO) has declared the Coronavirus disease (COVID-19) a global pandemic, the world has changed. All life's fields and daily habits have moved to adapt to this new situation. According to WHO, the probability of such virus pandemics in the future is high, and recommends preparing for worse situations. To this end, this work provides a framework for monitoring, tracking, and fighting COVID-19 and future pandemics. The proposed framework deploys unmanned aerial vehicles (UAVs), e.g.; quadcopter and drone, integrated with artificial intelligence (AI) and Internet of Things (IoT) to monitor and fight COVID-19. It consists of two main systems; AI/IoT for COVID-19 monitoring and drone-based IoT system for sterilizing. The two systems are integrated with the IoT paradigm and the developed algorithms are implemented on distributed fog units connected to the IoT network and controlled by software-defined networking (SDN). The proposed work is built based on a thermal camera mounted in a face-shield, or on a helmet that can be used by people during pandemics. The detected images, thermal images, are processed by the developed AI algorithm that is built based on the convolutional neural network (CNN). The drone system can be called, by the IoT system connected to the helmet, once infected cases are detected. The drone is used for sterilizing the area that contains multiple infected people. The proposed framework employs a single centralized SDN controller to control the network operations. The developed system is experimentally evaluated, and the results are introduced. Results indicate that the developed framework provides a novel, efficient scheme for monitoring and fighting COVID-19 and other future pandemics.     

基于DIS的防空训练空情保障系统

该文以新三打三防训练模拟为背景，建立了多种虚拟靶标，如巡航导弹、作战飞机、武装直升机的仿真模型，包括物理模型和决策模型，在此基础上，基于分布式交互仿真技术（Distributed Interactive Simulation,DIS）开发了一个用于防空训练模拟的空情保障系统。本文主要介绍了空情保障系统的模型建立、数据管理和系统集成方法。     

需求可拆分的无人机与卡车协同路径优化问题

基于无人机配送优势,提出一类无人机与卡车分区域协同配送问题。考虑需求稀疏地区由无人机配送,需求密集地区由卡车与无人机协同配送的情形。由于无人机的载重能力较小,无人机访问的顾客需求允许被拆分。结合无人机与卡车的访问路径关系,以最小化运输成本和使用卡车的人力成本为目标建立混合整数规划模型,并设计一种改进变邻域搜索算法对问题进行求解。小规模算例的特性分析表明,运输成本对无人机的载重能力及单位运输成本敏感性较高。多个不同规模的算例测试表明,改进变邻域搜索算法能在较短时间内有效求解各规模算例,且算法稳定性较好。     

无人机航拍辅助数字乡村微网站的设计与实现

构建数字乡村是弥合城乡差距的一项有效措施,是实现农村信息化、现代化的必要措施,是实现乡村振兴战略全面建设小康社会的重要组成部分.本文采用无人机航拍获取正射影像及其经纬度信息,在利用Photoscan与ArcMap等软件进行拼接并制作出正射实景地图的基础上,构建分辨率达到厘米级的乡村数字地图,并进一步搭建集地理信息与数字资源于一体的数字乡村服务平台.以福建省东山县樟塘村为例,探索此类数字乡村综合服务平台的构建方案,并开发了可通过微信、支付宝、浏览器等移动端程序扫码访问的"数字樟塘"微网站.     

玻璃钢蒙皮/聚氨酯泡沫塑料夹芯结构无人机机翼制造（一）工艺方案设计

某型号无人机机翼为内部结构复杂的玻璃钢蒙皮／全容腔填充聚氨酯泡沫塑料夹芯结构,本文介绍了一种采用灌注发泡法制造该机翼的工艺技术,采用该技术制造的杨翼已经过了飞行试验并通过了系统设计定型。     

基于DSP28335的某型靶机系统设计

针对某大型军用无人靶机的实际需求,根据飞行控制相关原理,进行了该靶机的控制系统设计。描述了靶机飞控系统的硬件组成结构,基于DSPF28335处理器,详细分析设计了各组件与飞控计算机的信号输入输出方式。仿真实验表明,该靶机飞控系统控制策略有效,且各机载子系统与飞控计算机能够协调工作。     

“天地一体化”技术在安徽输变电工程环水保监管核查中可靠性分析

输变电工程具有空间距离长、扰动区域分散等特点,而传统环水保监管手段受人力物力限制,已无法满足工程需要,急需引入一种新的监管手段加强环水保监管工作。本文基于卫星遥感、无人机及现场核查特点,将"天地一体化"技术应用于输变电工程。结果表明,"天地一体化"技术可靠,可有效获取工程线路环水保信息,包括施工扰动范围、施工便道长宽、环水保措施等,可提高监管能力,减少人员投入和经济损失,起到减员增效的作用。     

一种基于服务质量的多UAV容量优化方法

随着近些年来通信领域各种技术的高速发展,UAV因为其体积小、灵活性强、可快速部署等优点,越来越受到通信领域相关学者的青睐。同时,由于UAV在各个领域的应用逐渐增多,UAV网络的容量优化问题变得越来越重要。针对未来大规模用户的通信需求,提出了一种基于服务质量的多UAV容量优化方法。首先将服务质量设置为权重参数,联合用户调度、轨迹和功率分配,利用连续凸优化技术和块坐标下降算法,提出多UAV通信系统中的的容量优化算法。仿真结果证明该算法的收敛性,同时表明在相同条件下基于用户质量的多UAV容量算法能够使系统达到更高的平均传输速率。     

机载光电探测系统红外性能测试靶板的研究

为了对机载光电探测系统外场性能进行定量评价,设计了野外红外性能测试靶板,论述了该靶板的设计、原理、结构、标定和应用,利用该靶板可以开展不同气象条件下系统的作用距离、探测灵敏度、空间分辨率、温度分辨率、点目标探测等性能测试研究。