基于误差估值累加开环校正的诱导式欺骗检测方法

诱导式卫星欺骗干扰可诱导航空器逐渐偏离预定航迹，难以被发现，因此及时有效地检测干扰是飞行安全的保障。在现有紧组合导航体制基础上，设计了一种基于误差估值累加开环校正的紧组合导航结构，并证明了其性能与传统闭环校正紧组合导航性能等效。在此结构中，将紧组合导航系统与自适应序贯概率比检测方法结合，提出了一种基于误差估值累加开环校正的诱导式欺骗检测方法，融合紧组合导航信息与其他不受欺骗影响的导航信息，构建欺骗检测统计量进行诱导式欺骗检测。仿真结果表明，开环校正结构可避免随时间累加的惯性导航系统误差所导致的组合导航滤波器发散问题，同时欺骗检测方法可进一步提高算法对“最坏”情形下微小诱导式欺骗的检测效果。     

“数字信号处理”课程的几何视角

“数字信号处理”的经典课程体系以“系统”为中心，以离散时间傅立叶变换、z变换和离散傅立叶变换为分析工具，以滤波器设计为目的，注重定理和公式推导及其性质分析，很难让学生建立一个形象的、完整统一的知识体系框架。本文从几何视角将 “系统”化和“代数”化的经典课程体系重构为面向“信号”的“几何”知识体系，从而提升学生对本质问题的“洞察力”，帮助学生建立系统的知识体系。     

农村综合能源系统多层协同优化运行方法

农村综合能源系统通过多种能源的协同互补,在满足农村用户多元化用能需求的同时,能有效提升能源利用效率和用能经济性。首先,在考虑适应农村典型场景的基础上,提出了一种三层协同自律的农村综合能源分层协同运行优化框架。然后,基于农村综合能源系统典型设备,建立了三层农村综合能源系统源-储-荷联合优化调度模型以及相应的优化调度流程。调度模型中冬季通过对沼气发电机组的余热回收与空气源热泵协同对用户供热;夏季对沼气发电机组余热进行回收,并通过溴化锂制冷机与空气源热泵联合对农村用户供冷。最后,对农村综合能源系统多层协同优化方法进行了算例分析,结果表明该优化方法提高了农村居民用能的经济性,验证了所提方法的有效性。     

基于平均飞行航迹的CCO减噪效用研究

近年来，通过优化飞行程序降低机场飞机噪声影响成为机场环境保护的重要研究方向。文章首先建立了基于飞机“噪声-功率-距离”数据的噪声计算模型，介绍了平均飞行航迹以及连续爬升运行(Continuous Climb Opera-tion, CCO)离场程序的相关理论，最后以大型国际机场为实例，使用飞机平均飞行航迹进行噪声预测，运用综合噪声模型计算出噪声影响面积并绘制噪声影响等值线图，比较了CCO离场相对常规的标准仪表离场(Standard Instru-ment Departure, SID)的降噪效果。结果表明，CCO离场程序可有效降低机场噪声影响，在高噪声级影响区域的降噪效果更佳。     

Synthesis of hollow Fe,Co, and N-doped carbon catalysts from conducting polymer-metal-organic-frameworks core-shell particles for their application in an oxygen reduction reaction

Oxygen reduction reaction (ORR), one of the key reactions for fuel cells and zinc-air batteries, should be improved for higher performance. Herein, we fabricated hollow Fe, Co, and nitrogen co-doped carbon (H-FeCo-NC) catalyst, which was prepared by carbonization of core-shell particles made of polypyrrole (PPy)-coated polystyrene (PS) spheres as cores and (Zn, Co) bimetallic-zeolitic imidazolate frameworks (ZnCoBZIFs) as shells. PPy was used as a nitrogen and a carbon source. The H-FeCo-NC catalyst had a high surface area of 324.08 m² g^?1 with uniformly distributed Fe and Co species, and excellent ORR performance with the half-wave potential of 0.888 V vs. reversible hydrogen electrode in alkaline media. Furthermore, the H-FeCo-NC catalyst demonstrated exceptional stability, durability, and tolerance to methanol crossover.     

Design of frameworks for self-adaptive service-oriented applications: A systematic analysis

Self-adaptive service-oriented Applications (Self-Apps) must be able to understand themselves or the environment in which they are executed, and propose solutions to meet changing conditions. The development of these applications is not a trivial task, since it encompasses issues from different research areas. Despite the importance of frameworks for Self-Apps, there is a lack of comprehensive analysis of how the design of such applications is performed, and regarding the standardization of concepts and coverage of minimum requirements for Self-Apps. The main contribution of this article is to present this comprehensive analysis, providing the state of the art for this subject. This analysis was built through a Systematic Mapping Study, based on a total of 65 studies, from which we identify the main attributes for Quality of Service (QoS), search strategies, and service management strategies employed in the design of frameworks for Self-Apps. The main aspects of requirements involved in the design of Self-Apps were pointed out to stakeholders. For example, these applications must implement a method for evaluation of QoS based on metrics. We also put forward the S-Frame, a modular solution that brings together the main features for the design of Self-Apps, and describe the main challenges concerning these applications.     

Understand Students Feedback Using Bi-Integrated CRF Model Based Target Extraction

Educational institutions showing interest to find the opinion of the students about their course and the instructors to enhance the teaching-learning process. For this, most research uses sentiment analysis to track students’ behavior. Traditional sentence-level sentiment analysis focuses on the whole sentence sentiment. Previous studies show that the sentiments alone are not enough to observe the feeling of the students because different words express different sentiments in a sentence. There is a need to extract the targets in a given sentence which helps to find the sentiment towards those targets. Target extraction is the subtask of targeted sentiment analysis. In this paper, we proposed the innovative model to find the targets of the given sentence using Bi-Integrated Conditional Random Fields (CRF). A Parallel fusion neural network model is designed to perform this task. We evaluate the model using the Michigan dataset and we build a dataset for target extraction from student reviews. The experimental results show that our proposed fusion model achieves better results compared to baseline models.     

Molecular fingerprint and machine learning to accelerate design of high-performance homochiral metal–organic frameworks

Computational screening was employed to calculate the enantioseparation capabilities of 45 functionalized homochiral metal–organic frameworks (FHMOFs), and machine learning (ML) and molecular fingerprint (MF) techniques were used to find new FHMOFs with high performance. With increasing temperature, the enantioselectivities for (R,S)-1,3-dimethyl-1,2-propadiene are improved. The “glove effect” in the chiral pockets was proposed to explain the correlations between the steric effect of functional groups and performance of FHMOFs. Moreover, the neighborhood component analysis and RDKit/MACCS MFs show the highest predictive effect on enantioselectivities among the four ML classification algorithms with nine MFs that were tested. Based on the importance of MF, 85 new FHMOFs were designed, and a newly designed FHMOF, NO₂-NHOH-FHMOF, with high similarity to the optimal MFs achieved improved chiral separation performance, with enantioselectivities of 85%. The design principles and new chiral pockets obtained by ML and MFs could facilitate the development of new materials for chiral separation.