A 2.20 eV Bandgap Polymer Donor for Efficient Colorful Semitransparent Organic Solar Cells

Semitransparent organic solar cells (ST-OSCs) have attracted increasing attention due to their promising prospect in building-integrated photovoltaics. Generally, efficient ST-OSCs with good average visible transmittance (AVT) can be realized by developing active layer materials with light absorption far from the visible light range. Herein, the development of ultrawide bandgap polymer donors with near-ultraviolet absorption, paired with near-infrared acceptors, is proposed to achieve high-performance ST-OSCs. The key points for the design of ultrawide bandgap polymers include constructing donor–donor type conjugated skeleton, suppressing the quinoidal resonance effect, and minimizing the twist of conjugated skeleton via noncovalent conformational locks. As a proof of concept, a polymer named PBOF with an optical bandgap of 2.20 eV is synthesized, which exhibited largely reduced overlap with the human eye photopic response spectrum and afforded a power conversion efficiency (PCE) of 16.40% in opaque device. As a result, ST-OSCs with a PCE over 10% and an AVT over 30% are achieved without optical modulation. Moreover, colorful ST-OSCs with visual aesthetics can be achieved by tuning the donor/acceptor weight ratio in active layer benefiting from the ultrawide bandgap nature of PBOF. This study demonstrates the great potential of ultrawide bandgap polymers for efficient colorful ST-OSCs.     

Design of Online Vitals Monitor by Integrating Big Data and IoT

In this work, we design a multisensory IoT-based online vitals monitor (hereinafter referred to as the VITALS) to sense four bedside physiological parameters including pulse (heart) rate, body temperature, blood pressure, and peripheral oxygen saturation. Then, the proposed system constantly transfers these signals to the analytics system which aids in enhancing diagnostics at an earlier stage as well as monitoring after recovery. The core hardware of the VITALS includes commercial off-the-shelf sensing devices/medical equipment, a powerful microcontroller, a reliable wireless communication module, and a big data analytics system. It extracts human vital signs in a pre-programmed interval of 30 min and sends them to big data analytics system through the WiFi module for further analysis. We use Apache Kafka (to gather live data streams from connected sensors), Apache Spark (to categorize the patient vitals and notify the medical professionals while identifying abnormalities in physiological parameters), Hadoop Distributed File System (HDFS) (to archive data streams for further analysis and long-term storage), Spark SQL, Hive and Matplotlib (to support caregivers to access/visualize appropriate information from collected data streams and to explore/understand the health status of the individuals). In addition, we develop a mobile application to send statistical graphs to doctors and patients to enable them to monitor health conditions remotely. Our proposed system is implemented on three patients for 7 days to check the effectiveness of sensing, data processing, and data transmission mechanisms. To validate the system accuracy, we compare the data values collected from established sensors with the measured readouts using a commercial healthcare monitor, the Welch Allyn® Spot Check. Our proposed system provides improved care solutions, especially for those whose access to care services is limited.     

An Efficient Framework for Utilizing Underloaded Servers in Compute Cloud

In cloud data centers, the consolidation of workload is one of the phases during which the available hosts are allocated tasks. This phenomenon ensures that the least possible number of hosts is used without compromise in meeting the Service Level Agreement (SLA). To consolidate the workloads, the hosts are segregated into three categories: normal hosts, under-loaded hosts, and overloaded hosts based on their utilization. It is to be noted that the identification of an extensively used host or underloaded host is challenging to accomplish. Threshold values were proposed in the literature to detect this scenario. The current study aims to improve the existing methods that choose the underloaded hosts, get rid of Virtual Machines (VMs) from them, and finally place them in some other hosts. The researcher proposes a Host Resource Utilization Aware (HRUAA) Algorithm to detect those underloaded and place its virtual machines on different hosts in a vibrant Cloud environment. The mechanism presented in this study is contrasted with existing mechanisms empirically. The results attained from the study establish that numerous hosts can be shut down, while at the same time, the user's workload requirement can also be met. The proposed method is energy-efficient in workload consolidation, saves cost and time, and leverages active hosts.     

基于DCNN和数据增强的固体发动机烧蚀预示方法

针对固体发动机烧蚀率的预示,现有传统建模方法存在复杂度高、计算需求大、试验数据少、样本不平衡等问题,提出了一种基于深度卷积神经网络和数据增强的固体发动机烧蚀率预示方法。将传感器数据处理为长度相同、特征相近的序列数据,并使用自适应高斯噪声和随机漂移这2种数据增强方法扩充数据样本,再将扩充后的试验样本和伪样本作为深度卷积神经网络的输入进行训练,将训练得到的模型与传统方法计算得到的烧蚀率预示值进行对比。结果表明,该方法下烧蚀率预示值误差低至0.013 5 m/s,预示精度可达95%。     

Study on reaction time depending on display parameters of gaming displays

Display technology plays a significant role in gaming and, thus, display parameters are an important consideration when choosing a gaming display. In general, interest in display parameters such as contrast ratio, response time, refresh rate, and input lag in gaming displays is very high. In this article, we studied the reaction time of gaming displays, as a measure of game performance, with respect to different contrast ratios, response times, and refresh rates. Contrast ratio and response time turned out to be the key factors influencing reaction time. The higher the contrast ratio is, the shorter the reaction time tends to be. In addition, the faster the response time is, the shorter the reaction time is. Displays with high contrast ratio and fast response time are advantageous in terms of reaction time, which is also expected to be more advantageous in a fast-paced real gaming environment.     

航空发动机加工现场生产制造网络化协同系统

为解决航空发动机加工现场存在的物料管理手段缺乏、物料无法拉动、生产要素采集少、可视化手段少等问题,本文实现了一套面向航空发动机生产制造过程的网络化协同环境,对生产过程关键要素数据的采集、追踪、分析和处理,通过物料拉动配送系统、生产要素采集系统、可视化看板系统,实现物料线边拉动优化、数据展示端业务报表优化、生产关键要素信息采集、制造端数据集成等内容。     

基于SSA-SVM的网络入侵检测研究

互联网快速发展使得网络空间越来越复杂,网络入侵导致网络安全问题备受关注。为提升网络入侵的检测效率和精度,构建了基于支持向量机的网络入侵检测模型。支持向量机模型的惩罚系数和核函数参数直接影响入侵模型的检测精度,采用麻雀搜索算法对惩罚系数和核函数参数进行优化,提出了基于麻雀搜索算法和支持向量机的网络入侵检测模型。将提出的网络入侵检测模型应用于实际的网络入侵检测中,并与PSO-SVM和SVM模型进行对比。结果表明,所提出的网络入侵检测模型能够有效降低网络入侵的误报率,这对确保网络安全具有一定的现实意义。     

含氢储能的多源联合发电系统调度研究

风电和光伏发电具有间歇性和随机性,为了降低在多源联合发电系统中的弃风弃光率,采用含氢储能系统和火电机组配合来平滑风电和光电机组出力。文中以系统运行成本最小和弃电惩罚成本最小为目标,以系统功率平衡、火电机组出力和爬坡、热备用、风电和光电出力及储能系统储氢罐容量、电解槽和燃料电池功率等为约束条件构建了多源联合发电系统日前调度模型。通过YALMIP工具箱对模型进行编程,并调用CPLEX对编写的程序进行求解。对含有风电、光电、火电机组以及储能系统的多源联合发电系统进行算例分析,通过对比有无储能系统的弃风弃光量和系统总运行成本,证明了含氢储能系统可以有效降低系统的弃风弃光率,并提高系统的经济性。     

Short packet communication in underlay cognitive network assisted by an intelligent reflecting surface

We propose short packet communication in an underlay cognitive radio network assisted by an intelligent reflecting surface (IRS) composed of multiple reconfigurable reflectors. This scheme, called the IRS protocol, operates in only one time slot (TS) using the IRS. The IRS adjusts its phases to give zero received cumulative phase at the secondary destination, thereby enhancing the end-to-end signal-to-noise ratio. The transmitting power of the secondary source is optimized to simultaneously satisfy the multi-interference constraints, hardware limitations, and performance improvement. Simulation and analysis results of the average block error rates (BLERs) show that the performance can be enhanced by installing more reconfigurable reflectors, increasing the blocklength, lowering the number of required primary receivers, or sending fewer information bits. Moreover, the proposed IRS protocol always outperforms underlay relaying protocols using two TSs for data transmission, and achieves the best average BLER at identical transmission distances between the secondary source and secondary destination. The theoretical analyses are confirmed by Monte Carlo simulations.     

计及火电阶梯式爬坡率的耦合系统优化调度方法

北方地区普遍存在可再生能源和火力发电通过同一并网点接入电网的耦合方式。在该方式下,火电和可再生能源可作为一个整体共同参与调峰辅助服务,进一步挖掘两者的协同调度潜力。该文首先针对该类耦合方式,对耦合系统的定义进行研究,并分析耦合系统的特征和潜力;其次,根据火电机组深度调峰的运行特性,提出计及阶梯式爬坡率的火电机组运行模型,并在此基础上建立耦合系统优化调度模型;最后,以辽宁某局域电网为例的仿真结果表明,计及阶梯式爬坡率的火电机组运行模型能更精确地反映深度调峰时机组的实际运行状态,且耦合系统在现有政策下可进一步提升可再生能源和火力发电的整体控制性能和经济效益。