Nianyu Zou

This paper presents a UAV-based road illumination measurement system and evaluates its performance through experiments. The system employs a HUBSAN Zino 2+ UAV, STM32F103RCT6 microcontroller, BH1750 illuminance sensor, and GPS and integrates flight, processing, measurement, cloud platform, obstacle avoidance, communication, and power supply units via the OneNET cloud platform. Both hardware and software designs were implemented, using the Z-score algorithm to handle outliers in illumination data. The system showed a single-point measurement error rate of 1.14% and a MAPE of 5.08% for multi-point measurements. In experiments, the system’s horizontal and vertical illuminance RMSE were 1.92 lx and 1.75 lx, respectively. The real-time visualization interface improved operational efficiency, cutting labor costs by half and time costs by nearly four-fifths. UAV control and monitoring from the roadside ensured safety during measurements. The system’s efficiency and wide measurement range enabled extended experiments, collecting illuminance data across multiple horizontal and vertical planes. This resulted in the creation of both horizontal and innovative vertical-plane illuminance distribution maps. These findings provide valuable data for evaluating road lighting quality, enhancing road traffic safety, and improving road illumination design.

偏振调控技术能够直接满足偏光显微镜在可见光波段快速、高精度识别检测的需求，其关键是，在提高偏振消光比的同时，具有较高的光利用率。基于等效介质理论和谐振腔的共振增强机理，设计了一种多变量纳米线栅复合结构；采用时域有限差分方法，通过数值仿真，分析了纳米线栅各结构参数和偏振调控的变化关系，能够实现可见光波段宽角度入射、高透过率和高消光比的偏振快速调控功能；结合实验室现有条件，采用纳米压印技术进行样品制备和实验验证。实验和仿真结果表明：在入射角度±35°内，TM模偏振光的透射率和消光比分别高于60%和35 dB；并且在532 nm激光源垂直入射时，TM模偏振光的透射率和消光比与仿真结果误差范围为4%。与现有偏振显微镜设备中偏振器相比，所设计的纳米线栅偏振调控在保证高透过率的同时，消光比提升了1.5倍。

In this paper, an illumination measurement system is proposed and experimentally demonstrated. The system consists of two parts, including the illumination acquisition module mounted on the UAV and the real-time display interface of the cloud platform with control functions. The illuminance acquisition module consists of a light sensor, a distance sensor, a wireless communication module, and a power-supply component. For the other part, the OneNET cloud platform developed by China Mobile is chosen as the display interface for the system’s real-time data. In addition, the local-outlier factor (LOF) algorithm is used for outlier rejection to further improve the stability and accuracy of the system. The illuminance acquisition system designed and implemented was then applied to the illuminance measurement experiments in the stadium. In experiments, the illuminance data acquired by the system was used to investigate the illuminance distribution at different height levels in the stadium. It was learned that the root mean square error (RMSE) of the data acquired is calculated to be 2.45 compared to the illuminance standard values through experimentation, with an error range of −2.77% to −0.53% for dynamic measurements.

In order to solve the problem that students have insufficient understanding of the full-field goniophotometer instrumentation due to the expensive equipment and the high requirements of the experimental environment, virtual reality technology is used to complete the virtual laboratory of a light intensity distribution measurement of the full-field goniophotometer through Unity 3D. The virtual laboratory is designed for near-field measurement, mid-field measurement, and far-field measurement of light intensity. It combines 3D MAX and C# to realize the model building of laboratory, luminaire and full-field goniophotometer, the dynamic drawing of light distribution curves, and the simulation system can also be run on mobile phones. The experimental process is mainly through the manipulation of the virtual experimenter and each module to generate collision detection, pop-up buttons, and click on the button triggers an event and the operation prompt page appears. The light distribution curve is generated dynamically by reading and processing the IES file data to generate 3D coordinates. This virtual simulation laboratory is demonstrated on multiple platforms. The results proved that the virtual simulation restored the experimental process vividly and can complete the experimental teaching remotely.

With the rapid development of urbanization, ambient air pollution is becoming increasingly serious. Out of many pollutants, fine particulate matter (PM_2.5) is the pollutant that affects the urban atmospheric environment to the greatest extent. Fine particulate matter (PM_2.5) concentration prediction is of great significance to human health and environmental protection. This paper proposes a CNN-SSA-DBiLSTM-attention deep learning fusion model. This paper took the meteorological observation data and pollutant data from eight stations in Bijie from 1 January 2015 to 31 December 2022 as the sample data for training and testing. For the obtained data, the missing values and the data obtained from the correlation analysis performed were first processed. Secondly, a convolutional neural network (CNN) was used for the feature selection. DBILSTM was then used to establish a network model for the relationship between the input and actual output sequences, and an attention mechanism was added to enhance the impact of the relevant information. The number of units in the DBILSTM and the epoch of the whole network were optimized using the sparrow search algorithm (SSA), and the predicted value was the output after optimization. This paper predicts the concentration of PM_2.5 in different time spans and seasons, and makes a comparison with the CNN-DBILSTM, BILSTM, and LSTM models. The results show that the CNN-SSA-DBiLSTM-attention model had the best prediction effect, and its accuracy improved with the increasing prediction time span. The coefficient of determination (R²) is stable at about 0.95. The results revealed that the proposed CNN-SSA-DBiLSTM-attention ensemble framework is a reliable and accurate method, and verifies the research results of this paper in regard to the prediction of PM_2.5 concentration. This research has important implications for human health and environmental protection. The proposed method could inspire researchers to develop even more effective methods for atmospheric environment pollution modeling.

Stadium illumination is an important standard of the light environment when stadiums host major sporting events and broadcast sports. The rules of stadium illumination are strict and complex, and there are wide measuring ranges and variable measurement environments in different sports events. For fast and accurate acquisition of stadium illumination in different environments, in this paper, a micro control module for collecting illumination has been designed which is based on the STM32F103RCT6, and in the following work, the module is combined with the UAV because of the convenience of the function of the fight. According to the optical flow positioning function of UAV, the position information can be shown on the mobile application. We set up simulated experimental scenarios for illumination acquisition, and the system can transmit data to the OneNET cloud platform in 5-second intervals verified by experiments. Besides, changes in illumination data can be displayed in real-time on the cloud platform. Meanwhile, the Least Square Method is used as an algorithm for data processing to eliminate errors.

The predecessor of China Optics was China Optics and Applied Optics Digest, which was founded in 1985. At that time, it was the only retrieval journal in the field of optics in China. At the end of 2008, China Optics and Applied Optics Digest was renamed 《中国光学》的前身是1985年创办的《中国光学与应用光学文摘》，当时是国内光学领域唯一的检索刊物。2008年底《中国光学与应用光学文摘》转型更名为《中国光学与应用光学》期刊，刊载内容突破了文摘的限制，正式成为学术刊物。