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Image matching is a key step for remotely sensed image registration and digital elevation model (DEM) generation. Compared with point matching, few studies have focused on line matching for images, especially elimination algorithm of mismatched line segments. Therefore, this work systematically studies elimination algorithms of line segment mismatches by combining 2 transformation models (i.e., affine and homography) with 2 M-estimators or 2 sample consensus methods (i.e., random sample consensus, RANSAC, and least median of squares, LMedS). The main idea is as follows. After line segments are extracted and matched, the proposed algorithms can automatically remove mismatched line segments based on an error function of line segment. Aerial images with panchromatic bands and standard false color synthesis were selected for testing. Experiments were performed to compare different combinations of these models and methods and to quantitatively evaluate the performance of the algorithms in terms of accuracy and run time. The results show that the proposed algorithm can be effectively applied to automatically eliminate mismatched line segments, and among all combinations the homography model with LMedS performs the best. The algorithm can also ensure and control the quality of line segment matching from stereo pairs.

Land use and land cover (LULC) mapping is a basic research topic in geography. In deep-learning (DL)-based LULC mapping, there are primarily the following issues: training and testing samples for DL are typically annotated by indoor visual interpretation without field surveys; remotely sensed scene classification based on DL typically lacks fine geometric boundaries for ground objects; historical big data (HBD) (e.g., vector data) are underutilized in DL; studies of large-scale remote sensing mapping (LSRSM) using DL are rare. To solve above issues, this paper proposes an object-oriented (i.e., polygon-based) and DL-based (OODLB) image classification method assisted by HBD for LSRSM to serve for monitoring the soil erosion and water loss (SEWL) in the Yangtze River Basin that includes the following steps: (1) using HBD and OpenStreetMap data, ground objects are vectorized; (2) a remote sensing interpretation key database is established by field surveys and data augmentation; (3) object-oriented (i.e., polygon-based) and Inception-ResNet-V2-based LULC mapping is performed; (4) DL-based classification results are updated by man-machine mutual verification. The experimental results of one county of the Yangtze River Basin show state-of-the-art performance with an overall accuracy of 90.20% in comparison of 75.60% for eCognition. It provides an excellent framework for OODLB large-scene mapping.

The atmospheric pressure measurement is closed related to the aircraft flight and it is the key factor directly affecting the aviation flight safety. With the development of aircraft, the barometric altitude measurement technology is more and more prominent and important. According to the barometric altitude measurement principle and the general barometric altitude measurement methods, four kinds of measurement methods of bellows, vibration cylinder, silicon resonant and silicon piezoresistance are analyzed with the principle and compared from the application.

Agricultural ergonomics employs methods such as Rapid Upper Limb Assessment (RULA) and Rapid Entire Body Assessment (REBA) to assess postural risks. However, these methods may be inaccurate and time-consuming. The objective of this study is to compare the effectiveness of Artificial Intelligence (AI), specifically a software based on MediaPipe, with conventional methods (RULA-REBA) to identify and assess ergonomic risks due to postures in rice agriculture. The methodology employed involved the development of AI software with MediaPipe, which was designed to detect postures in real time. This model was capable of identifying 33 anatomical points, thereby enabling detailed analysis of movement and posture. The results demonstrated that the AI outperformed RULA and REBA in detecting forced postures. Furthermore, it provided faster and more accurate assessments. The findings indicated that AI could be a valuable tool in agricultural ergonomics, potentially outperforming traditional methods. This could significantly improve working conditions and reduce musculoskeletal disorders among farmers.

In order to satisfy the fast, accurate and high reliability of banknote image recognition, a banknote image recognition method based on hidden Markov models (MHH) is proposed in this paper. The grid method is used to extract banknote image features and the process of image preprocessing. By analysing the experimental data, the number of Markov model states, the number of iterations and the number of Gaussian models is finally determined. This method can be used to identify any currency. In this paper, 16000 pieces of RMB were collected by the self-developed banknote cleaner as experimental samples. Experimental results show that the proposed method achieves higher recognition rate than using neural network and support vector function.

Traffic accidents are closely related to the level of drivers’ safe driving skills. Many countries will add the evaluation of drivers’ safe driving quality to the evaluation of driving suitability. Safe driving quality refers to the driver’s own ideological quality, psychological quality and innate physiological quality which have an important impact on the safety of driving work. Including: awareness of compliance with traffic laws and regulations, strong sense of safety responsibility, agility in responding to sudden dangerous situations, whether the ability to judge road conditions is keen, character, personality, emotion, emotion, ability, attention, willpower, self-control, road rage, road rage, etc. At present, many countries in the world will test the safe driving level of drivers. Therefore, this paper combs the safe driving detection technology and methods of typical countries to explore the effective means and methods of safe driving and ensure traffic safety from the source.

Dust storms and wildfires occur frequently in southeastern Australia. Their effects on ecology, environment and population exposure have been the focus of many studies recently. Dust storms do not emit ground-sequestered carbon but wildfires emit significant quantities of carbon into the atmosphere. However, both natural events promote phytoplankton growth in water bodies because carbon, and other trace elements such as iron, deposit on the surface water of oceans and promote phytoplankton growth. Carbon di-oxide is reabsorbed by phytoplankton via photosynthesis. The carbon balance of dust storms and wildfires are not well known.This study focusses on the association of dust storms and wildfires in southeastern Australia with phytoplankton growth in the Southern Ocean and Tasman Sea due to the February 2019 dust storm event and the 2019-2020 black summer wildfires. The results show the similarities and differences in phytoplankton growth patterns and carbon reabsorption amount from these events.

With the continuous development of economic globalization, the trend of strengthening international exchanges and cooperation and introducing high-quality international education resources is becoming more and more obvious. Exploring a reasonable mode of international cooperation in running higher vocational colleges is not only conducive to promoting the development of higher vocational colleges, but also conducive to enhancing the international competitiveness of China’s higher education.

As an ancient historical city, where the Grand Canal meets the Yangtze River, Zhenjiang has more than 100 modern industrial architectural heritages and its scientific protection and utilization has become an important subject in the development and historical renewal of Zhenjiang city. By investigating and analyzing the basic types of industrial architectural heritage in Zhenjiang, this paper discusses the digital protection strategies and puts forward several specific digital protection methods such as heritage information collection, database construction, interactive experience. To promote the industrial architectural heritage in Zhenjiang to produce comprehensive value in the course of social development.

Case teaching method is a common teaching method in teaching. In recent years, the case teaching method is widely applied in the teaching of ideological and political courses, but in the actual operational process, the case teaching also has a series of problems. This paper aims to illustrate the important role of fit cases in improving the teaching effectiveness of ideological and political courses by sorting out various problems emerging when using cases during the teaching of ideological and political courses. In the end, the paper points out how to use the computer software technology and informationbased teaching method to precisely apply the fit cases into the teaching process of ideological and political courses in the background of information-based teaching, to make case teaching alive really and realize the true effectiveness of case teaching in the teaching of ideological and political courses.

In order to solve the problems of insufficient accuracy of long-term power load forecasting and poor applicability of the model, this paper considers the coupling of a number of macro indicators, such as regional economic development and social development indicators, with the time series data of regional power load. BP neural network and Autoregressive integrated moving average model (ARIMA) are used to integrate and improve the forecasting model, so as to improve the trend forecasting ability of annual load forecasting model. The non parametric function method is used to forecast the periodic load data in the monthly load data, the annual load forecast is combined with the monthly load forecast to improve the overall forecasting accuracy of the model. Finally, through the comparison of grey prediction and other models and the verification of MAPE error analysis method, the prediction accuracy of the model method considering the combination of data periodicity and trend is significantly improved, which is suitable for the long-term prediction of regional power load.

Temperature measurement is a very important measurement project in spacecraft vacuum thermal test, and thermocouple measurement system is generally used for temperature measurement. In order to reduce the number of wire measuring circuit and improving the measurement system of anti disturbance and high measurement accuracy in vacuum thermal test process, which can be used in digital temperature measurement system of vacuum thermal test design, realizes the digital temperature measuring equipment of spacecraft and ground equipment. The system is composed of digital temperature sensor DS18B20 and acquisition device. It can be connected with remote monitoring computer through LAN network to realize remote monitoring of temperature. The hardware structure, communication protocol and software design of the digital temperature measurement system are given in this paper. The vacuum thermal environment test and comparison with the platinum resistance temperature measurement system. The results show that the stability of the system is good and the difference between the measured value and the PT100 measurement is within 0.5 °C, and the linearity is about ±0.1% which satisfies the requirement of vacuum thermal test.

In order to compare and analyze Superpave and Marshall design method for asphalt mixture. Through the analysis about gradation selection, predicition asphalt content, the determination method of theoretical maximum density, the compaction methods of specimens, the determination method of optimum asphalt content and the test method of mixture performance of these two design methods. The following conclusions have been come to: Superpave design method is better than Marshall design method for asphalt mixture.