Research and Analysis of Grammatical Error Correction Technology for Chinese Documents

With the widespread use of Chinese globally, the number of Chinese learners has been increasing, leading to various grammatical errors among beginners. Additionally, as domestic efforts to develop industrial information grow, electronic documents have also proliferated. When dealing with numerous electronic documents and texts written by Chinese beginners, manually written texts often contain hidden grammatical errors, posing a significant challenge to traditional manual proofreading. Correcting these grammatical errors is crucial to ensure fluency and readability. However, certain special types of text grammar or logical errors can have a huge impact, and manually proofreading a large number of texts individually is clearly impractical. Consequently, research on text error correction techniques has garnered significant attention in recent years. The advent and advancement of deep learning have paved the way for sequence-to-sequence learning methods to be extensively applied to the task of text error correction. This paper presents a comprehensive analysis of Chinese text grammar error correction technology, elaborates on its current research status, discusses existing problems, proposes preliminary solutions, and conducts experiments using judicial documents as an example. The aim is to provide a feasible research approach for Chinese text error correction technology.

Research on information technology of state monitoring and fault prediction for mechatronics system

The safety and reliability of mechatronics systems,particularly the high-end,large and key mechatronics equipment in service,can strongly influence on production efficiency,personnel safety,resources and environment.Based on the demands of development of modern industries and technologies such as international industry 4.0,Made-in-China 2025 and Internet + and so on,this paper started from revealing the regularity of evolution of running state of equipment and the methods of signal processing of low signal noise ratio,proposed the key information technology of state monitoring and earlyfault-warning for equipment,put forward the typical technical line and major technical content,introduced the application of the technology to realize modern predictive maintenance of equipment and introduced the development of relevant safety monitoring instruments.The technology will play an important role in ensuring the safety of equipment in service,preventing accidents and realizing scientific maintenance.

A Novel Method for Cross-Subject Human Activity Recognition with Wearable Sensors

Human Activity Recognition (HAR) is an important way for lower limb exoskeleton robots to implement human-computer collaboration with users. Most of the existing methods in this field focus on a simple scenario recognizing activities for specific users, which does not consider the individual differences among users and cannot adapt to new users. In order to improve the generalization ability of HAR model, this paper proposes a novel method that combines the theories in transfer learning and active learning to mitigate the cross-subject issue, so that it can enable lower limb exoskeleton robots being used in more complex scenarios. First, a neural network based on convolutional neural networks (CNN) is designed, which can extract temporal and spatial features from sensor signals collected from different parts of human body. It can recognize human activities with high accuracy after trained by labeled data. Second, in order to improve the cross-subject adaptation ability of the pre-trained model, we design a cross-subject HAR algorithm based on sparse interrogation and label propagation. Through leave-one-subject-out validation on two widely-used public datasets with existing methods, our method achieves average accuracies of 91.77% on DSAD and 80.97% on PAMAP2, respectively. The experimental results demonstrate the potential of implementing cross-subject HAR for lower limb exoskeleton robots.

A Privacy Preserving Federated Learning System for IoT Devices Using Blockchain and Optimization

In this study, a blockchain based federated learning system using an enhanced weighted mean vector optimization algorithm, known as EINFO, is proposed. The proposed EINFO addresses the limitations of federated averaging during global update and model training, where data is unevenly distributed among devices and there are variations in the number of data samples. Using a well-defined structure and updating the vector positions by local searching, vector combining, and updating rules, the EINFO algorithm maximizes the shared model parameters. In order to increase the exploration and exploitation capabilities, the model convergence rate is improved and new vectors are generated through the use of a weighted mean vector based on the inverse square law. To choose validators, miners, and to propagate new blocks, a delegated proof of stake based on the reliability of blockchain nodes is suggested. Federated learning is included into the blockchain to protect nodes from both external and internal threats. To determine how well the suggested system performs in relation to current models in the literature, extensive simulations are run. The simulation results show that the proposed system outperforms existing schemes in terms of accuracy, sensitivity and specificity.

Analysis of the Drought Impact as an Extreme Climatic Event in Sudan

This study aimed to investigate drought as an extreme climatic event in Sudan by utilizing the Anomaly and Standard Precipitation Index (SPI). This research employed high-resolution Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data to analyze the temporal and spatial distribution of rainfall in Sudan from 2001 to 2015. Notably, the analysis compared the annual average precipitation to specific drought events and calculated the Standardized Precipitation Index (SPI). The results indicate a striking frequency of drought occurrence in Sudan, with SPI values suggesting similarities between drought and wet years. It is recommended to compute the SPI for individual stations rather than rely solely on area averages. Despite the comparable SPI values between the drought and wet years, this study emphasizes the profound impact of drought as a recurring phenomenon in Sudan. These findings emphasize the urgent need for proactive drought management and mitigation strategies in Sudan to effectively address the consequences of this climatic challenge.

Spatio-Temporal Variability of Rainfall and Temperature and Their Effects on Pasture Variability over East Africa: Implication on the Cattle Grazing Areas

Understanding the spatiotemporal variability of climatic parameters and their effects on pasture variability is vital for pasture management interventions over East Africa. The present study aims to assess the spatial-temporal variability of rainfall, temperature and Normalized Difference Vegetation Index (NDVI) (which is being used to assess pasture quality and productivity) over the region, between the period of 1982 and 2019. This study used annual mean values for rainfall, temperature and NDVI which were calculated for the period mentioned above. NDVI was derived from National Oceanic and Atmospheric Administration (NOAA) Global Area Cover (GAC) (NOAA-07-GAC) data. The rainfall data was acquired from the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) while temperature is ERA5 reanalysis data sourced from the European Centre for Medium-Range Weather Forecasts (ECMWF). The study utilized the empirical orthogonal function (EOF) to identify patterns and dominant relationships between the climate variables. The correlation was calculated between rainfall, temperature and NDVI to assess the relationship among them. A non-parametric Mann-Kendall trends test was used to determine whether annual precipitation, temperature and NDVI had statistically increasing or decreasing trend. Results revealed a positive correlation between rainfall and NDVI while a negative correlation between NDVI and temperature. Positive correlation between rainfall and NDVI indicates that pasture health (quality and productivity), will improve accordingly. A negative correlation between temperature and NDVI indicates that pasture health will decrease with increase in temperature while improving with decreasing temperature. Outcome from this study suggests that changes in climatic variables influence the distribution of pasture in East Africa’s cattle grazing areas. The study hence recommends prioritisation of climatic (weather) information during pasture management over East Africa.

CPG Human Motion Phase Recognition Algorithm for a Hip Exoskeleton with VSA Actuator

Due to the dynamic stiffness characteristics of human joints, it is easy to cause impact and disturbance on normal movements during exoskeleton assistance. This not only brings strict requirements for exoskeleton control design, but also makes it difficult to improve assistive level. The Variable Stiffness Actuator (VSA), as a physical variable stiffness mechanism, has the characteristics of dynamic stiffness adjustment and high stiffness control bandwidth, which is in line with the stiffness matching experiment. However, there are still few works exploring the assistive human stiffness matching experiment based on VSA. Therefore, this paper designs a hip exoskeleton based on VSA actuator and studies CPG human motion phase recognition algorithm. Firstly, this paper puts forward the requirements of variable stiffness experimental design and the output torque and variable stiffness dynamic response standards based on human lower limb motion parameters. Plate springs are used as elastic elements to establish the mechanical principle of variable stiffness, and a small variable stiffness actuator is designed based on the plate spring. Then the corresponding theoretical dynamic model is established and analyzed. Starting from the CPG phase recognition algorithm, this paper uses perturbation theory to expand the first-order CPG unit, obtains the phase convergence equation and verifies the phase convergence when using hip joint angle as the input signal with the same frequency, and then expands the second-order CPG unit under the premise of circular limit cycle and analyzes the frequency convergence criterion. Afterwards, this paper extracts the plate spring modal from Abaqus and generates the neutral file of the flexible body model to import into Adams, and conducts torque-stiffness one-way loading and reciprocating loading experiments on the variable stiffness mechanism. After that, Simulink is used to verify the validity of the criterion. Finally, based on the above criterions, the signal mean value is removed using feedback structure to complete the phase recognition algorithm for the human hip joint angle signal, and the convergence is verified using actual human walking data on flat ground.

Assessing the Relationship between October-November-December Rainfall and Indian Ocean Dipole in Recent Decades over Tanzania Following the 2011 Abrupt Change

The present study explored how the Indian Ocean Dipole (IOD) influences October-November-December (OND) rainfall over Tanzania in recent decade following the 2011 abrupt change. The study spans 50 years, from 1973 to 2022. Notable abrupt changes were observed in 1976 and 2011, leading us to divide our study into two periods: 1976-2010 and 2011-2022, allowing for a close investigation into the existing relationship between OND IOD and OND rainfall and their associated large-scale atmospheric circulations. It was found that the relationship between OND IOD and OND rainfall strengthened, with the correlation changed from +0.73 during 1976-2010 to +0.81 during 2011-2022. Further investigation revealed that, during 1976-2010, areas that received above- normal rainfall during positive IOD experienced below-normal during 2011- 2022 and vice versa. The same pattern relationship was observed for negative IOD. Spatial analysis demonstrates that the percentage departure of rainfall across the region mirrors the standardized rainfall anomalies. The study highlights that the changing relationship between OND IOD and OND rainfall corresponds to the east-west shift of Walker circulation, as well as the north-south shift of Hadley circulation. Analysis of sea surface temperature (SST) indicates that both positive and negative IOD events strengthened during 2011-2022 compared to 1976-2010. Close monitoring of this relationship across different timescales could be useful for updating OND rainfall seasonal forecasts in Tanzania, serving as a tool for reducing socio-economic impacts.

Spatial Variation and Trend of Extreme Precipitation in Africa during 1981-2019 and Its Projected Changes at the End of 21st Century

This study comprehensively examines the patterns and regional variation of severe rainfall across the African continent, employing a suite of eight extreme precipitation indices. The analysis extends to the assessment of projected changes in precipitation extremes using five General Circulation Models (GCMs) from Coupled Model Intercomparison Project Phase 6 (CMIP6) under four Shared Socioeconomic Pathways (SSPs) scenarios at the long-term period (2081-2100) of the 21st century. Furthermore, the study investigates potential mechanisms influencing precipitation extremes by correlating extreme precipitation indices with oceanic system indices, specifically Ni?o 3.4 for El Ni?o-Southern Oscillation (ENSO) and Dipole Mode Index (DMI) for the Indian Ocean Dipole (IOD). The findings revealed distinct spatial distributions in mean trends of extreme precipitation indices, indicating a tendency toward decreased extreme precipitation in North Africa, Sahel region, Central Africa and the Western part of South Africa. Conversely, West Africa, East Africa and the Eastern part of South Africa exhibit an inclination toward increased extreme precipitation. The changes in precipitation extreme indices indicate a general rise in both the severity and occurrence of extreme precipitation events under all scenarios by the end of the 21st century. Notably, our analysis projects a decrease in consecutive wet days (CWD) in the far-future. Additionally, correlation analysis highlights significant correlation between above or below threshold rainfall fluctuation in East Africa and South Africa with oceanic systems, particularly ENSO and the IOD. Central Africa abnormal precipitation variability is also linked to ENSO with a significant negative correlation. These insights contribute valuable information for understanding and projecting the dynamics of precipitation extreme in Africa, providing a foundation for climate adaptation and mitigation efforts in the region.

Evaluation of Mid-Depth Currents of NCEP Reanalysis Data in the Tropical Pacific Using ARGO Float Position Information

The global project of the Array for Real-time Geostrophic Oceanography （ARGO） provides a unique opportunity to observe the absolute velocity in mid-depths of the world oceans. A total of 1597 velocity vectors at 1000 （2000） db in the tropical Pacific derived from the ARGO float position information during the period November 2001 to October 2004 are used to evaluate the intermediate currents of the National Centers for Environmental Prediction reanalysis. To derive reliable velocity information from ARGO float trajectory points, a rigorous quality control scheme is applied, and by virtue of a correction method for reducing the drift error on the surface in obtaining the velocity vectors, their relative errors are less than 25%. Based on the comparisons from the quantitative velocity vectors and from the space-time average currents, some substantial discrepancies are revealed. The first is that the velocities of the reanalysis at mid-depths except near the equator are underestimated relative to the observed velocities by the floats. The average speed difference between NCEP and ARGO values ranges from about -2.3cm s^-1 to -1.8 cm s^-1. The second is that the velocity difference between the ocean model and the observations at 2000 dB seems smaller than that at 1000 dB. The third is that the zonal flow in the reanalysis is too dominant so that some eddies could not be simulated, such as the cyclonic eddy to the east of 160°E between 20°N and 30°N at 2000 dB. In addition, it is noticeable that many floats parking at 1000 dB cannot acquire credible mid-depth velocities due to the time information of their end of ascent （start of descent） on the surface in the trajectory files. Thus, relying on default times of parking, descent and ascent in the metadata files gravely confines their application to measuring mid-depth currents.

Effects of evacuation assistant's leading behavior on the evacuation efficiency: Information transmission approach

Evacuation assistants are expected to spread the escape route information and lead evacuees toward the exit as quickly as possible. Their leading behavior influences the evacuees＇ movement directly, which is confirmed to be a decisive factor of the evacuation efficiency. The transmission process of escape information and its function on the evacuees＇ movement are accurately presented by the proposed extended dynamic communication field model. For evacuation assistants and eevacuees, their sensitivity parameter of static floor field（SFF）, kL S, and kS, are fully discussed. The simulation results e indicate that the appropriate kL Sis associated with the maximum kSof evacuees. The optimal combinations of kL Sand e kSwere found to reach the highest evacuation efficiency. There also exists an optimal value for evacuation assistants＇ information transmission radius.

Information Leakage Problem in High-Capacity Quantum Secure Communication with Authentication Using Einstein-Podolsky-Rosen Pairs

The information leakage problem often exists in bidirectional quantum secure direct communication or quantum dialogue. In this work, we find that this problem also exists in the one-way quantum secure communication protocol [Chin. Phys. Lett. 32 （2015） 050301]. Specifically, the first bit of every four-bit message block is leaked out without awareness. A way to improve the information leakage problem is given.

Mobile Robot Positioning and Tracking Based on Ultra-wideband Technology

To solve the precision self-positioning problem for mobile robot,a positioning program based on ultra-wideband technology was proposed. Ultra-wideband pulse has very high bandwidth; ranging accuracy can achieve centimeter-level theoretically. The mobile robot obtained the distance to the reference node by sending ultra-wideband pulse. According to the geometric relations among the references and the robot,establish equations to calculate the position coordinates. Then Kalman filter algorithm was applied for mobile robot tracking. Simulation results show that robot positioning and tracking based on ultra-wideband technology can achieve indoor and outdoor seamless docking.

Digitization characteristics of geothermal information and structural analysis of 2011 off the Pacific coast of Tohoku Ms9.0 earthquake

Earthquake is one of the difficult problems that can not be solved as of this writing since the time when mod- em science was initiated over 300 years ago, and irregular events cannot be dealt with by using quantities examples before and followed by a view of the number of analysis systems in modem science ; meanwhile, it covers the problem that how to use changeable information. Structural analysis method is developed particularly for the study of evolutionary transitional processes of the changing events by employing irregular information, and emphasizes the primitiveness of changes in events. Based on the data of sounding observation every 08 o＇ clock and 20 o＇ clock per day from China Meteorological Administration, in this paper, we employ the digital structural analysis method to analyze the process of the special, structural characteristics of 2011 off the Pacific coast of Tohoku Ms9.0 earthquake. The result shows that the method can reflect the process of geothermal structures before and after earthquake; it can reflect the congruity between geothermal ＂heat level＂ and the level of earthquake energy. When the structure before the earthquake is ＂the counter clockwise rolling current and the unstable structures＂ with ＂dry in lower levels and wet in upper levels＂, we should be worry about the problem of earthquake. In a word, geothermal information could reveal that earthquakes really would be ＂heralded＂.

Review on the Sensor Technology Applied in the Intelligent Learning Environment

The development of sensor technology promotes the transformation of the intelligent learning environment. Through the research of the sensor technology application, this paper described the intelligent learning environment application system of the sensor technology and the management functions, conference organization, a library building, information collection, monitoring and equipment sharing function the role of sensor technology played in the intelligent learning environment.

Application of Optical Motion Capture Technology in Power Safety Entitative Simulation Training System

The safety production is critical to stable development of Chinese electric power industry. With the development of electric power enterprises, the requirements of its employees are also becoming higher and higher. In this paper, an optical motion capture system based on the virtual reality technology is proposed to meet the requirements of the power enterprise for the qualified business ability. Electric power equipment, power equipment model entitative operating environment and the human model are established by electric power simulation unit, ZigBee technology and OpenGL graphics library. The problem of missing feature points is solved by applying the human model driven algorithm and the Kalman filtering algorithm. The experimental results show that it is more accurate to use Kalman filtering algorithm to extract the feature point in tracking process of actual motion capture and real-time animation display. The average absolute error of 3D coordinates is 1.61 mm and the average relative error is 2.23%. The system can improve trainees’ sense of experience and immersion.

Analysis of Snowstorm Process in Northeast China during 5-9 November, 2021

In November 2021, Northeast China had more precipitation than in the same period. Among them, Heilongjiang and Jilin provinces in the Northeast China were the highest precipitation in the same period. I study a snowfall weather process from November 5 to 9, which mainly includes dynamic situation, synoptic background and situation. The results show that: In the middle and high latitudes of Eurasia, circulation is adjusted from zonal to meridional with large fluctuations. The northerly wind behind the trough continuously transports the polar cold air to the south. The northwest airflow behind the trough led the cold air to erupt to the southeast. In the process of moving southward, the cold air meets the warm and humid air in front of the trough, causing snowfall in the northeast. The southerly airflow and southeasterly airflow on the east side of the vortex continued to transport warm and humid airflow from the Yellow Sea and the Sea of Japan, which enhanced the snowfall.

Personnel Localization Method in Transformer Substation Based on Factor Graph

A SINS/GNSS location method based on factor diagram is proposed to meet the requirement of accurate location of substation construction personnel. In this paper, the inertial autonomous positioning, carrier motion information acquisition and satellite positioning technologies are integrated. The factor graph method is adopted to abstract the measurement information received by inertial navigation and satellite into factor nodes, and the state information into variable nodes, so as to construct the SINS/GNSS construction personnel positioning fusion factor graph model. The Gauss-Newton iterative method is used to implement the recursive updating of variable nodes, and the optimal estimate of the location information of the construction personnel is calculated, which realized the high precision location of the construction personnel. The factor graph method is verified by pedestrian navigation data. The results show that the factor graph method can continuously and stably output high-precision positioning results, and realize non-equidistant fusion of SINS and GNSS. The positioning accuracy is better than Kalman filter algorithm, and the horizontal positioning accuracy is less than 1 m. Therefore, the factor graph method proposed can provide accurate location information for substation construction personnel.

Analysis of the Microphysical Structure of Heavy Fog Using a Droplet Spectrometer:A Case Study

The microphysical properties of a long-lasting heavy fog event are examined based on the results from a comprehensive field campaign conducted during the winter of 2006 at Pancheng （32.2°N, 118.7°E）, Jiangsu Province, China. It is demonstrated that the key microphysical properties （liquid water content, fog droplet concentration, mean radius and standard deviation） exhibited positive correlations with one another in general, and that the 5-min-average maximum value of fog liquid water content was sometimes greater than 0.5 g m-3. Further analysis shows that the unique combination of positive correlations likely arose from the simultaneous supply of moist air and fog condensation nuclei associated with the advection of warm air, which further led to high liquid water content. High values of liquid water content and droplet concentration conspired to cause low visibility （〈50 m） for a prolonged period of about 40 h. Examination of the microphysical relationships conditioned by the corresponding autoconversion threshold functions shows that the collision-coalescence process was sometimes likely to occur, weakening the positive correlations induced by droplet activation and condensational growth. Statistical analysis shows that the observed droplet size distribution can be described well by the Gamma distribution.

Adaptive Sliding Mode Control for Re-entry Attitude of Near Space Hypersonic Vehicle Based on Backstepping Design

Combining sliding mode control method with radial basis function neural network (RBFNN), this paper proposes a robust adaptive control scheme based on backstepping design for re-entry attitude tracking control of near space hypersonic vehicle (NSHV) in the presence of parameter variations and external disturbances. In the attitude angle loop, a robust adaptive virtual control law is designed by using the adaptive method to estimate the unknown upper bound of the compound uncertainties. In the angular velocity loop, an adaptive sliding mode control law is designed to suppress the effect of parameter variations and external disturbances. The main benefit of the sliding mode control is robustness to parameter variations and external disturbances. To further improve the control performance, RBFNNs are introduced to approximate the compound uncertainties in the attitude angle loop and angular velocity loop, respectively. Based on Lyapunov stability theory, the tracking errors are shown to be asymptotically stable. Simulation results show that the proposed control system attains a satisfied control performance and is robust against parameter variations and external disturbances. © 2014 Chinese Association of Automation.