Engineering/Technology
A Data-driven and Deep Learning-based Mid- and Long-term Electric Vehicle Charging Load Forecasting Method
With the rapid popularization of electric vehicles their charging load influences the stable operation of the power grid. An accurate prediction of EV charging station load is crucial for optimal resource allocation in power systems. The objective of this study is to address the issue of insufficient accuracy in existing prediction methods this paper proposes a hybrid prediction model based on Bidirectional Long Short-Term Memory and Adaptive Boosting aiming to improve the accuracy and stability of medium and long-term EV charging station load forecasting.
The study employs a three-step approach: (1) The pearson correlation analysis was utilized to evaluate multi-dimensional influencing factors and reduce dataset dimensionality; (2) implementation of a BiLSTM neural network for temporal feature extraction and preliminary prediction; and (3) application of the Adaboost algorithm to construct a weighted combination of strong classifiers. The model’s effectiveness was validated through comprehensive simulation tests using real-world charging station data.
The proposed Pearson feature selection-based BiLSTM-Adaboost model outperforms traditional benchmark models (LSTM and SVM) effectively reduces data redundancy through feature selection achieves better performance in key indicators (MSE RMSE and MAPE) and demonstrates strong generalization ability and robustness while maintaining high accuracy.
Experimental results demonstrate that the proposed method effectively extracts key features of charging loads achieving superior prediction accuracy and generalization ability compared to traditional methods. This provides a reliable decision-making tool for power grid operation effectively supporting the resilience planning needs of urban power grids under continuously increasing EV penetration rates. But further research is needed to address robustness under extreme weather conditions.
This study provides an effective load forecasting methodology for power systems to address the challenges of large-scale electric vehicle integration. Future research will explore more robust feature engineering methods and deep learning architectures such as combining other more advanced time series prediction models and improving optimization algorithms to enhance model adaptability and generalization capability for complex data patterns.
Review on Stable Motion Control Methods of Whole Body for Hydraulic Quadruped Robots
The performance of the hydraulic quadruped robots has not yet reached the level of quadrupeds. The whole-body stable motion control method of the hydraulic quadruped robot is the key to determining the motion ability. The paper summarizes the current development status of hydraulic quadruped robots and whole-body control methods and the advantages of existing and future development trends of the main control methods with a focus on related research papers.
The various typical hydraulic quadruped robots and their characteristics that have been published are summarized in this study. Additionally the whole-body stable control methods of hydraulic quadruped robots are summarized and the characteristics of various control methods are analyzed especially the widely used model predictive control method and whole-body control method. Moreover a few research results on hybrid control methods are introduced.
By summarizing the research results of hydraulic quadruped robots it is evident that different control methods have different characteristics. The single control method is suitable for simple control tasks of hydraulic quadruped robots on flat road surfaces.
Due to the nonlinearity and time-varying parameters of hydraulic drive hydraulic drive errors are inevitably present. There are still shortcomings in the development of hydraulic quadruped robots such as energy utilization efficiency lightweight design of structures joint servo drive and intelligent control.
In order to achieve stable control and industrial application of hydraulic quadruped robots the control methods of whole body are summarized and analyzed. And it is pointed out that the future research work of hydraulic quadruped robots mainly focuses on the lightweight design of the system and the study of intelligent control algorithms with stronger adaptability.
Improved Time Difference of Arrival Algorithm for Partial Discharge Localization in Converter Transformer Bushings
Partial discharge in oil-paper insulated bushings represents a significant fault type in converter transformers during operation. Statistical analysis reveals that approximately 20% of insulation-related failures in converter transformers originate from partial discharges in bushings. When not detected promptly these partial discharges can lead to insulation breakdown within 3–6 months. Each failure incident typically causes an 8–12 hour power interruption resulting in substantial economic losses ranging from ¥500000 to ¥800000. This research addresses the critical challenge of precise partial discharge localization in bushings to enable effective casing maintenance.
The study initially developed an electromagnetic wave propagation simulation model for partial casing discharge to analyze the dynamic electromagnetic wave propagation process comprehensively. Subsequently the Time Difference of Arrival (TDOA) localization algorithm underwent optimization through integration with the neural network simulated annealing algorithm and Bayesian algorithm.
Simulation results demonstrate that electromagnetic wave signals propagate into outer space as spherical waves through the oil gap between the flange end screen and the upper casing section. The maximum electric field intensity direction exhibits substantial variations between the casing surface and the far end. The enhanced algorithms demonstrate improved localization accuracy. The neural network-based TDOA achieves a reduced Mean Absolute Percentage Error (MAPE) of 5% with over 80% of errors contained within 0.5 units of the actual position in each coordinate direction. The Bayesian-based improvement demonstrates a MAPE of 8% with 70% of errors within 0.8 units. The simulated annealing-based enhancement achieves a MAPE of 6% with 85% of errors within 0.6 units.
Based on the characteristics of the electromagnetic wave signal propagation process in the internal and external space of the oil-paper insulation sleeve this article further improves the of the TDOA positioning algorithm based on the neural network.
The enhanced TDOA localization algorithm incorporating neural network simulated annealing and Bayesian algorithms successfully improves the accuracy of partial discharge localization in bushings.
Advanced Internet of Things (IoT)-Based Intelligent Heavy Transport Vehicles (HTV) Monitoring System to Enhance Passenger Safety
Safety and efficiency have become critical issues in the quickly changing world of high-speed bus transit. The surge in high-speed bus-related traffic events is attributed to several factors such as reckless driving speeding improper overtaking vehicle health issues sleep deprivation alcohol consumption and driver distractions. This paper proposes a state-of-the-art Internet of Things (IoT) system particularly intended for tracking and enhancing the security of high-speed buses on roads as a solution to these problems.
Innovative technologies like image processing clever algorithms for computer and embedded vision (i.e. MobileNet Canny Edge Detection FaceMesh Model (Mediapipe) and Raspberry Pi 4 model B 8 GB are all included in the suggested solution. The system is made up of modules for online data visualization interfaces driver monitoring systems vehicle health and speed monitoring and image processing for safety.
Real-time interaction hardware implementation model training and web app integration are among the project's benchmarks.
Deliverables include creating a reliable IoT device installing sensors for vital metrics setting up a centralized interface for monitoring in real-time and creating a clever algorithm that will produce alerts promptly. The project entails extensive testing and validation to guarantee dependability accuracy and compliance with safety and privacy requirements by providing valuable information to law enforcement authorities improving the road safety and effectiveness of high-speed bus operations on highways.
A Pilot Protection of Negative Sequence and Additional Network Considering Photovoltaic Integration
Introducing pilot protection in active distribution networks containing PV can improve the reliability and selectivity of protection. However the basic communication facilities of the existing distribution network make it difficult to meet the requirements of data synchronization and the PV T-connection to the network leads to sudden changes in the impedance angle.
Therefore pilot protection of a negative sequence and additional network considering PV is proposed. The scheme is based on the feature that the PV model only outputs positive sequence components after a fault. For asymmetrical faults the negative sequence impedance detected at both ends of the protection is utilized to construct a comparative negative sequence impedance protection criterion. For symmetrical faults the voltage characteristics of the faulty additional network are utilized to construct a protection criterion.
The protection method requires less data information low dependence on communication and can quickly identify asymmetric faults occurring in the area. The operation results have high reliability and simple calculation; additional criteria can effectively avoid the impact of load current changes on the protection can effectively withstand different transition resistance access conditions and different penetration rates of photovoltaic power access. This study has two limitations: (1) The model considers only PV; (2) The proposed protection scheme applies only to local circuits.
Finally an actual distribution network model with PV is constructed in PSCAD to verify the effectiveness and reliability of the protection method.
The protection method is selective and reliable and is not affected by high penetration rate and PV fault characteristics.
An Improved Sliding Mode Observer-based Sensorless Control for the Three-phase PMSM with the Consideration of Stator Harmonic Compensation
The sensorless control technology for permanent magnet synchronous motors typically employs a sliding mode observer to obtain rotor position and speed information based on the back electromotive force. This study aims to improve the inherent chattering and poor observation performance of the traditional sliding mode observer (SMO) in the rotor position estimation of the surface-mounted permanent magnet synchronous motor.
The super twisting algorithm (STA) is introduced to improve the traditional SMO and the super twisting sliding mode observer (STA-SMO) is constructed to solve the chattering problem of the traditional SMO. According to different speeds the sliding mode variable gain coefficient is designed and a continuous function L(x) is introduced as a switching function to make the switching of the sliding mode surface smoother. Considering the problem of stator current distortion caused by dead zone the harmonic suppression strategy of adaptive notch filter (ANF) based on the least mean square (LMS) algorithm is studied and combined with the STA-SMO method to construct a position sensorless control system considering current harmonic compensation. Comparative verification under different speed conditions is carried out to verify the control performance of the method studied in this study under a wide speed range.
Firstly the speed information is introduced as a variable into the gain coefficient of the traditional STA-SMO and the parameters are adjusted with speed which solves the parameter matching problem in different speed domains of STA-SMO and effectively improves the stability of the observer. On this basis the current harmonic compensation strategy based on LMS-ANF is introduced. According to the characteristics of the adaptive filter the harmonic current of a specific wave can be extracted and the acquisition current is compensated to suppress the influence of current harmonics on the estimation results of the observer which further improves the accuracy of the observer.
The proposed STA-SMO with LMS-ANF harmonic compensation demonstrates superior performance over traditional SMO effectively reducing chattering and improving stability across wide speed ranges. Experimental results confirm its robustness under dynamic loads and adaptability to speed transitions with chattering reduced by 1.1%. The LMS-ANF strategy mitigates current harmonics enhancing low-speed accuracy. While the method balances simplicity and reliability future work could address near-zero-speed performance and computational efficiency for broader industrial applications.
The STA-SMO + LMS-ANF proposed in this study can effectively improve the anti-interference ability of the observer adapt to the application of a wide speed range and have strong robustness and higher observer accuracy.
Discussion on Distribution Network Operating Envelope for Providing Allowable Active and Reactive Power Injections
The high-proportion distributed energy resource (DER) connection in the distribution network deteriorates the condition of voltage violation when DERs control their power output arbitrarily. The operating envelope of distribution networks can decouple the safe operation of distribution networks from the regulation of distributed energy resources and map the voltage constraints of distribution networks into DER output constraints.
This provides an effective means to solve the problem of different ownership entities and regulation objectives between distribution networks and DER. Existing researches mainly focus on the active power operating envelopes while ignoring the reactive power. This paper studies the calculating method for the active-reactive power operating envelope of distribution networks. First the rectangle model of active-reactive power operating envelopes is constructed to calculate the operating envelopes that provide more flexibility for DER control while satisfying the voltage constraints robustly and meeting the actual demands in the operation of DER control. Second the area that the active-reactive operating envelope covers is enlarged by finding a new vertex on the right side of the p-q plane to generate a pentagon operating envelope which is called the expanded operating envelope.
This leads to a higher active power output limit for DER control.
Finally the effectiveness and safety of the proposed calculating method for distribution network operating envelopes is verified in distribution networks with different sizes
Fault Identification Algorithm for Transmission Line Integrated with MMC-HVDC Converter Station
Currently modular multilevel converter (MMC)-based HVDC technology is utilized in the power grid. As known the transmission line integrated with MMC-based station adopts travelling wave (TW) algorithm to identify partial discharge which is highly dependent on the calculation of the initial TW velocity that correlates with the precise acquisition of the TW spectrum.
Firstly in this work the frequency-dependent feature of underground cables was analysed. Secondly the correction algorithm for TW attenuation was obtained. Thirdly a detailed partial discharge location algorithm was derived.
Using PSCAD/EMTDC a ±400kV MMC-based power grid simulation model has been constructed followed by performing a typical case study to verify the robustness of the proposed algorithm. To overcome these shortcomings a novel partial discharge location principle for transmission line integrated with MMC-based station has been illustrated. However it should be noted that the proposed method has only achieved frequency domain corrections rather than the time-frequency domain which still requires further research. Furthermore the calculation has contained too many iterations causing significant computational pressure.
The comprehensive frequency correction algorithm has exhibited the ability to recover the initial frequency spectrum information from highly attenuated TW signal and the proposed fault location principle has been found suitable for transmission line integrated with MMC-based station.
Ultra-short-term Forecasting Study of Power Load in Mega Steel Industry Based on Multi-stage Modeling
In the large-scale steel industry significant power load variability especially during processes like steel smelting poses challenges to power system safety. Although there is an abundance of research and patents related to load forecasting studies and patents specifically addressing large industrial load forecasting are sparse. Hence accurate ultra-short-term load forecasting becomes particularly crucial.
This study proposes an innovative method for ultra-short-term load forecasting to improve prediction accuracy during peak periods and mitigate risks in high-load conditions.
We introduce an LSTM-XGBoost model enhanced by a random forest network and an improved grey wolf optimization algorithm (IGWO) for feature selection and parameter optimization respectively.
Compared to other advanced models our method demonstrates superior performance across key indicators such as MAPE (1.93%) RMSE (220.81) and R2 coefficient (0.99) and the prediction error is lower during both peak and off-peak periods. For instance the proposed model achieved a MAPE improvement of over 25% compared to traditional models. Validation with data from multiple time periods confirms the model's accuracy and robustness.
The proposed forecasting method effectively tackles load fluctuations in the steel industry supporting safe and economical power system operations. Future research will aim to further improve peak identification accuracy and enable continuous adaptive learning.
Patent Selections
Research Progress on Industrial Robots: A Review
The success of the fourth and upcoming fifth industrial resolution lies majorly in automation and robotics. Industrial robots perform various manufacturing-related tasks due to their autonomy flexibility and autonomous work in a complex environment. Applications including drilling material transfer loading and unloading machines processing assembling and inspection welding spray painting machining and so on are common. The present work comprehensively summarizes all the pertinent work related to the industrial robot based on extensive literature review and patents such as inverse kinematics problems robot design programming scheduling motion planning and trajectory planning. In addition the present work discusses various optimization algorithms employed in industrial robots. Furthermore several recommendations for future research have been addressed.
Recent Progress on Air, Liquid, PCM, Heat Pipe, and Hybrid Modes of Thermal Management in Lithium-Ion Batteries
This study emphasises lithium-ion batteries which have been the subject of extensive research due to their wide range of benefits including extended life cycle minimal discharge and high energy density. However the temperature sensitivity of the batteries presents a notable obstacle that can negatively impact their performance and longevity when operating under extreme conditions. To overcome this challenge implementing an effective battery thermal management system (BTMS) is imperative. Battery thermal management is crucial for ensuring the safety and longevity of lithium-ion batteries especially in high-demand applications like electric vehicles. This comprehensive review explores a variety of BTMS technologies including air-cooling methods liquid-cooling techniques heat pipes and PCM materials. While air-cooled BTMS is a safe and straightforward design its lower heat capacity and thermal efficiency limit is used to low-capacity batteries. However forced air-cooled BTMS is an excellent solution for high charging/discharging rates as air flows through channels within the battery packs to optimize cooling. Liquid-cooled BTMS also shows promise although designers must ensure the sealing cover is secure to prevent leaks. Heat pipes (HP) offer a unique approach to controlling battery temperature while Phase change materials (PCM) thermal management is notable for its ability to absorb significant heat by latent heat. Hybrid cooling combines fins nanofluids PCM and microchannels-based cooling and can significantly enhance battery performance under high charging/discharging rates. Furthermore lithium-ion batteries are extensively used in various applications including the Electric vehicle industry. Keeping the lithium-ion battery temperature within the optimal range is important and is accomplished by a suitable BTMS. Different methods such as air cooling Liquid cooling Heat pipe and PCM materials are used in BTMS. An effective thermal management system and efficient battery model are absolutely necessary. Each of the techniques in BTMS has its own benefits and drawbacks. The effectiveness of thermal management configurations and methods can vary. Thus evaluating performance and optimal configuration is crucial before implementation. The review also considers recent advancements and patent filings that underscore innovation in BTMS technologies.
Energy-saving Trajectory Planning Method for Electric Vehicles Based on Dynamic Programming Optimization
In autonomous driving systems the planning module serves as the link between environment perception and vehicle control directly influencing the safety and efficiency of autonomous driving. Despite the existence of numerous patents and publications related to trajectory planning there is still room for improvement in the economic efficiency of trajectory planning.
Given the limitations of the existing path planning algorithms in terms of search efficiency and path length this study introduces an innovative and improved strategy in the horizontal dimension. Based on the cost function of the distance between sampling points this strategy aims to improve the search efficiency of the dynamic planning algorithm and reduce the search path length. Furthermore the smoothness of the path is optimized to suit the actual driving conditions by applying a quadratic programming algorithm. An energy consumption model for pure electric vehicles is established in the vertical dimension effectively constraining energy use during speed dynamic planning to reduce consumption while driving. Finally the smoothness of speed planning is improved using a quadratic programming algorithm.
The results of simulation experiments show that compared with traditional methods the proposed algorithm achieves a substantial improvement in path length reduction of 5.8% average curvature reduction of 31.6% and average energy consumption reduction of 2.04% in static and dynamic obstacle avoidance environments.
The results show that the improved dynamic planning algorithm proposed in this study is significantly optimized in terms of mean path length mean curvature and energy consumption. Moreover the proposed algorithm can meet the requirements of energy efficiency of vehicle driving.
Research on Disturbance Rejection Control Algorithm for Aerial Operation Robots
This study addresses the challenges UAVs face during aerial operations particularly concerning external interference and slow localization response. The primary objective is to propose an algorithm that integrates admittance control and non-singular fast terminal sliding mode control verifying its effectiveness through simulation experiments while exploring its potential for patent application.
Due to their versatility and efficiency UAVs are increasingly utilized in various aerial operations. However they are susceptible to external disturbances which may affect their stability and accuracy during tasks such as contact operations. Additionally inherent delays in localization response speed may impact their performance in dynamic environments. Addressing these issues is essential for improving the reliability and robustness of UAV-based systems.
To achieve the objectives the kinematics and dynamics of a hexacopter aerial carrier robotic arm system were initially modeled. Subsequently an external admittance controller was designed to mitigate disturbances encountered during contact operations achieving smooth control of the robotic arm end-effector by adjusting the desired position to enhance system stability and disturbance rejection. Additionally to prevent performance degradation stemming from controller saturation an internal position control mechanism utilizing a non-singular fast terminal sliding mode control algorithm was implemented. This approach enhances system robustness and convergence speed ensuring accurate positioning.
To validate the effectiveness and feasibility of the proposed control algorithm numerical simulations were conducted. The outer loop's admittance control exhibited a smoother control process particularly during sudden stiffness changes when the actuator contacts the environment. The inner loop employing Non-Singular Fast Terminal Sliding Mode Control (NFTSMC) improved joint angle tracking speed by 41%-58% compared to PID control and by 20%-50% compared to traditional Sliding Mode Control (SMC). This algorithm demonstrated faster convergence rates and smoother transitions significantly reducing steady-state errors in contact force while exhibiting robustness to environmental parameters. The findings indicate that the algorithm effectively addresses the issues of external interference and sluggish localization response encountered by UAVs during aerial operations.
The algorithm based on admittance control and non-singular fast terminal sliding mode control demonstrates superior performance compared to traditional sliding mode control and PID control in mitigating external disturbances and enhancing the precision of UAV aerial operations. This ensures the resilience to disturbances and the speed of localization response of the rotary-wing flying robotic arm system during cleaning processes thus enhancing its reliability and robustness in dynamic environments.
The Structural Design and Analysis of the Multi Nozzle Relay Air-Jet Inserting System by Profile Reed Guiding for 3D Weaving Machine
Based on the author’s invention patent literature this article designs a parameterized simulation model of a multi-layer jet weft insertion system by profiles reed guidance for a 3D loom on the PTC Creo9.0 platform including the main supersonic nozzle supersonic auxiliary nozzle (relay nozzle) and multi-slot profiles reed components.
Based on the existing basic theory experimental results and empirical data of turbulent jet the parameters of the multi-layer jet weft insertion system as well as the structural parameters and the relative positions of the main auxiliary nozzle and profile reed components such as the center distance of each layer's main nozzle auxiliary nozzle spacing auxiliary nozzle installation angle spray direction angle and spray angle have been determined preliminarily.
Further the basic flow field parameters such as the supply pressure of the main and auxiliary nozzles and the shape of multi-layer profile reeds have been determined optimally on the Virtual Prototype Collaborative Simulation Platform (PTC Creo9.0/ANSYS Workbench/Fluent 2024R1) and the rationality of the structural design also been verified; on the premise of ensuring that the airflow velocity of each layer's profiles groove meets the requirements of weft insertion the design and simulation calculation is repeatedly modified and the optimal structural design parameters of the multi-layer weft insertion system and nozzle is finally determined.
To explore the feasibility of multi-layer jet weft insertion the design of three-dimensional multi-layer jet weft insertion looms has laid a theoretical foundation laying a good foundation for the emergence and industrial manufacturing of three-dimensional multi-layer jet weft insertion looms and providing an important reference for the innovative design of three-dimensional multi-layer water jet weft insertion looms.
Computational Fluid Dynamics Analysis and Optimization of a Double-suction Turbine Agitator
As one of the essential pieces of chemical equipment a reactor provides the necessary reaction space and conditions for the materials involved in the reaction during the stirring process there has been an increase in patents related to reactors. However under typical operating conditions issues such as uneven gas distribution suboptimal gas-liquid mixing and low product yield often arise in gas-liquid phase reactors.
To address the issues prevalent in current stirred reactors a new design for a stirred reactor equipped with a double-suction turbine agitator was developed.
In this paper a stirred reactor equipped with a double-suction turbine agitator was designed and its three-dimensional modeling was conducted using SolidWorks. Computational Fluid Dynamics (CFD) simulations based on the Euler-Euler two-phase approach with the RNG turbulence model were performed to assess variables such as stirring speed installation height blade diameter and agitator inner diameter. The dispersion characteristics and flow field behaviors of the gas-liquid two-phase under varying conditions were comparatively analyzed. Optimizations were conducted across various parameters to enhance the gas mixing efficiency in the liquid phase.
The results show that a diameter of 370 mm for the double-suction turbine agitator an installation height of 640 mm a blade diameter of 500 mm and an inner hole diameter of 200 mm yield optimal gas-liquid two-phase mixing performance. This configuration results in a broad and uniform gas distribution within the reactor maintaining a desired high level of gas holdup at specific positions.
The double suction turbine agitator is a type of radial agitator. During operation it induces significant centrifugal forces in the liquid exerts a robust shear effect and enhances the mixing of the gas-liquid phases thereby increasing the production efficiency of the product.
Preparation and Characterization of Fe3O4-Modified Graphene Oxide as Heat Transfer Additive for Paraffin Wax Applications
In phase change thermal management systems the development of magnetic phase change materials offers the possibility of effectively integrating passive and active heat control technologies. The low dispersibility of traditional heat transfer additives the high interfacial thermal resistance with phase change matrices and the restricted magnetic response characteristics are some of the current problems that must be resolved.
To overcome these challenges this study employed a co-precipitation method to composite magnetic nanoparticles Fe3O4 with graphene oxide (GO). The active sites on GO were functionalized with alkyl groups to prepare Fe3O4-modified graphene oxide (Fe3O4-MGO)/paraffin magnetic composite phase change materials. The morphology structure chemical composition and thermal properties of the resulting magnetic composite phase change materials were tested and characterized.
The results indicated that Fe3O4-MGO exhibits good dispersibility in paraffin which can enhance the thermal conductivity of the phase change material. The thermal conductivity of the composite phase change material with a Fe3O4-MGO mass fraction of 2.0% was measured to be 0.461 W/m·K representing a 47.3% increase compared to pure paraffin. Additionally Fe3O4-MGO demonstrated a certain phase change capability with a phase change enthalpy reaching 70.35 kJ/kg.
The findings of this study are expected to provide technical support for innovative applications of magnetic-controlled phase change thermal management. The emergence of magnetic phase change materials holds the promise of achieving efficient integration of passive and active heat control technologies within phase change thermal management systems. However several issues still need to be addressed in patents and related research.
Recent Advances on Upper Limb Rehabilitation Robot
With advances in medical technology and an aging population the number of patients with upper limb movement disorders has increased who are facing difficulties in self-care and occupational integration. Traditional rehabilitation methods have limitations such as unstable results long cycle times and insufficient resources. Therefore upper limb rehabilitation robotics has emerged combining robotics medicine and other fields to simulate upper limb movement for targeted rehabilitation training in order to improve effectiveness shorten the cycle and reduce the burden on therapists.
This study aimed to introduce the classification advantages and disadvantages and development trend in existing upper limb rehabilitation robots and provide a basis for other researchers to understand the current status of their development and future trends.
Various studies and patents on upper limb rehabilitation robots were reviewed revealing the structural characteristics along with the advantages and disadvantages of typical robotic arms used for upper limb rehabilitation.
Through the analysis of various upper limb rehabilitation robots the characteristics and problems of upper limb rehabilitation robots were identified and the development trend of upper limb rehabilitation robots was prospected.
Upper limb rehabilitation robots have many advantages such as good adaptability personalized customization safety and reliability etc. but they also have some disadvantages such as difficult control high manufacturing cost and short service life. In the future upper limb rehabilitation robots will develop towards simpler structures greater comfort affordability diverse functions better efficacy and increased safety.
Latest Patent for Rice Transplanter Mulching Device
The planting technology of rice mulching and rice transplanting can effectively control weed growth reduce fertilizer loss maintain surface temperature improve fertilizer utilization reduce the use of pesticides and chemical fertilizer and is a new mode of green rice cultivation. The rice mulching device is a key component and the quality of its mulching directly affects the productivity and planting quality of rice. Additionally obtaining an efficient rice transplanter with an integrated rice transplanter for transplanting and mulching is the hot spot of research in this field.
By analyzing and discussing the patents of rice mulching cultivation technology research and mechanical mulching devices their development trend is summarized which provides a reference for the development of rice transplanter mulching devices.
The research status of rice transplanter mulching devices in recent years is summarized and various mulching devices' structure types and applications are discussed.
By summarizing the patents on rice transplanter mulching devices the current status of patent applications was obtained and the problems and future research direction of rice transplanter mulching devices were discussed.
In-depth study of various types of rice transplanter mulching devices the existing rice transplanter mulching device needs further improvement regarding the laying mechanism the film-breaking mechanism and the structural type of the mulching device. With the application of GPS sensor detection information processing automatic control and other technologies in agricultural machinery rice transplanter filming is gradually developing in the direction of high speed automation and intelligence.
A Hybrid Framework for Fake News Detection Using Explainability of Artificial Intelligence
As the COVID-19 pandemic develops there is a lot of false information floating around on social media and the risks are huge. This is why identifying and countering disinformation campaigns is so important. Modern deep learning models that employ Natural-Language-Processing (NLP) approaches such as Bidirectional-Encoder-Representations-from-Transformers (BERT) have been quite effective at identifying disinformation.
To tackle the spread of false information on COVID-19 we present an explainable NLP approach that utilizes DistilBERT and Shapley-Additive-exPlanations (SHAP) two powerful and efficient frameworks. A dataset consisting of 984 assertions regarding COVID-19 that were factually verified was initially compiled. The DistilBERT model achieved superior performance in spotting COVID-19 disinformation after we doubled the dataset's sample size using back-translation.
Compared to more conventional machine learning models it performed better on both datasets. Additionally we used SHAP to enhance the explainability of the models which were then tested in amid-subjects experimentation with three constraints: text(T) text+SHAP explanation(TSE) and text+SHAP explanation+source and evidence(TSESE). The goal was to increase public trust in the models' predictions.
Compared to the T condition the TSE &TSESE constraints showed a substantial increase in participants' confidence and sharing of COVID-19-related information. Improving public trust and identifying COVID-19 disinformation were two important outcomes of our study.