UAV-assisted data collection for wireless sensor networks with dynamic working modes

Wireless Sensor Network(WSN)is a cornerstone of Internet of Things(IoT)and has rich application scenarios.In this work,we consider a heterogeneous WSN whose sensor nodes have a diversity in their Residual Energy(RE).In this work,to protect the sensor nodes with low RE,we investigate dynamic working modes for sensor nodes which are determined by their RE and an introduced energy threshold.Besides,we employ an Unmanned Aerial Vehicle(UAV)to collect the stored data from the heterogeneous WSN.We aim to jointly optimize the cluster head selection,energy threshold and sensor nodes’working mode to minimize the weighted sum of energy con-sumption from the WSN and UAV,subject to the data collection rate constraint.To this end,we propose an efficient search method to search for an optimal energy threshold,and develop a penalty-based successive convex approximation algorithm to select the cluster heads.Then we present a low-complexity iterative approach to solve the joint optimization problem and discuss the implementation procedure.Numerical results justify that our proposed approach is able to reduce the energy consumption of the sensor nodes with low RE significantly and also saves energy for the whole WSN.

Trajectory tracking guidance of interceptor via prescribed performance integral sliding mode with neural network disturbance observer

This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance systems of missiles is challenging.As our contribution,the velocity control channel is designed to deal with the intractable velocity problem and improve tracking accuracy.The global prescribed performance function,which guarantees the tracking error within the set range and the global convergence of the tracking guidance system,is first proposed based on the traditional PPF.Then,a tracking guidance strategy is derived using the integral sliding mode control techniques to make the sliding manifold and tracking errors converge to zero and avoid singularities.Meanwhile,an improved switching control law is introduced into the designed tracking guidance algorithm to deal with the chattering problem.A back propagation neural network(BPNN)extended state observer(BPNNESO)is employed in the inner loop to identify disturbances.The obtained results indicate that the proposed tracking guidance approach achieves the trajectory tracking guidance objective without and with disturbances and outperforms the existing tracking guidance schemes with the lowest tracking errors,convergence times,and overshoots.

Research on Blended Teaching Mode:A Case Study of Floristry

In order to better carry out research on education and teaching,the author consulted relevant literature on blended teaching mode from 2011-2021 through CNKI,Web of Science and other websites,summarized and analyzed the research status of blended teaching mode,in order to lay a good foundation for studying blended teaching mode in course teaching.

Electroencephalogram Signal Correlations between Default Mode Network and Attentional Functioning

Attentional issues may affect acquiring new information, task performance, and learning. Cortical network activities change during different functional brain states, including the default mode network (DMN) and attention network. We investigated the neural mechanisms underlying attentional functions and correlations between DMN connectivity and attentional function using the Trail-Making Test (TMT)-A and -B. Electroencephalography recordings were performed by placing 19 scalp electrodes per the 10 - 20 system. The mean power level was calculated for each rest and task condition. Non-parametric Spearman’s rank correlation was used to examine the correlation in power levels between the rest and TMT conditions. The most significant correlations during TMT-A were observed in the high gamma wave, followed by theta and beta waves, indicating that most correlations were in the parietal lobe, followed by the frontal, central, and temporal lobes. The most significant correlations during TMT-B were observed in the beta wave, followed by the high and low gamma waves, indicating that most correlations were in the temporal lobe, followed by the parietal, frontal, and central lobes. Frontoparietal beta and gamma waves in the DMN may represent attentional functions.

An Analysis on the Blended Teaching Mode of College English

This paper explores blended teaching in college English from five aspects:the connotation,theoretical basis,design principles,advantages,and challenges of blended teaching and blended teaching models.The purpose is to attract more foreign language practitioners and learners to pay attention to this model and put it into practice.

Design and Application of“STEM+”6E Teaching Mode in Information Technology Teaching

The concept of“STEM+”integrates art,humanistic literacy,and social values in the traditional STEM education concept,advocates cross-disciplinary integration,and aims to cultivate compound talents equipped to tackle future challenges.In 2022,the Ministry of Education issued the“Compulsory Education Information Technology Curriculum(2022 Edition),”emphasizing the core literacy of information science and technology and the integration of interdisciplinary disciplines,and encouraging the teaching mode suitable for discipline characteristics.The 6E teaching mode is a student-centered teaching strategy characterized by active exploration and cross-disciplinary integration.This article innovatively designed the“STEM+”6E teaching mode,which is applied to junior high school information technology teaching,which can better achieve core literacy teaching goals.

A Study on the Implementation Path of the Blended Teaching Model in Geological Hazards Investigation and Evaluation

Geological Hazards Investigation and Evaluation is the core course of Environmental Geological Engineering,aiming to cultivate skilled talents with solid theoretical knowledge and excellent practical skills.At present,the course faces several issues,including a teaching environment disconnected from real-world work scenarios,course content that deviates from job-related tasks,a lack of digital teaching resources,and reliance on a single teaching method,leading to students’poor feedback from employers.Based on the concept of outcome-based education,the course team of Geological Hazards Investigation and Evaluation establishes a“five-step double-rotation”blended teaching model with the help of a Small Private Online Course platform.The program is designed to improve the teaching environment and expand the digitalized teaching resources in order to improve students’learning motivation,enhance learning effectiveness,and cultivate skillful talents who meet employers’satisfaction.

Analysis of the Application Value of Virtual Reality Combined with Flipped Classroom Teaching Mode in Cardiopulmonary Resuscitation Teaching

Objective:To analyze the value of using virtual reality combined with the flipped classroom teaching model in teaching cardiopulmonary resuscitation(CPR).Methods:Two classes of our nursing program were randomly selected for the study from September 2022 to September 2023,Class A(52 students,conventional teaching method)and Class B(52 students,virtual reality combined with flipped classroom teaching mode).The assessment scores and independent learning ability scores of the students in the two classes were compared.Results:CPR theory and operation scores,passing rate,and independent learning ability scores of Class B were higher than those of Class A(P<0.05).Conclusion:the use of virtual reality combined with the flipped classroom teaching mode in CPR teaching is conducive to the improvement of students’assessment scores and independent learning ability.

Research on Online-Offline Hybrid Teaching Mode of College English in the Context of New Liberal Arts

English is an important course in colleges,and students’proficiency in the language can significantly affect their learning in multiple disciplines.At present,offline classroom teaching is still the primary mode of college English teaching,and online teaching has not been widely used.Under the background of new liberal arts,the teaching tasks and objectives of college English have changed,and it is imperative to carry out online-offline hybrid teaching mode.This paper analyzes the feasibility of the online-offline hybrid teaching mode of college English,summarizes its problems,and explores its implementation strategies in the context of the new liberal arts,to provide references for teachers.

Research on the Blended Online and Offline Teaching Mode of Ideological and Political Courses in Universities

With the development of information technology,the blended online and offline teaching mode has gradually become a new trend in the teaching of ideological and political theory courses in universities.This article analyzes the current situation and existing problems of blended online and offline teaching of ideological and political courses in universities,and explores how to effectively combine online and offline teaching resources to improve the teaching effectiveness of ideological and political courses in universities.

Prediction of column failure modes based on artificial neural network

To implement the performance-based seismic design of engineered structures,the failure modes of members must be classified.The classification method of column failure modes is analyzed using data from the Pacific Earthquake Engineering Research Center(PEER).The main factors affecting failure modes of columns include the hoop ratios,longitudinal reinforcement ratios,ratios of transverse reinforcement spacing to section depth,aspect ratios,axial compression ratios,and flexure-shear ratios.This study proposes a data-driven prediction model based on an artificial neural network(ANN)to identify the column failure modes.In this study,111 groups of data are used,out of which 89 are used as training data and 22 are used as test data,and the ANN prediction model of failure modes is developed.The results show that the proposed method based on ANN is superior to traditional methods in identifying the column failure modes.

Diffraction deep neural network based orbital angular momentum mode recognition scheme in oceanic turbulence

Orbital angular momentum(OAM)has the characteristics of mutual orthogonality between modes,and has been applied to underwater wireless optical communication(UWOC)systems to increase the channel capacity.In this work,we propose a diffractive deep neural network(DDNN)based OAM mode recognition scheme,where the DDNN is trained to capture the features of the intensity distribution of the OAM modes and output the corresponding azimuthal indices and radial indices.The results show that the proposed scheme can recognize the azimuthal indices and radial indices of the OAM modes accurately and quickly.In addition,the proposed scheme can resist weak oceanic turbulence(OT),and exhibit excellent ability to recognize OAM modes in a strong OT environment.The DDNN-based OAM mode recognition scheme has potential applications in UWOC systems.

Missing interpolation model for wind power data based on the improved CEEMDAN method and generative adversarial interpolation network

Randomness and fluctuations in wind power output may cause changes in important parameters(e.g.,grid frequency and voltage),which in turn affect the stable operation of a power system.However,owing to external factors(such as weather),there are often various anomalies in wind power data,such as missing numerical values and unreasonable data.This significantly affects the accuracy of wind power generation predictions and operational decisions.Therefore,developing and applying reliable wind power interpolation methods is important for promoting the sustainable development of the wind power industry.In this study,the causes of abnormal data in wind power generation were first analyzed from a practical perspective.Second,an improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)method with a generative adversarial interpolation network(GAIN)network was proposed to preprocess wind power generation and interpolate missing wind power generation sub-components.Finally,a complete wind power generation time series was reconstructed.Compared to traditional methods,the proposed ICEEMDAN-GAIN combination interpolation model has a higher interpolation accuracy and can effectively reduce the error impact caused by wind power generation sequence fluctuations.

Landslide displacement prediction based on optimized empirical mode decomposition and deep bidirectional long short-term memory network

There are two technical challenges in predicting slope deformation.The first one is the random displacement,which could not be decomposed and predicted by numerically resolving the observed accumulated displacement and time series of a landslide.The second one is the dynamic evolution of a landslide,which could not be feasibly simulated simply by traditional prediction models.In this paper,a dynamic model of displacement prediction is introduced for composite landslides based on a combination of empirical mode decomposition with soft screening stop criteria(SSSC-EMD)and deep bidirectional long short-term memory(DBi-LSTM)neural network.In the proposed model,the time series analysis and SSSC-EMD are used to decompose the observed accumulated displacements of a slope into three components,viz.trend displacement,periodic displacement,and random displacement.Then,by analyzing the evolution pattern of a landslide and its key factors triggering landslides,appropriate influencing factors are selected for each displacement component,and DBi-LSTM neural network to carry out multi-datadriven dynamic prediction for each displacement component.An accumulated displacement prediction has been obtained by a summation of each component.For accuracy verification and engineering practicability of the model,field observations from two known landslides in China,the Xintan landslide and the Bazimen landslide were collected for comparison and evaluation.The case study verified that the model proposed in this paper can better characterize the"stepwise"deformation characteristics of a slope.As compared with long short-term memory(LSTM)neural network,support vector machine(SVM),and autoregressive integrated moving average(ARIMA)model,DBi-LSTM neural network has higher accuracy in predicting the periodic displacement of slope deformation,with the mean absolute percentage error reduced by 3.063%,14.913%,and 13.960%respectively,and the root mean square error reduced by 1.951 mm,8.954 mm and 7.790 mm respectively.Conclusively,this model not only has high prediction accuracy but also is more stable,which can provide new insight for practical landslide prevention and control engineering.

Research on blended teaching Mode of Cultivating problem-solving ability--Take the course"Curriculum Design and Evaluation"as an example

Based on the literature analysis,this paper identifies the five factors of problem-solving skills:cognitive ability,knowledge,meta-cognitive ability,motivation,and emotion;teaching intervention strategies;and teaching sessions that affect the development of problem-solving ability.On this basis,a blended teaching mode for cultivating problem-solving ability is designed.The article uses the problem-solving ability evaluation questionnaire to test the effect of pre-test and post-test after teaching intervention on 25 college students.The results verify that the blended teaching model designed in this paper is effective in promoting the cultivation of problem-solving ability,and provide a reference for promoting the cultivation of students'problem-solving ability in the context of the normalization of blended teaching.

Control method based on DRFNN sliding mode for multifunctional flexible multistate switch

To address the low accuracy and stability when applying classical control theory in distribution networks with distributed generation,a control method involving flexible multistate switches(FMSs)is proposed in this study.This approach is based on an improved double-loop recursive fuzzy neural network(DRFNN)sliding mode,which is intended to stably achieve multiterminal power interaction and adaptive arc suppression for single-phase ground faults.First,an improved DRFNN sliding mode control(SMC)method is proposed to overcome the chattering and transient overshoot inherent in the classical SMC and reduce the reliance on a precise mathematical model of the control system.To improve the robustness of the system,an adaptive parameter-adjustment strategy for the DRFNN is designed,where its dynamic mapping capabilities are leveraged to improve the transient compensation control.Additionally,a quasi-continuous second-order sliding mode controller with a calculus-driven sliding mode surface is developed to improve the current monitoring accuracy and enhance the system stability.The stability of the proposed method and the convergence of the network parameters are verified using the Lyapunov theorem.A simulation model of the three-port FMS with its control system is constructed in MATLAB/Simulink.The simulation result confirms the feasibility and effectiveness of the proposed control strategy based on a comparative analysis.

Wave Azimuth Cutoff Compensation Method and the Multisatellite Networking Mode of SAR

Synthetic aperture radars(SARs)encounter the azimuth cutoff problem when observing sea waves.Consequently,SARs can only capture the waves with wavelengths larger than the cutoff wavelength and lose the information of waves with smaller wavelengths.To increase the accuracy of SAR wave observations,this paper investigates an azimuth cutoff compensation method based on the simulated multiview SAR wave synchronization data obtained by the collaborative observation via networked satellites.Based on the simulated data and the equivalent multiview measured data from Sentinel-1 virtual networking,the method is verified and the cutoff wavelengths decrease by 16.40%and 14.00%.The biases of the inversion significant wave height with true values decrease by 0.04 m and 0.14 m,and the biases of the mean wave period decrease by 0.17 s and 0.22 s,respectively.These results demonstrate the effectiveness of the azimuth cutoff compensation method.Based on the azimuth cutoff compensation method,the multisatellite SAR networking mode for wave observations are discussed.The highest compensation effect is obtained when the combination of azimuth angle is(95°,115°,135°),the orbital intersection angle is(50°,50°),and three or four satellites are used.The study of the multisatellite networking mode in this paper can provide valuable references for the compensation of azimuth cutoff and the observation of waves by a multisatellite network.

Application Practice of OMO Teaching Mode of"Two Courses,Three Links and Two Main Subjects"Based on the Wisdom Tree Platform

The shared course and flipped course of Modern Educational Technology were constructed and applied based on the wisdom tree platform.The OMO teaching mode of"two courses,three links and two main subjects"was constructed according to the course teaching practice,and the teaching practice was carried out.The main teaching links and teaching quality assurance system of the mode were expounded and the application effect was analyzed from the three aspects of student evaluation,course performance and course goal achievement value.The problems in the application of the mode were discussed,and some improvement measures were put forward from the aspects of students,teachers and schools.

The default mode network is affected in the early stage of simian immunodeficiency virus infection:a longitudinal study

Acquired immune deficiency syndrome infection can lead to cognitive dysfunction represented by changes in the default mode network.Most recent studies have been cross-sectional and thus have not revealed dynamic changes in the default mode network following acquired immune deficiency syndrome infection and antiretroviral therapy.Specifically,when brain imaging data at only one time point are analyzed,determining the duration at which the default mode network is the most effective following antiretroviral therapy after the occurrence of acquired immune deficiency syndrome.However,because infection times and other factors are often uncertain,longitudinal studies cannot be conducted directly in the clinic.Therefore,in this study,we performed a longitudinal study on the dynamic changes in the default mode network over time in a rhesus monkey model of simian immunodeficiency virus infection.We found marked changes in default mode network connectivity in 11 pairs of regions of interest at baseline and 10 days and 4 weeks after virus inoculation.Significant interactions between treatment and time were observed in the default mode network connectivity of regions of interest pairs area 31/V6.R and area 8/frontal eye field(FEF).L,area 8/FEF.L and caudal temporal parietal occipital area(TPOC).R,and area 31/V6.R and TPOC.L.ART administered 4 weeks after infection not only interrupted the progress of simian immunodeficiency virus infection but also preserved brain function to a large extent.These findings suggest that the default mode network is affected in the early stage of simian immunodeficiency virus infection and that it may serve as a potential biomarker for early changes in brain function and an objective indicator for making early clinical intervention decisions.

Research on College English Teaching Model Based on Multimedia and Network

Based on multimedia and network environment, college English teaching mode can effectively improve the teaching environment of traditional English teaching and students, and expand the capacity of English classroom information, and enrich the students’ English culture background. Therefore, as a teacher, we should first of all make efforts to learn new knowledge and teaching methods to improve our English professional quality. Strengthening the multimedia, network technology and educational psychology research, and put themselves in the teaching position. In addition, the college English teaching model based on multimedia and network environment can cultivate students’ independent thinking, autonomous learning, analysis and problem - solving ability, and promote the high efficiency of college English teaching.