A function-based behavioral modeling method for air combat simulation

Today’s air combat has reached a high level of uncertainty where continuous or discrete variables with crisp values cannot be properly represented using fuzzy sets. With a set of membership functions, fuzzy logic is well-suited to tackle such complex states and actions. However, it is not necessary to fuzzify the variables that have definite discrete semantics.Hence, the aim of this study is to improve the level of model abstraction by proposing multiple levels of cascaded hierarchical structures from the perspective of function, namely, the functional decision tree. This method is developed to represent behavioral modeling of air combat systems, and its metamodel,execution mechanism, and code generation can provide a sound basis for function-based behavioral modeling. As a proof of concept, an air combat simulation is developed to validate this method and the results show that the fighter Alpha built using the proposed framework provides better performance than that using default scripts.

Nonlinear Auto-Companding Method for Behavioral Modeling of Switched-Current Circuits

A wavelet collocation method with nonlinear auto companding is proposed for behavioral modeling of switched current circuits.The companding function is automatically constructed according to the initial error distribution obtained through approximating the input output function of the SI circuit by conventional wavelet collocation method.In practical applications,the proposed method is a general purpose approach,by which both the small signal effect and the large signal effect are modeled in a unified formulation to ease the process of modeling and simulation.Compared with the published modeling approaches,the proposed nonlinear auto companding method works more efficiently not only in controlling the error distribution but also in reducing the modeling errors.To demonstrate the promising features of the proposed method,several SI circuits are employed as examples to be modeled and simulated.

Behavioral modeling of RF power amplifiers with time-delay feed-forward neural networks

A novel behavioral model using three-layer time-delay feed-forward neural networks （TDFFNN）is adopted to model radio frequency （RF）power amplifiers exhibiting memory nonlinearities. In order to extract the parameters, the back- propagation algorithm is applied to train the proposed neural networks. The proposed model is verified by the typical odd- order-only memory polynomial model in simulation, and the performance is compared with different numbers of taped delay lines（TDLs） and perceptrons of the hidden layer. For validating the TDFFNN model by experiments, a digital test bench is set up to collect input and output data of power amplifiers at a 60 × 10^6 sample/s sampling rate. The 3.75 MHz 16-QAM signal generated in the vector signal generator（VSG） is chosen as the input signal, when measuring the dynamic AM/AM and AM/PM characteristics of power amplifiers. By comparisons and analyses, the presented model provides a good performance in convergence, accuracy and efficiency, which is approved by simulation results and experimental results in the time domain and frequency domain.

Multislice behavioral modeling based on envelope domain for power amplifiers

An envelope domain multislice behavioral modeling is introduced. The tradition AM-AM and AM- PM characteristics of power amplifiers axe extended to envelope domain and base-band filter is applied to distortion complex envelope signal for description of the envelope memory effect. Using traditional one and two-tone tests, the coefficients of nonlinear model and the FIR filter can be extracted. At last the model has been applied to a 10 W WCDMA Power amplifier to predict its output signal. And simulation results show that the model output conforms very well to the traditional transistor level simulation results.

Simulink Behavioral Modeling of a 10-bit Pipelined ADC

The increasing architecture complexity of data converters makes it necessary to use behavioral models to simulate their electrical performance and to determine their relevant data features. For this purpose, a specific data converter simulation environment has been developed which allows designers to perform time-domain behavioral simulations of pipelined analog to digital converters （ADCs）. All the necessary blocks of this specific simulation environment have been implemented using the popular Matlab simulink environment. The purpose of this paper is to present the behavioral models of these blocks taking into account most of the pipelined ADC non-idealities, such as sampling jitter, noise, and operational amplifier parameters （white noise, finite DC gain, finite bandwidth, slew rate, and saturation voltages）. Simulations, using a 10-bit pipelined ADC as a design example, show that in addition to the limits analysis and the electrical features extraction, designers can determine the specifications of the basic blocks in order to meet the given data converter requirements.

用于学习和解释行人预期行为的群体交互场

Anticipating others’actions is innate and essential in order for humans to navigate and interact well with others in dense crowds.This ability is urgently required for unmanned systems such as service robots and self-driving cars.However,existing solutions struggle to predict pedestrian anticipation accurately,because the influence of group-related social behaviors has not been well considered.While group relationships and group interactions are ubiquitous and significantly influence pedestrian anticipation,their influence is diverse and subtle,making it difficult to explicitly quantify.Here,we propose the group interaction field(GIF),a novel group-aware representation that quantifies pedestrian anticipation into a probability field of pedestrians’future locations and attention orientations.An end-to-end neural network,GIFNet,is tailored to estimate the GIF from explicit multidimensional observations.GIFNet quantifies the influence of group behaviors by formulating a group interaction graph with propagation and graph attention that is adaptive to the group size and dynamic interaction states.The experimental results show that the GIF effectively represents the change in pedestrians’anticipation under the prominent impact of group behaviors and accurately predicts pedestrians’future states.Moreover,the GIF contributes to explaining various predictions of pedestrians’behavior in different social states.The proposed GIF will eventually be able to allow unmanned systems to work in a human-like manner and comply with social norms,thereby promoting harmonious human-machine relationships.

Prospect Theory Based Individual Irrationality Modelling and Behavior Inducement in Pandemic Control

Understanding and modeling individuals’behaviors during epidemics is crucial for effective epidemic control.However,existing research ignores the impact of users’irrationality on decision-making in the epidemic.Meanwhile,existing disease control methods often assume users’full compliance with measures like mandatory isolation,which does not align with the actual situation.To address these issues,this paper proposes a prospect theorybased framework to model users’decision-making process in epidemics and analyzes how irrationality affects individuals’behaviors and epidemic dynamics.According to the analysis results,irrationality tends to prompt conservative behaviors when the infection risk is low but encourages risk-seeking behaviors when the risk is high.Then,this paper proposes a behavior inducement algorithm to guide individuals’behaviors and control the spread of disease.Simulations and real user tests validate our analysis,and simulation results show that the proposed behavior inducement algorithm can effectively guide individuals’behavior.

Observation on the Effect of Parental Participation in Nursing Under the IMCHB Model in Neonatal Hypoxic-Ischemic Encephalopathy

Objective:To investigate the clinical effects of parental participation in nursing under the Interaction Model of Client Health Behavior(IMCHB)model in neonatal hypoxic-ischemic encephalopathy(HIE).Methods:The First Affiliated Hospital of Gannan Medical University included 46 newborns with HIE admitted from October 2021 to October 2023 into the study population.They were divided into a control group and an observation group according to the random number table method,with the control group adopting routine nursing,and the observation group implementing parental participation in nursing under the IMCHB model.The indicators of physical,intellectual,and psychomotor development of the two groups were compared before and after nursing.Results:The physical,intellectual,and psychomotor development of the observation group was higher than that of the control group after 3 months of nursing,and the difference was statistically significant(P<0.05).Conclusion:The implementation of the IMCHB model of parental participation in the clinical care of HIE neonates can further promote their physical,intellectual,and psychomotor development.

Modeling for Ethernet passive optical network receiver

A behavior model for the receiver of the Ethernet passive optical network（EPON） is presented. The model consists of a fiber, a photodetector, a transimpedance amplifier （TIA） followed by a limiting amplifier and a clock and data recovery＇ circuit （CDR）. Each sub-model is constructed based on the architecture of a circuit. The noise and jitter in each block such as shot noise, thermal noise, deterministic and random jitter are also considered. The performance of the whole receiver can be evaluated by the simulation of the behavior model, which is faster than the ordinary circuit model and more accurate than the analytical model. The whole model is implemented with C ＋＋ and simulated in Microsoft Visual C ＋＋ 6. 0. Using the Monte Carlo method, the EPON receiver is simulated. The simulation results show a good agreement with experimental ones.

High-temperature Creep Behavior Characterization of Asphalt Mixture based on Micromechanical Modeling and Virtual Test

The high-temperature creep behavior of asphalt mixture was investigated based on micromechanical modeling and virtual test by using three-dimensional discrete element method（DEM）. A user-defined micromechanical model of asphalt mixture was established after analyzing the irregular shape and gradation of coarse aggregates, the viscoelastic property of asphalt mastic, and the random distribution of air voids within the asphalt mixture. Virtual uniaxial static creep test at 60 ℃ was conducted by using Particle Flow Code in three dimensions（PFC3D） and was validated by laboratory test. Based on virtual creep test, the micromechanical characteristics between aggregates, within asphalt mastic, and between aggregate and asphalt mastic were analyzed for the asphalt mixture. It is proved that the virtual test based on the micromechanical model can efficiently predict the creep deformation of asphalt mixture. And the high-temperature behavior of asphalt mixture was characterized from micromechanical perspective.

Non-Overlapping Conditions to Enable Multi- Dimensional Behavioral Models/DPDs for Multi-Band or Non-Continuous Carrier Aggregation Systems

To linearize the multi.band PAs/transmitters, a serial of multi.band predistortion models based on multi.dimensional architecture have been proposed. However, most of these models work properly only for the signals whose harmonic and intermodulation products of carriers' non.overlap with the interested fundamental bands. In this paper, the non.overlapping conditions for dual.band and tri.band signals are derived and denoted in the form of closed.form expression. It can be used to verify whether a given dual.band/multi.band signals can be linearized properly by these multi.dimensional behavioral models. Also the conditions can be used to plan the frequency spacing and maximum bandwidth of a multi.band or non.continuous carrier aggregation signal. Several dual.band and triband signals were tested on the same PA, by employing 2.D DPD and 3.D DPD behavioral models. The measurement results show that the signals which don't satisfy the non.overlapping conditions cannot be linearized well by the multi.dimensional behavioral models which does not take the harmonic and intermodulation products of carriers' into account.

Symbolic Macromodeling for Statistical Simulation of Operational Amplifiers

Symbolic circuit simulator is traditionally applied to the small-signal analysis of analog circuits. This paper establishes a symbolic behavioral macromodeling method applicable to both small-signal and large-signal analysis of general two-stage operational amplifiers （op-amps）. The proposed method creates a two-pole parametric macromodel whose parameters are analytical functions of the circuit element parameters generated by a symbolic circuit simulator. A moment matching technique is used in deriving the analytical model parameter. The created parametric behavioral model can be used for op-amps performance simulation in both frequency and time domains. In particular, the parametric models are highly suited for fast statistical simulation of op-amps in the time-domain. Experiment results show that the statistical distributions of the op-amp slew and settling time characterized by the proposed model agree well with the transistor-level results in addition to achieving significant speedup.

ANIMAL BEHAVIORAL MODELS OF TINNITUS

The pathophysiology of tinnitus is poorly understood and treatments are often unsuccessful. A number of animal models have been developed in order to gain a better understanding of tinnitus. A great deal has been learned from these models re- garding the electrophysiological and neuroanatomical correlates of tinnitus following exposure to noise or ototoxic drugs. Re- liable behavioral data is important for determining whether such electrophysiological or neuroanatomical changes are indeed related to tinnitus. Of the many documented tinnitus animal behavioral paradigms, the acoustic startle reflex had been pro- posed as a simple method to identify the presence or absence of tinnitus. Several behavioral models based on conditioned re- sponse suppression paradigms have also been developed. In addition to determining the presence or absence of tinnitus, some of the behavioral paradigms have provided signs of the onset, frequency, and intensity of tinnitus in animals. Although none of these behavioral models have been proved to be a perfect model, these studies provide useful information on understanding the neural mechanisms underlying tinnitus.

Student Behavior Modeling for an E-Learning System Offering Personalized Learning Experiences

With the advent of computing and communication technologies,it has become possible for a learner to expand his or her knowledge irrespective of the place and time.Web-based learning promotes active and independent learning.Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process.This digital learning improves the quality of teaching and also promotes educational equity.However,the challenges in e-learning platforms include dissimilarities in learner’s ability and needs,lack of student motivation towards learning activities and provision for adaptive learning environment.The quality of learning can be enhanced by analyzing the online learner’s behavioral characteristics and their application of intelligent instructional strategy.It is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning course.It is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user’s interest level.In this research work,a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner’s objective attainment.Catering to the demands of e-learner,an intelligent model is presented in this study for e-learning system that apply supervised machine learning algorithm.An adaptive e-learning system suits every category of learner,improves the learner’s performance and paves way for offering personalized learning experiences.

老年患者门诊预约挂号的影响因素:基于安德森健康行为模型的实证研究

目的基于安德森健康行为模型,研究老年患者门诊预约挂号的影响因素,为优化门诊预约挂号服务、推进老年患者预约挂号提供参考。方法以安德森健康行为模型为理论框架设计问卷,采用方便抽样法向上海某三级甲等综合医院门诊老年患者发放调查问卷。将各维度因素纳入二分类logistic回归模型,采用单因素分析和logistic回归分析探讨老年患者门诊预约挂号的影响因素。结果共回收282份有效问卷,预约挂号187人,占66.31%。曾使用过1~2种预约挂号服务、存在社会支持、距离医院路程80 min以上、收入为>5000~7500元、就诊科室预约率25.00%以上、有预约挂号使用意愿的老年患者更倾向于预约挂号。患慢性病数量在5种及以上的老年患者更不倾向于预约挂号。结论老年患者门诊预约需求较大且受多种因素影响。须关注老年人预约挂号“痛点”,关怀老年群体,共建支持网络,加强有效宣传,优化医疗资源配置,激发使用意愿。

Study on Volterra-Laguerre behavioral model for RF power amplifier

Voherra series behavioral model for radio frequency （RF） power amplifier （PA） has been widely used in system-level simulation, however, high computational complexity makes this kind of model limited to ＂weak＂ nonlinearity. In order to reduce the computational complexity and the number of coefficients of Volterra series kernels, a Volterra series improved behavioral model based on Laguerre orthogonal polynomials function, namely Voherra-Laguerre behavioral model, is proposed. Mathematical expressions of Volterra-Laguerre behavioral model is derived, and accuracy of the model is verified through comparison of measured and simulation output data from a freescale PA using MRF21030 transistor. Mathematical analysis and simulation results show that Voherra-Laguerre behavioral model has a simple structure, much less coefficients and better modeling performance than general Volterra series model. The model can be used more correctly for system-level simulation of RF PA with wideband signal.

Verilog HDL modeling and design of 10Gb/s SerDes full rate CDR in 65nm CMOS

Phase locked loop(PLL) is a typical analog-digital mixed signal circuit and a method of conducting a top level system verification including PLL with standard digital simulator becomes especially significant.The behavioral level model(BLM) of the PLL in Verilog-HDL for pure digital simulator is innovated in this paper,and the design of PLL based clock and data recovery(CDR)circuit aided with jitter attenuation PLL for SerDes application is also presented.The CDR employs a dual-loop architecture where a frequency-locked loop acts as an acquisition aid to the phase-locked loop.To simultaneously meet jitter tolerance and jitter transfer specifications defined in G.8251 of optical transport network(ITU-T OTN),an additional jitter attenuation PLL is used.Simulation results show that the peak-to-peak jitter of the recovered clock and data is 5.17 ps and 2.3ps respectively.The core of the whole chip consumes 72 mA current from a 1.0V supply.

Deep Neural Network Based Behavioral Model of Nonlinear Circuits

With the rapid growth of complexity and functionality of modern electronic systems, creating precise behavioral models of nonlinear circuits has become an attractive topic. Deep neural networks (DNNs) have been recognized as a powerful tool for nonlinear system modeling. To characterize the behavior of nonlinear circuits, a DNN based modeling approach is proposed in this paper. The procedure is illustrated by modeling a power amplifier (PA), which is a typical nonlinear circuit in electronic systems. The PA model is constructed based on a feedforward neural network with three hidden layers, and then Multisim circuit simulator is applied to generating the raw training data. Training and validation are carried out in Tensorflow deep learning framework. Compared with the commonly used polynomial model, the proposed DNN model exhibits a faster convergence rate and improves the mean squared error by 13 dB. The results demonstrate that the proposed DNN model can accurately depict the input-output characteristics of nonlinear circuits in both training and validation data sets.

Dynamic Behavior Modeling in Multi-Agent System By Evolutionary Programming

In a multi-agent system, each agent must adapt itself to the environment and coordinate with other agents dynamically. TO predict or cooperate with the behavior of oiller agents. An agent should dynamically establish and evolve the cooperative behavior model of itself. In this paper, we represent the behavior model of an agent as a f-mite state machine and propose a new method of dynamically evolving the behavior model of an agent by evolutionary programming.

Constitutive modeling for mechanical behaviors of geomaterials, newdesigns and techniques in geotechnical engineering

This Special Issue of the Journal of Rock Mechanics and GeotechnicalEngineering （JRMGE） contains 13 papers prepared by internationalexperts on various general topics in geomechanics, rockmechanics and geotechnical engineering. It represents a usefulmix of theoretical developments, testing and practical applications.We present in the following brief details in the papers, alphabeticallyin accordance with the last name of the first author.Barla presents a review of tunneling techniques with emphasison the full-face method combining full-face excavation and facereinforcement by means of fiber-glass elements with a yieldcontrolsupport. This method has been used successfully in difficultgeologic conditions, and for a wide spectrum of ground situations.The validation of the method with respect to the Saint Martin LaPorte access adit along the LyoneTurin Base tunnel experiencingseverely squeezing conditions during excavation is also includedin the paper. The numerical modeling with consideration of therock mass time-dependent behavior showed a satisfactory agreementwith monitoring results.