Modeling and Comprehensive Review of Signaling Storms in 3GPP-Based Mobile Broadband Networks:Causes,Solutions,and Countermeasures

Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.

Real-time model correction using Kalman filter for Raman-controlled cell culture processes

Raman spectroscopy has found extensive use in monitoring and controlling cell culture processes.In this context,the prediction accuracy of Raman-based models is of paramount importance.However,models established with data from manually fed-batch cultures often exhibit poor performance in Raman-controlled cultures.Thus,there is a need for effective methods to rectify these models.The objective of this paper is to investigate the efficacy of Kalman filter(KF)algorithm in correcting Raman-based models during cell culture.Initially,partial least squares(PLS)models for different components were constructed using data from manually fed-batch cultures,and the predictive performance of these models was compared.Subsequently,various correction methods including the PLS-KF-KF method proposed in this study were employed to refine the PLS models.Finally,a case study involving the auto-control of glucose concentration demonstrated the application of optimal model correction method.The results indicated that the original PLS models exhibited differential performance between manually fed-batch cultures and Raman-controlled cultures.For glucose,the root mean square error of prediction(RMSEP)of manually fed-batch culture and Raman-controlled culture was 0.23 and 0.40 g·L^(-1).With the implementation of model correction methods,there was a significant improvement in model performance within Raman-controlled cultures.The RMSEP for glucose from updating-PLS,KF-PLS,and PLS-KF-KF was 0.38,0.36 and 0.17 g·L^(-1),respectively.Notably,the proposed PLS-KF-KF model correction method was found to be more effective and stable,playing a vital role in the automated nutrient feeding of cell cultures.

Distributionally robust model predictive control for constrained robotic manipulators based on neural network modeling

A distributionally robust model predictive control(DRMPC)scheme is proposed based on neural network(NN)modeling to achieve the trajectory tracking control of robot manipulators with state and control torque constraints.First,an NN is used to fit the motion data of robot manipulators for data-driven dynamic modeling,converting it into a linear prediction model through gradients.Then,by statistically analyzing the stochastic characteristics of the NN modeling errors,a distributionally robust model predictive controller is designed based on the chance constraints,and the optimization problem is transformed into a tractable quadratic programming(QP)problem under the distributionally robust optimization(DRO)framework.The recursive feasibility and convergence of the proposed algorithm are proven.Finally,the effectiveness of the proposed algorithm is verified through numerical simulation.

Dynamics modeling and optimal control for multi-information diffusion in Social Internet of Things

As an ingenious convergence between the Internet of Things and social networks,the Social Internet of Things(SIoT)can provide effective and intelligent information services and has become one of the main platforms for people to spread and share information.Nevertheless,SIoT is characterized by high openness and autonomy,multiple kinds of information can spread rapidly,freely and cooperatively in SIoT,which makes it challenging to accurately reveal the characteristics of the information diffusion process and effectively control its diffusion.To this end,with the aim of exploring multi-information cooperative diffusion processes in SIoT,we first develop a dynamics model for multi-information cooperative diffusion based on the system dynamics theory in this paper.Subsequently,the characteristics and laws of the dynamical evolution process of multi-information cooperative diffusion are theoretically investigated,and the diffusion trend is predicted.On this basis,to further control the multi-information cooperative diffusion process efficiently,we propose two control strategies for information diffusion with control objectives,develop an optimal control system for the multi-information cooperative diffusion process,and propose the corresponding optimal control method.The optimal solution distribution of the control strategy satisfying the control system constraints and the control budget constraints is solved using the optimal control theory.Finally,extensive simulation experiments based on real dataset from Twitter validate the correctness and effectiveness of the proposed model,strategy and method.

Mathematical Modeling and Control Algorithm Development for Bidirectional Power Flow in CCS-CNT System

As the demand for more efficient and adaptable power distribution systems intensifies, especially in rural areas, innovative solutions like the Capacitor-Coupled Substation with a Controllable Network Transformer (CCS-CNT) are becoming increasingly critical. Traditional power distribution networks, often limited by unidirectional flow capabilities and inflexibility, struggle to meet the complex demands of modern energy systems. The CCS-CNT system offers a transformative approach by enabling bidirectional power flow between high-voltage transmission lines and local distribution networks, a feature that is essential for integrating renewable energy sources and ensuring reliable electrification in underserved regions. This paper presents a detailed mathematical representation of power flow within the CCS-CNT system, emphasizing the control of both active and reactive power through the adjustment of voltage levels and phase angles. A control algorithm is developed to dynamically manage power flow, ensuring optimal performance by minimizing losses and maintaining voltage stability across the network. The proposed CCS-CNT system demonstrates significant potential in enhancing the efficiency and reliability of power distribution, making it particularly suited for rural electrification and other applications where traditional methods fall short. The findings underscore the system's capability to adapt to varying operational conditions, offering a robust solution for modern power distribution challenges.

A Hybrid Approach to Modeling and Control of Vehicle Height for Electronically Controlled Air Suspension

The control problems associated with vehicle height adjustment of electronically controlled air suspension （ECAS） still pose theoretical challenges for researchers, which manifest themselves in the publications on this subject over the last years. This paper deals with modeling and control of a vehicle height adjustment system for ECAS, which is an example of a hybrid dynamical system due to the coexistence and coupling of continuous variables and discrete events. A mixed logical dynamical （MLD） modeling approach is chosen for capturing enough details of the vehicle height adjustment process. The hybrid dynamic model is constructed on the basis of some assumptions and piecewise linear approximation for components nonlinearities. Then, the on-off statuses of solenoid valves and the piecewise approximation process are described by propositional logic, and the hybrid system is transformed into the set of linear mixed-integer equalities and inequalities, denoted as MLD model, automatically by HYSDEL. Using this model, a hybrid model predictive controller （HMPC） is tuned based on online mixed-integer quadratic optimization （MIQP）. Two different scenarios are considered in the simulation, whose results verify the height adjustment effectiveness of the proposed approach. Explicit solutions of the controller are computed to control the vehicle height adjustment system in realtime using an offline multi-parametric programming technology （MPT）, thus convert the controller into an equivalent explicit piecewise affine form. Finally, bench experiments for vehicle height lifting, holding and lowering procedures are conducted, which demonstrate that the HMPC can adjust the vehicle height by controlling the on-off statuses of solenoid valves directly. This research proposes a new modeling and control method for vehicle height adjustment of ECAS, which leads to a closed-loop system with favorable dynamical properties.

Modeling and control design of a bronchoscope robotics system

For the pulmonary nodule biopsy problem,this paper investigates an intervention robotics system based on the bronchoscope.To achieve precise control of the system,the mathematical model is established at first.The system mainly consists of an inserting tube and a tube tip,which are modeled based on a moving flexible beam and a soft robot,respectively.In order to reduce the harm to the patient during the operation,the robot system should suppress the vibration of the inserting tube during the process of tracking the desired trajectory in the bronchus.Therefore,we use the boundary control algorithm to design a controller capable of suppressing vibration for the inserting tube.Meanwhile,in order for the robot system to accurately select the bronchus corresponding to the branch,we designed a soft robot controller that can bend or deflect the soft robot to a specified posture.Finally,we used theoretical proof and numerical simulation to verify the conclusion.

Sampled-data modeling and dynamical effect of output-capacitor time-constant for valley voltage-mode controlled buck-boost converter

By analyzing the output voltage ripple of a buck-boost converter with large equivalent series resistance（ESR） of output capacitor, one valley voltage-mode controller for buck-boost converter is proposed. Considering the fact that the increasing and decreasing slopes of the inductor current are assumed to be constant during each switching cycle, an especial sampleddata model of valley voltage-mode controlled buck-boost converter is established. Based on this model, the dynamical effect of an output-capacitor time-constant on the valley voltage-mode controlled buck-boost converter is revealed and analyzed via the bifurcation diagrams, the movements of eigenvalues, the Lyapunov exponent spectra, the boundary equations,and the operating-state regions. It is found that with gradual reduction of output-capacitor time-constant, the buck-boost converter in continuous conduction mode（CCM） shows the evolutive dynamic behavior from period-1 to period-2, period-4, period-8, chaos, and invalid state. The stability boundary and the invalidated boundary are derived theoretically by stability analysis, where the stable state of valley voltage-mode controlled buck-boost converter can enter into an unstable state, and the converter can shift from the operation region to a forbidden region. These results verified by time-domain waveforms and phase portraits of both simulation and experiment indicate that the sampled-data model is correct and the time constant of the output capacitor is a critical factor for valley voltage-mode controlled buck-boost converter, which has a significant effect on the dynamics as well as control stability.

A new modeling and control scheme for thyristor-controlled series capacitor

In order to design an optimal controller for the thyristor controlled series capacitor （TCSC）, a novel TCSC control model is developed. In the model, the delay angle of thyristor valves is the input, and the inductor current is chosen as the output. Theoretical analysis and simulation studies show that TCSC is a non-linear system and its parameters vary with the operating point. In consideration of the special characteristics of the TCSC, an improved model algorithmic control （1MAC） scheme is proposed to control TCSC effectively. The good performance can be observed from simulation results when IMAC is applied to a series compensated radial system.

Study of modeling and simulation of full digital controlled PMIG/MAG welding system based on Matlab/Simulink

This paper presents an integrated simulation model for full digital controlled PMIG/MAG welding system with Matlab/Simulink, and it consists of power inverter, digital control system and dynamic arc-load model. An integrated simulation study was done for full digital PMIG/MAG welding, and a method of connecting dynamic arc-load model to the system with controlled current source was presented, in addition, the simulation results were utilized to study the issues of digital control PMIG/MAG welding in this paper. The experimental results validated the developed simulation model, and this simulation study can be applied in implementation of the full digital PMIG/MAG welding and analysis of system dynamic process.

Generalized electro-biothermo-fluidic and dynamicalmodeling of cancer growth: state-feedback controlled cesium therapy approach

This paper develops a generalized dynamical model to describe the interactive dynamics between normal cells, tumor cells, immune cells, drug therapy, electromagnetic field of the human cells, extracellular heat and fluid transfer, and intercellular fractional mass of Oxygen, cell acidity and Pancreatin enzyme. The overall dynamics stability, controllability and observability have been investigated. Moreover, Cesium therapy is considered as a control input to the 11-dimensional dynamics using state-feedback controlled system and pole placement technique. This approach is found to be effective in driving the desired rate of tumor cell kill and converging the system to healthy equilibrium state. Furthermore, the ranges of the system dynamics parameters which lead to instability and growth of tumor cells have been identified. Finally, simulation results are demonstrated to verify the effectiveness of the applied approach which can be implemented successfully to cancer patients.

Theoretical modeling of airways pressure waveform for dual-controlled ventilation with physiological pattern and linear respiratory mechanics

The present paper describes the theoretical treatment performed for the geometrical optimization of advanced and improved-shape waveforms as airways pressure excitation for controlled breathings in dual-controlled ventilation applied to anaesthetized or severe brain injured patients, the respiratory mechanics of which can be assumed linear. Advanced means insensitive to patient breathing activity as well as to ventilator settings while improved-shape intends in comparison to conventional square waveform for a progressive approaching towards physiological transpulmonary pressure and respiratory airflow waveforms. Such functional features along with the best ventilation control for the specific therapeutic requirements of each patient can be achieved through the implementation of both diagnostic and compensation procedures effectively carried out by the Advance Lung Ventilation System (ALVS) already successfully tested for square waveform as airways pressure excitation. Triangular and trapezoidal waveforms have been considered as airways pressure excitation. The results shows that the latter fits completely the requirements for a physiological pattern of endoalveolar pressure and respiratory airflow waveforms, while the former exhibits a lower physiological behaviour but it is anyhow periodically recommended for performing adequately the powerful diagnostic procedure.

Analytical, Spectral, Thermal and Molecular Modeling Studies of Hg2+-2,3-Butanedionemonoxime Girard’s T Hydrazone Complex and Its Application

The coordination behavior of 2,3-butanedionemonoxime Girard’s T hydrazone (L1) towards Hg2+ ion has been investigated. The structure of Hg2+ complex, [Hg(L1)Cl]Cl·5H2O, is elucidated using elemental analyses, spectral (IR, UV-visible, 1H-NMR and mass) and TGA measurements. IR spectrum suggests that L1 behaves in a bidentate manner through the azomethine groups. The molecular modeling of L1 and its Hg2+ complex has been investigated. The bond lengths, bond angles, HOMO and LUMO have been calculated. The thermal behavior and kinetic parameters are determined using Coats-Redfern method. The use of L1 for preconcentration and separation via flotation of Hg2+ complex and determination using cold vapor atomic spectrometry (CVAAS) is described. The effects on the percentage of recovered Hg2+ by pH of sample solutions, oleic acid (HOL) concentration, Hg2+ and L1 concentrations are studied in details. The method is applied for the determination of the total Hg2+ (mg·mL-1) in natural water samples.

Fractional Order Modeling of Human Operator Behavior with Second Order Controlled Plant and Experiment Research

Modeling human operator's dynamics plays a very important role in the manual closed-loop control system, and it is an active research area for several decades. Based on the characteristics of human brain and behavior, a new kind of fractional order mathematical model for human operator in single-input single-output (SISO) systems is proposed. Compared with the traditional models based on the commonly used quasilinear transfer function method or the optimal control theory method, the proposed fractional order model has simpler structure with only few parameters, and each parameter has explicit physical meanings. The actual data and experiment results with the second-order controlled plant illustrate the effectiveness of the proposed method. © 2014 Chinese Association of Automation.

Propofol Target-Controlled Infusion Modeling in Rabbits:Pharmacokinetic and Pharmacodynamic Analysis

This study aimed to establish a new propofol target-controlled infusion（TCI） model in animals so as to study the general anesthetic mechanism at multi-levels in vivo. Twenty Japanese white rabbits were enrolled and propofol（10 mg/kg） was administrated intravenously. Artery blood samples were collected at various time points after injection, and plasma concentrations of propofol were measured. Pharmacokinetic modeling was performed using Win Nonlin software. Propofol TCI within the acquired parameters integrated was conducted to achieve different anesthetic depths in rabbits, monitored by narcotrend. The pharmacodynamics was analyzed using a sigmoidal inhibitory maximal effect model for narcotrend index（NI） versus effect-site concentration. The results showed the pharmacokinetics of propofol in Japanese white rabbits was best described by a two-compartment model. The target plasma concentrations of propofol required at light anesthetic depth was 9.77±0.23 μg/m L, while 12.52±0.69 μg/m L at deep anesthetic depth. NI was 76.17±4.25 at light anesthetic depth, while 27.41±5.77 at deep anesthetic depth. The effect-site elimination rate constant（ke0） was 0.263/min, and the propofol dose required to achieve a 50% decrease in the NI value from baseline was 11.19 μg/m L（95% CI, 10.25–13.67）. Our results established a new propofol TCI animal model and proved the model controlled the anesthetic depth accurately and stably in rabbits. The study provides a powerful method for exploring general anesthetic mechanisms at different anesthetic depths in vivo.

Principle, Modeling and Control of DC-DC Convertors for EV

DC DC convertors can convert the EV's high voltage DC power supply into the low voltage DC power supply. In order to design an excellent convertor one must be guided by theory of automatic control. The principle and the method of design, modeling and control for DC DC convertors of EV are introduced. The method of the system response to a unit step function input and the frequency response method are applied to researching the convertor's mathematics model and control characteristic. Experiments show that the designed DC DC convertor's output voltage precision is high, the antijamming ability is strong and the adjustable performance is fast and smooth.

Assessing the Impact of Using Different Land Cover Classification in Regional Modeling Studies for the Manaus Area,Brazil

Land cover classification is one of the main components of the modern weather research and forecasting models, which can influence the meteorological variable, and in turn the concentration of air pollutants. In this study the impact of using two traditional land use classifications, the United States Geological Survey (USGS) and the Moderate-resolution Imaging Spectroradiometer (MODIS), were evaluated. The Weather Research and Forecasting model (WRF, version 3.2.1) was run for the period 18 - 22 August, 2014 (dry season) at a grid spacing of 3 km centered on the city of Manaus. The comparison between simulated and ground-based observed data revealed significant differences in the meteorological fields, for instance, the temperature. Compared to USGS, MODIS classification showed better skill in representing observed temperature for urban areas of Manaus, while the two files showed similar results for nearby areas. The analysis of the files suggests that the better quality of the simulations favorable to the MODIS file is straightly related to its better representation of urban class of land use, which is observed to be not adequately represented by USGS.

The relationship between compartment models and their stochastic counterparts:A comparative study with examples of the COVID-19 epidemic modeling

Deterministic compartment models(CMs)and stochastic models,including stochastic CMs and agent-based models,are widely utilized in epidemic modeling.However,the relationship between CMs and their corresponding stochastic models is not well understood.The present study aimed to address this gap by conducting a comparative study using the susceptible,exposed,infectious,and recovered(SEIR)model and its extended CMs from the coronavirus disease 2019 modeling literature.We demonstrated the equivalence of the numerical solution of CMs using the Euler scheme and their stochastic counterparts through theoretical analysis and simulations.Based on this equivalence,we proposed an efficient model calibration method that could replicate the exact solution of CMs in the corresponding stochastic models through parameter adjustment.The advancement in calibration techniques enhanced the accuracy of stochastic modeling in capturing the dynamics of epidemics.However,it should be noted that discrete-time stochastic models cannot perfectly reproduce the exact solution of continuous-time CMs.Additionally,we proposed a new stochastic compartment and agent mixed model as an alternative to agent-based models for large-scale population simulations with a limited number of agents.This model offered a balance between computational efficiency and accuracy.The results of this research contributed to the comparison and unification of deterministic CMs and stochastic models in epidemic modeling.Furthermore,the results had implications for the development of hybrid models that integrated the strengths of both frameworks.Overall,the present study has provided valuable epidemic modeling techniques and their practical applications for understanding and controlling the spread of infectious diseases.

Discontinuity development patterns and the challenges for 3D discrete fracture network modeling on complicated exposed rock surfaces

Natural slopes usually display complicated exposed rock surfaces that are characterized by complex and substantial terrain undulation and ubiquitous undesirable phenomena such as vegetation cover and rockfalls.This study presents a systematic outcrop research of fracture pattern variations in a complicated rock slope,and the qualitative and quantitative study of the complex phenomena impact on threedimensional(3D)discrete fracture network(DFN)modeling.As the studies of the outcrop fracture pattern have been so far focused on local variations,thus,we put forward a statistical analysis of global variations.The entire outcrop is partitioned into several subzones,and the subzone-scale variability of fracture geometric properties is analyzed(including the orientation,the density,and the trace length).The results reveal significant variations in fracture characteristics(such as the concentrative degree,the average orientation,the density,and the trace length)among different subzones.Moreover,the density of fracture sets,which is approximately parallel to the slope surface,exhibits a notably higher value compared to other fracture sets across all subzones.To improve the accuracy of the DFN modeling,the effects of three common phenomena resulting from vegetation and rockfalls are qualitatively analyzed and the corresponding quantitative data processing solutions are proposed.Subsequently,the 3D fracture geometric parameters are determined for different areas of the high-steep rock slope in terms of the subzone dimensions.The results show significant variations in the same set of 3D fracture parameters across different regions with density differing by up to tenfold and mean trace length exhibiting differences of 3e4 times.The study results present precise geological structural information,improve modeling accuracy,and provide practical solutions for addressing complex outcrop issues.

Hybrid Dynamic Variables-Dependent Event-Triggered Fuzzy Model Predictive Control

This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.