Threshold Control Research Articles

Switching threshold event-triggered estimation and control for unmeasured oxygen excess ratio of automotive PEMFC air feeding system with input and prescribed performance constraints

Switching threshold event-triggered estimation and control for unmeasured oxygen excess ratio of automotive PEMFC air feeding system with input and prescribed performance constraints

Do Failed or Weak States Favor Resident Terrorist Groups’ Survival?

Employing two alternative measures of state failure, we investigate how state weakness influences resident terrorist groups’ survival. Theoretically, state failure favors resident terrorist groups’ survival, while state territorial control fosters resident groups’ termination until some control threshold. Empirically, we uncover a robust negative relationship between a country’s weakness and its control of terrorism through the lens of the resident terrorist groups’ survival prospects. The discovered relationship withstands a host of robustness tests – e.g., alternative estimates and samples. We apply an instrument designed to address endogeneity concerns. In particular, our novel instrument for failed states consists of the interaction between natural disasters and ethnic fractionalization. As a state’s percentage of territorial control increases, resident terrorist groups are more prone to ending until some threshold control percent. Our analysis can guide counterterrorism policy by exploiting the nuanced theoretical determinants identified here that foster resident groups’ termination in failing states.

Dynamics of a non-smooth pest-natural enemy model with the threshold control strategy

Pest issues have always been the focus of attention in agriculture. The Integrated Pest Management(IPM) method is currently the most popular way to be applied for pest control. In this study, according to the IPM strategy, we regard pest quantity as a threshold index and extend the Leslie-Gower model into a non-smooth Filippov system through combining chemical and biological control. To maintain the pest population at or below the given economic threshold(ET), we investigate the global dynamics of the proposed model, including the existence of sliding mode and various equilibria, sliding dynamics and bifurcations, and global stability of equilibria. The result shows that desired equilibria can be globally stable under some conditions, meaning that our control tactics work. In particular, the case where our strategy fails to be effective arouses interest. In the end, the biological implications of the results are discussed and given in detail.

Density-wave ordering in a unitary Fermi gas with photon-mediated interactions

A density wave (DW) is a fundamental type of long-range order in quantum matter tied to self-organization into a crystalline structure. The interplay of DW order with superfluidity can lead to complex scenarios that pose a great challenge to theoretical analysis. In the past decades, tunable quantum Fermi gases have served as model systems for exploring the physics of strongly interacting fermions, including most notably magnetic ordering1, pairing and superfluidity2, and the crossover from a Bardeen–Cooper–Schrieffer superfluid to a Bose–Einstein condensate3. Here, we realize a Fermi gas featuring both strong, tunable contact interactions and photon-mediated, spatially structured long-range interactions in a transversely driven high-finesse optical cavity. Above a critical long-range interaction strength, DW order is stabilized in the system, which we identify via its superradiant light-scattering properties. We quantitatively measure the variation of the onset of DW order as the contact interaction is varied across the Bardeen–Cooper–Schrieffer superfluid and Bose–Einstein condensate crossover, in qualitative agreement with a mean-field theory. The atomic DW susceptibility varies over an order of magnitude upon tuning the strength and the sign of the long-range interactions below the self-ordering threshold, demonstrating independent and simultaneous control over the contact and long-range interactions. Therefore, our experimental setup provides a fully tunable and microscopically controllable platform for the experimental study of the interplay of superfluidity and DW order.

A segmented optimal PID method to consider both regulation performance and damping characteristic of hydroelectric power system

Hydropower has become the main force of grid frequency regulation due to its regulation flexibility and rapid response characteristics. However, when the operating conditions are changed, the continued pursuit of a faster response speed deteriorates the damping characteristics of the system with wider water-head, causing low-frequency oscillations, threatening the power station's safety and stability and the power grid. In this study, an improved quantized damping method is applied. The settling time and damping coefficient variation under different PID parameters and operating conditions are recorded, revealing the contradiction between regulation performance and damping characteristics. Then the segmented optimal PID controller is proposed to balance this contradiction. The twin-delayed deep deterministic policy gradient learning algorithm enables the controller to find the optimal policy online. With the settling time-damping threshold control strategy, the controller optimizes parameters according to operating conditions, and changes parameters when reaching the threshold. The results show that, compared with using a set of PID parameters, the damping of the hydropower system is increased by 0.35 from negative to positive. In contrast, the settling time increases by 11.63s within limits. The proposed controller ensures the safe and coordinated operation of the power grid and the power station of the hydroelectric power system with a wide water-head.

Privacy-Aware Access Control in IoT-Enabled Healthcare: A Federated Deep Learning Approach

The traditional healthcare is overwhelmed by the processing and storage of massive medical data. The emergence and gradual maturation of Internet-of-Things (IoT) technologies bring the traditional healthcare an excellent opportunity to evolve into the IoT-enabled healthcare of massive data storage and extraordinary data processing capability. However, in IoT-enabled healthcare, sensitive medical data are subject to both privacy leakage and data tampering caused by unauthorized users. In this article, an attribute-based secure access control mechanism, coined (SACM), is proposed for IoT-Health utilizing the federated deep learning (FDL). Specifically, we manage to discover the relationship between users’ social attributes and their trusts, which is the trustworthiness of users rely on their social influences. By applying graph convolutional networks to the social graph with the susceptible–infected–recovered model-based loss function, users’ influences are obtained and then are transformed to their trusts. For each occupation, users’ trusts allow them to access specific medical data only if their trusts are higher than the corresponding threshold. Then, the FDL is applied to obtain the optimal threshold and relevant access control parameters for the improvement of access control accuracy and the enhancement of privacy preservation. The experimental results show that the proposed SACM achieves accurate access control in IoT-enabled healthcare with high data integrity and low privacy leakage.

Optimal threshold strategies for retrial systems with queue

In this article, we deal with Markov models of multi-channel [M|M|m|m+n](∞)–systems with repeated calls and queues with a variable rate of input flow controlled by threshold strategies without restrictions on the capacity of the orbit. An effective calculating algorithm for the characteristics of such systems in stationary regimes is proposed. First, stationary probabilities for a truncated model are investigated. Second, the obtained result is extended to a general model with an infinite number of servers. For threshold control strategies the optimization problem of the total income of the system is formulated and solved. In a particular case with one server and one place in the queue, the direct formulas for stationary probabilities are obtained, and the rate of convergence of the stationary distribution of a finite system to that of an infinite system under threshold strategies are estimated.

Dynamics analysis and Hamilton energy control of a class of Filippov neuron model

Electromagnetic induction plays a key role in regulating the electrical activity, excitability, and bistable structure of neurons. In this paper, a discontinuous control strategy with membrane potential as the threshold is introduced to the HR neuron model under the influence of electromagnetic field (EMFN model), and then we establish a Filipov EMFN neuron model to realize the regulation effect of electromagnetic field on the neuron system. Specific work of this paper reads as follows: first, the existence and stability of equilibrium points of two subsystems are analyzed by using MatCont software; second, the bistable region and its internal mechanism are discussed in detail by two-parameter bifurcation analysis; third, the mechanism of bistability and a series of complex sliding mode dynamics including sliding segment and sliding bifurcations are further revealed with the help of the fast–slow variable dissection method; finally, based on Hamilton energy, the regulation of threshold on chaotic discharge in the Filippov EMFN neuron model is explored. The numerical simulation results show that the EMFN neuron model can produce the corresponding sliding limit cycle and sliding bursting behaviors under the influence of the threshold control strategy, meanwhile, the chaotic behavior of the new system can be controlled effectively within a certain range. The results provide ideas for controlling the effects of electromagnetic fields on the biological neuronal system and exploring the regulation mechanism of neurological diseases.

Practical active traction control for independent‐wheel‐drive electric vehicle based on slip‐ratio threshold determination

AbstractThe traditional traction control system (TCS) based on hydraulic braking only works when the wheels are slipping, which will cause the problem of slow response to extreme slip. In addition, the TCS of four‐wheel‐independent‐drive electric vehicle (4WIDEV) is often based on road adhesion characteristics identification or optimal slip ratio identification to implement active control, which is difficult to achieve in engineering. Aiming at this problem, a practical active TCS is proposed in this paper. Firstly, according to the wheel slip state of the front and rear axles, the dynamic transfer of torque between axles is realized to maintain the vehicle propulsion power. Second, the adhesion conditions between road and tire are classified, and two sets of target slip ratio thresholds are formulated for high and low adhesion pavement, respectively. Then the current road adhesion coefficient is estimated by using the advantage that the in‐wheel motor torque can be obtained in real‐time. Thirdly, the overall framework of the control strategy is established, the logic threshold control algorithm is adopted for tracking the wheel target slip ratio. Finally, the simulation results show that the proposed active TCS can improve the vehicle power and avoid excessive wheel slipping.

Media impact research: a discrete SIR epidemic model with threshold switching and nonlinear infection forces.

The media's coverage has the potential to impact human behavior and aid in the control of emergent infectious diseases. We aim to quantify and evaluate the extent to which media coverage can influence infectious disease control through a mathematical model, thus proposing a switching epidemic model that considers the effect of media coverage. The threshold strategy incorporates media influence only when the number of infected cases surpasses a specific threshold; otherwise, it is disregarded. When conducting qualitative analysis of two subsystems, focusing on the existence and stability of equilibria. Using numerical methods, the codimension-2 bifurcation analysis is adopted here to investigate the various types of equilibria within the switching system that play a vital role in pest control. On the other hand, codimension-1 bifurcation analysis reveals the existence of periodic, chaotic solutions, period-doubling bifurcations, multiple attractors and other complexities within the proposed model, which could pose challenges in disease control. Additionally, the impact of key parameters on epidemic outbreaks is analyzed, such as the initial values of susceptible and infective individuals, and discuss the potential benefits of mass media coverage in preventing emerging infectious diseases. The modeling and analytical techniques developed for threshold control strategies can be applied to other disease control efforts.

Threshold control strategy for a Filippov model with group defense of pests and a constant-rate release of natural enemies.

In this paper, we establish an integrated pest management Filippov model with group defense of pests and a constant rate release of natural enemies. First, the dynamics of the subsystems in the Filippov system are analyzed. Second, the dynamics of the sliding mode system and the types of equilibria of the Filippov system are discussed. Then the complex dynamics of the Filippov system are investigated by using numerical analysis when there is a globally asymptotically stable limit cycle and a globally asymptotically stable equilibrium in two subsystems, respectively. Furthermore, we analyze the existence region of a sliding mode and pseudo equilibrium, as well as the complex dynamics of the Filippov system, such as boundary equilibrium bifurcation, the grazing bifurcation, the buckling bifurcation and the crossing bifurcation. These complex sliding bifurcations reveal that the selection of key parameters can control the population density no more than the economic threshold, so as to prevent the outbreak of pests.

Tuning parameters of phase retrieval algorithm for single-shot imaging based on object-modulated speckles by particle swarm optimization

Imaging through scattering media has become an important aspect in optical imaging. To achieve this, different approaches have been presented to recover the phase of an imaging object from its scattered speckle behind a scattering medium. However, all these algorithms require initial parameters which are obtained based on prior knowledge of the imaging object. This is impractical, as prior knowledge of the imaging object is not always available in real imaging applications. In this paper, we propose using a Particle Swarm Optimization algorithm to find these optimal parameters without requiring prior knowledge of the imaging object. These optimal values are then adopted in the Phase Retrieval algorithm based on object-modulated speckles for object image reconstruction behind a scattering medium in a single-shot imaging system. The obtention of the values for the standard deviation for Gaussian filter and the threshold control allows Phase Retrieval to converge faster and avoids the need for running the algorithm more than once in the search for the best reconstruction.

Impaired postural control in diabetes-a predictor of falls?

People with type 1 diabetes (T1D) and type 2 diabetes (T2D) are at risk of falling, but the association with impaired postural control is unclear. Therefore,the aim was to investigate postural control by measuring the center of pressure (CoP) during quiet standing and to estimate the prevalence ratio (PR) of falls and the fear of falling among people with diabetes compared to controls. In a cross-sectional study, participants with T1D (n = 111) and T2D (n = 106) and controls without diabetes (n = 328) were included. Study procedures consisted of handgrip strength (HGS), vibration perception threshold (VPT), orthostatism, visual acuity, and postural control during quiet stance measured by CoPArea (degree of body sway) and CoPVelocity (speed of the body sway) with "eyes open," "eyes closed" in combination with executive function tasks. A history of previous falls and fear of falling was collected by a questionnaire. CoPArea and CoPVelocity measurements were analyzed by using a multiple linear regression model. The PR of falls and the fear of falling were estimated by a Poisson regression model. Age, sex, BMI, previous falls, alcohol use, drug, HGS, VPT, orthostatism, episodes of hypoglycemia, and visual acuity were covariatesin multiple adjusted analyses. Significantly larger mean CoPArea measures were observed for participants with T1D (p = 0.022) and T2D (0.002), whereas mean CoPVelocity measures were only increased in participants with T2D (p = 0.027) vs. controls. Additionally, T1D and T2D participants had higher PRs for falls (p = 0.044, p = 0.014) and fear of falling (p = 0.006, p < 0.001) in the crude analyses, but the PRs reducedsignificantly when adjusted for mean CoPArea and mean CoPVelocity, respectively.Furthermore,multiple adjustedPRsweresignificantly higher than crude the analyses. CONCLUSION: Impaired postural control during quiet stance was seen in T1D and T2D compared with controlseven in the occurrence of well-known risk factors.and correlated well with a higher prevalence of falls.

Optimization of identification of micro-objects with error control and statistical image filtering mechanisms

Scientific and methodological foundations have been developed for the optimal identification of non-stationary objects transmitted by random time series in control systems of industrial and technological complexes, monitoring of environmental protection and ecology, palynology, medicine based on the use of statistical, dynamic, neural network models and mechanisms for extracting statistical, dynamic specific characteristics of information. The mechanisms for controlling the error in the identification of RTS, based on the use of statistical criteria - the rules of the sequential analysis, Bayesian estimation, ±3σ, confidence interval, have been investigated. The problem of control of the error in the identification of RTS by the confidence interval, the mechanisms of threshold control of the values of the random time series elements, their increments, the error of prediction by statistical, trend, dynamic models, algebraic polynomials, parabolic, cubic interpolation, extrapolation spline functions. Mechanisms for regulating the variables of the random time series identification models based on autoregressive, adaptive smoothing by R. Brown, Newton, Lagrange polynomials, orthogonal algebraic polynomials of 3, 5, 7 orders have been developed. The methodology for assessing the quality of the identification of RTS by mathematical expectation and variance and the criterion of correspondence between the relations of mathematical expectation and variance of two consecutive elements have been implemented. The experimental study was carried out according to the real data of the power grid enterprise. The effectiveness of the implemented mechanisms was studied according to the criteria of the minimum root-mean-square error, labor intensity, cost of information processing.

Research on Control Strategy of Light and CO2 in Blueberry Greenhouse Based on Coordinated Optimization Model

As essential environmental parameters in the greenhouse, appropriate light and CO2 will improve agricultural productivity and quality. Although many related studies have been carried out on the intelligent regulation of these environmental factors, the regulation of light and CO2 is usually controlled separately, and energy consumption is rarely considered. This paper proposed a coordinated control strategy for greenhouse light and CO2 based on the multi-objective optimization model. Firstly, the experiments on the net photosynthetic rate of blueberry under different temperatures, photon flux density, and CO2 concentration nesting were carried out to establish a blueberry net photosynthetic rate prediction model based on Support Vector Regression (SVR). Secondly, a model for calculating the energy cost of both light and CO2 was constructed. Thirdly, taking the maximum net photosynthetic rate and the minimum energy cost as the objective functions, the Non-dominated Sorting Genetic Algorithm (NSGA-II) was leveraged to obtain the Pareto optimal solutions of the target regulation values of light and CO2 concentration in different temperature ranges. Then, the optimal values were selected based on two different strategies. Finally, the multi-objective optimal control strategy proposed in this paper was compared with both the classical threshold control strategy and the Gaussian curvature maximization control strategy. The results indicated that the strategy which prioritized energy saving could reduce the energy cost by about 22.33% and 19.08%, respectively, under the premise that the net photosynthetic rate was consistent. Meanwhile, the strategy that prioritized production efficiency could increase the net photosynthetic rate by about 8.40% and 4.42%, respectively, with the same energy cost. In conclusion, the proposed multi-objective optimization control can improve the greenhouse climate control performance and reduce cost compared with other mentioned strategies.

Cooperative MEC With Non-Uniform User Density: Resource Planning and State Control Optimization

Mobile edge computing (MEC) allows mobile devices to offload computationally intensive tasks to nearby base stations equipped with computational resources. MEC also provides a solution for supporting low latency responses. However, MEC increases the system power consumption by adding extra computational units. It is important to carefully plan computational resources to achieve a balance between quality of service (QoS) and power consumption. Moreover, the MEC system control should shut down unused resources when traffic rates fall to further decrease the power consumption. In this letter, we investigate a workload-based core power state control for cooperative MEC systems and a heterogeneous resource planning method. We also formulate a joint resource planning and workload-threshold optimization problem for power consumption minimization. The problem is solved using a hybrid method using metaheuristic and heuristic algorithms. The problem is first solved for an equivalent uniform system using integer gaining and sharing knowledge (IGSK). The resources are then redistributed over the base stations using a heuristic that considers the effect of power state control on core distribution. The results show that the iterative algorithm can accomplish a satisfactory QoS with the same number of resources obtained by other approaches or less. Additionally, threshold control reduced the system power consumption by 19.6%.

Hierarchical energy management strategy for fuel cell/ultracapacitor/battery hybrid vehicle with life balance control

Fuel cell (FC) is an ideal power source for electric vehicles with high efficiency and little pollution. However, with its weak dynamic reaction, it needs to be used in combination with other energy storage devices, and the coupling of multiple energy sources makes it difficult to fully utilize the system performance. Meanwhile, the cost and durability of FC hinder its commercialization. This paper takes the composite energy storage system composed of FC, battery (BAT) and ultracapacitor (UC) as the research object. In order to improve the system performance, a hierarchical energy management strategy is proposed based on the analysis of the relationship between UC output power and vehicle demand power under dynamic programming (DP) strategy, taking into account the vehicle energy consumption and energy source lifetime. A fuzzy control based on speed prediction and a dynamic state-of-charge (SOC) threshold control optimization algorithm of UC based on working condition recognition are designed to optimize the hierarchical control strategy. In the upper layer strategy, for realizing the online real-time distribution of UC output power, the generalized regression neural network is used to learn the relationship between UC output power and demand power under DP strategy; in the lower layer strategy, the power of the FC and BAT is distributed using an equivalent energy minimization strategy. Based on above strategy, this paper proposes a life balance control strategy for energy sources to maximize the lifetime of the composite energy storage system. Simulation and experimental results show that the proposed strategy has a comparable life-cycle average operating cost to the DP strategy with better life mileage.

Thermal management control strategy of liquid-cooled fuel cell vehicle

The thermal management of proton exchange membrane fuel cells is an essential guarantee for the smooth operation of fuel cell vehicles. Most typical fuel cell cooling systems are controlled by threshold control, resulting in a strong coupling relationship between the inlet temperature and the difference of temperature between import and export. To address this problem, this paper proposes a decoupled control strategy and establishes a pump flow controller and a Fuzzy-PID fan controller. According to the fuel cell parameters, a Simulink-GT fuel cell cooling system model is built to verify the optimization strategy in the New European Driving Cycle (NEDC) and compared with the traditional threshold control strategy. The results show that the decoupled control strategy works well under the NEDC cycle test conditions. The fuel cell temperature can be stabilized around the set target value at all times, keeping the fuel cell operating in the high-efficiency range.

A Novel Deterministic Threshold Proxy Re-Encryption Scheme From Lattices

Aiming at the problem that it is difficult to flexibly realize, the sharing and efficient search of encrypted data in large data-bases, this paper proposes a deterministic threshold proxy re-encryption scheme under the auxiliary input model. This scheme uses Shamir's secret sharing technology to achieve threshold control, uses homomorphic signature technology to verify the legitimacy of ciphertext, and applies deterministic algorithms to solve the search problem in large databases, while ensuring the user's control over their own data, and proves its security can reach indistinguishable semantic security (PRIV1-INDr) under the standard model. Compared with other schemes, this scheme not only shortens the length of the ciphertext and improves the decryption efficiency, but it also has anti-auxiliary input, robustness, and multi-hop characteristics and can better meet actual needs.

Study on the Control Algorithm of Automatic Emergency Braking System (AEBS) for Commercial Vehicle Based on Identification of Driving Condition

Automatic emergency braking systems (AEBS) significantly improve the active safety performance of commercial vehicles, but their effectiveness is affected by the vehicle’s driving conditions, which mainly include the vehicle load and road conditions. In order to improve the adaptability of the AEBS, an AEBS control strategy with adaptive driving conditions was proposed and validated using a simulation and experimentation. This AEBS control strategy was designed based on an estimation of the vehicle mass, the center of gravity position, road grade, and the tire-road friction coefficient. In the simulation and experimental verification, the braking deceleration and braking distance under different driving conditions were compared. The results show that the AEBS control strategy proposed in this paper can avoid collisions in all test scenarios and maintain a parking spacing of approximately 5 m. In an extreme test scenario with a full load and low tire–road friction, as compared with the fixed threshold control strategy, the warning can be issued 0.2 s earlier and the maximum intensity braking can be carried out 0.5 s earlier.