Threshold Strategy Research Articles

Composite neural learning event-triggered control for dynamic positioning vehicles with the fault compensation mechanism

This paper focus on the novel composite neural learning event-triggered control for dynamic positioning vehicles with the fault compensation mechanism. In the proposed algorithm, the system uncertainties are tackled with by incorporating the dynamic surface control (DSC) and the robust neural damping techniques. Besides, the serial–parallel estimation model (SPEM) is developed to produce the prediction errors, which could be employed to derive the corresponding composite adaptive law. And the unknown actuator faults and gain uncertainties are effectively compensated for merits of the composite intelligent learning method. Furthermore, the idea of relative threshold strategy is utilized to construct the event-triggered mechanism. The event-triggered input could reduce the communication burden in the channel from controller to actuators. Through the direct Lyapunov theory, the parameters setting can be derived to guarantee the semi-global uniformly ultimately bounded (SGUUB) stability of all error signals in the closed-loop system. Finally, the effectiveness of the proposed algorithm is validated through the simulation experiments.

Cautious stochastic choice, optimal stopping and deliberate randomization

We study cautious stochastic choice (CSC) agents facing optimal timing decisions in a dynamic setting. In an expected utility setting, the optimal strategy is always a threshold strategy—to stop/sell the first time the price process exits an interval. In contrast, we show that in the CSC setting, where the agent has a family of utility functions and is concerned with the worst case certainty equivalent, the optimal strategy may be of non-threshold form and may involve randomization. We provide some carefully constructed examples, including one where we can solve explicitly for the optimal stopping rule and show it is a non-trivial mixture of threshold strategies. Our model is consistent with recent experimental evidence in dynamic setups whereby individuals do not play cut-off or threshold strategies.

Queueing Information Management of Primary Care Delivery With E-Visits

The goal of this work is to investigate the system configuration and information management of primary care delivery with electronic visits (e-visits). We consider a medical institution employing primary care physicians and other clinicians that offer office visits (in-person) and e-visits (through secure messaging from patient portals), and where different queue-joining behaviors: denoted as the mixed strategy, the duplication strategy and the threshold strategy are adopted by flexible patients based on different system configurations. Different queueing models are developed to capture flexible patients' queue-joining behaviors according to queueing information provision. In particular, we develop the equilibrium behavior of a dual server system where state information is available for one of the servers and the flexible patient exhibits a utility-maximizing behavior, which extends the literature on the analysis of queueing systems with strategic customers. The duplication strategy with deletion offers the least expected waiting time for the patients, and the threshold strategy provides the next best performance which is superior to the mixed strategy, which demonstrates the value of information. IEEE

Informative pairs mining based adaptive metric learning for adversarial domain adaptation

Adversarial domain adaptation has made remarkable in promoting feature transferability, while recent work reveals that there exists an unexpected degradation of feature discrimination during the procedure of learning transferable features. This paper proposes an informative pairs mining based adaptive metric learning (IPM-AML), where a novel two-triplet-sampling strategy is advanced to select informative positive pairs from the same classes and informative negative pairs from different classes, and a metric loss imposed with special weights is further utilized to adaptively pay more attention to those more informative pairs which can adaptively improve discrimination. Then, we incorporate IPM-AML into popular conditional domain adversarial network (CDAN) to learn feature representation that is transferable and discriminative desirably (IPM-AML-CDAN). To ensure the reliability of pseudo target labels in the whole training process, we select more confident target ones whose predicted scores are higher than a given threshold T, and also provide theoretical validation for this simple threshold strategy. Extensive experiment results on four cross-domain benchmarks validate that IPM-AML-CDAN can achieve competitive results compared with state-of-the-art approaches.

Energy-saving service management technology of internet of things using edge computing and deep learning

The purpose is to solve the problems of high transmission rate and low delay in the deployment of mobile edge computing network, ensure the security and effectiveness of the Internet of things (IoT), and save resources. Dynamic power management is adopted to control the working state transition of Edge Data Center (EDC) servers. A load prediction model based on long-short term memory (LSTM) is creatively proposed. The innovation of the model is to shut down the server in idle state or low utilization in EDC, consider user mobility and EDC location information, learn the global optimal dynamic timeout threshold strategy and N-policy through trial and error reinforcement learning method, reasonably control the working state switching of the server, and realize load prediction and analysis. The results show that the performance of AdaGrad optimization solver is the best when the feature dimension is 3, the number of LSTM network layers is 6, the time series length is 30–45, the batch size is 128, the training time is 788 s, the number of units is 250, and the number of times is 350. Compared with the traditional methods, the proposed load prediction model and power management mechanism improve the prediction accuracy by 4.21%. Compared with autoregressive integrated moving average (ARIMA) load prediction, the dynamic power management method of LSTM load prediction can reduce energy consumption by 12.5% and realize the balance between EDC system performance and energy consumption. The system can effectively meet the requirements of multi-access edge computing (MEC) for low delay, high bandwidth and high reliability, reduce unnecessary energy consumption and waste, and reduce the cost of MEC service providers in actual operation. This exploration has important reference value for promoting the energy-saving development of Internet-related industries.

Fuzzy-based dynamic event triggering formation control for nonstrict-feedback nonlinear MASs

This paper investigates the dynamic event triggering formation control issue of nonlinear multiagent systems (MASs) with asymmetric input saturation and unmeasured states. Based on fuzzy logic systems approximation technique, a high-gain fuzzy observer is constructed to estimate the unavailable states, where a leader-follower formation control strategy is proposed to form a desired time-varying formation. Incorporating the network-induced error and the relative threshold strategy, two new dynamic event triggering mechanisms and dynamic rules of threshold parameters are established, which reduce the amount of communication between controller and actuator. Moreover, to counteract the adverse effect of asymmetric input saturation, a modified auxiliary system containing the information interaction is developed with the same order as the plant. It is ensured that all closed-loop signals are semi-globally uniformly ultimately bounded. Finally, simulation results are presented to illustrate the effectiveness of the proposed event triggering fuzzy control scheme.

Evaluation of the effectiveness of frequency doubling technology perimetry in the diagnosis of optic neuropathies

PURPOSE. To compare the diagnostic effectiveness of two perimetric threshold strategies with frequency-doubling technology in patients with early primary open-angle glaucoma (POAG), the optical form of multiple sclerosis (MS) and papilledema in an open-label comparative clinical study.MATERIAL AND METHODS. The study involved 78 patients (105 eyes) with optic neuropathies (ON). The patients were divided into 3 groups depending on the etiology of optic neuropathy: the first group included 30 patients (46 eyes) with early POAG aged 30 to 65 years (54.9±1.3); the second group included 26 patients (26 eyes) diagnosed with the optic form of MS (an episode of retrobulbar optic neuritis in the medical history) aged 22 to 44 years (33.7±6.5); the third group consisted of 22 patients (33 eyes) aged from 18 to 66 years (35.7±14.9) with papilledema caused in the majority of cases by various brain tumors (25 eyes or 75.7%). The fourth (control) group consisted of 60 healthy individuals (60 eyes) aged 20 to 65 years, who were divided into 2 equal subgroups – younger (24.8±4.4) and older (56.4±3.9).Standard and non-standard perimetry was performed on all subjects during a comprehensive ophthalmic examination using the Humphrey 745i Visual Field Analyzer II (HFA II, «24-2» threshold strategy) (Germany-USA) and the author's own modification of Frequency Doubling Technology (FDT) Perimetry, in the form of 2 threshold strategies: the well-known «FDT-16» and the new «FDT-64».RESULTS. Both the «FDT-16» and the «FDT-64» threshold strategies were more effective in diagnosing glaucomatous optic neuropathy (GON), as confirmed by the higher sensitivity of their results to two criteria — the number of identified scotomas (n of scotomas n≥2), and the number of scotoma clusters in patients with POAG (88 and 100%; 95 and 83%, respectively) compared with those in patients with MS (61 and 76%; 85 and 54%, respectively) and papilledema (51 and 78%; 88 and 70%, respectively). The specificity of the «FDT-16» and «FDT-64» threshold strategies was significantly higher than the specificity of Humphrey perimetry (100, 80 and 63%, respectively).CONCLUSION. Both perimetric threshold strategies with frequency-doubling technology were found to be the most effective in detecting GON. This confirms that they are more sensitive in patients with early POAG when compared with the sensitivity in patients with MS and papilledema. The level of specificity of both FDT perimetry strategies far exceeds the level of specificity of Humphrey perimetry data, which indicates the advantage of FDT perimetry in separating healthy people from patients with ON, and not only of glaucoma genesis.

Active contour model based on local absolute difference energy and fractional-order penalty term

The active contour model (ACM) is one of the popular image segmentation methods. In practice, however, images are often corrupted by severe noise and intensity inhomogeneity, making traditional ACMs unavailable. In this paper, a novel ACM is presented to effectively cope with noise and intensity inhomogeneity. To better discriminate the object and background regions in inhomogeneous images, a new saliency fitting image (SFI), defined as the difference between the original image and the local-weighted image (LWI), is first constructed. In addition, the local mean absolute deviation (LMAD) based weight factor can reflect the degree of the intensity variation in a local region, and the LWI represents the bias component accounting for intensity inhomogeneity. Then, a local fitting energy term is defined as the absolute difference between two local fitting functions, which represent the local averages of image intensity inside/outside the contour in two windows with different sizes. And a threshold strategy is utilized to improve the robustness of the proposed model to noise. Finally, a fractional-order penalty term is defined to penalize the deviation between the level set function (LSF) and the signed distance function (SDF), which makes the proposed model avoid the side effect of the penalty energy term in traditional ACMs. Experimental results show the efficiency and accuracy of the proposed ACM.

Distributed Remote Estimation Over the Collision Channel With and Without Local Communication

Internet of Things networks are the large-scale distributed systems consisting of a massive number of simple devices communicating, typically, over a shared wireless medium. This new paradigm requires novel ways of coordinating access to limited communication resources without introducing unreasonable delays. Herein, the optimal design of a remote estimation system with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> sensors communicating with a fusion center via a collision channel of limited capacity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k\leq n$</tex-math></inline-formula> is considered. In particular, for independent and identically distributed observations with a symmetric probability density function, we show that the problem of minimizing the mean-squared error with respect to a threshold strategy is quasi-convex. When coordination among sensors via a local communication network is available, the online learning of possibly unknown parameters of the probabilistic model is possible, enabling each sensor to optimize its own threshold autonomously. We propose two strategies for remote estimation with local communication: 1) one strategy swiftly reaches the performance of the optimal decentralized threshold policy and 2) the second strategy approaches the performance of the optimal centralized scheme with a slower convergence rate. A hybrid scheme that combines the best of both approaches is proposed, offering fast convergence and excellent performance.

An improved algorithm of segmented orthogonal matching pursuit based on wireless sensor networks

Aiming at the problems of low data reconstruction accuracy in wireless sensor networks and users unable to receive accurate original signals, improvements are made on the basis of the stagewise orthogonal matching pursuit algorithm, combined with sparseness adaptation and the pre-selection strategy, which proposes a sparsity adaptive pre-selected stagewise orthogonal matching pursuit algorithm. In the framework of the stagewise orthogonal matching pursuit algorithm, the algorithm in this article uses a combination of a fixed-value strategy and a threshold strategy to screen the candidate atom sets in two rounds to improve the accuracy of atom selection, and then according to the sparsity adaptive principle, the sparse approximation and accurate signal reconstruction are realized by the variable step size method. The simulation results show that the algorithm proposed in this article is compared with the orthogonal matching pursuit algorithm, regularized orthogonal matching pursuit algorithm, and stagewise orthogonal matching pursuit algorithm. When the sparsity is 35 &lt; K &lt; 45, regardless of the size of the perception matrix and the length of the signal, M = 128, N = 256 or M = 128, N = 512 are improved, and the reconstruction time is when the sparsity is 10, the fastest time between 25 and 25, that is, less than 4.5 s. It can be seen that the sparsity adaptive pre-selected stagewise orthogonal matching pursuit algorithm has better adaptive characteristics to the sparsity of the signal, which is beneficial for users to receive more accurate original signals.

An improved time-domain multipath mitigation method based on the constraint of satellite elevation for low-cost single frequency receiver

Multipath effect seriously affects the accuracy of GNSS high precision positioning since it cannot be corrected by empirical model or eliminated by difference techniques. Sidereal filtering method can effectively mitigate multipath error for static positioning by using the characteristics of satellite time-domain repetition. The key of this method is to extract accurate multipath model on the reference day and mitigate multipath error by searching the multipath model accurately on the subsequent observation day. We analyze the relationship between the multipath error and satellite elevation first by using the data sets which collected from the Multi-GNSS Experiment (MGEX) stations. Based on the result, an improved time-domain sidereal filtering multipath mitigation method based on satellite elevation constraints is proposed. The information of satellite elevation is used to constrain the threshold strategy and decomposition layer of wavelet packets transform, which can effectively improve the denoising effect and extract a more precision multipath model. In addition, the performance of positioning of the proposed method at different mask angles is also evaluated. Real short baseline data sets are collected by low-cost single frequency receivers to evaluate the performance of the positioning of the proposed method, experimental results show that using the proposed method in multipath mitigation can improve approximately 25.00, 21.43 and 13.98% in the east, north and up components compared with the original results, respectively. Even compared with the traditional wavelet packets transform method, the improvement rate can reach approximately 10.78% for 3D positioning. Moreover, the proposed method can also improve the performance of positioning at different mask angles to a certain degree, especially for the low mask angle seriously affected by multipath effect.

A New Visual Inertial Simultaneous Localization and Mapping (SLAM) Algorithm Based on Point and Line Features

In view of traditional point-line feature visual inertial simultaneous localization and mapping (SLAM) system, which has weak performance in accuracy so that it cannot be processed in real time under the condition of weak indoor texture and light and shade change, this paper proposes an inertial SLAM method based on point-line vision for indoor weak texture and illumination. Firstly, based on Bilateral Filtering, we apply the Speeded Up Robust Features (SURF) point feature extraction and Fast Nearest neighbor (FLANN) algorithms to improve the robustness of point feature extraction result. Secondly, we establish a minimum density threshold and length suppression parameter selection strategy of line feature, and take the geometric constraint line feature matching into consideration to improve the efficiency of processing line feature. And the parameters and biases of visual inertia are initialized based on maximum posterior estimation method. Finally, the simulation experiments are compared with the traditional tightly-coupled monocular visual–inertial odometry using point and line features (PL-VIO) algorithm. The simulation results demonstrate that the proposed an inertial SLAM method based on point-line vision for indoor weak texture and illumination can be effectively operated in real time, and its positioning accuracy is 22% higher on average and 40% higher in the scenario that illumination changes and blurred image.

Large Scale Hotel Resource Retrieval Algorithm Based on Characteristic Threshold Extraction

At present, the hotel resource retrieval algorithm has the problem of low retrieval efficiency, low accuracy, low security and high energy consumption, and this study proposes a large scale hotel resource retrieval algorithm based on characteristic threshold extraction. In the large-scale hotel resource data, the mass sequence is decomposed into periodic component, trend component, random error component and burst component. Different components are extracted, the singular point detection is realized by the extraction results, and the abnormal data in the hotel resource data are obtained. Based on the attribute of hotel resource data, the data similarity is processed with variable window, the total similarity of data is obtained, and the abnormal detection of redundant resource data is realized. The abnormal data detection results and redundant data detection results are substituted into the space-time filter, and the data processing is completed. The retrieval problem is identified, and the data processing results are replaced in the hotel resource retrieval based on the characteristic threshold extraction to achieve the normalization of data source and rule knowledge. The characteristic threshold and retrieval strategy are determined, and data fusion reasoning is carried out. After repeated iteration, effective solutions are obtained. The effective solution is fused to get the best retrieval result. Experimental results showed that the algorithm has higher retrieval accuracy, efficiency and security coefficient, and the average search energy consumption is 56n J/bit.

Adaptive Event‐Triggered Finite‐Time Tracking of Output‐Constrained High‐Order Nonlinear Systems with Time‐Varying Powers

This paper studies the adaptive event‐triggered finite‐time tracking of output‐constrained high‐order nonlinear systems with time‐varying powers. Due to the presence of multiple unknown powers and the consideration of event‐triggered control, all the existing control methods of output‐constrained nonlinear systems are inapplicable. By introducing nonlinear mappings, finite‐time performance functions, and low‐power and high‐power terms into adding a power integrator technique and the relative threshold strategy, an adaptive state‐feedback controller is designed to eliminate the effects caused by the output constraint and time‐varying powers. It is proved that all the closed‐loop signals are bounded, the asymmetric time‐varying output constraint is not violated, and the tracking error converges to a prescribed arbitrarily small region around zero in a preassigned finite time. Furthermore, the Zeno phenomenon can be avoided. Two simulation examples demonstrate the effectiveness of this control scheme.

Color image perception based on stochastic spiking neural network

Our aim is to present an interpretable algorithm for enhancing low-illuminance color image based on the principle of stochastic resonance and the fundamental biophysical process of human brain perceiving object color. To this end, the phenomenon of stochastic resonance in a conductance-based integrate-and-fire neuronal network is first explored, with the effect of firing threshold, synaptic weight and the population size on the signal-to-noise ratio revealed, and the firing threshold is recognized as the key parameter for the resonance effects. And then, a color image enhancement algorithm, where the peak signal-to-noise ratio and the natural image quality evaluator are adopted as quantifying indexes, is developed by combining the stochastic spiking neuronal network and the involved biophysical process relating to visual perception. Note that the enhanced image is aperiodic, thus in order to optimize the performance of the algorithm, an illuminance distribution based threshold strategy is given by us for the first time. The numerical tests show that the algorithm has good enhancement performance and stability. We wish this algorithm could be applied to relevant signal processing fields such as military detection and medical image preprocessing.

Blood Group Classification System Based on Image Processing Techniques

The present paper proposes a novel system that automatically classifies the eight different blood groups according to the ABO and Rh blood group systems. The proposed system is developed by applying MATLAB’s image processing techniques on the blood sample images. These images are acquired from the laboratory using the slide test. It utilizes a mean filter for removing noise from blood sample images. In addition, the Contrast Limited Adaptive Histogram Equalization (CLAHE) is used for enhancing the image characteristics analysis. The proposed system also utilizes the automated threshold strategy (Otsu’s approach) for obtaining the blood samples binary images. Since, adding the three antigens (A, B, D) to the blood samples works on clumping the samples or not, and this, in turn, works on the variation number of objects and holes in the different blood groups’ samples. Therefore, the “Bwboundaries” Matlab function is applied for the first time in the proposed system to count the number of objects and holes for blood group classification. The database used in the proposed system consists of 600 images collected from different laboratories. The suggested system has many advantages such as simplicity, speeding up processing time, reducing human interference and errors, decreasing the risk of transfusion reactions, and high accuracy, even in remote places. As well, it can be used by a lab technician or any user who doesn’t have any previous knowledge of the blood group detection technique. In addition to using this system to assist in several applications such as premarital tests, blood transfusion as well as paternity tests.

Switching mechanism-based event-triggered fuzzy adaptive control with prescribed performance for MIMO nonlinear systems

&lt;p style='text-indent:20px;'&gt;This paper investigates the switching mechanism-based event-trig-gered fuzzy adaptive control issue of multi-input and multi-output (MIMO) nonlinear systems with prescribed performance (PP). Utilizing fuzzy logic systems (FLSs) to approximate unknown nonlinear functions. By using the switching threshold strategy, the system has more flexibility in strategy selection. The proposed control scheme can better solve the communication resource limitation. On account of the Lyapunov stability theory, the stability of the controlled system is proved. And all signals of the controlled system are bounded. Moreover, the tracking errors are controlled in a diminutive realm of the origin within the PP bounded. Simultaneously, the Zeno behavior is avoided. Finally, illustrate the effectiveness of the control scheme that has been proposed by demonstrating some simulation consequences.&lt;/p&gt;

Dynamic analysis of a predator-prey state-dependent impulsive model with fear effect in which action threshold depending on the prey density and its changing rate.

In ecology, the impact of predators goes beyond killing prey, the mere presence of predators reduces the ability of prey to reproduce. In this study, we extend the predator-prey model with fear effect by introducing the state-dependent control with a nonlinear action threshold depending on the combination of the density of prey and its changing rate. We initially defined the Poincaré map of the proposed model and studied its fundamental properties. Utilizing the properties of the Poincaré map, periodic solution of the model is further investigated, including the existence and stability of the order-1 periodic solution and the existence of the order-k (k≥2) periodic solutions. In addition, the influence of the fear effect on the system's dynamics is explored through numerical simulations. The action threshold used in this paper is more consistent with the actual growth of the population than in earlier linear threshold studies, and the results show that the control objectives are better achieved using the action threshold strategy. The analytical approach used in this study provided several novel methods for analyzing the complex dynamics that rely on state-dependent impulsive.

Global dynamics for a Filippov system with media effects.

In the process of spreading infectious diseases, the media accelerates the dissemination of information, and people have a deeper understanding of the disease, which will significantly change their behavior and reduce the disease transmission; it is very beneficial for people to prevent and control diseases effectively. We propose a Filippov epidemic model with nonlinear incidence to describe media's influence in the epidemic transmission process. Our proposed model extends existing models by introducing a threshold strategy to describe the effects of media coverage once the number of infected individuals exceeds a threshold. Meanwhile, we perform the stability of the equilibriua, boundary equilibrium bifurcation, and global dynamics. The system shows complex dynamical behaviors and eventually stabilizes at the equilibrium points of the subsystem or pseudo equilibrium. In addition, numerical simulation results show that choosing appropriate thresholds and control intensity can stop infectious disease outbreaks, and media coverage can reduce the burden of disease outbreaks and shorten the duration of disease eruptions.

94 GHz Asymmetric Antenna Radar for Speech Signal Detection and Enhancement via Variational Mode Decomposition and Improved Threshold Strategy

To further improve the detection distance and sensitivity of bio-radar, a 94 GHz asymmetric antenna radar sensor is employed to detect speech signal. However, the radar speech is often mixed with various noise, which will seriously affect the quality and intelligibility of the speech signal. Therefore, a novel method based on variational mode decomposition (VMD) and improved threshold strategy (ITS) is proposed in this paper for improving the quality and intelligibility of the radar speech. VMD is a novel adaptive decomposition method, which overcomes the problem of mode aliasing and end effect in empirical mode decomposition (EMD). ITS can overcome the limitation of traditional wavelet threshold and achieve the best compromise between speech intelligibility and noise reduction. Firstly, EMD is applied to determine the number of decomposition level, and then radar speech is decomposed into several limited bandwidth intrinsic mode functions by VMD. Secondly, ITS is employed to remove noise from useful modes which are determined by Pearson correlation coefficient (PCC). The performance of the proposed method is evaluated by perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI) and composite measures (CMs). The experimental results show that the radar sensor can detect long distance speech signal and the proposed method can effectively improve the quality and intelligibility of the radar speech signal. Due to the good performance, the proposed method will provide a promising alternative for various applications related to radar speech and traditional microphone speech signal enhancement.