Threshold Function Research Articles

Deep Learning-Based Nonparametric Identification and Path Planning for Autonomous Underwater Vehicles

As the nonlinear and coupling characteristics of autonomous underwater vehicles (AUVs) are the challenges for motion modeling, the nonparametric identification method is proposed based on dung beetle optimization (DBO) and deep temporal convolutional networks (DTCNs). First, the improved wavelet threshold is utilized to select the optimal threshold and wavelet basis functions, and the raw model test data are denoising. Second, the bidirectional temporal convolutional networks, the bidirectional gated recurrent unit, and the attention mechanism are used to achieve the nonlinear nonparametric model of the AUV motion. And the hyperparameters are optimized by the DBO. Finally, the lazy-search-based path planning and the line-of-sight-based path following control are used for the proposed AUV model. The simulation shows that the prediction accuracy of the DBO-DTCN is better than other artificial intelligence methods and mechanical models, and the path following of AUV is feasible. The methods proposed in this paper can provide an effective strategy for AUV modeling, searching, and rescue cruising.

A joint-threshold Filippov model describing the effect of intermittent androgen-deprivation therapy in controlling prostate cancer

Intermittent androgen-deprivation therapy (IADT) can be beneficial to delay the occurrence of treatment resistance and cancer relapse compared to the standard continuous therapy. To study the effect of IADT in controlling prostate cancer, we developed a Filippov prostate cancer model with a joint threshold function: therapy is implemented once the total population of androgen-dependent cells (AC-Ds) and androgen-independent cells (AC-Is) is greater than the threshold value ET, and it is suspended once the population is less than ET. As the parameters vary, our model undergoes a series of sliding bifurcations, including boundary node, focus, saddle, saddle-node and tangency bifurcations. We also obtained the coexistence of one, two or three real equilibria and the bistability of two equilibria. Our results demonstrate that the population of AC-Is can be contained at a predetermined level if the initial population of AC-Is is less than this level, and we choose a suitable threshold value.

Improved MSTOGI-PLL with enhanced control under non-ideal grid voltage conditions

Integration structure of second- and third-order generalized integrator phase-locking loop (MSTOGI-PLL) has received attention for the processing ability of harmonics and DC bias contained in grid voltage. However, due to that MSTOGI-PLL is sensitive to interharmonics and subharmonics, once low order harmonics dominate, the narrow bandwidth will result in slow response. Hence, based on the analysis on input and output frequency characteristics of existing MSTOGI, a frequency gain structure within the MSTOGI structure is added to improve the suppression ability, either to low-frequency or high-frequency harmonics. Secondly, in order to accelerate response and reduce overshoot of the output frequency of the phase-locked loop (PLL), a nonlinear PI dynamic regulator based on a hyperbolic threshold function is constructed. Furthermore, from the perspectives of voltage phase and frequency tracking error indicators, by comparing with three other types of PLL structures in weak grid application, the effectiveness of the proposed method is verified through simulation cases. Finally, a physical hardware platform based on dSPACE1104 is built to extract the grid voltage from the three-phase voltage regulating transformer, the theoretical derivation and simulation results are verified. The phase deviation is controlled within 0.44 rad s-1, and frequency deviation is controlled within 0.07 Hz.

Multi-timescale attention residual shrinkage network with adaptive global-local denoising for rolling-bearing fault diagnosis

In actual engineering scenarios, bearing fault signals are inevitably overwhelmed by strong background noise from various sources. However, most deep-learning-based diagnostic models tend to broaden the feature extraction scale to extract rich fault features for bearing-fault identification under noise interference, with little attention paid to multi-timescale discriminative feature mining with adaptive noise rejection, which affects the diagnostic performance. Thus, a multi-timescale attention residual shrinkage network with adaptive global-local denoising (AMARSN) was proposed for rolling-bearing fault diagnosis by learning discriminative multi-timescale fault features from signals and fully eliminating noise components in the multi-timescale fault features. First, a multi-timescale attention learning module (MALMod) was developed to capture multi-timescale fault features and enhance their discriminability under noise interference. Subsequently, an adaptive global-local denoising module (AGDMod) was constructed to fully eliminate noise in multiscale fault features by constructing specific global-local denoising thresholds and designing an adaptive smooth soft thresholding function. Finally, end-to-end bearing fault diagnosis tasks were realized using a softmax classifier located at the end of the AMARSN. The AMARSN was validated using two bearing datasets. The extensive results demonstrated that the AMARSN can mine more effective fault features from signals and achieve average diagnostic accuracies of 85.24% and 80.09% under different noise with different levels.

Threshold sampling

We consider the problem of sampling elements with some desired property from a large set, without testing the property of interest, but with the (probabilistic) assurance to have at least one match among the random sample. Like in ranked set sampling (RSS), we consider an infinite population under study, whose properties of interest are too expensive and/or time-consuming to measure. Unlike RSS, we are void of a ranking mechanism, so our sampling is done entirely blind. We show how it is nonetheless doable to assure, with controllably large likelihood, to either have at least one of the interesting elements in a random sample, or, contrarily, sample with the likewise assurance of not having one of the interesting elements in the sample. Our technique utilizes density bounds for distributions and threshold functions from random graph theory.

The relationship of clinical and morphological parameters with the BRAF status of the tumor in patients with stage I skin melanoma

Aim: to study the relationship between clinical and morphological parameters of skin melanoma and the BRAF status of the tumor in patients with stage I of the disease. Materials and methods. The study was retrospective and included 200 patients with stage I skin melanoma (pT1-2aN0M0), of which BRAF status was assessed in 88 patients. All patients underwent clinical data analysis, an extended morphological study and a molecular genetic study to determine the BRAF V600E mutation in the primary tumor. Results. The median age of patients in the total sample was 61.5 years. Mutation in the BRAF V600E gene was detected in 25 patients (28.4%). Patient age, tumor location and Breslow thickness were recognized as independent predictors of BRAF status of stage I skin melanoma. With an increase in the patient's age by 1 year, the chance of having a BRAF V600E mutation decreased by 3.4% or 1.04 times (OR = 0.966; 95% CI = 0.935–0.999; p = 0.045). When melanoma was localized in the lumbar region, the chance of having a BRAF V600E mutation increased by 15.311 times (95% CI = 1.239–189.142; p = 0.033). With a tumor thickness according to Breslow of more than 0.7 mm, the chance of having a BRAF V600E mutation increased by 2.939 times (95% CI = 1.031-8.376; p = 0.044). With a threshold value of the logistic function of 50%, the sensitivity and specificity of the proposed model were 28.0% and 93.7%, respectively. When the threshold function value is reduced to 25.3%, the sensitivity of the model increases to 68% with a simultaneous drop in specificity to 61.9%. Conclusion. Younger age, greater tumor thickness according to Breslow, and tumor localization in the lumbar region in patients with stage I skin melanoma increase the chance of having a BRAF V600E mutation, while other morphological parameters of the tumor are not associated with BRAF status. However, moderate sensitivity does not allow for a sufficiently accurate determination of the presence of a mutation, thereby strengthening the belief in the need for molecular genetic testing.

Subspace projection attention network: Deep weak signal recovery from complicated downhole distributed acoustic sensing data

Distributed acoustic sensing (DAS) is a new, rapidly developing acquisition technology. It has many advantages, such as full well coverage, high sampling density, and strong tolerance compared with conventional geophones in harsh environments. However, DAS data contain various new types of noise with different characteristics, which brings great difficulties to weak signal recovery. It is also not conducive to subsequent inversion, imaging, and interpretation. Several traditional methods and their developments can only remove particular types of DAS noise with serious energy loss. At the same time, the denoising performance is strongly dependent on the threshold function or manual setting parameters. In recent years, deep learning has attracted much attention in DAS data processing. However, convolutional neural networks can only capture local information. The commonly used channel attention assigns weights to each channel to reflect the importance and neglects the features within the channels. The popular self-attention mechanism (SAM) can efficiently obtain the dependencies between different locations or different patches that are a long distance from the input. Thus, a novel subspace projection attention network (SPANet) is designed from a new projection perspective. In the novel network, some feature basis vectors are generated to guide the decoupled projection of the noise components from the signal components. Low-level surface features are projected under the guidance of high-level abstract features through SAM before feature fusion. The projection operation helps to find the optimal subspace that preserves the fine structure of the input as much as possible, better capturing the potential relevance in DAS data. Finally, experimental results of synthetic and field data illustrate that SPANet recovers the effective reflection signals well, whether in a shallow layer with strong noise or a deep layer with weak energy. Its superior performance compared with some traditional and network methods is fully verified and analyzed.

Self-organizing hypercomplex-valued adaptive network

A novel, unsupervised, artificial intelligence system is presented, whose input signals and trainable weights consist of complex or hypercomplex values. The system uses the effect given by the complex multiplication that the multiplicand is not only scaled but also rotated. The more similar an input signal and the reference signal are, the more likely the input signal belongs to the corresponding class. The data assigned to a class during training is stored on a generic layer as well as on a layer extracting special features of the signal. As a result, the same cluster can hold a general description and the details of the signal. This property is vital for assigning a signal to an existing or a new class. To ensure that only valid new classes are opened, the system determines the variances by comparing each input signal component with the weights and adaptively adjusts its activation and threshold functions for an optimal classification decision. The presented system knows at any time all boundaries of its clusters. Experimentally, it is demonstrated that the system is able to cluster the data of multiple classes autonomously, fast, and with high accuracy.

Denoising method for X-ray images with poisson-Gaussian noise based on a new threshold function and shearlet transform

BackgroundX-ray imaging has been applied in various fields. However, the noise in X-ray images reduces the image quality and affects subsequent detection. AimsA denoising method is developed to solve the problem of the Poisson-Gaussian mixed noise caused by X-ray imaging. Methodology: A new threshold function and an improved threshold are proposed in this paper. Furthermore, an improved denoising method, namely the improved generalized Anscombe with shearlet transform (improved GA-ST), is developed based on the above proposed algorithms. After theoretical derivation, experiments and parameter analysis, the proposed method is applied to actual X-ray images. ResultsThe results show that the new threshold function is continuous, asymptotic, and has no inherent deviation, which solves the problems existing in traditional threshold functions. In addition, the improved GA-ST method can reduce Poisson-Gaussian mixed noise at different levels. As for actual X-ray images, the improved GA-ST method outperforms the other methods, and the BRISQUE descent ratios all exceed 25%. ConclusionsThe improved GA-ST method proposed in this paper can effectively reduce the noise in X-ray images and meet the requirements of actual applications based on MATLAB platform.

Spatial attention inference model for cascaded siamese tracking with dynamic residual update strategy

Target representation is crucial for visual tracking. Most Siamese-based trackers try their best to establish target models by using various deep networks. However, they neglect the exploration of correlation among features, which leads to the inability to learn more representative features. In this paper, we propose a spatial attention inference model for cascaded Siamese tracking with dynamic residual update strategy. First, a spatial attention inference model is constructed. The model fuses interlayer multi-scale features generated by dilation convolution to enhance the spatial representation ability of features. On this basis, we use self-attention to capture interaction between target and context, and use cross-attention to aggregate interdependencies between target and background. The model infers potential feature information by exploiting the correlations among features for building better appearance models. Second, a cascaded localization-aware network is introduced to bridge a gap between classification and regression. We propose an alignment-aware branch to resample and learn object-aware features from the predicted bounding boxes for obtaining localization confidence, which is used to correct the classification confidence by weighted integration. This cascaded strategy alleviates the misalignment problem between classification and regression. Finally, a dynamic residual update strategy is proposed. This strategy utilizes the Context Fusion Network (CFNet) to fuse the templates of historical and current frames to generate the optimal templates. Meanwhile, we use a dynamic threshold function to determine when to update by judging the tracking results. The strategy uses temporal context to fully explore the intrinsic properties of the target, which enhances the adaptability to changes in the target’s appearance. We conducted extensive experiments on seven tracking benchmarks, including OTB100, UAV123, TC128, VOT2016, VOT2018, GOT10k and LaSOT, to validate the effectiveness of our proposed algorithm.

Anomaly detection for atomic clocks using unsupervised machine learning algorithms

Atomic clocks are the most accurate timekeeping instruments in the world and play a crucial role in numerous applications, notably in satellite navigation. Nonetheless, the accuracy of these clock signals is subject to anomalies, highlighting the importance of anomaly characterization and detection. In our study, we identify three types of anomalies in the phase data of atomic clocks—outliers, phase jumps and inflection points, and also frequency jumps in the frequency data, which corresponds to the inflection points. The novelty of our work lies in the application of machine learning methods for atomic clock anomaly detection (AD), as opposed to traditional algorithms. We introduce a variety of unsupervised AD and change point detection (CPD) algorithms from the realms of statistics and machine learning, propose a customized thresholding function for AD, and evaluate AD and CPD algorithms on both synthetic and real-world atomic clock data. Our experiments reveal that the subsequence local outlier factor algorithm achieves excellent performance in detecting outliers and phase jumps, but struggles with inflection points. Crucially, we found that the application of a CPD algorithm specifically for detecting the frequency jumps in frequency data substantially improves performance. Our study adopts unsupervised machine learning methods for AD provides robust detection strategies, thereby contributing an expanded understanding of the use of machine learning for clock data monitoring.

Water limitation regulates positive feedback of increased ecosystem respiration.

Terrestrial ecosystem respiration increases exponentially with temperature, constituting a positive feedback loop accelerating global warming. However, the response of ecosystem respiration to temperature strongly depends on water availability, yet where and when the water effects are important, is presently poorly constrained, introducing uncertainties in climate-carbon cycle feedback projections. Here, we disentangle the effects of temperature and precipitation (a proxy for water availability) on ecosystem respiration by analysing eddy covariance CO2 flux measurements across 212 globally distributed sites. We reveal a threshold precipitation function, determined by the balance between precipitation and ecosystem water demand, which separates temperature-limited and water-limited respiration. Respiration is temperature limited for precipitation above that threshold function, whereas in drier areas water limitation reduces the temperature sensitivity of respiration and its positive feedback to global warming. If the trend of expansion of water-limited areas with warming climate over the last decades continues, the positive feedback of ecosystem respiration is likely to be weakened and counteracted by the increasing water limitation.

The heart sound classification of congenital heart disease by using median EEMD-Hurst and threshold denoising method.

Heart sound signals are vital for the machine-assisted detection of congenital heart disease. However, the performance of diagnostic results is limited by noise during heart sound acquisition. A limitation of existing noise reduction schemes is that the pathological components of the signal are weak, which have the potential to be filtered out with the noise. In this research, a novel approach for classifying heart sounds based on median ensemble empirical mode decomposition (MEEMD), Hurst analysis, improved threshold denoising, and neural networks are presented. In decomposing the heart sound signal into several intrinsic mode functions (IMFs), mode mixing and mode splitting can be effectively suppressed by MEEMD. Hurst analysis is adopted for identifying the noisy content of IMFs. Then, the noise-dominated IMFs are denoised by an improved threshold function. Finally, the noise reduction signal is generated by reconstructing the processed components and the other components. A database of 5000 heart sounds from congenital heart disease and normal volunteers was constructed. The Mel spectral coefficients of the denoised signals were used as input vectors to the convolutional neural network for classification to verify the effectiveness of the preprocessing algorithm. An accuracy of 93.8%, a specificity of 93.1%, and a sensitivity of 94.6% were achieved for classifying the normal cases from abnormal one.

A User-Focused Approach to Evaluating Probabilistic and Categorical Forecasts

Abstract A user-focused verification approach for evaluating probability forecasts of binary outcomes (also known as probabilistic classifiers) is demonstrated that (i) is based on proper scoring rules, (ii) focuses on user decision thresholds, and (iii) provides actionable insights. It is argued that when categorical performance diagrams and the critical success index are used to evaluate overall predictive performance, rather than the discrimination ability of probabilistic forecasts, they may produce misleading results. Instead, Murphy diagrams are shown to provide a better understanding of the overall predictive performance as a function of user probabilistic decision threshold. We illustrate how to select a proper scoring rule, based on the relative importance of different user decision thresholds, and how this choice impacts scores of overall predictive performance and supporting measures of discrimination and calibration. These approaches and ideas are demonstrated using several probabilistic thunderstorm forecast systems as well as synthetic forecast data. Furthermore, a fair method for comparing the performance of probabilistic and categorical forecasts is illustrated using the fixed risk multicategorical (FIRM) score, which is a proper scoring rule directly connected to values on the Murphy diagram. While the methods are illustrated using thunderstorm forecasts, they are applicable for evaluating probabilistic forecasts for any situation with binary outcomes. Significance Statement Recently, several papers have presented verification results for probabilistic forecasts using so-called categorical performance diagrams, which summarize multiple verification metrics. While categorical performance diagrams measure discrimination ability, we demonstrate how they can potentially lead to incorrect conclusions when evaluating overall predictive performance of probabilistic forecasts. By reviewing recent advances in the statistical literature, we show a comprehensive approach for the meteorological community that (i) does not reward a forecaster who “hedges” their forecast, (ii) focuses on the importance of the forecast user’s decision threshold(s), and (iii) provides actionable insights. Additionally, we present an approach for fairly comparing the skill of categorical forecasts to probabilistic forecasts.

An Adaptive Noise Reduction Method for High Temperature and Low Voltage Electromagnetic Detection Signals Based on SVMD Combined with ICEEMDAN.

In view of the low signal-to-noise ratio (SNR) of shear wave electromagnetic acoustic transducers (EMAT) in the detection of high-temperature equipment, the use of low excitation voltage (LEV) further deteriorates the detection results, resulting in the echo signal containing defects being drowned in noise. For the extraction of the EMAT signal, an adaptive noise reduction method is proposed. Firstly, the minimum envelope entropy is taken as the fitness function for the Harris Hawks Optimizer (HHO), and the optimal successive variational mode decomposition (SVMD) balance parameter is searched by HHO adaptive iteration to decompose LEV EMAT signals at high temperatures. Then the filter is carried out according to the excitation center frequency and correlation coefficient threshold function. Then, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used to decompose the filtered signal and combine the kurtosis factor to select the appropriate intrinsic mode functions. Finally, the signal is extracted by the Hilbert transform. In order to verify the effectiveness of the method, it is applied to the low-voltage detection of 40Cr from 25 °C to 700 °C. The results show that the method not only suppresses the background noise and clutter noise but also significantly improves the SNR of EMAT signals, and most importantly, it is able to detect and extract the 2 mm small defects from the echo signals. It has great application prospects and value in the LEV detection of high-temperature equipment.

Inventory rationing, admission control, and production capacity allocation in a make‐to‐stock/make‐to‐order manufacturing system

AbstractThis paper considers a manufacturing system in which products are produced in both make‐to‐stock (MTS) and make‐to‐order (MTO) modes. Production of MTS and MTO products is done in batches, incurs a setup cost, and is non‐preemptive. The inventory of MTS products fulfills the demand of multiple classes, and each class demand can be satisfied or rejected. Customer orders for MTO production can be accepted or rejected, and their size is the same as the production batch. The primary goal of this paper is to study a policy that coordinates inventory rationing, admission control, and production capacity allocation to maximize the system's profit. We formulate the problem as a Markov decision process model and identify the structure of optimal control policies. We investigate the effect of inventory rationing on the profit by comparing its performance to that of the system with a first‐come‐ first‐serve policy to allocate inventory to multiple demand classes and study the extent to which the benefit of inventory rationing can be affected by system parameter changes. We also propose a heuristic that manages control decisions from linear threshold functions. Our test results from numerical examples show that the average percentage difference between the optimal and heuristic policies is within 1.2%.

Joint sound denoising with EEMD and improved wavelet threshold for real-time drilling lithology identification

The present study proposes a joint denoising method for real-time drilling lithology identification based on drilling sound signals. The proposed method employs ensemble empirical mode decomposition (EEMD) for preliminary sound signal denoising while retaining the effective intrinsic mode function (IMF) components, which then undergo secondary denoising using the improved wavelet threshold function. Finally, the sound signal is reconstructed using the denoised IMF components. The experimental results show that the proposed method achieves a higher signal-to-noise ratio and lower root mean square error, reaching 12.94 and 0.0442, respectively. Furthermore, the proposed method demonstrated good performance in denoising rock drilling sound signals and successfully addressed issues including the susceptibility of sound signals to external interference, nonadaptive threshold selection in wavelet threshold denoising, and high reconstruction errors during EEMD in drilling environments. Simulation results show that the proposed method significantly improves the accuracy of lithology recognition by 23%, with a resulting accuracy of 72.58%.

The threshold and density function analysis of a stochastic SIVS model with saturation incidence

Mathematical model is the main tool to study the dynamics of infectious diseases, which has played an important role in controlling the spread of infectious diseases. We consider a stochastic SIVS model with saturation incidence in this paper. First of all, we establish the threshold [Formula: see text] for extinction and persistence for the stochastic epidemic model. Additionally, we give the specific expression of the probability density function of the stochastic model near the unique endemic quasi-equilibrium by solving the Fokker–Planck equation. In the end, the supporting theoretical results are verified by numerical simulation.

Action-dependent heuristic dynamic programming for hybrid-order systems based on dynamic event-triggered mechanism

We investigate the distributed optimal consensus control problem based on action-dependent heuristic dynamic programming (ADHDP). In order to strike a stable balance between the learning cost of reinforcement learning and the resource utilization efficiency of the hybrid-order multi-agent systems (MASs), we propose an improved dynamic event-triggered ADHDP (dET-ADHDP) method. This approach can non-periodically explore the control policy distribution using the online action-dependent actor–critic (ADAC) learning framework. Meanwhile, it can dynamically adjust the trigger lower bound by exploiting the designed trigger threshold function, and adaptively decide the signal trigger moment during the ADAC learning process. In addition, we demonstrate the boundedness of the ADAC network weights and show that under the designed dynamic event-triggering rules, the MASs can asymptotically achieve optimal tracking control without Zeno phenomenon. Finally, compared with the traditional static counterparts, simulation experiments demonstrate that the proposed dynamic event-triggered ADAC (dET-ADAC) algorithm has more efficient resource utilization while maintaining satisfactory learning performance.

Optimizing energy efficiency in multi-chiller systems: A comprehensive Modelica-based approach

To achieve sustainable development of buildings, this paper investigates the energy-saving potential of multi-chiller systems by examining the interactions between sequencing optimization, load optimization, and parameter optimization in systems with varying capacities. A Modelica-based simulation platform, adaptable to various programming languages optimization engines, was developed for this purpose. The study introduces a sequencing optimization threshold function that considers both wet-bulb temperature and cooling load. Additionally, a two-stage optimization strategy that prioritizes parameter adjustments before sequencing is proposed. Case studies demonstrate that this threshold function and reversed optimization order can further enhance overall energy savings. Focusing on a system with three large chillers and one small chiller, the study reveals the energy benefits of activating the small chiller under specific conditions, improving overall system efficiency. The proposed optimization achieved a cumulative energy saving of 650,463 kWh over the summer, with sequencing contributing 5.64 %. Frequency conversion control of the water pump is identified as a significant energy-saving factor. This research provides a comprehensive approach to optimizing diverse chiller systems, highlighting substantial potential for energy conservation.