Threshold Function Research Articles

Vibration Thresholds Using Conventional Audiometry are Clinically Useful Indicators of Postural Instability in Older Adults.

Falls are a major health concern with potentially dramatic consequences for people over 65 years of age. One crucial determinant in the risk of falls in older adults is postural control, a complex process that requires the contribution of different sensory modalities, namely visual, vestibular, auditory, and somatosensory. While there are well-established methods to screen for age-related vision, hearing, tactile, and vestibular impairments, there are very few widely available methods to screen for somatosensory function, but studies indicate that ankle audiometry (vibration thresholds) using a common B-71 bone vibrator can serve that purpose. To date, unfortunately, this technique has received little attention as a tool to measure postural instability in older adults. The objective of the present study was to examine postural control in older adults with and without degradation of the somatosensory functions, as determined with ankle audiometry. This was standard group comparison. In total, 36 healthy elderly aged between 65 and 80 years old were divided into two groups (low vibration threshold [n = 18] and high vibration threshold [n = 18]). Standard audiometry, video head impulse test, vibration thresholds (big toe, ankle, and tibia), and static postural control task using a force platform were performed. Greater postural instability in participants with higher (worse) vibration thresholds as compared with participants with lower (better) vibration thresholds was observed even though both groups were comparable on hearing threshold and vestibular function. The results indicate that performing a simple vibration threshold evaluation, using a clinically available B-71 with a cut-off value of 42 dB hearing loss, could be an effective, fast, and easy-to-use procedure for detecting people at risk of falls.

Mechanical equipment fault diagnosis method based on improved deep residual shrinkage network.

Fault diagnosis of mechanical equipment can effectively reduce property losses and casualties. Bearing vibration signals, as one of the effective sources of diagnostic information, are often overwhelmed by substantial environmental noise. To address this issue, we present a fault diagnosis method, CCSDRSN, which exhibits strong noise resistance. This method enhances the soft threshold function in the traditional deep residual shrinkage network, allowing it to extract useful information from the fault signal to the maximum extent, thus significantly improving diagnostic accuracy. Additionally, we have developed a novel activation function that can nonlinearly transform the time frequency map across multiple dimensions and the entire region. In pursuit of network optimization and parameter reduction, we have strategically incorporated depthwise separable convolutions, effectively replacing conventional convolutional layers. This architectural innovation streamlines the network. By verifying the effectiveness of the proposed method using Case Western Reserve University datasets, the results demonstrate that the proposed method not only possesses strong noise resistance in high noise environments but also achieves high diagnostic accuracy and good generalization performance under different load conditions.

A rolling bearing fault signal denoising algorithm that combines a new adaptive information entropy with a new wavelet threshold function

Abstract Mechanical fault diagnosis is of great significance to industrial automation, and extracting vibration fault signals is one of the important tasks in mechanical health monitoring and fault diagnosis. However, due to the complex working environment of rolling bearings, a large amount of noise makes it difficult to extract vibration fault signals. Denoising the vibration signal of bearings can remove interference noise, simplify the early identification of signal features, and thus improve diagnostic accuracy and mechanical maintenance efficiency. This paper proposes a rolling bearing fault signal denoising algorithm, which constructs a new feature extraction and denoising function. This method first decomposes the noisy signal into Intrinsic Mode Functions (IMFs) by Intrinsic Computing Expressive Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). Secondly, a new adaptive information entropy threshold function is constructed to extract the noisy IMFs from it. Then, the noisy IMF signal is denoised by a new wavelet threshold function. Finally, the noise-free IMFs are reconstructed to reconstruct the denoised signal. To verify the actual performance of the algorithm, comparative experiments were conducted on a self-collected dataset and a public dataset, the results show that this method improves the continuity of signal reconstruction and can remove various types of vibration fault noise signals more effectively and accurately, \hl{thereby improving the fault detection accuracy by 2%-9%.

Rolling bearing fault diagnosis method based on SSA-IWT-EMD

Aiming at the problem of insufficient wavelet threshold noise reduction and unclear extraction of characteristic frequency of EMD decomposition, a rolling bearing fault diagnosis method based on SSA-IWT-EMD is proposed. Two adjustment factors are introduced to propose an improved threshold function (IWT), which overcomes the shortcomings of traditional soft and hard thresholds, and the sparrow search optimization algorithm (SSA) is used to globally optimize its parameters to achieve rolling bearing signal noise reduction. A comprehensive index P is proposed to select and reconstruct the components generated by empirical mode decomposition (EMD) to highlight the fault characteristic information of the signal. Envelope spectrum analysis is used to realize bearing fault diagnosis. Simulation and measurement results verify the effectiveness of the proposed method; at the same time, compared with the method of selecting components with a single index and the literature method, it is shown that the comprehensive index P and the proposed method have stronger noise reduction and feature extraction capabilities, and the envelope spectrum amplitude and frequency doubling components are more obvious, which can better realize the fault diagnosis of rolling bearings.

Calretinin Staining Pattern is Variable in Hirschsprung Disease Occurring in Patients with Down Syndrome

Abstract Introduction/Objective Hirschsprung disease (HD), a congenital absence of enteric ganglion cells, affects 2-10% of patients with Down syndrome (DS). Submucosal nerve hypertrophy (SNH) and abnormal calretinin immunohistochemistry (IHC) support a diagnosis of HD, but the concordance of these findings can be variable in DS. Recognizing differences in HD occuring in DS can provide insight into the disease and help to improve outcomes and reduce delays in diagnosis for these patients. We sought to describe the histologic and calretinin staining patterns in HD occurring in patients with DS. Methods/Case Report From 2017-2023, 22 rectal biopsies from 20 patients with DS were retrospectively collected and reviewed. Subsequent pull-through (PT) resections were evaluated from 8 patients. Biopsies were evaluated for ganglion cells and SNH. Calretinin IHC was qualitatively evaluated for intensity (strong, weak) and density (many, partial, rare). Quantitative analysis used a calculation of the percent area above threshold (%AAT) function of the NIH ImageJ software version 2.1.0. Representative areas of the PT resections were also evaluated by ImageJ %AAT analysis. Results (if a Case Study enter NA) HD biopsies (12/22, 54.5%) were from male patients (age 3 days-6 weeks) and showed SNH in 11/12 (91.7%) with a mean nerve diameter of 57.3 microns (range 25-89 microns). Ganglionated biopsies (10/22, 45.5%) were collected from patients 3 days to 12 years old (M:F ratio 1:1.5) and showed SNH in 6/10 (60%) with a mean nerve diameter of 44.4 microns (range 10-70 microns). Strong normal calretinin staining was only seen in ganglionated biopsies (7/10 non-HD versus 0/12 HD). Negative calretinin staining was only seen in HD (0/10 non-HD vs 7/12 HD). Abnormal staining was seen in both non-HD (3/10, 30%) and HD (5/12, 41.7%) as partial strong positivity and rare weak positivity, respectively. ImageJ %AAT quantitative analysis revealed an average of 1.71 in biopsies with strong calretinin expression, 0.130 with weak staining, and 0.033 with negative staining. Evaluation of 8 PT resections (mean length of aganglionosis 7.1 cm) showed a mean nerve diameter in the aganglionic segment of 88.3 microns, 49.6 microns in the TZ, and 32.7 microns in the ganglionated segment. ImageJ %AAT quantitative analysis of calretinin revealed a mean of 0.044 in the aganglionic area, 0.66 in the TZ, and 1.56 in the ganglionated segment. Conclusion Recognition of the variable calretinin patterns seen in rectal biopsies from patients with DS can assist in accurate and timely diagnosis of HD.

An effective method for anomaly detection in industrial Internet of Things using XGBoost and LSTM

In recent years, with the application of Internet of Things (IoT) and cloud technology in smart industrialization, Industrial Internet of Things (IIoT) has become an emerging hot topic. The increasing amount of data and device numbers in IIoT poses significant challenges to its security issues, making anomaly detection particularly important. Existing methods for anomaly detection in the IIoT often fall short when dealing with data imbalance, and the huge amount of IIoT data makes feature selection challenging and computationally intensive. In this paper, we propose an optimal deep learning model for anomaly detection in IIoT. Firstly, by setting different thresholds of eXtreme Gradient Boosting (XGBoost) for feature selection, features with importance above the given threshold are retained, while those below are ignored. Different thresholds yield different numbers of features. This approach not only secures effective features but also reduces the feature dimensionality, thereby decreasing the consumption of computational resources. Secondly, an optimized loss function is designed to study its impact on model performance in terms of handling imbalanced data, highly similar categories, and model training. We select the optimal threshold and loss function, which are part of our optimal model, by comparing metrics such as accuracy, precision, recall, False Alarm Rate (FAR), Area Under the Receiver Operating Characteristic Curve (AUC-ROC), and Area Under the Precision–Recall Curve (AUC-PR) values. Finally, combining the optimal threshold and loss function, we propose a model named MIX_LSTM for anomaly detection in IIoT. Experiments are conducted using the UNSW-NB15 and NSL-KDD datasets. The proposed MIX_LSTM model can achieve 0.084 FAR, 0.984 AUC-ROC, and 0.988 AUC-PR values in the binary anomaly detection experiment on the UNSW-NB15 dataset. In the NSL-KDD dataset, it can achieve 0.028 FAR, 0.967 AUC-ROC, and 0.962 AUC-PR values. By comparing the evaluation indicators, the model shows good performance in detecting abnormal attacks in the Industrial Internet of Things compared with traditional deep learning models, machine learning models and existing technologies.

A fault diagnosis method for bearings and gears in rotating machinery based on data fusion and transfer learning

Abstract Rotating machinery is a crucial component of industrial equipment, and the fault diagnosis of bearings and gears, as vital elements of rotating machinery, is essential since they often fail under harsh working conditions, leading to significant property losses and serious personal safety problems. However, fault data for gears and bearings are often sparse in actual condition, and it is a challenge to ensure the reliability and stability of fault diagnosis results by extracting the features of a single data. To solve the above problems, this paper proposes a fault diagnosis method that combines Transfer Learning and data fusion techniques. Firstly, in this method, two kinds of fault signals are transformed into Gramian Angular Difference Fields and Recurrence Plot. Next, a U-shaped feature fusion dual discriminator generative adversarial network is used to fuse two-dimensional images from multiple sensor data. Its feature fusion module deeply integrates the features of the two images, thereby solving the impact of single data on the reliability and stability of fault diagnosis. Moreover, open-source datasets are used for Transfer Learning training to tackle the small sample problem. Finally, a decision-level information fusion classifier, the Dual-Branch Dempster-Shafer Classifier (DB-DSC), classifies the fused images. This classifier incorporates an improved soft threshold function and D-S evidence theory to achieve adaptive gradient changes and improve the robustness and accuracy of classification results. The experimental results show the effectiveness and stability of the proposed method, and the generated images get high score in several metrics. The average classification accuracy of the classification network reaches 93% and 92.5% on the two datasets, Therefore, the proposed method exhibits strong fault diagnosis capabilities under the small sample conditions of bearings and gears.

Smooth Sigmoid Surrogate (SSS): An Alternative to Greedy Search in Decision Trees

Greedy search (GS) or exhaustive search plays a crucial role in decision trees and their various extensions. We introduce an alternative splitting method called smooth sigmoid surrogate (SSS) in which the indicator threshold function used in GS is approximated by a smooth sigmoid function. This approach allows for parametric smoothing or regularization of the erratic and discrete GS process, making it more effective in identifying the true cutoff point, particularly in the presence of weak signals, as well as less prone to the inherent end-cut preference problem. Additionally, SSS provides a convenient means of evaluating the best split by referencing a parametric nonlinear model. Moreover, in many variants of recursive partitioning, SSS can be reformulated as a one-dimensional smooth optimization problem, rendering it computationally more efficient than GS. Extensive simulation studies and real data examples are provided to evaluate and demonstrate its effectiveness.

Neuronal threshold functions: Determining symptom onset in neurological disorders

Synaptic networks determine brain function. Highly complex interconnected brain synaptic networks provide output even under fluctuating or pathological conditions. Relevant to the treatment of brain disorders, understanding the limitations of such functional networks becomes paramount. Here we use the example of Parkinson’s Disease (PD) as a system disorder, with PD symptomatology emerging only when the functional reserves of neurons, and their interconnected networks, are unable to facilitate effective compensatory mechanisms. We have denoted this the “threshold theory” to account for how PD symptoms develop in sequence. In this perspective, threshold functions are delineated in a quantitative, synaptic, and cellular network context. This provides a framework to discuss the development of specific symptoms. PD includes dysfunction and degeneration in many organ systems and both peripheral and central nervous system involvement. The threshold theory accounts for and explains the reasons why parallel gradually emerging pathologies in brain and peripheral systems generate specific symptoms only when functional thresholds are crossed, like tipping points. New and mounting evidence demonstrate that PD and related neurodegenerative diseases are multisystem disorders, which transcends the traditional brain-centric paradigm. We believe that representation of threshold functions will be helpful to develop new medicines and interventions that are specific for both pre- and post-symptomatic periods of neurodegenerative disorders.

Contagion probability in linear threshold model

We study a linear threshold model on a simple undirected connected network G where each non-seed becomes active if and only if the proportion of its active neighbors exceeds its adoption threshold. Each threshold function ϕ:V→[0,1] is viewed as a point (ϕ(v1),…,ϕ(vn)) in the n-cube [0,1]n, where V={v1,…,vn} is the set of nodes in G. We define ϕ as a contagious point of a subset S of nodes if it can induce full contagion from S. Consequently, the volume of the set of contagious points of S in [0,1]n represents the probability of full contagion from S when the adoption threshold of each node is independently and uniformly distributed in [0,1], which we term the contagion probability of S and denote by pc(S). We derive an explicit formula for pc(S), showing that pc(S) is determined by how likely S can produce full contagion exclusively through each spanning tree of the quotient graph GS of G in which S is treated as a single node. Besides, we compare pc(S) with the contagion threshold of S, which is denoted by qc(S) and is the probability of full contagion from S when all nodes share a common adoption threshold q chosen uniformly at random from [0,1]. We show that the presence of a cycle in GS is necessary but not sufficient for pc(S) to exceed qc(S), which indicates that allowing threshold heterogeneity may not always increase the chance of full contagion. Our framework can be extended to study contagion under various threshold settings.

An optimal demodulation frequency band selection method based on spectral coherence fault energy factor for rolling bearing fault diagnosis

Identifying a frequency band with abundant fault information from the original vibration signal under the condition of complex background noise and the interference of other rotating parts is still problematic for envelope spectrum-based bearing diagnosis. To address this issue, a new indicator called spectral coherence fault energy factor (SCFE) to is proposed to select the optimal demodulation frequency band (ODFB) under complex noise interference for bearing fault diagnosis. Firstly, the fast spectral correlation (Fast-SC) is used to analyze the raw vibration signal and generate the Spectral Coherence (SCoh) image. Then, SCFE is employed to locate the initial center frequency from the whole carrier frequency, and the threshold function is used to optimize the center frequency and bandwidth adaptively to obtain ODFB. Finally, the envelope spectrum (ES) is used to identify bearing faults. The effectiveness of the developed method in identifying the fault related frequency band and bearing fault diagnosis is validated and compared using simulated signals with different interference noises. The results of experimental signals demonstrate that the developed indicator can efficiently evaluate bearing failure information, and the presented approach can accurately discriminate the failure-related frequency band and detect different bearing faults compared with the traditional methods.

A Tumor-Immune System with Switching Threshold Depending on Tumor Cells and Its Change Rate

Tumor prevention and control, taking into account the load of tumor cells or immune cells, are often described by Filippov systems, usually with a smooth function on the right-hand side describing the tumor immunity. However, these systems only consider the load of tumor cells or immune cells as a threshold function, which suggest that patients will only be treated if the load of tumor cells or immune cells exceeds a certain threshold. As a result, there may be an excessive growth of tumor cells or a rapid decrease in immune cells, which can lead to undesirable results. Considering this problem, we propose a weighted switching system using the load of tumor cells and its change rate as the threshold. Theoretical analysis and numerical simulation of the system are performed by changing the threshold value. We find that in the proposed nonlinear weighted threshold control method for the system, on the one hand, the system can produce rich dynamical behaviors, including many possible cases of bistability and sliding segments of the system, etc., and on the other hand, it is also very important for the prevention and control of tumor.

An enhanced proportional topology optimization method with new density filtering weight function for the minimum compliance problem

This article proposes an enhanced proportional topology optimization (EPTO) method to solve the structural topology optimization problem of minimizing compliance under material volume constraints. In the proposed method, a new filtering weight function based on the improved Heaviside threshold function is adopted to filter element density during the optimization process. The optimization process of the EPTO method consists of an inner loop and an outer loop. In the inner loop, density distribution is modified in combination with a new filtering weight function. By locally averaging the weighted element compliance, an improved density distribution function in the inner loop is put forward to make the topology configuration of the optimized structure more reasonable. In addition, the projection method is used to reduce the number of intermediate density elements, thereby obtaining optimization results with clear boundaries. In the outer loop, a new termination criterion is adopted, which terminates the optimization process by determining that the relative error of the objective function in several consecutive iterations is less than the specified value. The effectiveness and efficiency of the proposed method are demonstrated through several numerical examples involving two-dimensional (2D) and three-dimensional (3D) topology optimization problems. The results of numerical examples show that the proposed method can not only accelerate the convergence of optimization iterations, but also obtain optimized structures with smaller objective function values and better topology configurations. HIGHLIGHTS A new density filtering weight function is proposed based on an improved Heaviside threshold function. An improved inner loop density distribution formula is proposed by combining a new filtering weight function. Using projection based methods to suppress the appearance of intermediate density elements. Verify the effectiveness of the proposed algorithm by comparing the optimization results with existing algorithms such as top88 and PTO.

Denoising method for colonic pressure signals based on improved wavelet threshold.

The colonic peristaltic pressure signal is helpful for the diagnosis of intestinal diseases, but it is difficult to reflect the real situation of colonic peristalsis due to the interference of various factors. To solve this problem, an improved wavelet threshold denoising method based on discrete wavelet transform is proposed in this paper. This algorithm can effectively extract colonic peristaltic pressure signals and filter out noise. Firstly, a threshold function with three shape adjustment factors is constructed to give the function continuity and better flexibility. Then, a threshold calculation method based on different decomposition levels is designed. By adjusting the three preset shape factors, an appropriate threshold function is determined, and denoising of colonic pressure signals is achieved through hierarchical thresholding. In addition, the experimental analysis of bumps signal verifies that the proposed denoising method has good reliability and stability when dealing with non-stationary signals. Finally, the denoising performance of the proposed method was validated using colonic pressure signals. The experimental results indicate that, compared to other methods, this approach performs better in denoising and extracting colonic peristaltic pressure signals, aiding in further identification and treatment of colonic peristalsis disorders.

A computer vision system and machine learning algorithms for prediction of physicochemical changes and classification of coated sweet cherry

The current research utilized visual characteristics obtained from RGB images and qualitative characteristics to investigate changes in surface defects, predict physical and chemical characteristics, and classify sweet cherries during storage. It was achieved with the help of ANN (Artificial Neural Network) and ANFIS (Adaptive Neuro-Fuzzy Inference System) models. The ANN used in this study was a Multilayer Perceptron (MLP) with SigmoidAxon and TanhAxon threshold functions, trained with the Momentum training function. Additionally, ANFIS with a Mamdani system and Triangle, Gauss, and Trapezoidal membership functions, was employed to predict sweet cherries' physical and chemical properties and their quality classification. Both models incorporate four algorithms. Additionally, the algorithms use color statistical features and color texture features combined with physical and chemical properties, including weight loss, firmness, titratable acidity, and total anthocyanin content. The image color and texture characteristics were used by ANN and ANFIS models to predict physical and chemical properties with high accuracy. ANN and ANFIS models accurately estimate sweet cherry quality grades in all four algorithms with over 90% accuracy. According to the findings, the ANN and ANFIS models have demonstrated satisfactory performance in the qualitative classification and prediction of sweet cherries' physical and chemical properties.

Adaptive Distributed Control of Nonlinear Multiagent Systems With Event-Triggered for Communication Faults and Dead-Zone Inputs.

This article studies the containment control problem of nonlinear multiagent systems (MASs) subjected to communication link faults and dead-zone inputs. In case of an unknown fault in the communication link, there is no constant Laplacian matrix anymore and each follower agent cannot be informed of the global information simultaneously. To deal with this problem, an adaptive compensating estimator is constructed to estimate the signal spanned by the leaders. Instead of using the linear filter, a nonlinear filter is employed, which both solves the classical complexity explosion in the traditional backstepping method and flushes out the usefulness of the boundary layer error. Considering the dead zone input, we propose two event-triggered schemes, that is, the update-triggered scheme and the transmit-triggered scheme. In the former, the threshold function involves the tracking errors and additional dynamic variable, which can provide the desirable tradeoff between the containment control performance of the considered MASs and saving communication resources. In the latter, the triggered condition is designed according to the characteristic of dead zone, which makes the communication burden be reduced further. Following the backstepping design framework, an adaptive containment control is constructed, it is shown that the containment error can converge to an adjustable residual set even if MASs are subjected to the unknown and bounded communication link faults and dead-zone inputs. Finally, an example is given to show the effectiveness of the proposed results.

Fault diagnosis of the harmonic reducer based on dual-path convolutional network with multi-channel hybrid attention mechanism

Abstract Harmonic reducers are prone to tooth breakage and other failures due to continuous deformation of the flexure wheel during operation. To identify the fault types of harmonic reducers under different working conditions, a fault diagnosis method (MADCNN-BiGRU) based on Dual-path Convolution Neural Network (DCNN) with Multi-channel Hybrid Attention Mechanism (MCHAM) and Bidirectional Gated Recurrent Unit (BiGRU) was proposed. Firstly, a novel MCHAM focuses on the critical information part of the vibration signal and can effectively suppress the noise and redundant information. Secondly, a new soft threshold function is constructed to regulate the attention weights dynamically. Thirdly, BiGRU obtains feature information for different time series locations to identify the faults correctly. Fourthly, the harmonic reducer test rig is established to collect vibration signals of harmonic reducers with different faults under different working conditions. The comparative experimental results show that MADCNN-BiGRU has excellent capacity for generalization and robustness, and can effectively diagnose under various complicated situations.

Thresholding Computing with Heterogeneous Integration of Memristive Kernel with Metal-Oxide-Semiconductor Capacitor for Temporal Data Analysis.

Precise event detection within time-series data is increasingly critical, particularly in noisy environments. Reservoir computing, a robust computing method widely utilized with memristive devices, is efficient in processing temporal signals. However, it typically lacks intrinsic thresholding mechanisms essential for precise event detection. This study introduces a new approach by integrating two Pt/HfO2/TiN (PHT) memristors and one Ni/HfO2/n-Si (NHS)metal-oxide-semiconductor capacitor (2M1MOS) to implement a tunable thresholding function. The current-voltage nonlinearity of memristors combined with the capacitance-voltage nonlinearity of the capacitor forms the basis of the 2M1MOS kernel system. The proposed kernel hardware effectively records feature-specified information of the input signal onto the memristors through capacitive thresholding. In electrocardiogram analysis, the memristive response exhibited a more than ten-fold difference between arrhythmia and normal beats. In isolated spoken digit classification, the kernel achieved an error rate of only 0.7% by tuning thresholds for various time-specific conditions. The kernel is also applied to biometric authentication by extracting personal features using various threshold times, presenting more complex and multifaceted uses of heartbeats and voice data as bio-indicators. These demonstrations highlight the potential of thresholding computing in a memristive framework with heterogeneous integration.

Quantum Rectification Based on Room Temperature Multidirectional Nonlinearity in Bi2Te3.

Recent interest in quantum nonlinearity has spurred the development of rectifiers for harvesting energy from ambient radiofrequency waves. However, these rectifiers face efficiency and bandwidth limitations at room temperature. We address these challenges by exploring Bi2Te3, a time-reversal symmetric topological quantum material. Bi2Te3 exhibits robust room temperature second-order voltage generation in both the longitudinal and transverse directions. We harness these coexisting nonlinearities to design a multidirectional quantum rectifier that can simultaneously extract energy from various components of an input signal. We demonstrate the efficacy of Bi2Te3-based rectifiers across a broad frequency range, spanning from existing Wi-Fi bands (2.45 GHz) to frequencies relevant to next-generation 5G technology (27.4 GHz). Our Bi2Te3-based rectifier surpasses previous limitations by achieving a high rectification efficiency and operational frequency, alongside a low operational threshold and broadband functionality. These findings enable practical topological quantum rectifiers for high-frequency electronics and energy conversion, advancing wireless energy harvesting for next-generation communication.

An adaptive trajectory segmentation and simplification algorithm based on vessel behavioral features

Existing algorithms for trajectory simplification in AIS data processing have issues such as loss of key feature points, high subjectivity in threshold selection, and incomplete consideration of factors. In this paper, an adaptive trajectory segmentation and simplification algorithm based on vessel behavioral features is proposed. The algorithm identifies behavioral feature intervals by extracting vessel behavioral features and constructs an adaptive segmentation threshold function to achieve adaptive segmentation of trajectories and extraction of key feature points. Then, considering the quality, similarity, and spatial variability of trajectory simplification, an adaptive simplification threshold function is constructed to automatically select the best threshold for each sub-trajectory to generate the simplified trajectory. Our experiments demonstrate that the algorithm maintains a high simplification rate while enabling adaptive simplification of the trajectory, and also retains the key feature points and trajectory shapes to the maximum extent. The effectiveness and robustness of the algorithm are verified through comparison with other algorithms, effectively avoiding the distortion of navigational behavior that may be caused by traditional methods. This provides reliable data support for maritime traffic management and decision-making.