Adaptive Interval Research Articles

Feasibility Study of Efficient and Adaptive Interval Type-2.0 Fuzzy Logic Controller for Hybrid Electric Vehicle Performance Optimization

Feasibility Study of Efficient and Adaptive Interval Type-2.0 Fuzzy Logic Controller for Hybrid Electric Vehicle Performance Optimization

Adaptive hierarchical interval type-2 fuzzy control for trajectory tracking of an underactuated AUV with uncertain dynamics

In this study, an adaptive hierarchical interval type-2 fuzzy control (AHIT2FC) scheme is presented for three-dimensional (3D) trajectory tracking control of an underactuated autonomous underwater vehicle (AUV), which has only three independent control inputs and is subject to partially parametric uncertainty and unknown external disturbances. To decrease reliance on hydrodynamic parameters, an interval type-2 fuzzy logic system (IT2FLS) is utilized, which exhibits superior capabilities in handling high levels of uncertainties prevalent in the underwater environment as compared to a type-1 fuzzy logic system. However, the traditional fuzzy system structure encounters the “curse of dimensionality” issue, which results in high computational complexity and imposes a significant burden on the limited computing resources underwater. The proposed AHIT2FC framework, based on a hierarchical structure, can substantially enhance computational speed while ensuring excellent control performance. Subsequently, Lyapunov’s theory is adopted to guarantee that all the tracking errors in the closed-loop system are semiglobally uniformly bounded (SGUB). Finally, comparative simulation results demonstrate the effectiveness and superiority of the proposed AHIT2FC scheme.

An adaptive interval many-objective evolutionary algorithm with information entropy dominance

Interval many-objective optimization problems (IMaOPs) involve more than three conflicting objectives with interval parameters. Various real-world applications under uncertainty can be modeled as IMaOPs to solve, so effectively handling IMaOPs is crucial for solving practical problems. This paper proposes an adaptive interval many-objective evolutionary algorithm with information entropy dominance (IMEA-IED) to tackle IMaOPs. Firstly, an interval dominance method based on information entropy is proposed to adaptively compare intervals. This method constructs convergence entropy and uncertainty entropy related to interval features and innovatively introduces the idea of using global information to regulate the direction of local interval comparison. Corresponding interval confidence levels are designed for different directions. Additionally, a novel niche strategy is designed through interval population partitioning. This strategy introduces a crowding distance increment for improved subpopulation comparison and employs an updated reference vector method to adjust the search regions for empty subpopulations. The IMEA-IED is compared with seven interval optimization algorithms on 60 interval test problems and a practical application. Empirical results affirm the superior performance of our proposed algorithm in tackling IMaOPs.

Least squares regression under weak moment conditions

In this paper we consider the robust regression problem when the output variable may be heavy-tailed. In such scenarios, the traditional least squares regression paradigm is usually thought to be not a good choice as it lacks robustness to outliers. By projecting the outputs onto an adaptive interval, we show the regularized least squares regression can still work well when the conditional distribution satisfies a weak moment condition. Fast convergence rates in various norm are derived by tuning the projection scale parameter and regularization parameter in according with the sample size and the moment condition.

Prediction of Crohn's disease based on deep feature recognition

BackgroundCrohn's disease is a complex genetic disease that involves chronic gastrointestinal inflammation and results from a complex set of genetic, environmental, and immunological factors. By analyzing data from the human microbiome, genetic information can be used to predict Crohn's disease. Recent advances in deep learning have demonstrated its effectiveness in feature extraction and the use of deep learning to decode genetic information for disease prediction. MethodsIn this paper, we present a deep learning-based model that utilizes a sequential convolutional attention network (SCAN) for feature extraction, incorporates adaptive additive interval losses to enhance these features, and employs support vector machines (SVM) for classification. To address the challenge of unbalanced Crohn's disease samples, we propose a random noise one-hot encoding data augmentation method. ResultsData augmentation with random noise accelerates training convergence, while SCAN-SVM effectively extracts features with adaptive additive interval loss enhancing differentiation. Our approach outperforms benchmark methods, achieving an average accuracy of 0.80 and a kappa value of 0.76, and we validate the effectiveness of feature enhancement. ConclusionsIn summary, we use deep feature recognition to effectively analyze the potential information in genes, which has a good application potential for gene analysis and prediction of Crohn's disease.

An adaptive photovoltaic power interval prediction based on multi-objective optimization

Photovoltaic (PV) power interval prediction can provide a variation range of prediction results, which is of great significance to promoting the optimization of power grid dispatching and maintaining the stability of the power system. However, the existing PV interval prediction fails to accurately reflect error fluctuations in their construction process due to dependency on data distribution assumptions and unreasonable estimation of prediction error standard deviations (PEStds). To address these issues, we propose an adaptive interval prediction method based on multi-objective optimization. Firstly, K-means clustering is used to segment similar days. Subsequently, Convolutional Neural Network-Gated Recurrent Unit-Attention (CNN-GRU-Attention) is utilized for point prediction. Furthermore, Grey Relation Analysis (GRA) is used to screen dates similar to the target date under the same weather type, enabling a more precise estimation of prediction error fluctuations. Ultimately, the estimated values are multiplied by non-fixed parameters to construct intervals, which avoids the dependency on data distribution assumptions. Among them, the optimal values for non-fixed parameters are obtained through multi-objective optimization considering interval coverage probability, interval width, and deviation. To verify the effectiveness of the proposed model, we conduct comparative experiments on two datasets with different resolutions. The results demonstrate that the proposed model offers more flexible and higher-quality intervals. Not only does this research improve the accuracy of PV power generation interval prediction, but it also helps to promote the development of smart grid technology and improve the adaptive ability of power systems facing dynamic environments and complex data, which has an important impact on the future energy management and power market.

Real-time traffic condition uncertainty quantification using adaptive grey prediction interval model

Uncertainty quantification is important for making reliable transportation decisions. For grey-based uncertainty quantification approaches, the data classification methods for most models cannot yield real-time upper and lower limited data sequence, limiting their application in dynamic transportation systems. Therefore, this paper proposes an adaptive grey prediction interval model to quantify real-time traffic condition uncertainty. To this end, polynomial regression is first used to fit the traffic flow trend function in real time, generating dynamically the upper and lower limited data sequence. Then, upper grey (UG) and lower grey (LG) models are built with parameters optimised using particle swarm optimisation. Finally, based on the real-time upper and lower bounds generated by the UG and LG models, prediction intervals are constructed. Using real-world traffic flow data, the proposed model was shown to be able to generate workable prediction intervals in real time. Future studies are recommended to advance traffic condition uncertainty quantification.

Design of a novel intelligent adaptive fractional-order proportional-integral-derivative controller for mitigation of seismic vibrations of a building equipped with an active tuned mass damper

The primary objective of this study is to introduce a novel adaptive fractional order proportional–integral–derivative (FOPID) controller. The adaptive FOPID controller’s parameters are dynamically adjusted in real-time using five distinct multilayer perceptron neural networks. The extended Kalman filter (EKF) is employed to facilitate the parameter-tuning process. A multilayer perceptron neural network, trained using the error Backpropagation algorithm, is employed to identify the structural system and estimate the plant. The real-time estimated Jacobian is applied to the controller to control the model. The stability and robustness of the adaptive interval type-2 fuzzy neural networks controller are enhanced by utilizing the EKF and the feedback error learning strategy for compensator tuning. This improvement increases resilience against estimation errors, seismic disturbances, and unknown nonlinear functions. The primary objective is to address the challenges posed by maximum displacement, acceleration, and drift, as well as the uncertainties arising from variations in stiffness and mass. In order to validate the reliability of the proposed controller, the performance investigation is carried out on an 11-story building equipped with an active tuned mass damper under far and near-field earthquakes. Numerical findings show the remarkable effectiveness of the proposed controllers compared to their predecessors. In addition, it is revealed that the inclusion of the adaptive interval type-2 fuzzy neural networks compensator has increased the performance of the proposed controller and shows significant capabilities in reducing the seismic responses of structures during severe earthquake events.

Hamiltonian Identification via Quantum Ensemble Classification.

Identifying the Hamiltonian of an unknown quantum system is a critical task in the area of quantum information. In this article, we propose a systematic Hamiltonian identification approach via quantum ensemble multiclass classification (HI-QEMC). This approach is implemented by a three-step iterative refining process, i.e., parameter interval guess, verification, and judgment. In the parameter interval guess step, the parameter interval is divided into several sub-intervals and the true Hamiltonian parameter is guessed in one of them. In the parameter interval verification step, cross verification is applied to verify the accuracy of the guess. In the parameter interval judgment step, an adaptive interval judgment (AIJ) algorithm is designed to determine the sub-interval containing the true Hamiltonian parameter. Numerical results on two typical quantum systems, i.e., two-level quantum systems and three-level quantum systems, demonstrate the effectiveness and superior performance of the proposed approach for quantum Hamiltonian identification.

Output Feedback Active Fault Tolerant Control for a 3-DOF Laboratory Helicopter With Sensor Fault

In this paper, an output feedback-based active fault tolerant control (FTC) scheme is proposed for an unstable three degree-of-freedom (3-DOF) helicopter equipped only with angular position sensors, of which a single sensor can be faulty. Limited by the available outputs, existing fault estimation (FE) and FTC schemes cannot be applied to this helicopter because when the sensor measuring the elevation or travel angle is faulty, it does not satisfy the minimum-phase and matching conditions required by standard observers. To circumvent this problem, an adaptive interval observer is firstly designed as a fault detection and isolation (FDI) unit to indicate the occurrence and location of a fault, in contrast to the existing FDI methods that require a bank of observers and incur a high computational cost. Then a high-gain observer is combined with a sliding mode observer to form an FE unit that does not require the matching and minimum-phase conditions. Based on the estimate of the fault, an FTC scheme is constructed to ensure an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{H}_{\infty}$</tex-math> </inline-formula> performance of the faulty system. Finally, physical experiments on the 3-DOF helicopter verify the effectiveness of the proposed scheme. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The 3-DOF lab helicopter serves as an ideal experimental platform for control strategies. In this paper, an active fault tolerant control (FTC) scheme is developed for a 3-DOF lab helicopter when any single sensor can be faulty. The helicopter is nonlinear and subject to external disturbances. There are only encoders measuring the attitude angles and no sensors for angular velocities. In this paper, we firstly design a fault detection and isolation (FDI) unit based on an adaptive interval observer to detect the fault and identify its location; it incurs a lower computational cost compared to existing methods that use a bank of observers. Then a high-gain observer is combined with a sliding mode observer to estimate the fault. Based on the estimate of the fault, an FTC scheme is constructed and designed using Linear Matrix Inequalities to ensure an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{H}_{\infty}$</tex-math> </inline-formula> performance of the faulty system.

Condition monitoring of wind turbine based on a novel spatio-temporal feature aggregation network integrated with adaptive threshold interval

Condition monitoring (CM) technology based on supervisory control and data acquisition (SCADA) data is crucial for ensuring reliable operation and reducing maintenance costs of wind turbine (WT). However, SCADA data is highly dimensional and sophisticated, making it challenging to extract features and determine fault thresholds to improve CM accuracy. In this paper, we propose a new approach for effectively estimating the operating condition and diagnose the faults of WTs, which is founded on the integration of spatio-temporal feature aggregation network and adaptive threshold interval. Briefly, the Kalman filter is used to preprocess the SCADA data and the variables are selected by Pearson correlation coefficient to improve data quality. Then, a novel stacked sample convolution and interaction network (SCINet) embedded in a coordinate attention mechanism (CA) is designed to extract and merge the multi-dimensional and multi-resolution spatial and temporal features. Moreover, an adaptive threshold interval is proposed for early warning of fault occurrence based on Monte Carlo (MC) dropout and kernel density estimation (KDE). The effectiveness of the proposed method is demonstrated through two real cases from a wind farm, which show superior performance compared to other comparative methods.

A phase 1/2 study of ONCT-534, a dual-action androgen receptor inhibitor (DAARI), in patients with metastatic castration-resistant prostate cancer.

TPS5111 Background: Current therapeutic strategies for metastatic castration-resistant prostate cancer (mCRPC) include treatment with next-generation androgen receptor pathway inhibitors (ARPIs), which target the ligand-binding domain (LBD) of the AR. Approximately 5% to 10% of patients will have primary resistance to ARPIs and most initial responders will develop resistance within 1 to 3 years. Eventually, nearly all men with prostate cancer treated with ARPIs develop resistance via several mechanisms including AR genomic alterations, epigenetic alterations, and expression of truncated constitutively active AR splice variants. Hence, there is substantial unmet need for treatment of men with mCRPC resistant to at least one next-generation ARPI. ONCT-534 is a DAARI with a novel mechanism of action that combines inhibition of AR function with degradation of the AR protein. Importantly, this activity includes interaction with both LBD and the N-terminal domain (NTD) of the AR, rendering it effective against splice variants and LBD mutants, unlike existing ARPIs. ONCT-534 has demonstrated preclinical activity in prostate cancer models against unmutated AR and multiple forms of AR alteration, including amplification, mutations in the LBD, and splice variants with loss of LBD. Methods: ONCT-534-101 is a phase 1/2, multi-center study to evaluate the safety, tolerability, antitumor activity, and pharmacokinetics of ONCT-534 in subjects with histologically confirmed mCRPC without neuroendocrine differentiation or small cell features who have relapsed or are refractory (R/R) to at least one next-generation ARPI. The study is separated into Phase 1 Dose Escalation and a Phase 2 Dose Expansion portions. The Phase 1 portion will evaluate approximately 27 subjects in 5 dose levels using an adaptive Bayesian Optimal Interval (BOIN) design to assess safety, tolerability and dose limiting toxicities (DLTs) at escalating doses and to determine the maximum tolerated dose and inform the 2 dose levels or schedules to be tested in Phase 2. DLTs will be assessed during the first 28 days of treatment. AR phenotype and AR levels will be evaluated for each subject pre- and post-treatment. The Phase 2 portion will evaluate approximately 32 subjects in 2 randomized cohorts to assess safety and tolerability of ONCT-534, compare the 2 different dose levels or schedules to select the optimal dose for further study, and assess the preliminary antitumor activity of ONCT-534. In both phases, after a screening period, eligible subjects with mCRPC will receive their assigned dose regimen of ONCT-534, which will be administered orally daily for 2 years or until disease progression and no longer clinically benefiting from treatment or development of unacceptable toxicity. Cohorts 1 and 2 have been completed without DLT. Enrollment into Cohort 3 began in January 2024 (NCT 05917470). Clinical trial information: NCT05917470 .

Phase 1 study of BDTX-1535, an oral 4th generation covalent EGFR inhibitor, in patients with recurrent glioblastoma: Preliminary dose escalation results.

2068 Background: Epidermal growth factor receptor ( EGFR) gene is the most frequently altered oncogenic driver in glioblastoma (GBM). In-frame deletion alterations (e.g., EGFRvIII) and missense mutations co-occur in the setting of EGFR gene amplification and are characterized as a hallmark of disease pathogenesis in GBM. BDTX-1535 is an oral, highly potent, brain penetrant, selective, irreversible 4th generation tyrosine kinase inhibitor that targets EGFR alterations in GBM and NSCLC. Preliminary results of the Phase 1 dose escalation study (NCT05256290) of patients with recurrent GBM (rGBM) are presented here. Methods: BDTX-1535-101 is a first-in-human study that enrolled patients with either rGBM harboring EGFR alterations following standard of care or patients with locally advanced or metastatic EGFR mutated NSCLC that progressed on prior EGFR TKIs. Using an adaptive Bayesian optimal interval design in the Phase 1 part, patients were enrolled at increasing dose cohorts and were treated daily for 21-day cycles until treatment discontinuation. The primary objective was to determine the BDTX-1535 recommended Phase 2 dose based on the overall safety, PK, pharmacodynamics, and preliminary antitumor activity. Results: Twenty-seven patients with rGBM were enrolled in the Phase 1 cohort that consisted of 54 patients in total including 27 patients with NSCLC. Patients were treated across seven dose levels (15mg – 400mg QD). The mean age of patients with rGBM was 58.7 years (range 41-85) with 96% of patients with previous temozolomide (TMZ) treatment and a median of 2 lines of prior therapy (range 1-4). The most common all-grade treatment-related AEs across all cohorts were rash (78%), diarrhea (41%), fatigue (15%), stomatitis (11%), decreased appetite (11%), nausea (11%) and paronychia (11%). Gr 3 TRAEs ≥ 10% included rash (19%) reported at 300 or 400 mg QD doses. Plasma exposure of BDTX-1535 increased dose proportionally and had a half-life of ~15h, supporting once daily dosing. The maximum tolerated dose (MTD) is 300 mg QD. Among 19 evaluable patients for response based on RANO criteria, 1 confirmed partial response was observed and 8 patients achieved stable disease. Five patients remained on BDTX-1535 with stable disease for an extended period (&gt;5 months) who previously performed poorly on TMZ with short treatment duration, and 1 patient continues on BDTX-1535 after 16 months of treatment. Conclusions: BDTX-1535 was well-tolerated up to 300mg daily, which is the MTD, and promising preliminary clinical activity was observed in patients with rGBM after relapse on standard of care treatment. Given the inability to reconfirm EGFR status at the time of treatment with BDTX-1535 in this Phase 1 trial, further exploration of BDTX-1535 in a “window of opportunity” study is ongoing (NCT06072586). Clinical trial information: NCT05256290 .

Deep Lossy Plus Residual Coding for Lossless and Near-Lossless Image Compression.

Lossless and near-lossless image compression is of paramount importance to professional users in many technical fields, such as medicine, remote sensing, precision engineering and scientific research. But despite rapidly growing research interests in learning-based image compression, no published method offers both lossless and near-lossless modes. In this paper, we propose a unified and powerful deep lossy plus residual (DLPR) coding framework for both lossless and near-lossless image compression. In the lossless mode, the DLPR coding system first performs lossy compression and then lossless coding of residuals. We solve the joint lossy and residual compression problem in the approach of VAEs, and add autoregressive context modeling of the residuals to enhance lossless compression performance. In the near-lossless mode, we quantize the original residuals to satisfy a given ℓ∞ error bound, and propose a scalable near-lossless compression scheme that works for variable ℓ∞ bounds instead of training multiple networks. To expedite the DLPR coding, we increase the degree of algorithm parallelization by a novel design of coding context, and accelerate the entropy coding with adaptive residual interval. Experimental results demonstrate that the DLPR coding system achieves both the state-of-the-art lossless and near-lossless image compression performance with competitive coding speed.

A Dual-Hierarchy Synchronization Method for Signal Preambles with High Detection Rates for Satellite-Based ADS-B Receivers with Different Sensitivities

Existing methods are unable to achieve high detection rates and low false alarm rates of satellite-based Automatic Dependent Surveillance-Broadcast (ADS-B) signal preambles at extremely low signal-to-noise ratios (SNRs) using limited on-star resources. In this paper, a dual-hierarchy synchronization method is proposed, including a first-level coarse synchronization and a second-level fine synchronization. The coarse synchronization process involves three steps: (1) detection of unknown signals, (2) soft decision, and (3) adaptive interval output. The first step introduces the threshold (TMSED) of the minimum signal energy to be detected to guarantee a high detection rate. In the soft decision step, a value (SV) designed to improve the robustness of the system curbs false detection caused by noise interference. In the last step, the coarse synchronization interval radius (r) is mapped out according to the SNR to reduce resource consumption. The fine synchronization process is based on the coarse synchronization output, and the correlation peak is calculated to complete the synchronization of the signal preambles. The results show that the proposed method achieves a high detection rate of 96% at an extremely low SNR using a low sampling frequency of 10 MHz. Furthermore, the adjustment of TMSED allows this method to be applied to ADS-B receivers with different sensitivities. The comprehensive performance of this method to achieve high detection rates and acceptable false alarm rates at extremely low SNRs with limited on-star resources is verified by final simulations to be superior to other methods.

A combined interval prediction system based on fuzzy strategy and neural network for wind speed

Wind energy exhibits strong fluctuations and intermittencies. The accurate prediction of wind speed is of considerable significance for the operation and maintenance of wind farms and the safety of the power grid. However, previous studies have often ignored the impact of data noise on trend prediction, and lacked effective data pre-processing methods and adaptive interval prediction schemes, resulting in poor prediction results. To improve the accuracy of prediction, this paper proposes a wind-speed combination interval prediction system based on a fuzzy strategy and neural network. This study used a fuzzy strategy to pre-process the data and proposed a joint optimization algorithm that uses multiple multi-objective metaheuristic algorithms to optimize the neural network jointly. An improved prediction hybrid algorithm was used to reconstruct the predicted results. Finally, based on the fuzzy theory and neural network, interval prediction schemes that can adapt to different situations were proposed. Wind-speed data from the Penglai Wind Farm in China were used for verification. The mean absolute percentage error of the wind-speed point prediction was 3.75%, and the prediction interval coverage probability of the wind-speed interval prediction was 97.92%. The experimental results showed that the proposed model not only outperforms other comparable models but also improves the prediction interval coverage probability by 176.4% compared with the baseline model. This proves that the integrated hybrid model can improve the accuracy and effectiveness of wind-speed prediction. The accurate interval prediction of wind speed can result in the complementary scheduling of renewable energy generation and the sustainable development of the energy field, and support intelligent upgrades.

A phase 1/2 study of ONCT-534, a dual-action androgen receptor inhibitor (DAARI), in patients with metastatic castration-resistant prostate cancer.

TPS241 Background: Current therapeutic strategies for metastatic castration-resistant prostate cancer (mCRPC) include treatment with next-generation androgen receptor signaling inhibitors (ARSIs), such as enzalutamide, darolutamide, and apalutamide, which all target the ligand-binding domain (LBD) of the AR, and abiraterone, an androgen biosynthesis inhibitor. Approximately 5% to 10% of patients will have primary resistance to ARSIs and most initial responders will develop resistance within 1 to 3 years. Eventually, nearly all men with prostate cancer treated with ARSIs develop resistance via several mechanisms including activation of AR genomic alterations, epigenetic alterations, and expression of truncated constitutively active AR splice variants. Hence, there is substantial unmet need for treatment of men with mCRPC resistant to at least one next-generation ARSI. ONCT-534 is a DAARI with a novel mechanism of action that combines inhibition of AR function with degradation of the AR protein. Importantly, this activity includes interaction with the N-terminal domain (NTD), rendering it effective against splice variants and LBD mutants, unlike existing ARSIs. ONCT-534 has demonstrated preclinical activity in prostate cancer models against unmutated AR and multiple forms of AR alteration, including amplification, mutations in the LBD, and splice variants with loss of LBD. Methods: ONCT-534-101 is a phase 1/2, multi-center study to evaluate the safety, tolerability, antitumor activity, and pharmacokinetics of ONCT-534 in subjects with mCRPC without neuroendocrine differentiation or small cell features who have relapsed or are refractory (R/R) to at least one next-generation ARSI. The study will be separated into a Phase 1 Dose Escalation and a Phase 2 Dose Expansion. The Phase 1 portion will evaluate approximately 27 subjects in 5 dose levels using an adaptive Bayesian Optimal Interval (BOIN) design to assess safety, tolerability and DLT at escalating doses and to determine the MTD and inform the 2 dose levels or schedules to be tested in Phase 2. DLTs will be assessed during the first 28 days of treatment. The AR phenotype and AR levels of each subject’s disease will be evaluated pre- and post-treatment. The Phase 2 portion will evaluate approximately 32 subjects in 2 randomized cohorts to assess safety and tolerability of ONCT-534, compare the 2 different dose levels or schedules to select the optimal dose for further study, and assess the preliminary antitumor activity of ONCT-534. In both phases, after a screening period, eligible subjects with mCRPC will receive their assigned dose regimen of ONCT-534, which will be administered orally daily for 2 years or until disease progression and no longer clinically benefiting from treatment or development of unacceptable toxicity. The study was opened for enrollment September 2023. Clinical trial information: NCT05917470 .

Controllable cone for horizontal landing on asteroids using a flexible probe

The recently proposed flexible probe for asteroids provides a novel scheme for landing on low-gravity surface. To understand the feasible initial condition of the flexible probe's horizontal landing trajectory under multiple constraints, a controllable cone is defined and investigated. The boundary of the controllable cone is calculated using a double-layer optimization method. In the inner-layer, the switching time of the thrust profile is fixed, and the boundary point is solved through a multi-constraint trajectory optimization problem that considers the influence caused by the flexible deformation. In the outer-layer, the switching time of the thrust profile is optimized to maximize the cone boundary. To improve the optimization efficiency, an adaptive interval is used to search the switching time. With the double-layer optimization method, the controllable cone boundaries can be generated for both a specific height and a three-dimensional space. The controllable cone of the flexible probe is simulated for the landing on asteroid 433 Eros, and the influence of the flexible deformation is also analyzed.

An Accelerated Approach on Adaptive Gradient Neural Network for Solving Time-Dependent Linear Equations: A State-Triggered Perspective.

To improve the acceleration performance, a hybrid state-triggered discretization (HSTD) is proposed for the adaptive gradient neural network (AGNN) for solving time-dependent linear equations (TDLEs). Unlike the existing approaches that use an activation function or a time-varying coefficient for acceleration, the proposed HSTD is uniquely designed from a control theory perspective. It comprises two essential components: adaptive sampling interval state-triggered discretization (ASISTD) and adaptive coefficient state-triggered discretization (ACSTD). The former addresses the gap in acceleration methods related to the variable sampling period, while the latter considers the underlying evolutionary dynamics of the Lyapunov function to determine coefficients greedily. Finally, compared with commonly used discretization methods, the acceleration performance and computational advantages of the proposed HSTD are substantiated by the numerical simulations and applications to robotics.

Deep Spiking Neural Networks Driven by Adaptive Interval Membrane Potential for Temporal Credit Assignment Problem

Deep Spiking Neural Networks Driven by Adaptive Interval Membrane Potential for Temporal Credit Assignment Problem