Space Model Research Articles

Wind and Wave-Induced Vibration Reduction Control for Floating Offshore Wind Turbine Using Delayed Signals

Active vibration control is a critical issue of the wind turbine in the field of marine energy. First, based on a three-degree-of-freedom wind turbine, a state space model subject to wind and wave loads is obtained. Then, a delayed state feedback control scheme is illustrated to reduce the vibration of platform pitch angle and tower top foreaft displacement, where the control channel includes time-delay state signals. The designed controller’s existence conditions are investigated. The simulation results show that the delayed feedback H∞ controller can significantly suppress wind- and wave-induced vibration of the wind turbine. Furthermore, it presents potential advantages over the delay-free feedback H∞ controller and the classic linear quadratic regulator in two aspects: vibration control performance and control cost.

Sediment Cover Modulates Landscape Erosion Patterns and Channel Steepness in Layered Rocks: Insights From the SPACE Model

AbstractErosional perturbations from changes in climate or tectonics are recorded in the profiles of bedrock rivers, but these signals can be challenging to unravel in settings with non‐uniform lithology. In layered rocks, the surface lithology at a given location varies through time as erosion exposes different layers of rock. Recent modeling studies have used the Stream Power Model (SPM) to highlight complex variations in erosion rates that arise in bedrock rivers incising through layered rocks. However, these studies do not capture the effects of coarse sediment cover on channel evolution. We use the “Stream Power with Alluvium Conservation and Entrainment” (SPACE) model to explore how sediment cover influences landscape evolution and modulates the topographic expression of erodibility contrasts in horizontally layered rocks. We simulate river evolution through alternating layers of hard and soft rock over million‐year timescales with a constant and uniform uplift rate. Compared to the SPM, model runs with sediment cover have systematically higher channel steepness values in soft rock layers and lower channel steepness values in hard rock layers. As more sediment accumulates, the contrast in steepness between the two rock types decreases. Effective bedrock erodibilities back‐calculated assuming the SPM are strongly influenced by sediment cover. We also find that sediment cover can significantly increase total relief and timescales of adjustment toward landscape‐averaged steady‐state topography and erosion rates.

Uranus and Neptune as methane planets: Producing icy giants from refractory planetesimals

Uranus and Neptune are commonly considered ice giants, and it is often assumed that, in addition to a solar mix of hydrogen and helium, they contain roughly twice as much water as rock. This classical picture has led to successful models of their internal structure and has been understood to be compatible with the composition of the solar nebula during their formation (Reynolds and Summers, 1965; Podolak and Cameron, 1974; Podolak and Reynolds, 1984; Podolak et al., 1995; Nettelmann et al., 2013). However, the dominance of water has been recently questioned (Teanby et al., 2020; Helled and Fortney, 2020; Podolak et al., 2022). Planetesimals in the outer solar system are composed mainly of refractory materials, leading to an inconsistency between the icy composition of Uranus and Neptune and the ice-poor planetesimals they accreted during formation (Podolak et al., 2022). Here we elaborate on this problem, and propose a new potential solution. We show that chemical reactions between planetesimals dominated by organic-rich refractory materials and the hydrogen in gaseous atmospheres of protoplanets can form large amounts of methane ’ice’. Uranus and Neptune could thus be compatible with having accreted refractory-dominated planetesimals, while still remaining icy. Using random statistical computer models for a wide parameter space, we show that the resulting methane-rich internal composition could be a natural solution, giving a good match to the size, mass and moment of inertia of Uranus and Neptune, whereas rock-rich models appear to only work if a rocky interior is heavily mixed with hydrogen. Our model predicts a lower than solar hydrogen to helium ratio, which can be tested. We conclude that Uranus, Neptune and similar exoplanets could be methane-rich, and discuss why Jupiter and Saturn cannot.

Solution and numerical simulation of thermal-structural characteristics model of wire deflagration based on partition coupling time domain propulsion method

The dynamic analysis of transmission lines under the thermal shock load of mountain fire is a complex nonlinear coupling problem of thermal structure. Taking the transmission line as the research object, based on the temperature rise model of mountain fire deflagration space, considering the temperature characteristics of the wire, the mathematical model of the thermal structure coupling characteristics of the wire deflagration is established. In this paper, the partition coupling time domain propulsion method is used to solve the problem. Through MATLAB programming and ANSYS secondary development, the transient characteristics and dynamic response of the coupling effect of the heated structure of the LGJ-400/35 wire with a span of 100 m under different positions and numbers of deflagration points are simulated. It is concluded that the temperature rise rate is positively correlated with the displacement rate and negatively correlated with the stress reduction rate. When the temperature rise rate increases by 0.1 °C/s, the displacement rate increases by 0.0004 m/s. The global deflagration is the most unfavorable mountain fire condition, and the temperature rise rate and displacement rate of the wire under this condition are about 1.3 and 1.9 times that of each single deflagration point condition. The wire temperature is symmetrically distributed with the detonation point symmetrically arranged relative to the mid-span.

Ultra-fast semantic map perception model for autonomous driving

Autonomous driving relies on real-time perception of environmental semantic maps to make decisions, accurate and real-time perception of environmental semantics is crucial for safe navigation, as well as for efficient construction of high-definition maps in the backend. Our research found that when the output space of the semantic map perception model is an orthogonal projection subspace of the input perception space, it significantly improves the accuracy and real-time performance of map semantic segmentation while reducing learning pressure on deep network models. Based on this, we propose a simple yet efficient semantic map segmentation network that aims to achieve extremely fast speed and precision. Furthermore, we implement a semantic map fusion process using a Bayesian framework that promotes global semantic information accumulation in local semantics over time and space. Our experimental results demonstrate the superiority of our method, achieving state-of-the-art performance in network speed and mIoU metrics. At the same spatial resolution of semantic maps, our method reaches over 350+ frames per second, which is at least 15 times faster than the previous state-of-the-art method. Code released at https://github.com/xzx-ai/MDSegMap.

Ultra-fast semantic map perception model for autonomous driving

Autonomous driving relies on real-time perception of environmental semantic maps to make decisions, accurate and real-time perception of environmental semantics is crucial for safe navigation, as well as for efficient construction of high-definition maps in the backend. Our research found that when the output space of the semantic map perception model is an orthogonal projection subspace of the input perception space, it significantly improves the accuracy and real-time performance of map semantic segmentation while reducing learning pressure on deep network models. Based on this, we propose a simple yet efficient semantic map segmentation network that aims to achieve extremely fast speed and precision. Furthermore, we implement a semantic map fusion process using a Bayesian framework that promotes global semantic information accumulation in local semantics over time and space. Our experimental results demonstrate the superiority of our method, achieving state-of-the-art performance in network speed and mIoU metrics. At the same spatial resolution of semantic maps, our method reaches over 350+ frames per second, which is at least 15 times faster than the previous state-of-the-art method. Code released at https://github.com/xzx-ai/MDSegMap.

LPV interpolation modeling and modal-based pole placement control for ball screw drive with dynamic variations

This paper presents a linear parameter varying (LPV) interpolation modeling method and modal-based pole placement (PP) control strategy for the ball screw drive (BSD) with varying dynamics. The BSD is modeled as a global LPV model with position-load dependence by selecting position and load as scheduling variables. The global LPV model is obtained from local subspace closed-loop identification and LPV interpolation modeling. A modal-based global LPV model is obtained through the similarity transformation. Based on this model, a modal-based LPV PP control strategy is proposed to achieve various modal control. Specifically, a state feedback control structure with an LPV state observer is designed to realize online state estimation and real-time state feedback control of modal state variables which cannot be measured directly. The steady-state error is minimized by introducing an error state space (SS) model with the integral effects. Moreover, the stability of the closed-loop system is analyzed according to the controllable decomposition and principle of separation. It is experimentally demonstrated that the proposed modal-based LPV PP control strategy can effectively achieve precise tracking and outstanding robustness meantime.

HTQ: Exploring the High-Dimensional Trade-Off of mixed-precision quantization

Mixed-precision quantization, where more sensitive layers are kept at higher precision, can achieve the trade-off between accuracy and complexity of neural networks. However, the search space for mixed-precision grows exponentially with the number of layers, making the brute force approach infeasible on deep networks. To reduce this exponential search space, recent efforts use Pareto frontier or integer linear programming to select the bit-precision of each layer. Unfortunately, we find that these prior works rely on a single constraint. In practice, model complexity includes space complexity and time complexity, and the two are weakly correlated, thus using simply one as a constraint leads to sub-optimal results. Besides this, they require manually set constraints, making them only pseudo-automatic. To address the above issues, we propose High-dimensional Trade-off Quantization (HTQ), which automatically determines the bit-precision in the high-dimensional space of model accuracy, space complexity, and time complexity without any manual intervention. Specifically, we use the saliency criterion based on connection sensitivity to indicate the accuracy perturbation after quantization, which performs similarly to Hessian information but can be calculated quickly (more than 1000× speedup). The bit-precision is then automatically selected according to the three-dimensional (3D) Pareto frontier of the total perturbation, model size, and bit operations (BOPs) without manual constraints. Moreover, HTQ allows for the joint optimization of weights and activations, and thus the bit-precisions of both can be computed concurrently. Compared to state-of-the-art methods, HTQ achieves higher accuracy and lower space/time complexity on various model architectures for image classification and object detection tasks. Code is available at: https://github.com/zkkli/HTQ.

Design and implementation of a collaborative modeling architecture based on MDE (Multi-DDS Edge)

Abstract Virtual space is an integral part of the Metaverse. Typical virtual spaces are modeled step by step using artificial means. With the development needs of Metaverse modeling and simulation technology and the need for simulation fidelity and content richness in virtual space construction, efficient and correct modeling methods are required. It has become a bottleneck restricting the efficiency of virtual space modeling. This paper designs a multi-person collaborative virtual space modeling method, which improves the modeling efficiency, communication security, and interaction stability of the virtual space collaborative modeling method. Compared with existing collaborative modeling methods, this paper proposes a virtual space collaborative modeling method based on multi-edge DDS technology. By constructing MDE (multi-DDS edges) and MDE on user migration, collaborative construction in multiple virtual spaces is completed. Model data communication extends collaborative modeling technology from the virtual space creation stage to the virtual space simulation and deduction stage, and designs and verifies collaborative content, collaborative architecture, and collaborative communication. Experiments show that the architecture synergy efficiency is 145.6%, the architecture synergy efficiency is 212.5%, and the architecture synergy efficiency is 105.4%. As the number of people in collaborative modeling increases, the operational complexity and demonstration take a logarithmic form, meeting the needs of the future development of the metaverse.

A novel model-based Cauchy-Schwarz divergence condition indicator for gears monitoring during fluctuating speed conditions

Gear monitoring and fault diagnosis are vital for preventing accidents and minimizing economic losses in transportation and industrial systems. Traditional methods use vibration sensors and a two-stage analysis approach: preprocessing data to remove noise and extract relevant components, and generating a condition indicator to detect behavioral anomalies in the gears over time.Time synchronous averaging is a notable tool for monitoring gears at constant speeds. Such a tool filters sensor signals and extracts rotation-related components by using statistical measurements as condition indicators. However, it has limitations in scenarios with time-varying sampling rates and fluctuating speeds, where statistical measures may not fully capture changes in system parameters.This article proposes a novel methodology for monitoring gears in multivariate rotordynamical systems under fluctuating speed conditions. The method integrates time synchronous averaging, system identification algorithms, and statistical tools. It generates a time-synchronous average signal considering speed fluctuations, computes a state–space model of gear behavior in healthy states, extracts residual data from a data-driven model, and generates a condition indicator based on the Cauchy–Schwarz divergence.The proposed methodology was evaluated using experimental data from three rotor dynamical setups under different operational conditions. Validation showed its effectiveness, especially under high-load conditions with significant speed fluctuations.

Color Image Hybrid Noise Filtering Algorithm based on Deep Convolution Neural Network

To solve the problems of the classical color image hybrid noise filtering method, a deep convolutional neural network improved by evolutionary strategy and jump connection is proposed and applied to the filtering noise reduction of color images. First, the color information of the image is described quantitatively by digital means. The common method is to build color space model. According to the characteristics of color and the needs of human vision, mathematical algorithms are used to convert images into machine recognizable data. The distance between pixels is measured according to the difference of pixels in the color image determined above. Then, the probability density function and noise probability density function of Gaussian noise are calculated to determine the hybrid noise feature points of color image. The filtering algorithm structure designed this time is as follows: A color image hybrid noise filter is used to map the noise points in the mapped image to the feature space, and linear regression is performed on the noise point data. Relaxation variables are introduced in the network to improve the denoising ability. The experimental results show that the Peak Signal to Noise Ratio and structural similarity index values of the filtering algorithm designed in this study are higher than the two methods in the literature. The color image hybrid noise filtering model designed in this study has good filtering performance, good image cleanliness, and high filtering efficiency.

Enhancing autonomous driving safety: A robust traffic sign detection and recognition model TSD-YOLO

As autonomous driving technology rapidly advances, Traffic Sign Detection and Recognition (TSDR) has become pivotal in ensuring the safety and regulatory compliance of autonomous vehicles. Despite progress, existing technologies struggle under challenging conditions such as adverse weather and complex roadway environments. To overcome these obstacles, we introduce a novel model, TSD-YOLO, which leverages Mamba and YOLO technologies to enhance the accuracy and robustness of traffic sign detection. Our innovative YOLO-MAM dual-branch module merges convolutional layer-based local feature extraction with the long-distance dependency capabilities of the State Space Model (SSM). We conducted experimental validations using the Tsinghua-Tencent 100K (TT-100K) dataset and the Mapillary Traffic Sign Detection (MTSD) dataset, demonstrating our model’s efficacy across various datasets. Furthermore, cross-dataset validations affirm the model’s exceptional generalization and robustness across diverse environments. This study not only bolsters traffic sign detection and recognition in autonomous driving systems but also paves the way for future advancements in autonomous driving technology.

Associations between Delay Discounting and Unhealthy Behaviors in Substance Use Recovery

BackgroundResearch on delay discounting (DD) is mixed on whether DD is a domain-specific component related to specific behaviors or a domain-general process that cuts across various behaviors. A pivotal group to test the associations between DD and unhealthy behaviors is individuals in recovery from substance use disorders (SUD), as they are moving away from a disorder toward a healthier state. MethodsIndividuals in SUD recovery (n = 317) completed the Temptation Scale, the Health Behaviors Questionnaire, and an Adjusting Delay Discounting Task. An exhaustive model space search was performed using linear regression to examine associations between DD with temptation, engagement in unhealthy behaviors, and the total number of unhealthy behaviors participants engage in. We also tested whether remission status is associated with the total number of unhealthy behaviors participants engage in. ResultsResults revealed that DD was positively associated with poor eating (p<.001), physical inactivity (p=.003), financial irresponsibility (p<.001), risky behaviors (p<.001), lack of personal development goals (p<.001), lack of household savings (p=.004), and lack of health behaviors (p=.003). DD was also positively associated with the total number of unhealthy behaviors participants engage in (p<.001). Participants who were not in remission engaged in more unhealthy behaviors compared to those who were in remission (p<.001). ConclusionIn a sample of individuals in recovery from SUD, DD is not domain-specific and undergirds engagement in several maladaptive health behaviors that can negatively impact recovery. Thus, DD can be a target for interventions aiming to reduce other maladaptive behaviors in SUD recovery.

Trajectory tracking control of wearable upper limb rehabilitation robot based on Laguerre model predictive control

Wearable rehabilitation robots have become an important auxiliary tool in rehabilitation therapy, providing effective rehabilitation training and helping to recover damaged muscles and joints. In response to the difficulty of traditional control methods in solving various constraints in the trajectory tracking process of the Upper Limb Rehabilitation Robot (ULRR), this study uses model predictive control to study the trajectory tracking problem of the upper limb rehabilitation robot. Firstly, based on the Lagrangian dynamic model of wearable rehabilitation robots, an extended state space model with pseudo linearization of the system was established. Given the performance indicators and various constraints of the system, a corresponding model predictive controller is designed based on the Laguerre model to ensure system performance while greatly reducing the computational complexity of predictive control. Secondly, the stability of the model predictive controller is demonstrated, and a disturbance observer is introduced into the controller to achieve compensation for slow-varying perturbations; a joint space sliding mode variable is also introduced to achieve simultaneous tracking of the joint’s desired position and desired velocity. Finally, taking a planar two bar robot as an example, comparative simulation verification was conducted on unconstrained joint trajectory tracking and constrained joint trajectory tracking. The simulation results show that the model predictive controller can achieve simultaneous tracking of joint expected trajectory and expected speed while meeting various constraints. It has good effects in improving patient motion control ability and reducing patient fatigue, providing new research ideas and methods for the field of rehabilitation therapy.

Tongue Disease Prediction Based on Machine Learning Algorithms

The diagnosis of tongue disease is based on the observation of various tongue characteristics, including color, shape, texture, and moisture, which indicate the patient’s health status. Tongue color is one such characteristic that plays a vital function in identifying diseases and the levels of progression of the ailment. With the development of computer vision systems, especially in the field of artificial intelligence, there has been important progress in acquiring, processing, and classifying tongue images. This study proposes a new imaging system to analyze and extract tongue color features at different color saturations and under different light conditions from five color space models (RGB, YcbCr, HSV, LAB, and YIQ). The proposed imaging system trained 5260 images classified with seven classes (red, yellow, green, blue, gray, white, and pink) using six machine learning algorithms, namely, the naïve Bayes (NB), support vector machine (SVM), k-nearest neighbors (KNN), decision trees (DTs), random forest (RF), and Extreme Gradient Boost (XGBoost) methods, to predict tongue color under any lighting conditions. The obtained results from the machine learning algorithms illustrated that XGBoost had the highest accuracy at 98.71%, while the NB algorithm had the lowest accuracy, with 91.43%. Based on these obtained results, the XGBoost algorithm was chosen as the classifier of the proposed imaging system and linked with a graphical user interface to predict tongue color and its related diseases in real time. Thus, this proposed imaging system opens the door for expanded tongue diagnosis within future point-of-care health systems.

A Survey on Visual Mamba

State space models (SSM) with selection mechanisms and hardware-aware architectures, namely Mamba, have recently shown significant potential in long-sequence modeling. Since the complexity of transformers’ self-attention mechanism is quadratic with image size, as well as increasing computational demands, researchers are currently exploring how to adapt Mamba for computer vision tasks. This paper is the first comprehensive survey that aims to provide an in-depth analysis of Mamba models within the domain of computer vision. It begins by exploring the foundational concepts contributing to Mamba’s success, including the SSM framework, selection mechanisms, and hardware-aware design. Then, we review these vision Mamba models by categorizing them into foundational models and those enhanced with techniques including convolution, recurrence, and attention to improve their sophistication. Furthermore, we investigate the widespread applications of Mamba in vision tasks, which include their use as a backbone in various levels of vision processing. This encompasses general visual tasks, medical visual tasks (e.g., 2D/3D segmentation, classification, image registration, etc.), and remote sensing visual tasks. In particular, we introduce general visual tasks from two levels: high/mid-level vision (e.g., object detection, segmentation, video classification, etc.) and low-level vision (e.g., image super-resolution, image restoration, visual generation, etc.). We hope this endeavor will spark additional interest within the community to address current challenges and further apply Mamba models in computer vision.

Integrating distributive justice in system dynamics models of sustainability transitions

AbstractSocial‐ecological‐technological transitions for decarbonisation and sustainable development goals can perpetuate or introduce environmental and socio‐economic injustices. The just transitions literature provides useful perspectives for assessing transition pathways. This paper explores integrating distributive justice into system dynamics modelling for sustainability transitions. For this purpose, we adapt existing requirements to enable the evaluation of distributive justice in model‐based support for climate planning to the specific case of system dynamics modelling. We test the requirements by using a previously developed system dynamics model to (1) gain insights into the application of the requirements and (2) reflect on their further development. Our findings underscore the importance of transparency in model assumptions and results. They suggest exploring the behaviour space of models to account for uncertainty and plurality of stakeholder values. Furthermore, they emphasise the need to discuss the implications of model results and the plurality of stakeholder values to foster deliberation and inclusive decision‐making.

Variational Model with Nonstandard Growth Condition in Image Restoration and Contrast Enhancement

AbstractWe propose a new variational model in Sobolev-Orlicz spaces with non-standard growth conditions of the objective functional and discuss its applications to the simultaneous contrast enhancement and denoising of color images. The characteristic feature of the proposed model is that we deal with a constrained non-convex minimization problem that lives in variable Sobolev-Orlicz spaces where the variable exponent is unknown a priori and it depends on a particular function that belongs to the domain of the objective functional. In contrast to the standard approach, we do not apply any spatial regularization to the image gradient. We discuss the consistency of the variational model, give the scheme for its regularization, derive the corresponding optimality system, and propose an iterative algorithm for practical implementations.

Weyl Points on Nonorientable Manifolds.

Weyl fermions are hypothetical chiral particles that can also manifest as excitations near three-dimensional band crossing points in lattice systems. These quasiparticles are subject to the Nielsen-Ninomiya "no-go" theorem when placed on a lattice, requiring the total chirality across the Brillouin zone to vanish. This constraint results from the topology of the (orientable) manifold on which they exist. Here, we ask to what extent the concepts of topology and chirality of Weyl points remain well defined when the underlying manifold is nonorientable. We show that the usual notion of chirality becomes ambiguous in this setting, allowing for systems with a nonzero total chirality. This circumvention of the Nielsen-Ninomiya theorem stems from a generic discontinuity of the vector field whose zeros are Weyl points. Furthermore, we discover that Weyl points on nonorientable manifolds carry an additional Z_{2} topological invariant which satisfies a different no-go theorem. We implement such Weyl points by imposing a nonsymmorphic symmetry in the momentum space of lattice models. Finally, we experimentally realize all aspects of their phenomenology in a photonic platform with synthetic momenta. Our work highlights the subtle but crucial interplay between the topology of quasiparticles and of their underlying manifold.

Designing for health: A study of architecturally oriented models of public space

AbstractIn the post-pandemic period, cities are committed to creating healthier and more healing urban environments. In high-density urban environments, urban complexes are increasingly assuming the function of enriching the external space and activating the vitality of cities, and their architecturally oriented public spaces have great potential for the promotion of residents' health. However, current research on public space and health has focused on green spaces and neighborhoods, and has not yet systematically sorted out the pathways that influence how building-oriented public space can contribute to health. In order to fill the gap, this study attempt to establish a model of health-promoting architecturally oriented public space, which can provide a reference for the similar projects.