Space Model Research Articles

Attribute reduction using self-information uncertainty measures in optimistic neighborhood extreme-granulation rough set

The pessimistic and optimistic neighborhood multi-granulation rough sets (PNMRS and ONMRS) have been applied to attribute reduction. Nonetheless, the setting of the granulation space in both models is usually based on expert experience or continuous trials, which are poorly interpretable. Besides, the dependency, an important attribute evaluation function in PNMRS and ONMRS, only considers classification information in the lower approximation and ignores that of the upper approximation, affecting its evaluation effect. Thus, we firstly propose the pessimistic and optimistic neighborhood extreme-granulation rough sets (PNERS and ONERS) based on the minimum granulation space, avoiding the unobjective setting of the granulation space. However, PNERS describes knowledge with low accuracy, and therefore we construct two self-information uncertainty measures in ONERS and propose the joint self-information that is more suitable for attribute reduction as it takes full consideration of classification information in the lower and upper approximations. Then, an attribute reduction method using the joint self-information is designed, and Fisher Score is introduced into our method to decrease time cost when processing high dimensional data. Extensive experiments validate that our method is able to achieve superior reduction and classification results on different dimensional datasets.

Dynamic data reconciliation based on elman neural network and particle filter

In the process of modern industries, complex nonlinear dynamic systems present high requirements for measured data. In the actual industrial process system, the measurement data obtained by sensors will inevitably be subject to noise disturbances from the equipment itself or from the outside environment. These noise disturbances will deteriorate the dynamic performance of the system to a certain extent and affect the industrial production. Particle filter (PF) can be used to infer the accurate outputs of nonlinear dynamic system from the contaminated measurement data, but PF is limited to the pre-known state space model. In the actual industrial process, it is difficult to summarize the internal behavior of the system and obtain the pre-known state space model. Therefore, it is impossible to directly use PF in the nonlinear dynamic system with unknown model. In order to solve the above problems, this paper proposes a dynamic data reconciliation method called ENN-PF, which combines Elman neural network (ENN) data-driven modeling with PF. In this method, ENN is used for data-driven modeling, that is, the system model is dynamically identified by using the input and output data of the system, and then the dynamic data reconciliation is carried out by using PF according to the model identified by ENN. Finally, the proposed ENN-PF method is validated by simulations and practical experiments to effectively reduce the interference of measurement noise and improve the dynamic performance of the system.

A local enhanced mamba network for hyperspectral image classification

Deep learning has significantly advanced hyperspectral image (HSI) classification, primarily due to its robust nonlinear feature extraction capabilities. The vision transformer has achieved notable performance but is limited by the quadratic computational burden of its self-attention mechanism. Recently, a network based on state space model named Mamba, has attracted considerable attention for its linear complexity and commendable performance. Nevertheless, Mamba was originally designed for one-dimensional causal sequence modeling, and its effectiveness in inherent non-causal HSI classification remains to be fully validated. To address this issue, we propose a novel Local Enhanced Mamba (LE-Mamba) network for hyperspectral image classification, which mainly comprises a Local Enhanced Spatial SSM (LES-S6), a Central Region Spectral SSM (CRS-S6), and a Multi-Scale Convolutional Gated Unit (MSCGU). The LES-S6 improves non-causal local feature extraction by incorporating a multi-directional local spatial scanning mechanism. Additionally, the CRS-S6 employs a bidirectional scanning mechanism in the spectral dimension to capture fine spectral details and integrate them with spatial information. The MSCGU utilizes a convolutional gating mechanism to aggregate features from diverse scanning routes and extract high-level semantic information. The overall accuracies of LE-Mamba on Indian Pines, WHU-Hi-HanChuan, WHU-Hi-LongKou, and Pavia University datasets are 99.16 %, 98.16 %, 99.57 %, and 99.63 %, respectively. Extensive experimental results on these four public datasets demonstrate that the LE-Mamba outperforms eight mainstream deep learning models in classification performance.

Comparative analysis of control strategies impact on power losses in IGBT power modules with a multiphysics‐circuit coupling method

AbstractPower losses in semiconductor devices present a significant impediment to advancing power electronics systems for achieving higher frequencies, improved efficiency, and greater integration. A major challenge lies in directly coupling power losses with various control strategies, considering the variations in manufacturers, packaging, and process structures within the power electronics field. This paper introduces a field‐circuit coupling simulation methodology of the insulated gate bipolar transistor (IGBT) within the Simulink/COMSOL environment, using a conventional three‐phase six‐switch voltage source inverter (VSI) IGBT module as a case study. A composite transfer function interconnects the electrical and thermal aspects, enabling bidirectional coupling between IGBT temperature and losses. This study aims to comprehensively compare the effects of various control strategies, namely, single‐vector, dual‐vector, and three‐vector current model predictive control (CMPC) and space vector pulse width modulation (SVPWM), on the losses and temperature of IGBT power modules. The findings emphasize that losses are significantly influenced by the selection of weighting factor coefficients and power factor settings under the same CMPC control strategy with a fixed switching frequency. Additionally, the selection of control strategies, such as CMPC and SVPWM, substantially impacts power losses in power electronic devices.

Distributed state estimation with compressed and synchronized auxiliary particle filters using graph theory

In this paper, we propose a novel compressed distributed auxiliary particle filter that uses graph theory (CDAPF-GT) to reduce the communication cost and improve the estimation accuracy of a nonlinear state space model. Our method compresses the global loglikelihood function into a set of parameters that are updated by an average consensus algorithm over a network of nodes. Unlike the existing methods, our method synchronizes the particle sets among all the nodes and uses the latest measurements to construct a better proposal distribution. Our method is suitable for applications that require fast and reliable distributed state estimation. We show through various simulation scenarios that our method outperforms the common counterpart method in terms of estimation accuracy with the same level of communication.

A new dynamic state estimation method for distribution networks based on modified SVSF considering photovoltaic power prediction

The fluctuations brought by the renewable energy access to the distribution network make it difficult to accurately describe the state space model of the distribution network’s dynamic process, which is the basis of the existing dynamic state estimation methods such as the Kalman filter. The inaccurate state space model directly causes an error of dynamic state estimation results. This paper proposed a new dynamic state estimation method which can mitigates the impact of renewable energy fluctuation by considering PV power prediction in establishing distribution network state space model. Firstly, the proposed method mitigates the impact of renewable energy fluctuation by considering PV power prediction in establishing distribution network state space model. Secondly, SVSF filter is introduced to achieve more accurate estimation under noise. The case study and evaluations are carried out based on MATLAB simulation. The results prove that the smooth variable structure filter with photovoltaic power prediction has a better dynamic state estimation effect under the fluctuation of the distribution network compared with the existing Kalman filter.

Illumination and Shadows in Head Rotation: Experiments with Denoising Diffusion Models

Accurately modeling the effects of illumination and shadows during head rotation is critical in computer vision for enhancing image realism and reducing artifacts. This study delves into the latent space of denoising diffusion models to identify compelling trajectories that can express continuous head rotation under varying lighting conditions. A key contribution of our work is the generation of additional labels from the CelebA dataset, categorizing images into three groups based on prevalent illumination direction: left, center, and right. These labels play a crucial role in our approach, enabling more precise manipulations and improved handling of lighting variations. Leveraging a recent embedding technique for Denoising Diffusion Implicit Models (DDIM), our method achieves noteworthy manipulations, encompassing a wide rotation angle of ±30°. while preserving individual distinct characteristics even under challenging illumination conditions. Our methodology involves computing trajectories that approximate clouds of latent representations of dataset samples with different yaw rotations through linear regression. Specific trajectories are obtained by analyzing subsets of data that share significant attributes with the source image, including light direction. Notably, our approach does not require any specific training of the generative model for the task of rotation; we merely compute and follow specific trajectories in the latent space of a pre-trained face generation model. This article showcases the potential of our approach and its current limitations through a qualitative discussion of notable examples. This study contributes to the ongoing advancements in representation learning and the semantic investigation of the latent space of generative models.

SLAKA-IoD: A Secure and Lightweight Authentication and Key Agreement Protocol for Internet of Drones

The existing authentication and key agreement (AKA) schemes for the internet of drones (IoD) still suffer from various security attacks and fail to ensure required security properties. Moreover, drones generally have limited memory and computation capability. Motivated by these issues, a secure and lightweight AKA protocol for IoD (SLAKA-IoD) is proposed based on physical unclonable function (PUF), “exclusive or” (XOR) operation and hash function, which are simple cryptographic operations and functions that can provide better performance. In the SLAKA-IoD protocol, a drone and the ground station (GS) perform mutual authentication and establish a secure session key between them, and any two drones can also perform mutual authentication and establish a secure session key between them. Via informal security analysis, formal security analysis using the strand space model, and security verification based on the Scyther tool, the SLAKA-IoD protocol is proven to resist various security attacks and ensure required security properties. Further comparative analysis shows that the SLAKA-IoD protocol can provide more security features, and is generally lightweight as compared with these related AKA protocols for IoD, so it is suitable for IoD.

City Bus Reliability Measurement Based on Sparse Field Data Supported by Selected State Space Models

City Bus Reliability Measurement Based on Sparse Field Data Supported by Selected State Space Models

VIRTUAL SIMULATOR FOR PROCESSING CONFIDENTIAL INFORMATION ON PHYSICAL MEDIA

Acquiring digital competencies is an important area in modern education. Using and applying digital tools and technologies allows to increase the level of students' competencies. A modern digital solution based on virtual reality technology is proposed. The virtual simulator is a set of interactive models in a three-dimensional virtual space, allowing to implement scenarios of interaction between users and special software for the practical application of information security tasks. An overview of existing virtual simulators and justification for the choice of software and processes for processing confidential information on physical media are provided. Digital competencies are an important area in modern education. A modern digital solution based on virtual reality technology is proposed. The virtual simulator is designed and represents a set of interactive models in a three-dimensional virtual space, allowing to implement scenarios of interaction between users and special software for the practical application of information security tasks. Experimental studies were conducted on working with the virtual simulator. Testing of the simulator confirmed its operability. The virtual simulator can be used by both practitioners in the field of information security software operation. In the future, it is planned to refine the simulator in terms of developing augmented reality scenarios and modeling information security incidents. The implementation of the virtual simulator is described. Statistical data on the results of the experiment are provided. On average, the training result using the simulator increased by 35,8 %.

DeepTree: Modeling Trees With Situated Latents.

In this article, we propose DeepTree, a novel method for modeling trees based on learning developmental rules for branching structures instead of manually defining them. We call our deep neural model "situated latent" because its behavior is determined by the intrinsic state -encoded as a latent space of a deep neural model- and by the extrinsic (environmental) data that is "situated" as the location in the 3D space and on the tree structure. We use a neural network pipeline to train a situated latent space that allows us to locally predict branch growth only based on a single node in the branch graph of a tree model. We use this representation to progressively develop new branch nodes, thereby mimicking the growth process of trees. Starting from a root node, a tree is generated by iteratively querying the neural network on the newly added nodes resulting in the branching structure of the whole tree. Our method enables generating a wide variety of tree shapes without the need to define intricate parameters that control their growth and behavior. Furthermore, we show that the situated latents can also be used to encode the environmental response of tree models, e.g., when trees grow next to obstacles. We validate the effectiveness of our method by measuring the similarity of our tree models and by procedurally generated ones based on a number of established metrics for tree form.

Central similarity consistency hashing for asymmetric image retrieval

Asymmetric image retrieval methods have drawn much attention due to their effectiveness in resource-constrained scenarios. They try to learn two models in an asymmetric paradigm, i.e., a small model for the query side and a large model for the gallery. However, we empirically find that the mutual training scheme (learning with each other) will inevitably degrade the performance of the large gallery model, due to the negative effects exerted by the small query one. In this paper, we propose Central Similarity Consistency Hashing (CSCH), which simultaneously learns a small query model and a large gallery model in a mutually promoted manner, ensuring both high retrieval accuracy and efficiency on the query side. To achieve this, we first introduce heuristically generated hash centers as the common learning target for both two models. Instead of randomly assigning each hash center to its corresponding category, we introduce the Hungarian algorithm to optimally match each of them by aligning the Hamming similarity of hash centers to the semantic similarity of their classes. Furthermore, we introduce the instance-level consistency loss, which enables the explicit knowledge transfer from the gallery model to the query one, without the sacrifice of gallery performance. Guided by the unified learning of hash centers and the distilled knowledge from gallery model, the query model can be gradually aligned to the Hamming space of the gallery model in a decoupled manner. Extensive experiments demonstrate the superiority of our CSCH method compared with current state-of-the-art deep hashing methods. The open-source code is available at https://github.com/dubanx/CSCH.

Adversarial EM for variational deep learning: Application to semi-supervised image quality enhancement in low-dose PET and low-dose CT

In positron emission tomography (PET) and X-ray computed tomography (CT), reducing radiation dose can cause significant degradation in image quality. For image quality enhancement in low-dose PET and CT, we propose a novel theoretical adversarial and variational deep neural network (DNN) framework relying on expectation maximization (EM) based learning, termed adversarial EM (AdvEM). AdvEM proposes an encoder–decoder architecture with a multiscale latent space, and generalized-Gaussian models enabling datum-specific robust statistical modeling in latent space and image space. The model robustness is further enhanced by including adversarial learning in the training protocol. Unlike typical variational-DNN learning, AdvEM proposes latent-space sampling from the posterior distribution, and uses a Metropolis–Hastings scheme. Unlike existing schemes for PET or CT image enhancement which train using pairs of low-dose images with their corresponding normal-dose versions, we propose a semi-supervised AdvEM (ssAdvEM) framework that enables learning using a small number of normal-dose images. AdvEM and ssAdvEM enable per-pixel uncertainty estimates for their outputs. Empirical analyses on real-world PET and CT data involving many baselines, out-of-distribution data, and ablation studies show the benefits of the proposed framework.

Asymptotic smoothness effects and global attractor for a peridynamic model with energy damping

AbstractIn this paper, we consider a peridynamic model with energy damping inspired by the works of Balakrishnan and Taylor on “damping models” based on the instantaneous total energy of the system. We study the asymptotic behavior of solutions, in the sense of attractors, of these peridynamic models in suitable phase space; more precisely, we prove a result of existence and characterization of compact global attractors with a nonlinear strongly continuous semigroup approach based in the asymptotic smoothness property thanks to Chueshov and Lasiecka and Nakao's lemma.

Efficient Exploration and Exploitation for Sequential Music Recommendation

Music streaming services heavily rely upon recommender systems to acquire, engage, and retain users. One notable component of these services are playlists, which can be dynamically generated in a sequential manner based on the user’s feedback during a listening session. Online learning to rank approaches have recently been shown effective at leveraging such feedback to learn users’ preferences in the space of song features. Nevertheless, these approaches can suffer from slow convergence as a result of their random exploration component and their session-agnostic exploitation component. To overcome these limitations, we propose a novel online learning to rank approach which efficiently explores the space of candidate recommendation models by restricting itself to the orthogonal complement of the subspace of previous underperforming exploration directions. Moreover, we propose a session-aware exploitation component which leverages the momentum of the current best model during updates. Our thorough evaluation using simulated listening sessions from two large Last.fm datasets demonstrates substantial improvements over state-of-the-art approaches in terms of early-stage performance, which results in an improved user experience during online learning. In addition, we demonstrate that long-term convergence can be further enhanced by adaptively relaxing exploration constraints along the way.

Content Classification of the Official Website of the Ministry of Foreign Affairs of the Republic of Indonesia (MoFA RI) using Vector Space Model (VSM)

The official website of the Ministry of Foreign Affairs of the Republic of Indonesia (MoFA RI) is an important platform for disseminating information to a diverse audience. Efficiently categorizing the vast amount of content available on the website is essential for enhancing user experience and optimizing information retrieval. These categories will also become an identifier and topic classification based on the content inside the article. This study presents a systematic approach to content classification of the Official Website of the Ministry of Foreign Affairs of the Republic of Indonesia (MoFA RI) using the Vector Space Model (VSM). The methodology involves preprocessing the text data, constructing a term-document matrix, and implementing cosine similarity to measure the relevance of documents to predefined categories. The study demonstrates the effectiveness of VSM in accurately classifying content, thus facilitating streamlined access to information for users navigating the website. Furthermore, the findings offer insights into enhancing the organization and accessibility of governmental online platforms, contributing to improved user experience and information dissemination.

Research on multi-beam survey line system based on dynamic programming algorithm

This paper establishes a calculation model for the coverage width and overlap rate of a multi-beam survey line system [1], and optimizes the multi-beam bathymetric survey plan for marine areas. Through the dynamic programming algorithm, a reasonable survey line plan is designed based on the actual marine area depth. The research content includes: Model Establishment: Construct a calculation model for coverage width and overlap rate, determining the coverage width and overlap rate at each position. Three-Dimensional Space Model: Using the survey vessel as the origin and combining marine area information, solve for the coverage width at different angles. Shortest Survey Line Plan: Using the dynamic programming algorithm, determine the shortest survey line plan, with the dynamic transfer equation determined by the overlap rate index. The total length of the survey line in this plan is calculated to be 114,824 meters. Specific Survey Line Plan Design: Using sea depth data and cubic spline interpolation to complete the discrete point plane, construct approximate width and slope. Under the condition of relaxing the overlap rate to 5%-30%, the shortest survey line length is obtained as 398,059 meters, with a missing survey area ratio of 0%, and the total length of parts with an overlap rate exceeding 20% is 25,759 meters, accounting for 6.4%. This study provides theoretical basis and calculation methods for optimizing multi-beam bathymetric survey plans.

X‐Ray Computed Tomography Meets Robust Chemometric Latent Space Modeling for Lean Meat Percentage Prediction in Pig Carcasses

ABSTRACTThis study presents a case of processing X‐ray computed tomography (CT) data for pork scans using chemometric latent space modeling. The distribution of voxel intensities is shown to exemplify a multivariate, multi‐collinear signal mixture. While this concept is not novel, it is revisited here from a chemometric perspective. To extract meaningful information from such multivariate signals, latent space modeling based on partial least squares (PLS) is an ideal solution. Furthermore, a robust PLS approach is even more effective for latent space modeling, as it can extract latent spaces unaffected by outliers, thereby enhancing predictive modeling. As an example, lean meat percentage is predicted using X‐ray CT data and robust PLS regression. This method is applicable to X‐ray CT quantification analysis, particularly in cases where unclear, erroneous, and outlying observations are suspected in the data.

Periodic traveling waves of a SIR model with renewal and delay in one-dimensional lattice

This paper is primarily addressed to the (non)existence and asymptotic behaviors of periodic traveling waves for a SIR model with renewal and delay in one-dimensional lattice, which generalizes the conclusions of SIR models in one-dimensional continuous space. Especially, the challenge of establishing the asymptotic behaviors of periodic traveling waves when periodicity arises is solved by integral and analytic techniques. Moreover, the spreading speed is equal to the minimal wave speed can be obtained by our results.

Modified Vector-Controlled DFIG Wind Energy System Using Robust Model Predictive Rotor Current Control

AbstractAs wind energy (WE) technologies become more prevalent, there are significant concerns about the electrical grid’s stability. Despite their many advantages, a WE system based on a doubly fed induction generator is vulnerable to power grid disruptions. Due to being built on traditional controllers, the generator systems with standard vector control (VC) cannot resist disturbances. This paper seeks to provide a novel VC that is resistant to outer perturbations. For this purpose, a finite state space model predictive control (FS-MPC) is utilized instead of the internal current loop of the standard VC. The objective of the proposed system is to minimize the error between the measured currents and their reference values and, also, reduces the total harmonic distortion (THD) of the current. The cost function can optimize this requirement, which reduces the computation time. The VC-FS-MPC was implemented using the MATLAB, where a 1.5-MW generator operating under different conditions was used. The necessary graphical and numerical results were extracted to show the efficiency, effectiveness, and ability of the VC-FS-MPC to improve the characteristics of the studied energy system. The results show the flexibility and distinctive performance of the VC-FS-MPC in the various tests used, as the THD of stator current was reduced in the second test compared to the first test by an estimated percentage of 61.79%. Moreover, the THD of rotor current was reduced compared to the first test by an estimated percentage of 23.56%. These ratios confirm the effectiveness of the VC-FS-MPC in improving the characteristics of the proposed system.