Space Model Research Articles

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

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.

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.

Multi-Autonomous Underwater Vehicle Full-Coverage Path-Planning Algorithm Based on Intuitive Fuzzy Decision-Making

Aiming at the difficulty of realizing full-coverage path planning in a multi-AUV collaborative search, a multi-AUV full-coverage path-planning algorithm based on intuitionistic fuzzy decision-making is proposed. First, the state space model of the search environment was constructed using the raster method to provide accurate environment change data for the AUV. Second, the full-coverage path-planning algorithm for the multi-AUV collaborative search was constructed using intuition-based fuzzy decision-making, and more uncertain underwater information was modeled using the intuition-based fuzzy decision algorithm. A priority strategy was used to avoid obstacles in the search area. Finally, the simulation experiment verified the proposed algorithm. The results demonstrate that the proposed algorithm can effectively realize full-coverage path planning of the search area, and the priority strategy can effectively reduce the generation of repeated paths.

Measuring Reliable Accessibility to High-Speed Railway Stations by Integrating the Utility-Based Model and Multimodal Space–Time Prism under Travel Time Uncertainty

Measuring Reliable Accessibility to High-Speed Railway Stations by Integrating the Utility-Based Model and Multimodal Space–Time Prism under Travel Time Uncertainty

K. Polke's Theorem in Computer Model Space in 2D Modeling

K. Polke's Theorem in Computer Model Space in 2D Modeling

A comprehensive cutting tool adaptation method for multi-feature part based on evolutionary game algorithm

The adaptation of cutting tools is the key link in machining. Although a large amount of work has been done to achieve tool selection for a single feature, there is relatively little research on tool scheme adaptation for multi-feature parts. Considering the factors of matching, efficiency, cost, and environment, a comprehensive optimization method for a multi-feature part tool scheme is proposed in this work. First, the critical tool of a complex feature is defined, and a tool combination method with complex single features is proposed to generate an efficient tool strategy. Then, a tool scheme optimization model for multi-feature parts is constructed in which each machining feature is mapped to a game player in the model space, and the set of available tools is mapped to the player strategies. In addition, the classical evolutionary game algorithm is improved to adapt to the model, according to the difference in the features number and type of different parts. Finally, a square test piece is taken as a case to verify the proposed method. The result shows that the method presented in this work can efficiently obtain the customer-preferred tool scheme for the part.

Spatial syntax analysis on the renewal of the external space of mining heritage buildings —— A case study of Zollverein Coal Mine Industrial Complex

ABSTRACT The renovation of external spaces of mining heritage buildings should respect their historical and cultural value and be adapted to the specific characteristics of the mining area for spatial restructuring and functional replacement. Current research often relies on subjective experience and focuses on qualitative analysis, lacking in-depth quantitative analysis. Building upon the summary of research on mining heritage exterior spaces and spatial syntax studies, this paper proposes a research methodology that quantitatively analyzes the accessibility and integration of mining heritage exterior spaces using spatial syntax convex space and axis map models. Taking the exterior connecting spaces and characteristic spaces before and after the renovation of the German Zollverein Coal Mine Industrial Complex as the research objects, the impact of spatial location on different circulation organizations and functions of spatial places is visually expressed, providing a more intuitive explanation of the complex relationships in mining heritage exterior spaces. The research findings reveal that the spatial syntax model is adaptable in evaluating the renovation of mining heritage exterior spaces, with spaces exhibiting high integration being essentially consistent with key and highly utilized exterior spaces within the renovated mining area. In summary, it can be concluded that the integration of mining heritage exterior spaces and consequently the utilization rate and value can be improved through spatial “Connection”, “Growth”, and “Darning”, thereby offering direct reference for subsequent research and practical organization of mining heritage architectural exterior space renewal.

A comprehensive exploration of the druggable conformational space of protein kinases using AI-predicted structures.

Protein kinase function and interactions with drugs are controlled in part by the movement of the DFG and ɑC-Helix motifs that are related to the catalytic activity of the kinase. Small molecule ligands elicit therapeutic effects with distinct selectivity profiles and residence times that often depend on the active or inactive kinase conformation(s) they bind. Modern AI-based structural modeling methods have the potential to expand upon the limited availability of experimentally determined kinase structures in inactive states. Here, we first explored the conformational space of kinases in the PDB and models generated by AlphaFold2 (AF2) and ESMFold, two prominent AI-based protein structure prediction methods. Our investigation of AF2's ability to explore the conformational diversity of the kinome at various multiple sequence alignment (MSA) depths showed a bias within the predicted structures of kinases in DFG-in conformations, particularly those controlled by the DFG motif, based on their overabundance in the PDB. We demonstrate that predicting kinase structures using AF2 at lower MSA depths explored these alternative conformations more extensively, including identifying previously unobserved conformations for 398 kinases. Ligand enrichment analyses for 23 kinases showed that, on average, docked models distinguished between active molecules and decoys better than random (average AUC (avgAUC) of 64.58), but select models perform well (e.g., avgAUCs for PTK2 and JAK2 were 79.28 and 80.16, respectively). Further analysis explained the ligand enrichment discrepancy between low- and high-performing kinase models as binding site occlusions that would preclude docking. The overall results of our analyses suggested that, although AF2 explored previously uncharted regions of the kinase conformational space and select models exhibited enrichment scores suitable for rational drug discovery, rigorous refinement of AF2 models is likely still necessary for drug discovery campaigns.

Leading impactful research: applying platform thinking to drive collaborative inquiry in the innovation field

PurposeThis study investigates the application of collaborative inquiry within innovation management, employing platform thinking to address challenges of generalizability and relevance. The aim is to integrate Collaborative Inquiry methods, characterized by participatory, diffuse, and reflective practices, to transform research into a tool for impactful change in organizations in the field of innovation management.Design/methodology/approachA longitudinal participatory case study approach focuses on the IDeaLs case—a research platform that collaborated with multiple companies over several years. The data gathered and analyzed comes from the research project within the research platforms over the first two editions and from the research platform management and coordination activities.FindingsThe study introduces the Collaborative Research Platform Approach (CRPA), demonstrating its effectiveness in addressing typical constraints of traditional research methodologies through a real-world application within the IDeaLs case. The findings highlight the CRPA's potential in fostering a dynamic, co-creative research environment that bridges theoretical knowledge with practical applications, thus enhancing both scholarly and organizational outcomes while pursuing a future change within the organizations.Research limitations/implicationsThere are two main research implications. First, it proposes platform thinking as a theoretical lens to read a multi-stakeholder phenomenon in the research domain, confirming its nature of value-creation mechanisms, using it outside the business model and strategic space. Second, it offers a methodological contribution by presenting the CRPA framework.Practical implicationsThe CRPA framework offers organizations a structured approach to managing collaborative research projects that align with both academic rigor and practical relevance. Companies engaged in the study reported enhanced ability to implement actionable insights from research, influencing real-time decision-making processes.Social implicationsBy fostering collaborative engagements across multiple stakeholders, the CRPA promotes a research culture that values inclusivity and practical impact, potentially leading to broader societal benefits through improved innovation management practices.Originality/valueThis paper contributes to the innovation management field by proposing the CRPA, which integrates principles of Platform Thinking with Collaborative Inquiry. This novel approach is designed to improve the applicability and scope of innovation research, offering a robust framework that enhances engagement and utility across academic and business domains. It uses platforms as a theoretical lens to read a multi-stakeholder environment in the research domain.

Prototipe Sistem Rekomendasi Film Indonesia Menggunakan Pendekatan Content Based Filtering dan Metode Vector Space Model

Film represents a combination of narrative and cinematographic aspects in an audio-visual form. Films also provide an engaging visual, auditory, and emotional experience. The film industry in Indonesia has shown significant growth. In 2015, cinema audiences numbered 16.2 million, increasing to 51.2 million in 2019. The COVID-19 pandemic caused a drastic decline in the number of viewers, with only 4.5 million in 2021. However, the industry quickly recovered, reaching 54.07 million viewers in 2022 and 55 million in 2023, and is projected to reach 60 million in 2024. With the increasing availability of films and streaming platforms, finding movies that match user preferences often becomes a challenge. This study proposes a film recommendation system using the Content-Based Filtering method and the Vector Space Model to help users more easily find movies that match their preferences. Content-Based Filtering recommends films based on content similarity, while the Vector Space Model measures content similarity using metrics such as cosine similarity or euclidean distance. Based on experiments using 20 sample films, the results show that the designed system can provide accurate and precise movie recommendations based on keywords provided by users. The film "Pengabdi Setan 2: Communion" has the highest similarity score of 0.3018, followed by "Nana," "Menjelang Magrib," "Jailangkung: Sandekala," and "Qorin," with similarity scores of 0.0865, 0.0138, 0.0136, and 0.0125, respectively

Vehicle adaptive cruise control strategy based on improved safe distance model for off-roads with changeable pavement and slope

Compared with the structured environment, off-road environment has complex and ever-changing road conditions. This paper is focused on the vehicle adaptive cruise task for vehicles driving on complex off-road terrain. Traditional ACC strategies do not take the complex road conditions into consideration, thus easily make the relative distance between vehicles unreasonable. To solve this problem, an off-road adaptive cruise control (OACC) strategy is proposed for off-roads with changeable pavement and slope. Firstly, the influence of road conditions and vehicle relative motion states on safe distance between vehicles have analyzed with the aim of developing a more reasonable space strategy for off-road conditions. Then, based on the analysis results, an improved safe distance model (ISDM) is proposed which take the influence factors into account comprehensively. The concept of road impact factor is proposed to prevent model degradation and effectively balance the influence of single road conditions and comprehensive factors on safe distance. Besides, target to improve the adaptability of ACC, a novel state space model has developed which can handle not only the change of vehicle motion states but also road conditions. In addition, the MPC-based OACC for off-road environment is proposed, which can enable the vehicle to better adapt to relative motion states and road conditions based on the novel state space model. Finally, the performance of OACC is verified by co-simulation in MATLAB/Simulink and Carsim, and a hardware-in-the-loop simulation system. Furthermore, the analysis of ISDM has conducted to illustrate the differences and similarities compared with the traditional safe distance models and to verify the effectiveness of ISDM. Simulation results show that the OACC and ISDM proposed in this paper have great performance in different off-road working conditions.

Modelling systematic anatomical uncertainties of head and neck cancer patients during fractionated radiotherapy treatment

Objective. Head and neck cancer patients experience systematic as well as random day to day anatomical changes during fractionated radiotherapy treatment. Modelling the expected systematic anatomical changes could aid in creating treatment plans which are more robust against such changes. Approach. Inter- patient correspondence aligned all patients to a model space. Intra- patient correspondence between each planning CT scan and on treatment cone beam CT scans was obtained using diffeomorphic deformable image registration. The stationary velocity fields were then used to develop B-Spline based patient specific (SM) and population average (AM) models. The models were evaluated geometrically and dosimetrically. A leave-one-out method was used to compare the training and testing accuracy of the models. Main results. Both SMs and AMs were able to capture systematic changes. The average surface distance between the registration propagated contours and the contours generated by the SM was less than 2 mm, showing that the SM are able to capture the anatomical changes which a patient experiences during the course of radiotherapy. The testing accuracy was lower than the training accuracy of the SM, suggesting that the model overfits to the limited data available and therefore, also captures some of the random day to day changes. For most patients the AMs were a better estimate of the anatomical changes than assuming there were no changes, but the AMs could not capture the variability in the anatomical changes seen in all patients. No difference was seen in the training and testing accuracy of the AMs. These observations were highlighted in both the geometric and dosimetric evaluations and comparisons. Significance. In this work, a SM and AM are presented which are able to capture the systematic anatomical changes of some head and neck cancer patients over the course of radiotherapy treatment. The AM is able to capture the overall trend of the population, but there is large patient variability which highlights the need for more complex, capable population models.