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Journal articles on the topic 'Interactive Multiple Model algoritmus'

Author: Grafiati

Published: 28 June 2021

Last updated: 29 July 2025

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1

Liu, Qiaoran, and Xun Yang. "Improved Interacting Multiple Model Particle Filter Algorithm." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 1 (2018): 169–75. http://dx.doi.org/10.1051/jnwpu/20183610169.

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For the issue of limited filtering accuracy of interactive multiple model particle filter algorithm caused by the resampling particles don't contain the latest observation information, we made improvements on interactive multiple model particle filter algorithm in this paper based on mixed kalman particle filter algorithm. Interactive multiple model particle filter algorithm is proposed. In addition, the composed methods influence to tracking accuracy are discussed. In the new algorithm the system state estimation is generated with unscented kalman filter (UKF) first and then use the extended

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Liu, Ming Yong, Yang Li, and Xiao Jian Zhang. "A Research on the Weight of Interactive Multiple Model in Maneuvering Target Tracking." Applied Mechanics and Materials 568-570 (June 2014): 1008–11. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.1008.

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The establishment of the target model is the key of maneuvering target tracking. The previous research on interactive multiple model, which is applied on tracking extensively, focused on the design of the model set and fusion with other algorithms, while there is less study on change mechanisms of the model weight. In light of this, the impetus behind this paper is to do some analysis which based on the model weight of different trajectories, reveal the change rule. Finally, the validity of the proposed approach is demonstrated by simulation.

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V, Dr Manjunatha, Tejashwani H, Sahana K. R, Kishore S. D, and Vishal. "Multiple Disease Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 1 (2024): 612–15. http://dx.doi.org/10.22214/ijraset.2024.58018.

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Abstract: In this comprehensive health analysis project, we delve into the evaluation of Diabetes, heart disease, and Parkinson's disease. Basic health parameters like Pulse Rate, Cholesterol, Blood Pressure, and Heart Rate are scrutinized, enabling the identification of associated risk factors through a prediction model known for its accuracy and precision. The implementation involves leveraging machine learning algorithms, employing Streamlit for interactive interfaces, and employing Python pickling to store model behaviour effectively. Future expansions may encompass diverse health domains

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Tanaka, Misato, Yasunari Sasaki, Mitsunori Miki, and Tomoyuki Hiroyasu. "Crossover Method for Interactive Genetic Algorithms to Estimate Multimodal Preferences." Applied Computational Intelligence and Soft Computing 2013 (2013): 1–16. http://dx.doi.org/10.1155/2013/302573.

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We apply an interactive genetic algorithm (iGA) to generate product recommendations. iGAs search for a single optimum point based on a user’s Kansei through the interaction between the user and machine. However, especially in the domain of product recommendations, there may be numerous optimum points. Therefore, the purpose of this study is to develop a new iGA crossover method that concurrently searches for multiple optimum points for multiple user preferences. The proposed method estimates the locations of the optimum area by a clustering method and then searches for the maximum values of th

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Funkhouser, Thomas, Seth Teller, Carlo Séquin, and Delnaz Khorramabadi. "The UC Berkeley System for Interactive Visualization of Large Architectural Models." Presence: Teleoperators and Virtual Environments 5, no. 1 (1996): 13–44. http://dx.doi.org/10.1162/pres.1996.5.1.13.

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Realistic-looking architectural models with furniture may consist of millions of polygons and require gigabytes of data—far than today's workstations can render at interactive frame rates or store in physical memory. We have developed data structures and algorithms for identifying a small portion of a large model to load into memory and render during each frame of an interactive walkthrough. Our algorithms rely upon an efficient display database that represents a building model as a set of objects, each of which can be described at multiple levels of detail, and contains an index of spatial ce

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Wang, Junxiang, Xin Wang, Yingying Chen, Mengting Yan, and Hua Lan. "Model Adaptive Kalman Filter for Maneuvering Target Tracking Based on Variational Inference." Electronics 14, no. 10 (2025): 1908. https://doi.org/10.3390/electronics14101908.

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This study introduces a new variational Bayesian adaptive estimator that enhances traditional interactive multiple model (IMM) frameworks for maneuvering target tracking. Conventional IMM algorithms struggle with rapid maneuvers due to model-switching delays and fixed structures. Our method uses Bayesian inference to update change-point statistics in real-time for quick model switching. Variational Bayesian inference approximates the complex posterior distribution, transforming target state estimation and model identification into an optimization task to maximize the evidence lower bound (ELBO

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Kim, Taeheon, Jun Ma, and Min Hong. "Real-Time Cloth Simulation in Extended Reality: Comparative Study Between Unity Cloth Model and Position-Based Dynamics Model with GPU." Applied Sciences 15, no. 12 (2025): 6611. https://doi.org/10.3390/app15126611.

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This study proposes a GPU-accelerated Position-Based Dynamics (PBD) system for realistic and interactive cloth simulation in Extended Reality (XR) environments, and comprehensively evaluates its performance and functional capabilities on standalone XR devices, such as the Meta Quest 3. To overcome the limitations of traditional CPU-based physics simulations, we designed and optimized highly parallelized algorithms utilizing Unity’s Compute Shader framework. The proposed system achieves real-time performance by implementing efficient collision detection and response handling with complex enviro

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Sari, Noer Nilam, Tiyar Tohirotun Anisah, and Risma Fitriani. "Implementasi Machine Learning Untuk Prediksi Harga Laptop Menggunakan Algoritma Regresi Linear Berganda." Jurnal Manajemen Informatika (JAMIKA) 14, no. 2 (2024): 162–77. http://dx.doi.org/10.34010/jamika.v14i2.12923.

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This article discusses the implementation of machine learning using multiple linear regression algorithms to predict laptop prices. The main objective of this research is to design an appropriate predictive model based on various features such as technical specifications and laptop brands. The research stages include literature study, collection of relevant datasets, pre-processing or data cleaning, Exploratory Data Analysis, Feature Engineering, data splitting, model building, and website development. The results show that the proposed model is able to provide price predictions with a high le

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Chanyaswad, Thee, Changchang Liu, and Prateek Mittal. "RON-Gauss: Enhancing Utility in Non-Interactive Private Data Release." Proceedings on Privacy Enhancing Technologies 2019, no. 1 (2019): 26–46. http://dx.doi.org/10.2478/popets-2019-0003.

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Abstract A key challenge facing the design of differential privacy in the non-interactive setting is to maintain the utility of the released data. To overcome this challenge, we utilize the Diaconis-Freedman-Meckes (DFM) effect, which states that most projections of high-dimensional data are nearly Gaussian. Hence, we propose the RON-Gauss model that leverages the novel combination of dimensionality reduction via random orthonormal (RON) projection and the Gaussian generative model for synthesizing differentially-private data. We analyze how RON-Gauss benefits from the DFM effect, and present

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Čertický, Martin, Michal Čertický, Peter Sinčák, Gergely Magyar, Ján Vaščák, and Filippo Cavallo. "Psychophysiological Indicators for Modeling User Experience in Interactive Digital Entertainment." Sensors 19, no. 5 (2019): 989. http://dx.doi.org/10.3390/s19050989.

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Analyses of user experience in the electronic entertainment industry currently rely on self-reporting methods, such as surveys, ratings, focus group interviews, etc. We argue that self-reporting alone carries inherent problems—mainly the misinterpretation and temporal delay during longer experiments—and therefore, should not be used as a sole metric. To tackle this problem, we propose the possibility of modeling consumer experience using psychophysiological measures and demonstrate how such models can be trained using machine learning methods. We use a machine learning approach to model user e

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Zhu, Yifei, Luran Chen, and Dejun Yin. "Slope estimation based on multi-source information fusion." Journal of Physics: Conference Series 2785, no. 1 (2024): 012142. http://dx.doi.org/10.1088/1742-6596/2785/1/012142.

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Abstract This study presents a novel methodology for slope estimation, addressing limitations in current unidirectional estimation research. A comprehensive vehicle kinematics and dynamics model is developed using Inertial Measurement Unit (IMU) data, enabling a thorough assessment of vehicle slope. The proposal advocates employing the Interactive Multiple Model Unscented Kalman Filter algorithm to enhance algorithmic stability and prevent divergence from singular model errors. This approach ensures precise computation of dynamic slopes. The algorithm’s efficacy is demonstrated through collabo

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Imanov, Alisher, and Yelbek Utepov. "Conceptual model of noise monitoring system for construction projects in cramped conditions, based on sensors and GIS." Technobius 2, no. 3 (2022): 0025. http://dx.doi.org/10.54355/tbus/2.3.2022.0025.

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Existing sound measuring equipment are mostly designed for indoor use, and those of outdoor are costly and demands human involvement multiple times. This study proposes the concept of a compact and cheap sensor and GIS-based system that runs algorithms of sound distribution and visualizes interpolated and extrapolated data as heatmaps in an interactive map. According to the concept, the system consists of several noise measuring devises wirelessly connected to a data collector that transmits the measurement data real-time through internet to a server, where the data is analyzed and visualized.

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Wu, Weidong, Xiaoyan Sun, Guangyi Man, Shuai Li, and Lin Bao. "Interactive Multifactorial Evolutionary Optimization Algorithm with Multidimensional Preference Surrogate Models for Personalized Recommendation." Applied Sciences 13, no. 4 (2023): 2243. http://dx.doi.org/10.3390/app13042243.

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Interactive evolutionary algorithms (IEAs) coupled with a data-driven user surrogate model (USM) have recently been proposed for enhancing personalized recommendation performance. Since the USM relies on only one model to describe the full range of user preferences, existing USMbased IEAs have not investigated how knowledge migrates between preference models to improve the diversity and novelty of recommendations. Motivated by this, an interactive multifactorial evolutionary optimization algorithm with multidimensional preference user surrogate models is proposed here to perform a multi-view o

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Shen, Jing, Ling Chen, Xiaotong He, Chuanlin Zuo, Xiangjun Li, and Lin Dong. "An Interactive Human-in-the-Loop Framework for Skeleton-Based Posture Recognition in Model Education." Biomimetics 10, no. 7 (2025): 431. https://doi.org/10.3390/biomimetics10070431.

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This paper presents a human-in-the-loop interactive framework for skeleton-based posture recognition, designed to support model training and artistic education. A total of 4870 labeled images are used for training and validation, and 500 images are reserved for testing across five core posture categories: standing, sitting, jumping, crouching, and lying. From each image, comprehensive skeletal features are extracted, including joint coordinates, angles, limb lengths, and symmetry metrics. Multiple classification algorithms—traditional (KNN, SVM, Random Forest) and deep learning-based (LSTM, Tr

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Sundaravadivel, Prabha, Ashton Fitgerald, Prosenjit Kumar Ghosh, and Md Sharif Ahmed. "HAPT-EDGE: HAPTICS IN SOFT EFFECTORS FOR SMART INTERACTIVE ASSISTIVE FRAMEWORKS." Biomedical Sciences Instrumentation 58, no. 3 (2022): 162–67. http://dx.doi.org/10.34107/lwwj5713162.

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Tactile sensors help in modeling the intrinsic and external touch sensation. Tactile sensing can refer to the touch in multiple points of contact and refer to the process of detecting and measuring a given property of a contact event in a predetermined area at multiple points of contact. The Haptic system helps in creating a “sense of touch” about the environment through stimuli at a single contact point. Effectors are the end point of robotic frameworks that interact with the real world. From prosthetic hands to precision grippers, effectors made of soft flexible materials have been used for

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Zhou, Fansen, Yidi Wang, Wei Zheng, Zhao Li, and Xin Wen. "Fast Distributed Multiple-Model Nonlinearity Estimation for Tracking the Non-Cooperative Highly Maneuvering Target." Remote Sensing 14, no. 17 (2022): 4239. http://dx.doi.org/10.3390/rs14174239.

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The newly developed near-space vehicle has the characteristics of high speed and strong maneuverability, being able to perform vertical skips and a wide range of lateral maneuvers. Tracking this kind of target with ground-based radars is difficult because of the limited detection range caused by the curvature of the Earth. Compared with ground-based radars, satellite tracking platforms equipped with Synthetic Aperture Radars (SARs) have a wide detection range, and can keep the targets in custody, making them a promising approach to tracking near-space vehicles continuously. However, this appro

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17

Wu, Jie. "A Product Styling Design Evaluation Method Based on Multilayer Perceptron Genetic Algorithm Neural Network Algorithm." Computational Intelligence and Neuroscience 2021 (December 2, 2021): 1–11. http://dx.doi.org/10.1155/2021/2861292.

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Products no longer exist simply as carriers of useful functions, but more and more consumers are beginning to pay attention to the spiritual aspects of the feelings brought by products. This paper brings machine learning algorithms to the discipline of industrial design and proposes a method to evaluate the design of product shapes using a multilayer perceptron genetic algorithm neural network (GA-MLP-NN) algorithm, quantifying the product shape, using computer-aided design technology to achieve shape optimization, shape, and color scheme generation, and using interactive feedback with users t

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Tavana, Madjid, Dawn A. Trevisani, and Jerry L. Dussault. "The Stability Model." International Journal of Information Technology Project Management 4, no. 2 (2013): 18–34. http://dx.doi.org/10.4018/jitpm.2013040102.

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The increasing complexity and tight coupling between people and technology in military Command and Control (C2) systems has led to greater vulnerability due to system failure. Although system vulnerabilities cannot be completely eliminated, the accidental or anticipated failures have to be thoroughly understood and guarded. Traditionally, the failure in C2 systems has been studied with resiliency and the concept of self-healing systems represented with reactive models or robustness and the concept of self-protecting systems represented with proactive models. The authors propose the stability m

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19

Okkan, Umut, and Umut Kirdemir. "Towards a hybrid algorithm for the robust calibration of rainfall–runoff models." Journal of Hydroinformatics 22, no. 4 (2020): 876–99. http://dx.doi.org/10.2166/hydro.2020.016.

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Abstract In this study, the hybrid particle swarm optimization (HPSO) algorithm was proposed and practised for the calibration of two conceptual rainfall–runoff models (dynamic water balance model and abcde). The performance of the developed method was compared with those of several metaheuristics. The models were calibrated for three sub-basins, and multiple performance criteria were taken into consideration in comparison. The results indicated that HPSO was derived significantly better and more consistent results than other algorithms with respect to hydrological model errors and convergence

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Wu, Jianghang, Senyao Qiao, Haocheng Li, et al. "Goal-Guided Graph Attention Network with Interactive State Refinement for Multi-Agent Trajectory Prediction." Sensors 24, no. 7 (2024): 2065. http://dx.doi.org/10.3390/s24072065.

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The accurate prediction of the future trajectories of traffic participants is crucial for enhancing the safety and decision-making capabilities of autonomous vehicles. Modeling social interactions among agents and revealing the inherent relationships is crucial for accurate trajectory prediction. In this context, we propose a goal-guided and interaction-aware state refinement graph attention network (SRGAT) for multi-agent trajectory prediction. This model effectively integrates high-precision map data and dynamic traffic states and captures long-term temporal dependencies through the Transfor

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Kurt, Yagiz, and Halil Ersin Soken. "Vision-Based Real-Time Motion Estimation of Ground Vehicles from Aerial Imagery." Journal of Physics: Conference Series 2526, no. 1 (2023): 012089. http://dx.doi.org/10.1088/1742-6596/2526/1/012089.

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Abstract This research presents vision-based maneuvering object motion estimation in case of occlusion. Unequal dimension Interactive Multiple Model (UDIMM) approach is applied to increase the motion prediction accuracy when no measurement is available. Current deep learning-based multi-object tracking algorithms cannot track the objects when an occlusion exists, even for quite a short time. The main reason is the motion model used in tracking algorithms. If the measurement coming from the object detector is not available, the prediction model propagates the Kalman Filter’s motion model. In th

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SHINDE, AMIT, MOEED HAGHNEVIS, MARCO A. JANSSEN, GEORGE C. RUNGER, and MANI JANAKIRAM. "SCENARIO ANALYSIS OF TECHNOLOGY PRODUCTS WITH AN AGENT-BASED SIMULATION AND DATA MINING FRAMEWORK." International Journal of Innovation and Technology Management 10, no. 05 (2013): 1340019. http://dx.doi.org/10.1142/s0219877013400191.

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A framework is presented to simulate and analyze the effect of multiple business scenarios on the adoption behavior of a group of technology products. Diffusion is viewed as an emergent phenomenon that results from the interaction of consumers. An agent-based model is used in which potential adopters of technology product are allowed to be influenced by their local interactions within the social network. Along with social influence, the effect of product features is important and we ascribe feature sensing attributes to the consumer agents along with sensitivities to social influence. The mode

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Sakawa, Masatoshi, and Kosuke Kato. "An Interactive Fuzzy Satisficing Method for Multiobjective Nonlinear Integer Programming Problems with Block-Angular Structures through Genetic Algorithms with Decomposition Procedures." Advances in Operations Research 2009 (2009): 1–17. http://dx.doi.org/10.1155/2009/372548.

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We focus on multiobjective nonlinear integer programming problems with block-angular structures which are often seen as a mathematical model of large-scale discrete systems optimization. By considering the vague nature of the decision maker's judgments, fuzzy goals of the decision maker are introduced, and the problem is interpreted as maximizing an overall degree of satisfaction with the multiple fuzzy goals. For deriving a satisficing solution for the decision maker, we develop an interactive fuzzy satisficing method. Realizing the block-angular structures that can be exploited in solving pr

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Barlowe, Scott, Heather B. Coan, and Robert T. Youker. "SubVis: an interactive R package for exploring the effects of multiple substitution matrices on pairwise sequence alignment." PeerJ 5 (June 27, 2017): e3492. http://dx.doi.org/10.7717/peerj.3492.

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Understanding how proteins mutate is critical to solving a host of biological problems. Mutations occur when an amino acid is substituted for another in a protein sequence. The set of likelihoods for amino acid substitutions is stored in a matrix and input to alignment algorithms. The quality of the resulting alignment is used to assess the similarity of two or more sequences and can vary according to assumptions modeled by the substitution matrix. Substitution strategies with minor parameter variations are often grouped together in families. For example, the BLOSUM and PAM matrix families are

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Yuxuan Zhou, Yuxuan Zhou, Wanzhong Chen Yuxuan Zhou, Linlin Li Wanzhong Chen, Linlin Gong Linlin Li, and Chang Liu Linlin Gong. "The Energy-Efficient Resource Allocation of Multi-Modal Perception for Affective Brain-Computer Interactions Based on Non-Linear Iterative Prediction Scheme." 網際網路技術學刊 24, no. 3 (2023): 641–50. http://dx.doi.org/10.53106/160792642023052403009.

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<p>For the whole environmental settings in this research, the conventional affective brain-computer interactions can not build a good performance on energy-efficient resource of network&rsquo;s forwarding ports and routing paths due to its poor allocation function of cognitive radio networks, based on the novel interactive networking architecture, the model of non-linear iterative prediction scheme in interaction was successively proposed. This research proposes a modified LSTM algorithm with a structure of non-linear iterative in complexity prediction, joins the multiple k modes sel

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Ding, Li, Min Xia, Haifeng Lin, and Kai Hu. "Multi-Level Attention Interactive Network for Cloud and Snow Detection Segmentation." Remote Sensing 16, no. 1 (2023): 112. http://dx.doi.org/10.3390/rs16010112.

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The ground is typically hidden by cloud and snow in satellite images, which have a similar visible spectrum and complex spatial distribution characteristics. The detection of cloud and snow is important for increasing image availability and studying climate change. To address the issues of the low classification accuracy and poor generalization effect by the traditional threshold method, as well as the problems of the misdetection of overlapping regions, rough segmentation results, and a loss of boundary details in existing algorithms, this paper designed a Multi-level Attention Interaction Ne

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Li, Keda, Xiangyue Zheng, Jingxin Bi, Gang Zhang, Yi Cui, and Tao Lei. "RMVAD-YOLO: A Robust Multi-View Aircraft Detection Model for Imbalanced and Similar Classes." Remote Sensing 17, no. 6 (2025): 1001. https://doi.org/10.3390/rs17061001.

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Aircraft detection technology plays a vital role in civilian applications, with significant attention being devoted to research on related algorithms in recent years. However, most existing research predominantly focuses on aircraft detection from a single top–down viewpoint, which constrains the applicability of detection technology across diverse scenarios. To overcome this limitation, we propose RMVAD-YOLO, a multi-view aircraft detection model built upon YOLOv8. First, we propose a novel Robust Multi-Link Scale Interactive Feature Pyramid Network (RMSFPN), which robustly extracts features

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Jeong, G., N. Freitas, Y. Cho, and C. Han. "[AI-Machine Learning] Optimized Sensorless Human Pose Estimation for a Kpop Dance Application." Volume 5 - 2020, Issue 8 - August 5, no. 8 (2020): 893–96. http://dx.doi.org/10.38124/ijisrt20aug003.

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There has been a great effort to use technology to make exercise more interactive, measurable and gamified. However, in order to optimize the detection accuracy, these efforts have always translated themselves into motion detection with multiple sensors including purpose specific hardware, which results in extra expenses on both the content production and consumption and induces limitations on the final mobility of the user. In this paper we aim to improve the accuracy, learning speed and detail range of Posenet’s AI sensorless human pose detection by using an artificial neural network to opti

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Cheni Sruneethi and Bale Rajesh. "Intelligent Water Quality Assessment: Predictive Modeling for Potable Water Using Advanced Machine Learning Techniques." International Journal of Scientific Research in Science and Technology 12, no. 3 (2025): 92–98. https://doi.org/10.32628/ijsrst251238.

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Access to safe drinking water remains a global priority, necessitating robust and accurate methods for potability assessment. This research implements advanced machine learning approaches to develop a predictive framework for water quality classification. The study utilizes a comprehensive dataset containing critical physicochemical parameters including pH levels, sulfate concentration, and trihalomethane content. The methodology encompasses extensive data preparation protocols such as missing value imputation, outlier identification, and feature normalization. Multiple classification algorith

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Zhang, Qingsong, Yibo He, Meng Shu, et al. "A Level-Based Learning Swarm Optimizer with Stochastic Fractal Search for Parameters Identification of Solar Photovoltaic Models." Mathematical Problems in Engineering 2023 (February 22, 2023): 1–16. http://dx.doi.org/10.1155/2023/3397430.

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As the most popular renewable energy, solar energy could be converted into electricity by photovoltaic (PV) systems directly. To maximize the effectiveness of the conversion, it is critical to find the precise and accurate parameters of the PV model. In this paper, we propose a level-based learning swarm optimizer with stochastic fractal search (LLSOF) to tackle the parameter estimation of several kinds of solar PV models. The population is separated into multiple levels according to their fitness at first. The individuals at the lower levels evolve through learning from the individuals at the

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Amari, Mohamad Dzulhelmy. "Augmented Reality Monitoring System for Cross-Belt Conveyor in Advanced Automation Line." Journal of Mechanical Engineering 22, no. 2 (2025): 47–57. https://doi.org/10.24191/jmeche.v22i2.2933.

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Remote monitoring systems are increasingly adopted as one of the control strategies in contemporary industrial operations. However, the integration of Augmented Reality (AR) within industrial applications remains limited due to a deficit in comprehensive research. In alignment with Industry 4.0 principles, the deployment of AR in automation introduces a spectrum of possibilities for manufacturing sectors, particularly in critical production processes like material handling. This motivates our initiative to develop a simulation of a cross-belt conveyor system. The algorithms for the conveyor sy

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Butterworth, Erik, Bartholomew E. Jardine, Gary M. Raymond, Maxwell L. Neal, and James B. Bassingthwaighte. "JSim, an open-source modeling system for data analysis." F1000Research 2 (December 30, 2013): 288. http://dx.doi.org/10.12688/f1000research.2-288.v1.

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JSim is a simulation system for developing models, designing experiments, and evaluating hypotheses on physiological and pharmacological systems through the testing of model solutions against data. It is designed for interactive, iterative manipulation of the model code, handling of multiple data sets and parameter sets, and for making comparisons among different models running simultaneously or separately. Interactive use is supported by a large collection of graphical user interfaces for model writing and compilation diagnostics, defining input functions, model runs, selection of algorithms

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Butterworth, Erik, Bartholomew E. Jardine, Gary M. Raymond, Maxwell L. Neal, and James B. Bassingthwaighte. "JSim, an open-source modeling system for data analysis." F1000Research 2 (May 12, 2014): 288. http://dx.doi.org/10.12688/f1000research.2-288.v2.

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JSim is a simulation system for developing models, designing experiments, and evaluating hypotheses on physiological and pharmacological systems through the testing of model solutions against data. It is designed for interactive, iterative manipulation of the model code, handling of multiple data sets and parameter sets, and for making comparisons among different models running simultaneously or separately. Interactive use is supported by a large collection of graphical user interfaces for model writing and compilation diagnostics, defining input functions, model runs, selection of algorithms

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Butterworth, Erik, Bartholomew E. Jardine, Gary M. Raymond, Maxwell L. Neal, and James B. Bassingthwaighte. "JSim, an open-source modeling system for data analysis." F1000Research 2 (July 1, 2014): 288. http://dx.doi.org/10.12688/f1000research.2-288.v3.

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JSim is a simulation system for developing models, designing experiments, and evaluating hypotheses on physiological and pharmacological systems through the testing of model solutions against data. It is designed for interactive, iterative manipulation of the model code, handling of multiple data sets and parameter sets, and for making comparisons among different models running simultaneously or separately. Interactive use is supported by a large collection of graphical user interfaces for model writing and compilation diagnostics, defining input functions, model runs, selection of algorithms

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Shah, Ghawas Ali, Sumair Khan, Sufyan Ali Memon, Mohsin Shahzad, Zahid Mahmood, and Uzair Khan. "Improvement in the Tracking Performance of a Maneuvering Target in the Presence of Clutter." Sensors 22, no. 20 (2022): 7848. http://dx.doi.org/10.3390/s22207848.

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The proposed work uses fixed lag smoothing on the interactive multiple model-integrated probabilistic data association algorithm (IMM-IPDA) to enhance its performance. This approach makes use of the advantages of the fixed lag smoothing algorithm to track the motion of a maneuvering target while it is surrounded by clutter. The suggested method provides a new mathematical foundation in terms of smoothing for mode probabilities in addition to the target trajectory state and target existence state by including the smoothing advantages. The suggested fixed lag smoothing IMM-IPDA (FLs IMM-IPDA) me

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Ke, Xiangyu, Arijit Khan, and Francesco Bonchi. "Multi-relation Graph Summarization." ACM Transactions on Knowledge Discovery from Data 16, no. 5 (2022): 1–30. http://dx.doi.org/10.1145/3494561.

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Graph summarization is beneficial in a wide range of applications, such as visualization, interactive and exploratory analysis, approximate query processing, reducing the on-disk storage footprint, and graph processing in modern hardware. However, the bulk of the literature on graph summarization surprisingly overlooks the possibility of having edges of different types. In this article, we study the novel problem of producing summaries of multi-relation networks, i.e., graphs where multiple edges of different types may exist between any pair of nodes. Multi-relation graphs are an expressive mo

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Nath, Gopal, Yawei Wang, Austin Coursey, Krishna K. Saha, Srikanth Prabhu, and Saptarshi Sengupta. "Incorporating a Machine Learning Model into a Web-Based Administrative Decision Support Tool for Predicting Workplace Absenteeism." Information 13, no. 7 (2022): 320. http://dx.doi.org/10.3390/info13070320.

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Productivity losses caused by absenteeism at work cost U.S. employers billions of dollars each year. In addition, employers typically spend a considerable amount of time managing employees who perform poorly. By using predictive analytics and machine learning algorithms, organizations can make better decisions, thereby increasing organizational productivity, reducing costs, and improving efficiency. Thus, in this paper we propose hybrid optimization methods in order to find the most parsimonious model for absenteeism classification. We utilized data from a Brazilian courier company. In order t

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Cao, Yi, and Hongwei Du. "IDDNet: a deep interactive dual-domain convolutional neural network with auxiliary modality for fast MRI reconstruction." JUSTC 54, no. 3 (2024): 0302. http://dx.doi.org/10.52396/justc-2023-0169.

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Reconstructing a complete image accurately from an undersampled <i>k</i>-space matrix is a viable approach for magnetic resonance imaging (MRI) acceleration. In recent years, numerous deep learning (DL)-based methods have been employed to improve MRI reconstruction. Among these methods, the cross-domain method has been proven to be effective. However, existing cross-domain reconstruction algorithms sequentially link the image domain and <i>k</i>-space networks, disregarding the interplay between different domains, consequently leading to a deficiency in reconstruction a

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Liu, Hongqiang, Zhongliang Zhou, and Lei Yu. "Maneuvering Acceleration Estimation Algorithm Using Doppler Radar Measurement." Mathematical Problems in Engineering 2018 (June 4, 2018): 1–13. http://dx.doi.org/10.1155/2018/4984186.

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An algorithm to estimate the tangential and normal accelerations directly using the Doppler radar measurement in an online closed loop form is proposed. Specific works are as follows: first, the tangential acceleration and normal acceleration are taken as the state variables to establish a linear state transition equation; secondly, the decorrelation unbiased conversion measurement Kalman filter (DUCMKF) algorithm is proposed to deal with the strongly nonlinear measurement equation; thirdly, the geometric relationship between the range rate and the velocity direction angle is used to obtain tw

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Yang, Xiaohui, Jiating Long, Peiyun Liu, Xiaolong Zhang, and Xiaoping Liu. "Optimal Scheduling of Microgrid with Distributed Power Based on Water Cycle Algorithm." Energies 11, no. 9 (2018): 2381. http://dx.doi.org/10.3390/en11092381.

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Microgrid, taking advantage of distributed power generation technology, plays an important role in maximizing the utilization of renewable energy. Based on the problems of the energy crisis, environmental contamination and the high operating cost of the microgrid, the microgrid model can effectively ease energy pressure. We can dispatch the output of each part in the microgrid to obtain the optimal economy. Since many traditional optimization algorithms have limitations of local optimization, multiple iterations, and slow convergence speed, this paper proposes a method that applies the Water C

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Tian, Shunyu, Changyun Wei, Yajun Li, and Ze Ji. "FGRL: Federated Growing Reinforcement Learning for Resilient Mapless Navigation in Unfamiliar Environments." Applied Sciences 14, no. 23 (2024): 11336. https://doi.org/10.3390/app142311336.

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In this paper, we propose a federated growing reinforcement learning (FGRL) approach for solving the mapless navigation problem of unmanned ground vehicles (UGVs) facing cluttered unfamiliar obstacles. Deep reinforcement learning (DRL) has the potential to provide adaptive behaviors for autonomous agents through interactive learning, but standard episodic DRL algorithms often struggle with out-of-distribution observations. For navigation tasks, UGVs often encounter unfamiliar situations where novel obstacles differ from prior experience. To address this problem, the proposed FGRL approach enab

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Zhao, Jin Xiao, Jian Qiu Zhang, and Dong Ming Zhou. "Polynomial Model Set and its Interacting Multiple Model Algorithms." Advanced Materials Research 562-564 (August 2012): 2038–44. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.2038.

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From the maneuvering target orbit on the geometrical properties, according to different motion modes track corresponding to different order number polynomial curve, using the least squares fitting structure, this paper gives out a group of various motion modes matching the mathematical model—polynomial model set (PMS), and gives distinct mathematical process. PMS covers all the motion modes theoretically, easy to choose according to the practical situation and expand, especially suitable for single model can not accurately describe the complex sports scene. The model need not consider sampling

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Wang, Yanyue, Guohua Fang, and Zhenni Wang. "The Benefit Realization Mechanism of Pumped Storage Power Plants Based on Multi-Dimensional Regulation and Leader-Follower Decision-Making." Energies 15, no. 16 (2022): 5931. http://dx.doi.org/10.3390/en15165931.

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The roles and benefits of pumped storage are reflected in different stakeholders of the power system. The multi-dimensionality and non-linearity of pumped storage multi-stakeholder decision-making make pumped storage benefit realization a hot research topic with challenges. This paper takes pumped storage benefit sharing as the breakthrough. It adopts multiple regulation strategies and multi-level decision-making measures based on the multiple objectives of different stakeholders. A method and a framework for pumped storage benefit realization are proposed. This paper proposes the objective fu

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Qin, Li, Hongyu Wang, Yazhou Yuan, and Shufan Qin. "Multi-Sensor Perception Strategy to Enhance Autonomy of Robotic Operation for Uncertain Peg-in-Hole Task." Sensors 21, no. 11 (2021): 3818. http://dx.doi.org/10.3390/s21113818.

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The peg-in-hole task with object feature uncertain is a typical case of robotic operation in the real-world unstructured environment. It is nontrivial to realize object perception and operational decisions autonomously, under the usual visual occlusion and real-time constraints of such tasks. In this paper, a Bayesian networks-based strategy is presented in order to seamlessly combine multiple heterogeneous senses data like humans. In the proposed strategy, an interactive exploration method implemented by hybrid Monte Carlo sampling algorithms and particle filtering is designed to identify the

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Zheng, Nan, Meng Sun, and Ye Yang. "Visual Analysis of College Sports Performance Based on Multimodal Knowledge Graph Optimization Neural Network." Computational Intelligence and Neuroscience 2022 (July 1, 2022): 1–12. http://dx.doi.org/10.1155/2022/5398932.

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In this paper, through data analysis of multimodal knowledge graph optimized neural network and visual analysis of college students’ sports performance, we use huge graph, a graph database supporting distributed storage, to store domain knowledge in the form of the knowledge graph, use Spring Boot to build a server-side framework, use Vue framework combined with vis.js to visualize relational network graphs, and design and implement a knowledge-oriented. This paper proposes a visual analytics system based on the theory of visual analytics. Based on the idea of visual analytics, this paper pres

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Vakil, Vahideh, and Wade Trappe. "Drug Combinations: Mathematical Modeling and Networking Methods." Pharmaceutics 11, no. 5 (2019): 208. http://dx.doi.org/10.3390/pharmaceutics11050208.

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Treatments consisting of mixtures of pharmacological agents have been shown to have superior effects to treatments involving single compounds. Given the vast amount of possible combinations involving multiple drugs and the restrictions in time and resources required to test all such combinations in vitro, mathematical methods are essential to model the interactive behavior of the drug mixture and the target, ultimately allowing one to better predict the outcome of the combination. In this review, we investigate various mathematical methods that model combination therapies. This survey includes

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Reiser, Christian, Stephan Garbin, Pratul Srinivasan, et al. "Binary Opacity Grids: Capturing Fine Geometric Detail for Mesh-Based View Synthesis." ACM Transactions on Graphics 43, no. 4 (2024): 1–14. http://dx.doi.org/10.1145/3658130.

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While surface-based view synthesis algorithms are appealing due to their low computational requirements, they often struggle to reproduce thin structures. In contrast, more expensive methods that model the scene's geometry as a volumetric density field (e.g. NeRF) excel at reconstructing fine geometric detail. However, density fields often represent geometry in a "fuzzy" manner, which hinders exact localization of the surface. In this work, we modify density fields to encourage them to converge towards surfaces, without compromising their ability to reconstruct thin structures. First, we emplo

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Szymański, Zbigniew, Michał Maj, Jacek Piwkowski, Ewelina Jurczak, Tomasz Cieplak, and Damian Pliszczuk. "Optimizing delivery time with an intelligent forecasting model: leveraging ai and machine learning for efficient logistics." Journal of Modern Science 57, no. 3 (2024): 485–97. http://dx.doi.org/10.13166/jms/191216.

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Business analytics involves using various technologies to analyze data. Data mining focuses on the automated search for knowledge, patterns, or regularities in data. As a business analyst, it is essential to recognize the type of analytical technique appropriate for solving a specific problem. Exploratory Data Analysis (EDA) describes data using statistical and visualization techniques to highlight important aspects of that data for further analysis. This involves examining a data set from many angles, describing it, and summarizing it without making assumptions about its content. Exploratory

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Gupta, Suneet, Sumit Kumar, Sunil L. Bangare, Shibili Nuhmani, Arnold C. Alguno, and Issah Abubakari Samori. "Homogeneous Decision Community Extraction Based on End-User Mental Behavior on Social Media." Computational Intelligence and Neuroscience 2022 (March 8, 2022): 1–9. http://dx.doi.org/10.1155/2022/3490860.

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Aiming at the inadequacy of the group decision-making method with the current attribute value as interval language information, an interval binary semantic decision-making method is proposed, which considers the decision maker’s psychological behavior. The scope of this research is that this paper is based on localized amplification method. The localized amplification method used in this research may amplify physiological movement after removing unwanted noise, allowing the movement trend to be seen with the naked eye, improving the CNN network’s mental identification accuracy. These two algor

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Semerdjiev, Emil, Ludmila Mihaylova, Tzvetan Semerdjiev, and Violeta Bogdanova. "Interacting Multiple Model Algorithms for Manoeuvring Ship Tracking Based On New Ship Models." Information & Security: An International Journal 2 (1999): 122–37. http://dx.doi.org/10.11610/isij.0211.

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