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Performance analysis for Upper Limb Rehabilitation in Non-Immersive and Immersive Scenarios 25

Performance Analysis for Upper Limb Rehabilitation in Non-Im...

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25th International conference on Enterprise Information Systems, ICEIS 2023

作者： Herrera, Vanesa Reyes-Guzmán, Ana Vallejo, David Castro-Schez, José Monekosso, Dorothy N. Carlos, González-Morcillo Albusac, Javier School of Computer Science University of Castilla-La Mancha Ciudad Real Spain Hospital Nacional de Parapléjicos de Toledo Toledo Spain Department of Computer Science University of Durham County Durham Durham United Kingdom

ISBN: (纸本)9789897586484

In recent years, new technologies have contributed to an improvement in the upper limb rehabilitation process as a complement to the conventional therapy received by patients. In this context, technologies should facilitate accurate monitoring of the hands and serve to collect data on patient performance during functional tasks in order to objectively assess the patient’s potential evolution. Mechanical and wearable devices provide greater accuracy in measurements. However, the physical limitations of patients requires the use of hands-free solutions. This article investigates controller-free hand technologies for accurate hand tracking in the Box and Block test (BBT) benchmarked against the real test, validated and standardized in the context of the Hospital Nacional de Parapléjicos (Toledo, Spain). In particular, the performance in the execution of therapeutic exercises is analyzed in three different scenarios: i) physical environment without the use of technologies, ii) non-immersive virtual environment and, finally, iii) fully immersive virtual environment. The results demonstrate the similarity between therapies developed in physical scenarios without the use of technologies, and those carried out in virtual reality-based scenarios. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Virtual reality

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DAS Recorded Body and Tube Wave Generated by Perforation Shots: analysis and Numerical modeling for Completion Monitoring and Reservoir Characterization

DAS Recorded Body and Tube Wave Generated by Perforation Sho...

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2021 SPE/AAPG/SEG Unconventional Resources Technology conference, URTC 2021

作者： Zhang, Shuang Titov, Aleksei Jayaram, Vikram Bello, Hector Hurt, Robert Jin, Ge Pioneer Natural Resources United States Colorado School of Mines United States

During the perforation stage of the hydraulic fracturing process, the distributed acoustic sensing (DAS) data are recorded and used to determine the location of perforated holes. In our study, we aim to quantitatively analyze DAS data to study the generated seismic wavefields by the perforation process in order to evaluate completion designs and characterize the reservoir. Our study is open for research as we seek to improve our understanding of the subsurface and advance well control. In this study, we developed a workflow to characterize the reservoir by quantitatively analyzing the DAS data recorded during the perforation process. We used the dynamics and kinematics properties of the generated seismic wavefields;amplitude and velocity of both body and tube waves were analyzed to determine heel-going and toe-going wavefield variation. We created a theoretical framework by conducting full wavefield seismic modeling using finite-difference elastic modeling techniques. We studied the influence of wellbore anomalies (e.g., perforation gun) and near-wellbore anomalies (e.g., fractures, stress-induced velocity changes) on the derived seismic attributes. The extracted P-wave velocity is comparable with the sonic log with a slight discrepancy in values. Two factors may cause the observable discrepancy. First, the perforation shot seismic data has a lower frequency than the sonic data. Second, the sonic log measurement is acquired in unstimulated rocks, while toe-going P-wave generated by the perforation process propagates in stimulated rocks. The DAS extracted velocity can be used to create a better velocity model for microseismic analysis. The amplitude decay attribute can be used to infer near-wellbore conductivity. The toe-going tube wave energy dissipates quicker than the heel-going energy as the wave propagates through the previously stimulated rock. This observation was validated using numerical modeling with two influencing factors. We found that the distance betwe

关键词： Seismic waves

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Contextual Mathematical modeling Using the Formation of a Capital Size control Algorithm in the Music Industry 23

Contextual Mathematical Modeling Using the Formation of a Ca...

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Proceedings of the 7th International conference on Future Networks and Distributed Systems

作者： Dmitriy Rodionov Mansur Eshov Irina Smirnova Bokhodir Isroilov Darya Kryzhko Evgenii Konnikov Faculty of Economics Peter the Great St.Petersburg Polytechnic University Russia Faculty of Economics Tashkent State University of Economics Uzbekistan Financial Analysis and Audit Department Tashkent State University of Economics Uzbekistan

ISBN: (纸本)9798400709036

Today's volumes of information streams allow to mathematize a wide spectrum of processes and regularities, which qualitatively improves the possibilities of data analysis, prediction and planning. A mathematical model of an object of study, which has reached a satisfactory level of confidence in a particular situation, is a flexible tool in the hands of a knowledgeable analyst, providing, first and foremost, information about the relationships in the considered system. The understanding of situational relationships - within the framework of a limited model and system - is an effective means of control. Thus, a person possessing information can not only evaluate their expectations - making the most productive choices - but also transform influencing factors, exerting an appropriate influence on them, in order to achieve a targeted change in an specific index that requires adjustments. The algorithms for such modeling, descriptive analysis, and the process of forming conclusions are considered in this study on the example of the change in the volume of private capital in the music industry. The personal capital of popular musical artists is described in the context of its existence in the socio-economic environment of the music industry, which is mathematically estimated by the genre, quantitative composition of musical works, musical activity duration, number of streaming (estimated by a well-known entertainment-information edition that is relevant to the described environment), the number of Grammys awarded, and the number of nominations for this prestigious award, as well as the fact that the artist is included in the number of living or deceased.

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Enhanced Cross-Modal Retrieval with Low-Rank Multi-Head Self-Attention

Enhanced Cross-Modal Retrieval with Low-Rank Multi-Head Self...

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Computer Engineering and Intelligent control (ICCEIC) International conference on

作者： Hancong Liu Jingzhong Qi Qing Zhu Beijing University of Technology Beijing China

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

In today's rapidly developing technological innovation environment, cross-modal retrieval plays a vital role in the field of modern information retrieval and data analysis. It can map data of different modalities such as images and text into a common semantic space, thereby realizing cross-modal similarity measurement and retrieval. In the field of patent analysis, image-text cross-modal retrieval is particularly important. Patent documents usually contain complex technical descriptions and related instructions. The ability to efficiently and accurately retrieve images and text from massive patent data is of great significance for technological innovation, competition analysis and intellectual property protection. This paper proposes a novel visual semantic embedding model for patents using a low-rank multi-head self-attention mechanism to improve the cross-modal retrieval performance in the field of patent analysis. Our model uses EfficientNet as an image encoder to extract richer and more detailed feature representations from patent images. The text encoder uses BERT to effectively handle the complex text structure and polysemy problems in patent documents. By optimizing the embedding generation process of the image-text encoder, the feature expression ability and retrieval accuracy of the model are significantly enhanced. At the same time, we introduced a multi-instance embedding network and used a new low-rank multi-head self-attention mechanism to combine global features and local features, and calculated multiple different representations of instances, further improving the model's ability to handle ambiguity and data diversity. To verify the effectiveness of the model, we conducted a large number of experiments on multiple public cross-modal datasets and a patent-specific dataset. The experimental results show that the model significantly outperforms most existing methods in multiple indicators, especially in patent image and text retrieval tasks, showing hig

关键词： Patents Technological innovation Analytical models Accuracy Correlation Computational modeling Semantics Feature extraction data models Robustness

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An Efficient Approach to Decision-making in the Education process: Markov Chain for Fuzzy States

An Efficient Approach to Decision-making in the Education Pr...

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International conference on control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Anna E. Loskutova Mikhail G. Matveev Peter A. Meleshenko Computer Science Department Voronezh State University Voronezh Russia

ISBN: (数字)9798350309126

ISBN: (纸本)9798350309133

This paper reports on an efficient approach within the control of the school education process. Namely, we use the discrete Markov chain for a system with fuzzy states that based on processing data from time series (a set of student’s grades within some time period). In this case, the transition matrix has stochastic nature. Such an approach makes it possible to obtain stationary states of the system that characterize its status at a certain given time interval. This result can be used in a decision support system for targeted status changes. A feature of the resulting Markov chain for fuzzy states of a system is the ability to take into account the dynamics of changes in system states when calculating average status values, in contrast to the widely used averaging based on calculating arithmetic averages.

关键词： Decision support systems Education Stationary state Time series analysis process control Markov processes Numerical simulation

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Distributed Photovoltaic Power Forecasting Considering Perceived data Quality

Distributed Photovoltaic Power Forecasting Considering Perce...

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2025 International conference on Electrical Automation and Artificial Intelligence, ICEAAI 2025

作者： Liu, Xiaodan Hou, Chunlei Du, Lishi Beijing FibrLINK Communications Co. Ltd. Beijing China

ISBN: (纸本)9798331506797

In distributed photovoltaic (PV) power generation systems, data quality plays a critical role in the accuracy of predictive models. However, the complexity of distributed PV sensor data, including issues such as missing values, noise and anomalous bias, often leads to data quality degradation, which seriously affects the reliability of the prediction. To address these challenges, this study proposes a novel forecasting method that integrates sensor data quality into the forecasting process to improve the reliability and robustness of forecasts. The method starts with a detailed analysis of distributed PV sensor data characteristics to identify patterns of data quality degradation and their impact on forecast accuracy. data recovery algorithm based on adaptive mean drift clustering with multi-paradigm optimisation is presented to effectively deal with data loss and reduce noise. In addition, an adversarial graphical neural network (GNN) is used for knowledge migration and spatio-temporal correlation modelling to make accurate predictions even in the presence of perceived data quality degradation. The GNN framework exploits spatial dependence and temporal dynamics to create robust prediction models. Simulation results show that the proposed method significantly improves the data quality and prediction accuracy of distributed PV power generation, providing a reliable basis for decision making in energy management systems. © 2025 IEEE.

关键词： adaptive mean drift clustering component distributed photovoltaic generation perceived data quality spatio-temporal correlation modeling style

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Single-Step and Multi-Step Prediction for Dissolved Oxygen of Huangpu River Based on Feature Engineering and the BiGRU-Attention-TCN Model

Single-Step and Multi-Step Prediction for Dissolved Oxygen o...

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IEEE International conference on Networking, Sensing and control

作者： Zijian Wang Jing An Kezhu Wang Kefan Wang School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Water quality prediction is an important tool for environmental protection and public health. It can prevent water pollution, maintain ecological balance, and provide crucial scientific evidence for water resource management. To address the low prediction accuracy of traditional methods, this study uses data from the water quality monitoring station on the Huangpu River in the Taihu Basin of Shanghai to predict dissolved oxygen levels. We propose a water quality prediction method based on feature engineering and the BiGRU-Attention-TCN model. The proposed method employs feature engineering to extract monitoring station status information, improving model robustness and stability. It uses Bidirectional Gated Recurrent Unit (BiGRU) to process time series data, capturing bi-directional dependencies, and applies the Attention mechanism to enhance the model's focus on key time steps and features. Additionally, Temporal Convolutional Network (TCN) is introduced to further extract long-term dependencies and global features. Our method was compared with several commonly used baseline methods, and experimental results show that the proposed method outperforms other advanced baseline methods.

关键词： Biological system modeling Time series analysis Water quality Predictive models Feature extraction Water pollution Stability analysis Rivers Monitoring Water resources

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Research on the Application of Big data in Transient Stability Simulation of Power Systems

Research on the Application of Big Data in Transient Stabili...

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Energy, Power and Electrical Engineering (EPEE), International conference on

作者： Jie Zhang Haiping Guo Yanjun Zhao Libin Huang Binjiang Hu Hui Luo Jingyue Zhang State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

With the rapid expansion of smart grids, there is a growing complexity in power system dynamics, which increases the availability of real-time operational data and underscores the need for advanced transient stability simulations. Traditional transient stability analysis methods are constrained by their computational demands and may compromise accuracy. Furthermore, deep learning-based methods often focus on the application of a single algorithm, lacking a systematic consideration of the entire simulation process. To address these challenges, this study proposes a novel approach that leverages big data to enhance transient stability simulations. By integrating deep learning and ensemble learning techniques, the model significantly improves the accuracy and efficiency of predicting transient instabilities. The methodology utilizes a multi-source data acquisition and fusion scheme to ensure data comprehensiveness and a distributed storage architecture to handle large-scale datasets effectively. Advanced visualization technologies are also employed to enhance the interpretability of the simulation results. This comprehensive framework aims to support the safe and stable operation of power systems amid increasing complexity and data availability.

关键词： Accuracy Computational modeling Power system stability Big data Predictive models Stability analysis data models Real-time systems Transient analysis Thermal stability

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Full Wavelength Selection and Deep Spectra Feature Extraction Network for Near-Infrared Spectroscopy Quantitative analysis

Full Wavelength Selection and Deep Spectra Feature Extractio...

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data Driven control and Learning Systems (DDCLS)

作者： Jinlong Liu Xiaoli Luan Fei Liu Key Laboratory for Advanced Process Control of Light Industry of the Ministry of Education Jiangnan University Wuxi Jiangsu P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Near-infrared spectroscopy (NIRS) has been widely used in the chemical industry and has attracted much attention because of its fast and non-destructive characteristics. However, spectra are susceptible to interference from external environmental factors, leading to spectral overlap and noise problems. To solve these problems, spectral wavelength selection can be performed. Traditional selection methods require manually choosing the appropriate method based on spectral data and combining it with appropriate regression modeling, which is complicated and time-consuming. In contrast, this study proposes a novel spectral wavelength selection method that intelligently filters the entire wavelength based on the correlation between spectra and output variables. Simultaneously, it utilizes a deep spectra feature extraction module to obtain a more abstract spectral representation applied in regression prediction. Evaluation results on the training and test sets demonstrate the outstanding performance of this model: RMSE of 0.007796 and 0.01036 for the training and test sets, respectively, R2 of 0.9918 and 0.9842, with an impressive RPD of 7.9703. These metrics indicate that this model meets the rigorous requirements for high-performance models in the chemical engineering domain.

关键词： Training Analytical models Statistical analysis Noise Predictive models Feature extraction Prediction algorithms

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Intelligent Traffic Signal control System Based on Improved YOLOv8 and Deep Reinforcement Learning

Intelligent Traffic Signal Control System Based on Improved ...

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Frontiers Technology of Information and Computer (ICFTIC), IEEE International conference on

作者： Ziyuan Liu Hongxiang Chen Jiajie Wang Tianqi Deng Mingshuo Liu Guodong Zhao School of computer science and technology Harbin Engineering University Harbin China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

As urban traffic pressure continues to increase, existing traffic systems have achieved some success in improving vehicle throughput. However, they commonly face issues such as insufficient precision in visual recognition models, traffic signal control algorithms that neglect pedestrian needs, and excessive reliance on hardware infrastructure, leading to high deployment complexity. To address these challenges, an intelligent traffic system based on an improved YOLOv8 model and Deep Q-Network (DQN) algorithm is proposed. The system consists of a road dataization module and a traffic signal control module. In the road dataization module, the improved YOLOv8 model incorporates an attention mechanism to efficiently recognize vehicle information and uses OpenCV to process traffic images, enabling accurate data modeling. In the traffic signal control module, the enhanced DQN algorithm dynamically adjusts signal durations to balance the needs of pedestrians and vehicles, thereby improving traffic efficiency. Verified through the SUMO simulation platform, this approach demonstrates significant ad-vantages in optimizing traffic flow and throughput. Furthermore, by leveraging algorithmic data analysis of the environment, the system reduces dependency on hardware infrastructure, significantly lowering deployment complexity and showing strong potential for practical application. Additionally, the design of a visualized front-end interface and an embedded display system enhances the system's usability and integrity, validating its feasibility.

关键词： Visualization Pedestrians Roads Heuristic algorithms Face recognition Throughput Deep reinforcement learning Hardware data models Optimization

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