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第十四届中国卫星导航年会

作者： Yu Lei Danning Zhao School of Computer Science and Technology Xi'an University of Posts and Telecommunications School of Electronic and Electrical Engineering Baoji University of Arts and Sciences

This paper presents the application of weighted least squares(WLS)extrapolation and vector autoregressive(VAR) modelling in polar motion prediction. The simultaneous predictions of pole coordinatespx,py are generated by the combination of(1) WLS extrapolation of harmonic models for the linear trend, Chandler and annual wobbles, and(2) VAR stochastic prediction of the WLS residuals(WLS+VAR). For WLS fit the weights are computed by a power function to accurately extract the Chandler and annual wobbles. Moreover, the VAR technology is used to model and predict the WLS residuals of pole coordinatespx,py together considering the correlation betweenpx andpy. The results show that the accuracy of the ultra short-term predictions up to 10 days into the future obtained by WLS+VAR is equal to or slightly better than that by LS+AR, whilst the predictions out 10 days are substantially more accurate than those by LS+AR. Furthermore, the medium-term predictions beyond 90 days can be improved by WLS+VAR in comparison with the prediction values provided by the IERS Bulletin A. The improvement in the prediction accuracy can reach up to20%.

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technology, Movement, and Play Is Hampering and Boosting Interactive Play 23

Technology, Movement, and Play Is Hampering and Boosting Int...

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10th ACM SIGCHI Annual Symposium on computer-Human Interaction in Play, CHI PLAY 2023

作者： Van Delden, Robby Reidsma, Dennis Postma, Dees Weijdom, Joris Márquez Segura, Elena Turmo Vidal, Laia Vega-Cebrián, José Manuel Tajadura-Jiménez, Ana Waern, Annika Park, Solip Hämäläinen, Perttu Font, José Maria Johnsson, Mats Rasmussen, Lærke Schjødt Elbæk, Lars Human Media Interaction University of Twente Enschede Netherlands Hku Theatre Hku University of the Arts Netherlands Department of Computer Science and Engineering Universidad Carlos Iii de Madrid Madrid Spain Ucl Interaction Centre University College London London United Kingdom Department of Informatics and Media Uppsala University Uppsala Sweden Aalto University Finland Department of Computer Science and Media Technology Faculty of Technology and Society Malmö University Sweden Department of Sport Sciences Faculty of Education and Society Malmö University Sweden Department of Sports Science and Clinical Biomechanics University of Southern Denmark Odense Denmark

ISBN: (纸本)9798400700293

In this paper, we highlight how including technology, movement or play can boost a design process but with unbalanced amounts can also hamper the process. We provide a set of examples where we miscalculated the amount of technology, movement, or play that was needed in a design activity in such a way that it became counterproductive and for each example mention possible adaptations. Finally, we highlight three existing approaches that can balance the overabundance of technology, movement, and play in design processes: activity-centered design, somaesthetic design, and perspective-changing movement-based design. © 2023 Owner/Author.

关键词： and process balancing movement play product technology

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HCAI4U 2023 - Preface to the First Workshop on User Perspectives in Human-Centred Artificial Intelligence 1

HCAI4U 2023 - Preface to the First Workshop on User Perspect...

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1st Workshop on User Perspectives in Human-Centred Artificial Intelligence, HCAI4U 2023

作者： De Luca, Ernesto W. Purificato, Erasmo Boratto, Ludovico Marrone, Stefano Sansone, Carlo Otto von Guericke University Magdeburg Faculty of Computer Science Magdeburg39106 Germany Leibniz Institute for Educational Media Georg Eckert Institute Brunswick38118 Germany University of Cagliari Department of Mathematics and Computer Science Cagliari09124 Italy University of Naples Federico II Department of Electrical Engineering and Information Technology Naples80125 Italy

The emerging concept of Human-Centred Artificial Intelligence (HCAI) involves the amplification, augmentation, empowerment, and enhancement of individuals. The goal of HCAI is to ensure that AI meets our needs while also operating transparently, delivering fair and equitable outcomes, and respecting privacy, all while preserving human control. This approach involves multiple stakeholders, such as researchers, developers, business leaders, policymakers, and users, who are affected in various ways by the implementation and evaluation of AI systems. The primary focus of the First Workshop on User Perspectives in Human-Centred Artificial Intelligence (HCAI4U) is to examine the potential positive and negative impacts of automated decision-making systems on end-users, as well as how their interaction with AI is influenced by human-centred aspects of reliability, safety, and fairness. The workshop aims to facilitate discussion and exchange of ideas among the community on advances in developing trustworthy, fair, and privacy-preserving systems, as well as user interfaces that are explainable, with a specific focus on the users’ perception in real-world scenarios rather than solely on the algorithmic and model performance. Additionally, HCAI4U aims to foster cross-disciplinary and interdisciplinary discussions between experts from various research fields, such as computer science, psychology, sociology, law, medicine, business, etc., to discuss problems and synergies in this exciting research topic. © 2023 CEUR-WS. All rights reserved.

关键词： Human computer interaction

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Cutting-Edge Robotic Arm Design: Harnessing Flex Sensors and Novel Controller Framework to Meet Real-World Innovations

Cutting-Edge Robotic Arm Design: Harnessing Flex Sensors and...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Syambabu, V. Kalaiselvi, K. Mahil, S. Derisha Hemalatha, S. Alekhya, Badi Kanagavalli, N. Mlr Institute of Technology Department of Electronics and Communication Engineering Hyderabad India Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Data Analytics Tamil Nadu Chennai India Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India R.M.D Engineering College Department of Aiml Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

This research paper introduces a sophisticated arm movement tracking system, leveraging a multi-sensor fusion approach for enhanced precision and versatility. The comprehensive system comprises various components, including a dedicated cabinet, six Leap Motion sensors, six microcomputers, a Kinect depth camera, a KVM switcher, a router, and flex sensors. In addition to the aforementioned components, the integration of flex sensors enhances the system's capability to capture nuanced movements and gestures of the human arm. Flex sensors contribute valuable data to the multi-sensor fusion, further refining the arm movement tracking and providing a comprehensive understanding of the intricacies involved. By quantifying the Root Mean Square Error (RMSE) for each filtering technique, including the data from flex sensors, we gained valuable insights into the accuracy and precision achieved by Particle Filtering in refining data from the specified angles. This approach ensures a thorough validation of Particle Filtering's superior performance when compared to Existing filtering Methods. The systematic evaluation of RMSE serves as a robust method for assessing the effectiveness of Particle Filtering, affirming its potential as a high-performing solution for precise angle estimation and tracking within the context of the presented arm movement tracking system that incorporates flex sensors. © 2024 IEEE.

关键词： Cameras

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A transformer generative adversarial network for multi-track music generation

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CAAI Transactions on Intelligence technology 2022年第3期7卷 369-380页

作者： Cong Jin Tao Wang Xiaobing Li Chu Jie Jiessie Tie Yun Tie Shan Liu Ming Yan Yongzhi Li Junxian Wang Shenze Huang School of Information and Communication Engineering Communication University of ChinaBeijingChina School of Information and Engineering Zhengzhou UniversityZhengzhouChina Central Conservatory of Music BeijingChina Department of computer Science University of TorontoTorontoCanada School of Data Science and Media Intelligence Communication University of ChinaBeijingChina School of Light Industry and Engineering South China University of TechnologyGuangzhouChina School of Animation and Digital Arts Communication University of ChinaBeijingChina

This study proposes a new generation network based on transformers and guided by the music theory to produce high-quality music *** this study,the decoding block of the transformer is used to learn the internal information of single-track music,and cross-track transformers are used to learn the information amongst the tracks of different musical instruments.A reward network based on the music theory is proposed,which optimizes the global and local loss objective functions while training and discriminating the network so that the reward network can provide a reliable adjustment method for the generation of the *** method of combining the reward network and cross entropy loss is used to guide the training of the generator and produce high-quality music *** with other multi-track music generation models,the experimental results verify the validity of the model.

关键词： artificial intelligence deep learning multimedia

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Behavioral Changes Based Trust Computation in Mutli-Agent Predictive Maintenance Applications

Behavioral Changes Based Trust Computation in Mutli-Agent Pr...

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Advanced Research in Computing (ICARC), International Conference on

作者： Uthpala Subodhani Premarathne Shalini Rajasingham Department of Electrical and Computer Engineering The Open University of Sri Lanka Nawala Sri Lanka Center for Education Technology and Media The Open University of Sri Lanka Nawala Sri Lanka

Predictive maintenance practices rely on reliable scheduling and allocation of resources to sustain seamless operations. Advent of agent technology has increased the avenues to manage efficient schedules in distributed industry settings. This paper proposes a multi-agent architecture with behavior based trust computations and reinforcement learning to effectively manage multiple schedule assignments. To formalize the schedule maintenance problem, bigraph was used to model the centralized resource allocations among multiple schedules. Reinforcement learning algorithms was used to compute and update trust values based on the agent behaviors. Results reveal that the cooperation among agents helps to maintain a variation in assignments to less than 40%.

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Intelligent Control of Power Converters using Reinforcement Learning 9

Intelligent Control of Power Converters using Reinforcement ...

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9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Krishna, R. Sathya Barna Sudharani, R. Devi, S. Rukmani Kulkarni, Pramodkumar H. Rohini, G. Sindhu, M. Sathyabama Institute of Science And Technology Department of Computer Science Engineering Tamil Nadu Chennai600119 India Panimalar Engineering College Department of Mathematics Tamil Nadu Poonamallee600123 India Saveetha College of Liberal Arts And Sciences Deemed To Be University SIMATS Department of Computer Science Tamil Nadu Chennai600124 India Dr. D. Y. Patil Institute of Technology Department of Electronics And Telecommunications Maharashtra Pimpri411018 India S.A. Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Thiruverkadu600077 India R.M.K. Engineering College Department of Computer Science And Business Systems Tamil Nadu Thiruvallur601206 India

ISBN: (纸本)9798350365092

Power converter control techniques must evolve to accommodate power system complexity and renewable energy integration. Traditional control methods may be difficult to adapt to these dynamic and uncertain systems. Reinforcement learning (RL) can build optimal control policies in complex and unpredictable situations, making it a promising intelligent control solution in this scenario. This research examines how reinforcement learning can control power converters in energy systems. Advanced reinforcement learning methods, notably deep reinforcement learning, are used to improve power converter efficiency in varied operational scenarios. This research examines the integration of reinforcement learning (RL)-based controllers into power converter systems, focusing on their autonomous learning and adaptation. The explored method uses Reinforcement Learning (RL) flexibility to address power system time-varying conditions, non-linearities, and uncertainties. This research considers practical concerns such RL-based control's effects on real-time execution, processing requirements, and safety. The report concludes with suggestions for future research including expanding RL-based intelligent control systems to power system applications. Reinforcement learning keywords include stability, efficiency, reliability, renewable energy, deep reinforcement learning, power converters, intelligent control, and energy systems. © 2024 IEEE.

关键词： Power converters

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A Hybrid Deep Learning Approach to Classify the Plant Leaf Species

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computers, Materials & Continua 2023年第9期76卷 3897-3920页

作者： Javed Rashid Imran Khan Irshad Ahmed Abbasi Muhammad Rizwan Saeed Mubbashar Saddique Mohamed Abbas Department of CS&SE Islamic International UniversityIslamabad44000Pakistan Department of IT Services University of OkaraOkara56310Pakistan Faculty of Science and Arts Belqarn University of BishaSabtul Alaya61985Saudi Arabia Department of CS University of OkaraOkara56310Pakistan Department of Computer Science&Engineering University of Engineering&Technology LahoreNarowalCampusNarowal51601Pakistan Electrical Engineering Department College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Research Center for Advanced Materials Sciences(RCAMS) King Khalid UniversityAbhaSaudi Arabia

Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them *** plant species can be challenging to classify and segment using deep *** using deep learning architectures has helped improve classification accuracy,the resulting models often need to be more flexible and require a large dataset to *** the sake of taxonomy,this research proposes a hybrid method for categorizing guava,potato,and java *** new approaches are used to formthe hybridmodel suggested *** guava,potato,and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET *** a second model,we use a Plant Species Detection Stacking Ensemble Deep Learning Model(PSD-SE-DLM)to identify potatoes,java plums,and *** proposed models were trained using data collected in Punjab,Pakistan,consisting of images of healthy and sick leaves from guava,java plum,and *** datasets are known as PLSD and *** levels of 99.84%and 96.38%were achieved for the suggested PSD-SE-DLM and MobileNetV2-UNET models,respectively.

关键词： Plant leaf species stacking ensemble model guava potato java plum MobileNetV2-UNET hybrid deep learning segmentation

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Hyperparameter Tuned Deep Learning Enabled Intrusion Detection on Internet of Everything Environment

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computers, Materials & Continua 2022年第12期73卷 6579-6594页

作者： Manar Ahmed Hamza Aisha Hassan Abdalla Hashim Heba G.Mohamed Saud S.Alotaibi Hany Mahgoub Amal S.Mehanna Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computers and Information Computer Science DepartmentMenoufia UniversityEgypt Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt

Internet of Everything(IoE),the recent technological advancement,represents an interconnected network of people,processes,data,and *** recent times,IoE gained significant attention among entrepreneurs,individuals,and communities owing to its realization of intense values from the connected *** the other hand,the massive increase in data generation from IoE applications enables the transmission of big data,from contextawaremachines,into useful *** and privacy pose serious challenges in designing IoE environment which can be addressed by developing effective Intrusion Detection Systems(IDS).In this background,the current study develops Intelligent Multiverse Optimization with Deep Learning Enabled Intrusion Detection System(IMVO-DLIDS)for IoT *** presented IMVO-DLIDS model focuses on identification and classification of intrusions in IoT *** proposed IMVO-DLIDS model follows a three-stage *** first,data pre-processing is performed to convert the actual data into useful *** addition,Chaotic Local Search Whale Optimization Algorithm-based Feature Selection(CLSWOA-FS)technique is employed to choose the optimal feature ***,MVO algorithm is exploited with Bidirectional Gated Recurrent Unit(BiGRU)model for ***,the novelty of the work is the application of MVO algorithm in fine-turning the hyperparameters involved in BiGRU *** experimental validation was conducted for the proposed IMVO-DLIDS model on benchmark datasets and the results were assessed under distinct *** extensive comparative study was conducted and the results confirmed the promising outcomes of IMVO-DLIDS approach compared to other approaches.

关键词： Internet of everything deep learning feature selection classification intrusion detection cybersecurity

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Assessments on Linearized Feedback Control in Regulating electrical Stimulations for Revival of Sitting-to-Standing Movement on Subjects with Injuries of the Spinal Cord 8

Assessments on Linearized Feedback Control in Regulating Ele...

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8th IET Smart Cities Symposium, SCS 2024

作者： Ahmed, M. Muhammad, M.H. Ibrahim, B.S.K.K. Huq, M.S. Abubakar Tafawa Balewa University P. M. B. 0248 Bauchi Nigeria Gulf University for Science & Technology Department of Electrical and Computer Engineering Hawally Kuwait London Metropolitan University London School of Computing and Digital Media United Kingdom

ISBN: (纸本)9781837242672

Functional electrical Stimulation (FES) entails utilising a suitable form of electric current for reviving functions. Literature showed that applying FES for Sit-to-Stand (STS) restoration needed improved performance towards achieving clinical acceptability which imposed challenges for researchers. Control systems have been applied for providing solutions and results were highly encouraging. Therefore, to contribute in that vein the study the evaluation of fatigue, spasms and tremors on the Linearized Feedback Control (LFC) scheme. LFC was a nonlinear control method which is characterized by being robust. The integral/Time Integral amount of currents, Integral/Time Integral Rate of Change in Current, RMSE, Integral/Time Integral of Tracking Error and Maximum Control Signal without disturbance, fatigue, spasm and tremor are all by far higher or with the PID compared to the LFC;making it the worse. The parameters are greater than the threshold specified for the restored movement. Results indicated that the LFC had better and superior performance in comparison to the Proportional-Integral-Derivative (PID) for the restoration of the movement function. The LFC again portrayed a high level of robustness and disturbance rejection capabilities and also was stable. Hence, the nonlinear control method was successfully applied for FES-induced STS restoration. Results therefore portrayed the suitability of LFC for the achievement of disturbance rejection. The assessments showed that most likely the LFC might with further research lead to remarkable results when applied to such or even similar systems. © The Institution of engineering & technology 2024.

关键词： functional electrical stimulation linearized feedback control movement restoration nonlinear control method sit-to-stand

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