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Hybrid Sphere Decoder for Long prediction Horizon FCS-MPC

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2023年第6期70卷 5484-5492页

作者： Zafra, Eduardo Vazquez, Sergio Alcaide, Abraham M. Martin, Emilia Perez Franquelo, Leopoldo G. Leon, Jose I. Univ Seville Elect Dept Seville 41092 Spain Univ Seville Lab Engn Energy & Environm Sustainabil Seville 41092 Spain

In finite control set model predictive control strategies, extending the prediction horizon length provides important closed-loop performance improvements. However, the computational costs are increased in exponential fashion. Transforming the problem to an equivalent box-constrained integer least-squares formulation enables the usage of sphere decoding algorithms (SDA) that can efficiently solve this problem. Recently, a K-best sphere decoder was proposed and designed for hardware platforms. This algorithm follows a breadth-first strategy different to the conventional SDA. In this work, a hybrid SDA that combines the merits of both the K-best SDA and the conventional SDA is proposed with the objective of increasing optimality likelihood and improve control performance. In particular, it is proposed that a K-best sphere decoder delivers a preliminary optimal solution. Then, a conventional SDA uses the available calculation time to search for a better solution. Simulation and experimental results confirm the validity of the proposal in terms of performance and computational efficiency.

关键词： prediction algorithms Switches Predictive models Decoding Uninterruptible power systems Filtering algorithms Computational modeling DC-AC power conversion digital control predictive control

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BoB: Bandwidth prediction for Real-Time Communications Using Heuristic and Reinforcement Learning

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IEEE TRANSACTIONS ON MULTIMEDIA 2023年 25卷 6930-6945页

作者： Bentaleb, Abdelhak Akcay, Mehmet N. Lim, May Begen, Ali C. Zimmermann, Roger Concordia Univ Gina Cody Sch Engn & Comp Sci Montreal PQ H3H 2R9 Canada Natl Univ Singapore Sch Comp Singapore 119077 Singapore Ozyegin Univ TR-34794 Istanbul Turkiye

Bandwidth prediction is critical in any Real-time Communication (RTC) service or application. This component decides how much media data can be sent in real time. Subsequently, the video and audio encoder dynamically adapts the bitrate to achieve the best quality without congesting the network and causing packets to be lost or delayed. To date, several RTC services have deployed the heuristic-based Google Congestion Control (GCC), which performs well under certain circumstances and falls short in some others. In this paper, we leverage the advancements in reinforcement learning and propose BoB(Bang-on-Bandwidth) - a hybrid bandwidth predictor for RTC. At the beginning of the RTC session, BoBuses a heuristic-based approach. It then switches to a learning-based approach. BoBpredicts the available bandwidth accurately and improves bandwidth utilization under diverse network conditions compared to the two winning solutions of the ACM MMSys'21 grand challenge on bandwidth estimation in RTC. An open-source implementation of BoBis publicly available for further testing and research.

关键词： Bandwidth Quality of experience prediction algorithms Optimization Estimation Bit rate Streaming media Bandwidth prediction real-time communications reinforcement learning RTC WebRTC AlphaRTC

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Region-Based Template Matching prediction for Intra Coding

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2023年 32卷 779-790页

作者： Venugopal, Gayathri Muller, Karsten Pfaff, Jonathan Schwarz, Heiko Marpe, Detlev Wiegand, Thomas Fraunhofer HHI D-10587 Berlin Germany Free Univ Berlin D-14195 Berlin Germany Tech Univ Berlin Informat Technol D-10623 Berlin Germany

Copy prediction is a renowned category of prediction techniques in video coding where the current block is predicted by copying the samples from a similar block that is present somewhere in the already decoded stream of samples. Motion-compensated prediction, intra block copy, template matching prediction etc. are examples. While the displacement information of the similar block is transmitted to the decoder in the bit-stream in the first two approaches, it is derived at the decoder in the last one by repeating the same search algorithm which was carried out at the encoder. Region-based template matching is a recently developed prediction algorithm that is an advanced form of standard template matching. In this method, the reference area is partitioned into multiple regions and the region to be searched for the similar block(s) is conveyed to the decoder in the bit-stream. Further, its final prediction signal is a linear combination of already decoded similar blocks from the given region. It was demonstrated in previous publications that region-based template matching is capable of achieving coding efficiency improvements for intra as well as inter-picture coding with considerably less decoder complexity than conventional template matching. In this paper, a theoretical justification for region-based template matching prediction subject to experimental data is presented. Additionally, the test results of the aforementioned method on the latest H.266/Versatile Video Coding (VVC) test model (version VTM-14.0) yield an average Bjontegaard-Delta (BD) bit-rate savings of -0.75% using all intra (AI) configuration with 130% encoder run-time and 104% decoder run-time for a particular parameter selection.

关键词： Decoding Video coding Image coding Indexes Encoding Distortion prediction algorithms Template matching video coding intra-picture prediction AVC HEVC VVC

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Flight Control Method Using Neural Network in prediction for Suppressing Ship Airwake Impact in Carrier Landing

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IEEE AEROSPACE AND ELECTRONIC SYSTEMS MAGAZINE 2023年第7期38卷 20-32页

作者： Meng, Yue Wang, Wei Han, Hao Beihang Univ Inst Unmanned Syst Beijing 100191 Peoples R China Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China

Ship airwake is an important factor affecting the accurate carrier landing of carrier-based aircraft. To suppress the effects of airwake, this article presents a flight control method using neural network prediction. Different from most studies focusing on improving the adaptability of the flight control methods, the emphasis of the research is placed on modeling the effects of the ship airwake in the carrier landing environment pertinently and reflecting the effects in the control model. From a large amount of carrier landing flight data, the impact of airwake on the landing control can be extracted and summarized into models based on neural network algorithm. Then the airwake impact in the next period of time can be predicted. A flight control law is designed combining the predicting model of airwake impact with model predictive control. The simulation demonstrated that the presented method can reduce the trajectory deviation influenced by airwake and decrease the touchdown dispersions.

关键词： Atmospheric modeling Marine vehicles Aircraft Aerospace control Predictive models Wind speed prediction algorithms Carrier landing flight control ship airwake neural network prediction

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Automated Stroke prediction Using Machine Learning: An Explainable and Exploratory Study With a Web Application for Early Intervention

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IEEE ACCESS 2023年 11卷 52288-52308页

作者： Mridha, Krishna Ghimire, Sandesh Shin, Jungpil Aran, Anmol Uddin, Md. Mezbah Mridha, M. F. Marwadi Univ Dept Comp Engn Rajkot 360003 India Univ Aizu Dept Comp Sci & Engn Aizu Wakamatsu 9658580 Japan Amer Int Univ Bangladesh Dept Comp Sci Dhaka 1229 Bangladesh

Stroke is a dangerous medical disorder that occurs when blood flow to the brain is disrupted, resulting in neurological impairment. It is a big worldwide threat with serious health and economic implications. To solve this, researchers are developing automated stroke prediction algorithms, which would allow for early intervention and perhaps save lives. The number of people at risk for stroke is growing as the population ages, making precise and effective prediction systems increasingly critical. wo In a comparison examination with six well-known classifiers, the effectiveness of the proposed ML technique was explored in terms of metrics relating to both generalization capability and prediction accuracy. To give insight into the black-box machine learning models, we also studied two kinds of explainable techniques, namely SHAP and LIME, in this study. SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) are well-established and reliable approaches for explaining model decision-making, particularly in the medical industry. The findings of the experiment revealed that more complicated models outperformed simpler ones, with the top model obtaining almost 91% accuracy and the other models achieving 83-91% accuracy. The proposed framework, which includes global and local explainable methodologies, can aid in standardizing complicated models and gaining insight into their decision-making, which can enhance stroke care and treatment.

关键词： Stroke (medical condition) Machine learning Predictive models prediction algorithms Machine learning algorithms Medical services Medical diagnostic imaging Stroke prediction data leakage explainable machine learning ANOVA test SHAPE LIME

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Comparative Study of Multi-Combination Models for Medium- and Long-Term Runoff prediction in Weihe River

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IEEE ACCESS 2023年 11卷 97099-97106页

作者： Liu, Hao Liu, Wei Luo, Jungang Li, Jing Changjiang Schinta Software Technol Co Ltd Wuhan 430010 Peoples R China Changjiang Survey Planning Design & Res Co Ltd Wuhan 430010 Peoples R China Xian Univ Technol Sch Water Conservancy & Hydropower Dept Hydrol & Water Resources Wuhan 430010 Peoples R China Hydrol Bur Changjiang Water Resources Commiss Wuhan 430010 Peoples R China

The characteristics of hydrological data include nonconsistency and nonlinearity. The prediction accuracy can be improved through the combination of both the decomposition algorithm and the runoff model. Previous studies have typically focused on the combination of a single decomposition algorithm and model. These studies have compared the prediction accuracy before and after decomposition, ignoring the role of multiple decomposition algorithms and models. Considering the limitations of previous single combinations of decomposition algorithms and models, this study will explore the unique features of hydrological data by using a combination of five algorithms, including Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), TIME series decomposition (TIME), Variational Mode Decomposition (VMD), and Singular Spectrum Analysis (SSA). The study constructed models for Prophet, Long Short-Term Memory (LSTM), Multiple Regression (MLR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and Support Vector Regression (SVR). Thirty combined prediction models were then developed and used to forecast medium and long-term runoff at Xianyang Station. To comprehensively evaluate the forecasted runoff results, multiple evaluation metrics were used. The prediction accuracy improved after using EMD and TIME decomposition, but the difference was insignificant, and TIME decomposition was the least effective. VMD, EEMD, and SSA, on the other hand, yielded higher data quality. The combined model achieved an NSE above 0.70, demonstrating good prediction results. Of the thirty combined models, the SSA-SVR and SSA-LSTM models were most accurate, with a verification NSE of 0.90. This study developed a comprehensive, reliable, and accurate combination prediction model by employing multiple decomposition algorithms and models. These findings provide a framework for characteristics-driven watershed runoff prediction and water resources scheduling.

关键词： Predictive models prediction algorithms Data models Analytical models Empirical mode decomposition Time series analysis Support vector machines Long short term memory Hydrologic measurements Rivers Decomposition algorithm singular spectrum analysis LSTM combination model medium and long term runoff forecast

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DeepTrip: A Deep Learning Model for the Individual Next Trip prediction With Arbitrary prediction Times

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第6期24卷 5842-5855页

作者： Zhang, Pengfei Koutsopoulos, Haris N. Ma, Zhenliang Henan Acad Sci Inst Phys Zhengzhou 450007 Peoples R China Northeastern Univ Dept Civil & Environm Engn Boston MA 02115 USA KTH Royal Inst Technol Dept Civil & Architectural Engn S-11428 Stockholm Sweden

The increasing availability of travel trajectory data allows for a better understanding of travel behavior. In the individual mobility analysis, the problem of next trip prediction assumes a central role and is beneficial for applications such as personalized services and mobility management. This paper addresses the next trip prediction problem with arbitrary prediction times (the time when the prediction is made). This problem has not been studied adequately in the literature and it is important for applications driven by system events, such as proactive travel recommendations under disruptions or crowding in transport systems. It predicts an individual's next trips given their historical trip sequences and the prediction time. We formulate the next trip prediction problem as on-board and off-board predictions depending on an individual's travel status (i.e. on-board/off-board). Using historical/real-time travel trajectories, a DeepTrip model is proposed based on a trip sequence-to-sequence deep learning structure coupled with an attention mechanism. A novel overlapped embedding method is proposed to represent continuous travel attributes capturing simultaneously the categorical and numerical feature information. We also develop a random-sampling training algorithm to learn the impact of the prediction time. The model is validated using trip data in urban rails. The results show that DeepTrip outperforms statistical-based models by more than 10% in terms of accuracy and other deep learning models by 2%-3%. The impact analysis shows that different representations are appropriate for the two prediction cases (on-board/off-board), and the prediction performance does not monotonically improve as the prediction time approaches the next trip.

关键词： Predictive models Numerical models Data models Deep learning Trajectory prediction algorithms Global Positioning System Next trip prediction individual mobility deep learning metro systems

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Location-Aware Web Service QoS prediction via Deep Collaborative Filtering

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第6期10卷 3524-3535页

作者： Jia, Zhaohong Jin, Li Zhang, Yiwen Liu, Chuang Li, Kai Yang, Yun Anhui Univ Sch Comp Sci & Technol Hefei 230039 Peoples R China Anhui Univ Sch Internet Hefei 230039 Peoples R China Hefei Univ Technol Sch Management Hefei 230009 Peoples R China Swinburne Univ Technol Dept Comp Technol Melbourne Vic 3122 Australia

Nowadays, there is a large number of web services with similar functions, from which users choose the best according to the quality of service (QoS). Hence, QoS prediction is a primary challenge in service recommendation. Most existing approaches model the user-service interaction relationship. However, the low-dimensional linear and the high-dimensional nonlinear relationships between users and services are seldom considered simultaneously. In addition, although location information, including the local location information of users and services, is incorporated to overcome data sparsity in most approaches. The global location information is seldom considered. Aiming at the above shortcomings, we propose a new QoS prediction model that fuses local and global location information of users and services in the interaction layer of the model. The proposed model uses a multilayer perceptron (MLP) to acquire high-dimensional nonlinear relationships of users and services, where the dot product is employed in complementing the learning of low-dimensional linear relationships. Experimental results on the real world dataset WS-Dream validate the prediction performance of the proposed model.

关键词： Quality of service Predictive models Web services Data models prediction algorithms Computational modeling Collaboration Data sparsity quality of service (QoS) service recommendation web service

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A Trajectory prediction-Based and Dependency-Aware Container Migration for Mobile Edge Computing

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2023年第5期16卷 3168-3181页

作者： Zhang, Weiwen Luo, Jinzhou Chen, Lei Liu, Jianqi Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou 510006 Peoples R China

Edge computing and container technologies offer more possibilities for the development of Internet of Vehicles (IOV). However, many studies have neglected the dependencies among containers and the mobility of users. In this article, we propose a container migration strategy based on trajectory prediction, with the consideration of dependencies among containers. Given a set of containers with dependencies, we aim to reduce the service latency while distributing the containers as evenly as possible for load balance. Specifically, we leverage Recurrent Neural Network (RNN) to train a model for trajectory prediction. Based on the prediction, we can identify the location of the vehicles, after which we develop a mathematical model for service delay. We formulate the problem as a 0-1 program and solve the problem by Hunger Games Search (HGS). The proposed algorithm is validated with a real vehicle dataset and an Alibaba cluster dataset. Experiment results demonstrate that we can predict the trajectory of vehicle accurately and the container migration strategy can effectively reduce service latency and improve server load balancing, compared to alternative algorithms. In addition, we characterize the impact of error correction mechanism and also the effect of bandwidth on the optimization.

关键词： Containers Servers Delays Trajectory Optimization Load management prediction algorithms Mobile edge computing trajectory prediction dependency-aware migration service latency load balancing IOV

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Modified Bald Eagle Search Algorithm With Deep Learning-Driven Sleep Quality prediction for Healthcare Monitoring Systems

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IEEE ACCESS 2023年 11卷 135385-135393页

作者： Alabdan, Rana Mengash, Hanan Abdullah Maray, Mohammed Alotaibi, Faiz Abdelbagi, Sitelbanat Mahmud, Ahmed Majmaah Univ Coll Comp & Informat Sci Dept Informat Syst Al Majmaah 11952 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh 11671 Saudi Arabia King Khalid Univ Coll Comp Sci Dept Informat Syst Abha 62529 Saudi Arabia King Saud Univ Dept Informat Sci Coll Humanities & Social Sci Riyadh Saudi Arabia Prince Sattam Bin Abdulaziz Univ Dept Comp & Self Dev Preparatory Year Deanship Al Kharj 11437 Saudi Arabia Future Univ Egypt FUE Res Ctr New Cairo 11835 Egypt

Sleep habits are strongly related to health behaviors, with sleep quality serving as a major health indicator. Current approaches for evaluating sleep quality, namely polysomnography and questionnaires, are often time-consuming, costly, or invasive. Thus, there is a pressing need for a more convenient, nonintrusive, and cost-effective method. The applications of deep learning (DL) in sleep quality prediction represent a groundbreaking technique for addressing sleep-related disorders. In this aspect, the article offers the design of a Modified Bald Eagle Search Algorithm with Deep Learning-Driven Sleep Quality prediction (MBES-DLSQP) for Healthcare Monitoring Systems. The MBES-DLSQP technique combines the strengths of a DL model with a hyperparameter tuning strategy to provide precise sleep quality predictions. At the primary stage, the MBES-DLSQP technique undergoes data pre-processing. Besides, the MBES-DLSQP technique uses a stacked sparse autoencoder (SSAE)-based prediction model, which can extract and encode high-dimensional sleep data. The MBES-DLSQP incorporates MBESA-based hyperparameter tuning which assures its optimal configurations to further boost the efficiency of the SSAE model. The experimental outcome of the MBES-DLSQP algorithm is tested on the sleep dataset from the Kaggle repository. The experimental value infers that the MBES-DLSQP technique shows promising performance in sleep quality prediction with a maximum accuracy of 98.33%.

关键词： Sleep Tuning Sleep apnea Monitoring Predictive models prediction algorithms Medical services Deep learning Sleep quality prediction healthcare monitoring deep learning parameter tuning artificial intelligence

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