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Trajectory prediction control for digital control DC-DC converters with fast transient response

MICROELECTRONICS JOURNAL 2014年第6期45卷 767-774页

作者： Wang, Qing Chen, Ning Xu, Shen Sun, Weifeng Shi, Longxing Southeast Univ Natl ASIC Syst Engn Res Ctr Nanjing 210096 Jiangsu Peoples R China

A novel trajectory prediction control algorithm for digital control DC-DC converters has been presented in this paper. The proposed trajectory prediction control algorithm can provide an accurate prediction of the duty ratio of the next several switching cycles, so as to overcome the inherent time delay of the digital control loop, and to improve the transient response of digital control DC-DC converters, including load response, line response and reference tracking response. A digital control buck DC-DC converter was implemented to verify the effectiveness of the proposed prediction control algorithm. The recovery time is about 8 its and 4 mu s respectively, when the load current changes from a full load to a 17% load and the input voltage changing between 5 V to 6 V. The fastest reference tracking speed is about 26.7 mu s/V. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Transient response Time optimization DC-DC converter prediction algorithm Digital control

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Earthquake prediction and Alert System Using IoT Infrastructure and Cloud-Based Environmental Data Analysis

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APPLIED SCIENCES-BASEL 2024年第22期14卷 10169页

作者： Rosca, Cosmina-Mihaela Stancu, Adrian Petr Gas Univ Ploiesti Fac Mech & Elect Engn Dept Automat Control Comp & Elect 39 Bucharest Ave Ploiesti 100680 Romania Petr Gas Univ Ploiesti Fac Econ Sci Dept Business Adm 39 Bucharest Ave Ploiesti 100680 Romania

Earthquakes are one of the most life-threatening natural phenomena, and their prediction is of constant concern among scientists. The study proposes that abrupt weather parameter value fluctuations may influence the occurrence of shallow seismic events by focusing on developing an innovative concept that combines historical meteorological and seismic data collection to predict potential earthquakes. A machine learning (ML) model utilizing the *** framework was designed and implemented. An analysis was undertaken to identify which modeling approach, value prediction, or data classification performs better in forecasting seismic events. The model was trained on a dataset of 8766 records corresponding to the period from 1 January 2001 to 5 October 2024. The achieved accuracy of the model was 95.65% for earthquake prediction based on weather conditions in the Vrancea region, Romania. The authors proposed a unique alerting algorithm and conducted a case study that evaluates multiple predictive models, varying parameters, and methods to identify the most effective model for seismic event prediction in specific meteorological conditions. The findings demonstrate the potential of combining Internet of Things (IoT)-based environmental monitoring with AI to improve earthquake prediction accuracy and preparedness. An IoT-based application was developed using C# with *** framework to enhance earthquake prediction and public warning capabilities, leveraging Azure cloud infrastructure. The authors also created a hardware prototype for real-time earthquake alerting, integrating the M5Stack platform with ESP32 and MPU-6050 sensors for validation. The testing phase and results describe the proposed methodology and various scenarios.

关键词： earthquake detection earthquake prediction processing of seismic data AI methods machine learning IoT infrastructure prediction algorithm *** weather parameters Azure services

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PCSPred&x005F;SC: prediction of Protein Citrullination Sites Using an Effective Sequence-Based Combined Method

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IEEE ACCESS 2020年 8卷 88453-88463页

作者： Zhang, Lina Chen, Jingui Zhang, Chengjin Gao, Rui Yang, Runtao Shandong Univ Weihai Sch Mech Elect & Informat Engn Weihai 264209 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250100 Peoples R China

As one of post-translational modifications (PTMs), protein citrullination is crucial in a diverse array of cellular processes and implicated in a slew of human pathology. Therefore, accurate identification of protein citrullination sites (PCSs) is urgently needed to illuminate the reaction details and the complex pathogenesis related to the protein citrullination. In view of the limitations of the existing PCS predictors, this study proposes a novel and powerful sequence-based combined method named PCSPred & x005F;SC to further enhance the prediction performance. Various feature extraction methods are developed to mine sequence-derived biological information. Under the feature space, the predictive capabilities of different prediction algorithms, over-sampling methods, and feature selection methods are respectively explored. Experimental results indicate that the over-sampling methods are effective to solve the imbalanced dataset problem and the feature selection methods are significant in removing irrelevant and redundant features. On the same dataset using 10-fold cross validation, PCSPred & x005F;SC constructed by the combination of support vector machine (SVM), Adasyn, and t-distributed stochastic neighbor embedding (t-SNE) achieves much more outstanding performance than the competing methods, while reducing the number of features used for this task remarkably. It is anticipated that the proposed method will provide significant information to broaden our knowledge of citrullination-related biological processes.

关键词： Citrullination prediction algorithm over-sampling feature selection

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Bit Loading Using Imperfect CSIT for prediction-Based Resource Allocation in Mobile OFDMA

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2011年第8期60卷 4082-4088页

作者： Schmidt, Jorge F. Cousseau, Juan E. Wichman, Risto Werner, Stefan Univ Nacl Sur Consejo Nacl Invest Cient & Tecn CONICET Inst Invest Ingn Elect IIIE RA-8000 Bahia Blanca Buenos Aires Argentina Aalto Univ Sch Elect Engn Aalto 00076 Finland

We present a prediction-based resource allocation algorithm (RA) for orthogonal frequency-division multiple-access (OFDMA) downlink, where inaccuracies in the wireless channel predictions are accounted for in the problem formulation. As the prediction quality significantly degrades with the prediction horizon, we propose a solution based on the histogram of the prediction error. This characterization also enables different mobile stations (MSs) to use different channel predictors as it does not rely on a specific prediction scheme. Using this characterization of the prediction error and based on classical resource allocation strategies, we derive an algorithm that incorporates imperfect channel prediction information of future time slots. We evaluate the proposed algorithm using a practical low-complexity channel predictor suitable for implementation at the MSs. Simulation results show that the proposed algorithm outperforms previous prediction-based RA strategies without the characterization of the prediction error, and the system throughput is comparable with the case with perfect channel state information in the transmitter (CSIT).

关键词： Channel state information histograms orthogonal frequency-division multiple-access (OFDMA) prediction algorithm scheduling algorithm time-varying channels wireless communications

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Heart Motion prediction Based on Adaptive Estimation algorithms for Robotic-Assisted Beating Heart Surgery

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IEEE TRANSACTIONS ON ROBOTICS 2013年第1期29卷 261-276页

作者： Tuna, E. Erdem Franke, Timothy J. Bebek, Ozkan Shiose, Akira Fukamachi, Kiyotaka Cavusoglu, M. Cenk Case Western Reserve Univ Dept Elect Engn & Comp Sci Cleveland OH 44106 USA Cleveland Clin Dept Biomed Engn Lerner Res Inst Cleveland OH 44195 USA Ozyegin Univ Mech Engn Istanbul Turkey

Robotic-assisted beating heart surgery aims to allow surgeons to operate on a beating heart without stabilizers as if the heart is stationary. The robot actively cancels heart motion by closely following a point of interest (POI) on the heart surface-a process called active relative motion canceling. Due to the high bandwidth of the POI motion, it is necessary to supply the controller with an estimate of the immediate future of the POI motion over a prediction horizon in order to achieve sufficient tracking accuracy. In this paper, two least-squares-based prediction algorithms, using an adaptive filter to generate future position estimates, are implemented and studied. The first method assumes a linear system relation between the consecutive samples in the prediction horizon. On the contrary, the second method performs this parametrization independently for each point over the whole the horizon. The effects of predictor parameters and variations in heart rate on tracking performance are studied with constant and varying heart rate data. The predictors are evaluated using a three-degree-of-freedom (DOF) test bed and prerecorded in vivo motion data. Then, the one-step prediction and tracking performances of the presented approaches are compared with an extended Kalman filter predictor. Finally, the essential features of the proposed prediction algorithms are summarized.

关键词： Active relative motion canceling beating heart surgery prediction algorithm signal estimation surgical robotics

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HEPre: Click frequency prediction of applications based on heterogeneous information network embedding

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第6期41卷 7511-7526页

作者： Li, Chao Yan, Yeyu Zhao, Zhongying Luo, Jun Zeng, Qingtian Shandong Univ Sci & Technol Coll Comp Sci & Engn Shandong Prov Key Lab Wisdom Mine Informat Techno Qingdao 266510 Peoples R China Lenovo Grp Ltd Lenovo Machine Intelligence Ctr Hong Kong Peoples R China

Owing the continuous enrichment of mobile application resources, mobile applications carry almost all user behaviors and preferences. The analysis of user behavior regarding mobile terminals has become an important research direction. The frequency with which users click on mobile applications reflects their preferences to a certain extent. In this study, we propose a mobile application click-frequency prediction model based on heterogeneous information network representation. This model first constructs a heterogeneous information network between users' mobile devices and mobile applications. To generate a meaningful sequence of network-embedded nodes, we perform a random walk on a specified meta-path. Finally, the prediction of users' mobile application click frequency is completed using representation fusion and matrix factorization. Experiments show that our method outperforms other baseline methods in terms of the mean absolute error and root mean square error. Therefore, the application of a heterogeneous information network representation method to the prediction model is effective. This study is significant to the behavior research of mobile terminal users.

关键词： Heterogeneous information network network representation learning prediction algorithm mobile application

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Comparative Study of Markov Chain With Recurrent Neural Network for Short Term Velocity prediction Implemented on an Embedded System

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IEEE ACCESS 2021年 9卷 24755-24767页

作者： Shin, Jaewook Yeon, Kyuhwan Kim, Sunbin Sunwoo, Myoungho Han, Manbae Hanyang Univ Dept Automot Engn Seoul 04763 South Korea Hyundai Motor Co Res & Dev Div Hwaseong 18280 South Korea Keimyung Univ Dept Mech & Automot Engn Daegu 42601 South Korea

Short-term prediction models for an ego-vehicle's speed contributes to the improvement of vehicle safety, driveability, and fuel economy. To achieve these desired outcomes, an accurate forward speed prediction model and its successful implementation in a real system is a prerequisite. This paper compares six velocity prediction models based on two types of data-driven models, a Markov chain and a Recurrent Neural Network (RNN), by implementing them in an embedded system to evaluate their prediction accuracy and execution time. The inputs to each model are the driving information acquired on a specific route, such as internal vehicle information, relative speed and distance to the vehicle in the front of the ego-vehicle, and ego-vehicle's location estimated by the GPS signal along with the B-spline roadway model. The proposed prediction models predict the velocity profile of the ego-vehicle up to the prediction horizon of 150 m. The parameters of the proposed models have been optimized using Hyper-parameter Optimization via Radial basis function and Dynamic coordinate search. By applying real driving data, the Markov chain-based models show slightly lower prediction accuracy but shorter execution time than those of the RNN-based models.

关键词： Predictive models Data models Markov processes Roads Biological system modeling Recurrent neural networks Encoding Embedded system execution time gated recurrent unit (GRU) long short-term momory (LSTM) Markov chain prediction accuracy prediction algorithm recurrent neural network (RNN)

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Online Data-Driven Adaptive prediction of Train Event Times

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2015年第1期16卷 465-474页

作者： Kecman, Pavle Goverde, Rob M. P. Delft Univ Technol Dept Transport & Planning NL-2628 CN Delft Netherlands

This paper presents a microscopic model for accurate prediction of train event times based on a timed event graph with dynamic arc weights. The process times in the model are dynamically obtained using processed historical track occupation data, thus reflecting all phenomena of railway traffic captured by the train describer systems and preprocessing tools. The graph structure of the model allows applying fast algorithms to compute prediction of event times even for large networks. The accuracy of predictions is increased by incorporating the effects of predicted route conflicts on train running times due to braking and reacceleration. Moreover, the train runs with process times that continuously deviate from their estimates in a certain pattern are detected, and downstream process times are adaptively adjusted to minimize the expected prediction error. The tool has been tested and validated in a real-time environment using train describer log files.

关键词： Data driven graph model prediction algorithm rail transportation

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prediction of Interstitial Glucose Level

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IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE 2012年第1期16卷 136-142页

作者： Koutny, Tomas Univ W Bohemia Dept Comp Sci & Engn Plzen 30614 Czech Republic

Glucose is an important source of energy for cells. In clinical practice, we measure glucose level in blood and interstitial fluid. Each method has its pros and cons, and both levels correlate with each other. As the body tries to maintain the glucose level within a particular range to avoid adverse effects, it is desirable to predict future glucose levels in order to aid provided health care. We can see this desire in research, e.g., research on glucose transporters of cells. As yet another example, we can see it with diabetic patients, patients in a metabolic intensive care unit, particularly. In this paper, a glucose level prediction method is proposed.

关键词： Blood glucose interstitial glucose prediction algorithm

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MicroRNA Target prediction: Problems and Possible Solutions

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CURRENT BIOINFORMATICS 2010年第1期5卷 81-88页

作者： Szabo, Peter M. Tombol, Zsofia Molnar, Viktor Falus, Andras Racz, Karoly Igaz, Peter Semmelweis Univ Dept Med 2 Fac Med H-1088 Budapest Hungary Semmelweis Univ Dept Genet Cell & Immunobiol Fac Med H-1088 Budapest Hungary

MicroRNAs (miRNA) are small non-coding RNA molecules involved in the posttranscriptional regulation of gene expression. miRNAs bind specifically to the 3' untranslated region of messenger RNA (mRNA) molecules and induce translational repression or mRNA degradation. Potential miRNA targets can be predicted by various computational algorithms that take several parameters into consideration and calculate probability scores for each miRNA-mRNA interaction. In this review, three of the most frequently used algorithms (TargetScan, PicTar and miRBase) are compared, and their strengths and weaknesses are highlighted. These algorithms use different input databases and mathematical models that may lead to discrepancies of the outputs. As currently there is no unambiguous evidence for the preference of any of these algorithms, simultaneous analysis by all can be an effective approach. For this purpose a novel software was developed which is capable of collecting all available data about each miRNA-mRNA interaction retrieved by these databases and identifying common putative targets. Another major problem is related to difficulties of experimental validation, therefore only a minority of in silico predicted targets could be validated to date. Following a brief description of the available experimental target validation methods, the authors attempt to provide suggestions for designing in vitro validation approaches.

关键词： microRNA mRNA target seed region prediction algorithm target validation

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