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International Conference on Computing and Networking Technology (ICCNT)

作者： R. Premkumar Amit Kumar Shrivastav Shiv Shankar Shankar Department of Electronics and Communication Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India ARKA JAIN University Jamshedpur Jharkhand India Department of Mathematics Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This examine aimed to discover biomarkers associated to precise lung diseases from gene expression statistics thru the use of prediction analysis algorithms. To this end, the authors advanced a framework based totally on a Euclidean distance help vector machine (DSVM) and a recursive feature removal (RFE) manner. The dataset beneath evaluation changed into made from gene expression information from 380 bronchial brushings people- 188 of wholesome people and 192 of diseased individuals with muscular dystrophy, chronic obstructive pulmonary sicknesses, bronchial asthma and post-radio-chemotherapy. The DSVM version became able to achieve a mean popularity rate of ninety five. 6%, without compromising the specificity price at 88. n i n e %. The diagnosed biomarkers have been further enriched towards many databases and the authors located an association of a number of them with bronchial sicknesses. This result recommend the identification of potentially new biomarkers, supplying novel findings that is probably beneficial in future studies about lung diseases.

关键词： Support vector machines Radio frequency Accuracy Lung Biomarkers prediction algorithms Vectors Classification algorithms Gene expression Standards

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Ship-motion prediction: algorithms and simulation results

Ship-motion prediction: algorithms and simulation results

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： X. Zhao R. Xu C. Kwan Intelligent Automation Inc. Rockville MD USA

Ship-motion prediction is very useful for several naval operations such as aircraft landing, cargo transfer, off-loading of small boats, and ship "mating" between a big transport ship and some small ships. The prediction information is extremely useful in sea states above 3. Five to ten seconds of ship motion prediction can give the operator ample time to avoid serious collisions. The paper summarizes the development of a high performance ship-motion prediction algorithm using minor component analysis (MCA). Simulation results show that this method can predict ship motion a long time ahead with consistent accuracy. That is, the prediction error is almost the same for the 5 second and 20 second predictions. Other conventional algorithms, such as neural networks (NN), autoregressive methods (AR), and Wiener prediction, were also studied for comparative purposes.

关键词： prediction algorithms Predictive models Marine vehicles Aircraft Neural networks Eigenvalues and eigenfunctions Computer crashes Automation Boats Performance analysis

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Approximate stochastic realization and robust prediction: algorithms for iterative solution

Approximate stochastic realization and robust prediction: al...

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IEEE Conference on Decision and Control

作者： H.V. Poor Department of Electrical Engineering Princeton University Princeton NJ USA

The related problems of (finite-length) robust prediction and maximum-entropy approximate stochastic realization are considered. Such problems are of interest in situations where there is uncertainty in the finite-length covariance data of an observed signal or time series. General properties of iterative solutions of these problems are developed, and two iterative algorithms that converge monotonically to such solutions are presented for the situation in which the uncertainty class is a simplex.< >

关键词： Stochastic processes Robustness prediction algorithms Iterative algorithms Uncertainty Minimax techniques Predictive models Entropy Centralized control Data compression

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Adaptive Dynamic State prediction Control algorithms for Quantitative Weighing System

Adaptive Dynamic State Prediction Control Algorithms for Qua...

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International Conference on Future Generation Communication and Networking (FGCN)

作者： Jun-Feng Gao Guang-Yi Tang Yao Wang School of Software Harbin University of Science and Technology HRBUST Harbin China

In the most industrial processes, weighing machines are designed to automatically fill one or more types of the containers with a predetermined weight of particulates raw material using a controller. The performance of the machine is closely related to the accuracy of measurement and speed of dynamic response, which are contradictory. For a gravimetric filling machine, a novel adaptive dynamic state prediction control (ADSPC) algorithm running in the controller is proposed in this paper to regulate conflicts. This algorithm will consider the weighing process as a finite state. State migration is depended on the resulting weight and current state. The resulting weight is determined by the weight of measurement and current state. Compared with normal algorithm and simple prediction algorithm separately under the same condition, the result indicated that the algorithm is effective and feasible.

关键词： Materials Heuristic algorithms prediction algorithms Logic gates Weight measurement Accuracy Switches

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Almost sure convergence of adaptive identification prediction and control algorithms

Almost sure convergence of adaptive identification predictio...

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IEEE Conference on Decision and Control

作者： Rajendra Kumar Division of Applied Mathematics Brown University Providence RI USA

The paper is concerned with the almost sure convergence of the adaptive parameter estimation, N-step ahead prediction and control algorithms based upon standard least square algorithm. With the usual stability and passivity assumptions for the prediction problem, it is demonstrated that the state estimation and the N-step ahead prediction errors converge to the optimum such errors achievable with known plant parameters, in the Cesaro sense. An additional regularity assumption on the signal model establishes the result that the state estimation and prediction errors also converge in the strong sense at an asymptotically arithmetic rate. Under an additional persistency of excitation condition it is shown that the parameter estimation error converges to zero at a rate specified by the degree of excitation. With the regularity condition holding, the convergence is also established for the adaptive control algorithms.

关键词： Convergence Programmable control Adaptive control prediction algorithms Parameter estimation State estimation Least squares approximation Stability Predictive models Arithmetic

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Neural Networks for Epileptic Seizure prediction: algorithms and Hardware Implementation

Neural Networks for Epileptic Seizure Prediction: Algorithms...

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Annual IEEE Northeast Workshop on Circuits and Systems (NEWCAS)

作者： Laura Gagliano Frédéric Lesage Elie Bou Assi Dang K. Nguyen Mohamad Sawan Institute of Biomedical Engineering Polytechnique Montréal Montreal Canada University of Montreal Hospital Research Center Montreal Canada CenBRAIN School of Engineering Westlake University Hangzhou China

ISBN: (数字)9781728170442

ISBN: (纸本)9781728170459

The quality of life of patients with refractory epilepsy can be significantly improved by designing algorithms capable of forecasting seizures and implementing them into closed-loop advisory/intervention devices. Over the last decade, several algorithms based on neural networks and deep learning have been proposed and showed promising performances. Nevertheless, the computational requirements of such algorithms were major obstacles towards their use in clinical devices. In this work, we overview recently proposed neural network-based seizure forecasting algorithms and summarize the state of the art regarding advancement in hardware design and implementation of deep neural network inferences. The paper ends with a list of recommendation for future seizure forecasting endeavors.

关键词： Hardware prediction algorithms Feature extraction Inference algorithms Computer architecture Forecasting Artificial neural networks

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Development and Evaluation of a Machine Learning Model for Cobot Action prediction: Optimizing algorithms Through Cognitive Workload Metrics

Development and Evaluation of a Machine Learning Model for C...

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International Conference on Robotics and Artificial Intelligence (ICRAI)

作者： Lala Rukh Yasir Ahmad Uzair Khaleeq Uz Zaman Mohsin Islam Tiwana Afshan Naseem Department of Engineering Management NUST College of Electrical and Mechanical Engineering Islamabad Pakistan Mechanical Engineering Department NUST College of Electrical and Mechanical Engineering Islamabad Pakistan Mechatronics Engineering Department NUST College of Electrical and Mechanical Engineering Islamabad Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

Collaborative robots (Cobots) have replaced traditional industrial robots on the factory floors, enhancing automation and efficiency. From the perspective of Industry 5.0, centering around H.F. (Human Factors), concerns about cognitive ergonomics persist as Cobots' unpredictable actions can increase cognitive workload and reduce productivity. Identifying and prioritising the operators' mental and physical well-being is necessary for safer and more efficient Human Robot Collaboration (HRC). This paper proposes a novel machine-learning model for Cobot action prediction and evaluates which algorithm is best for predicting Cobot actions based on operators' cognitive states. 75 participants with different levels of technophobia, technical expertise, and age collaborated with a Cobot to solve a complex maze puzzle. Firstly, each participant's Cognitive Workload (CWL) was measured using a combination of subjective and objective metrics (NASA-Task Load index, Heart Rate Variability, and Error Rate), followed by the development of a machine learning model that takes human factors as input parameters and predicts Cobot's actions accordingly. This study evaluates various machine learning models for Cobot action prediction and identifies that the Random Forest Classifier outperforms other models with an accuracy of 90%. The model can be implemented in various industrial settings to improve HRC safety and operators' cognitive ergonomics.

关键词： Productivity Service robots Ergonomics Collaborative robots Human factors Predictive models prediction algorithms Safety Random forests Load modeling

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Optimal Performance prediction of ADAS algorithms on Embedded Parallel Architectures

Optimal Performance Prediction of ADAS Algorithms on Embedde...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Romain Saussard Boubker Bouzid Marius Vasiliu Roger Reynaud Renault S.A.S Guyancourt France Instutut d'Electronique Fondamentale Université Paris Sud Orsay France

ADAS (Advanced Driver Assistance Systems) algorithms increasingly use heavy image processing operations. To embed this type of algorithms, semiconductor companies offer many heterogeneous architectures. These SoCs (System on Chip) are composed of different processing units, with different capabilities, and often with massively parallel computing unit. Due to the complexity of these SoCs, predicting if a given algorithm can be executed in real time on a given architecture is not trivial. In fact it is not a simple task for automotive industry actors to choose the most suited heterogeneous SoC for a given application. Moreover, embedding complex algorithms on these systems remains a difficult task due to heterogeneity, it is not easy to decide how to allocate parts of a given algorithm on the different computing units of a given SoC. In order to help automotive industry in embedding algorithms on heterogeneous architectures, we propose a novel approach to predict performances of image processing algorithms applicable on different types of computing units. Our methodology is able to predict a more or less wide interval of execution time with a degree of confidence using only high level description of algorithms, and a few characteristics of computing units.

关键词： Kernel Computer architecture Image processing Graphics processing units prediction algorithms Parallel processing Computational modeling

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A hybrid conditional averaging technique for performance prediction of algorithms with continuous and discrete uncertainties

A hybrid conditional averaging technique for performance pre...

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American Control Conference (ACC)

作者： X. Rong Li Y. Bar-Shalom Department of Electrical Engineering University of Hartford West Hartford CT USA Department of Electrical and Systems Engineering University of Connecticut Storrs CT USA

Increasing attention has been given to hybrid algorithms those that involve both continuous-valued and discrete-valued uncertainties. The performance of these algorithms are, however, difficult to evaluate without recourse to costly and time-consuming Monte Carlo simulations. In this paper, a general and accurate technique for nonsimulation performance evaluation of hybrid algorithms is presented. This technique gives full consideration to the important scenario dependence of the performance by using a scenario-conditional expectation of the performance. The system mode sequence is adopted as the essential description of the scenario. Two versions of the technique are given: mode-sequence-conditional, and current-mode-conditional. The first one is applied to the notable interacting multiple model algorithm and the second one to the popular probabilistic data association filter for tracking in clutter. The accuracy of the technique is demonstrated via examples.

关键词： prediction algorithms Uncertainty Error correction Analytical models Air traffic control Stochastic systems Target tracking Stochastic processes Numerical analysis State-space methods

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Construction and Performance Evaluation of Grain Porosity prediction Models Based on Metaheuristic algorithms and Machine Learning

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IEEE ACCESS 2025年 13卷 43360-43383页

作者： Chen, Jiahao Li, Jiaxin Zheng, Deqian Liu, Chaosai Zhao, Runmei Zhang, Jiayi Henan Univ Technol Coll Civil Engn Zhengzhou 450001 Peoples R China Henan Key Lab Grain Storage Facil & Safety Zhengzhou 450001 Peoples R China

Grain porosity is a key parameter affecting the heat and moisture transfer in grain piles, and is related to the accuracy of heat and moisture transfer calculations and simulation studies in grain piles. To effectively measure the porosity of grains under different pressures, this study used a powerful machine learning algorithm to set six input parameters affecting porosity and experimentally acquired the data required for machine learning modeling. Eight machine learning-based models for grain porosity prediction were developed. Four meta-heuristic algorithms (i.e., Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Harris Hawks Optimization (HHO), and Multi-Verse Optimizer (MVO)) were combined with Support Vector Regression (SVR) to obtain four hybrid models (i.e., PSO-SVR, SCA-SVR, HHO-SVR, and MVO-SVR), respectively, in order to improve the accuracy and methodology of prediction. In addition, four other intelligent models, namely SVR, least-squares support vector regression, random forest, and partial least-squares regression, were constructed and compared with the four hybrid models. Finally, the accuracies of the eight models were evaluated and compared using several metrics: coefficient of determination (R2), variance accounted for (VAF), Nash-Sutcliffe efficiency (NSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and scatter index (SCI). The results indicate that the HHO-SVR model achieved the highest accuracy during both the training and testing phases, with R2 values of 0.97660 and 0.95613, respectively. The HHO-SVR model predicts grain porosity with reasonable accuracy and exhibits superior performance compared to other models.

关键词： Predictive models Machine learning Accuracy Moisture Temperature measurement Machine learning algorithms Support vector machines Media prediction algorithms Food security Grain porosity Harris hawks optimization machine learning support vector regression

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