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2022 International Conference Laser Optics, ICLO 2022

作者： Oliveira, L.M. Goncalves, T.M. Botelho, A.R. Martins, I.S. Silva, H.F. Carneiro, I. Carvalho, S. Henrique, R. Tuchin, V.V. Porto Portugal Center of Innovation in Engineering and Industrial Technology Isep Porto Portugal Department of Electrical and Computer Engineering Porto University-School of Engineering Porto Portugal Department of Pathology and Cancer Biology and Epigenetics Group Portuguese Oncology Institute of Porto Porto Portugal Porto Portugal Viana do Castelo Portugal Dept. of Pathol. and Molecular Immunology Porto University-Institute of Biomedical Sciences Abel Salazar Porto Portugal Science Medical Center Saratov State University Saratov Russia Laboratory of Laser Molecular Imaging and Machine Learning National Research Tomsk State University Tomsk Russia Laboratory of Laser Diagnostics of Technical and Living Systems Institute of Precision Mechanics and Control Frc-Saratov Scientific Centre-of the Russian Academy of Sciences Saratov Russia

ISBN: (纸本)9781665466646

The direct calculation of the absorption coefficient spectra of various tissues from spectral measurements allowed to retrieve the contents of melanin and lipofuscin. In the rabbit brain cortex, 1.8 times higher melanin content is explained by the neuron degeneration process. Similar melanin and lipofuscin contents were found in the rabbit pancreas as a result of the tissue aging process. The conversion of 83 % of the melanin in the human normal kidney into lipofuscin in the cancer kidney indicates that lipofuscin can be considered a kidney cancer marker in humans. © 2022 IEEE.

关键词： Tissue

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Multi-level Coordinated Energy Management for Energy Hub in Hybrid Markets with Distributionally Robust Scheduling

arXiv

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arXiv 2022年

作者： Cao, Jiaxin Yang, Bo Zhu, Shanying Chung, Chi Yung Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai200240 China Department of Electrical and Computer Engineering University of Saskatchewan SaskatoonSKS7N 5A9 Canada

Maintaining energy balance and economical operation is significant for multi-energy systems such as the energy hub (EH). However, it is usually challenged by the frequently changing and unpredictable uncertain parameters at different timescales. Under this scope, this paper investigates the coordinated energy management problem for day-ahead and intra-day conditions considering uncertainties of source-load and market prices concurrently. Note that the precise knowledge of distributions about uncertainties may be unaccessible before the decision-making in day-ahead phase. A two-stage chance-constrained model based on distributionally robust approach with ambiguous moment information is proposed to immunize scheduling strategies against the worst-case probability distributions. The first stage is dedicated to obtaining more energy arbitrage and operation flexibility by optimizing bidding strategies in day-ahead power, natural gas and carbon trading markets. The second stage focuses on the optimization of the worst-case expected operation cost. It provides a robust energy equipment and load scheduling strategy for the reference of subsequent intra-day arrangements. With respect to different variations of electrical and thermal components, an intra-day two-timescale coordination is implemented step by step. The energy scheduling is re-dispatched circularly to minimize the operation and penalty costs. The possible energy imbalance is also compensated by this way. As the energy management program is nonlinear, chance-constrained and multi-stage, some linearization and dual transformation techniques are designed to enhance tractability of the program. Experimental results show that the developed multi-level framework results in a carbon emission decrease of 37%, and reduces energy cost averagely 3% compared with corresponding contrasting cases. The obtained strategy validates a good tradeoff between robustness and optimality. Copyright © 2022, The Authors. All rights reser

关键词： Power markets

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A Global Asymptotically Stable Nonlinear Robust Bias Observer for MEMS Gyros

TechRxiv

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TechRxiv 2022年

作者： Tang, Ning Chang, Jieling Wang, Lingling Fu, Li Neusypin, Konstantin A. Selezneva, Maria S. Peng, Linping The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The Faculty of Computer Science and Control Systems Bauman Moscow State Technical University Moscow105005 Russia Experimental Teaching Demonstration Center of Mechanical and Control Engineering Beihang University Beijing100191 China The School of Mathematics and System Sciences Beihang University Beijing100191 China

In practical applications, commercial-grade Micro-Electro-Mechanical System (MEMS) gyros tend to exhibit dynamics sensitivities which are resistant to modeling by linear drift. To address the problem, a new nonlinear robust bias observer (NRBO) is proposed in this paper. Unlike the existing attitude and gyro bias nonlinear observers, we propose a dynamics-sensitive gyro bias decomposition method to tackle unknown nonlinear dynamic models of MEMS gyro bias. We expose the potential advantages of the proposed method: the global asymptotic stability of the NBRO and the robustness to the bias instability of MEMS gyros can be guaranteed through a rational design of the attitude-angular rate nonlinear dynamic coupling (AARNDC) term and the attitude-linear coupling (ALC) term. In addition, the attitude estimation with bias correction is given in the framework of NBRO. The experiment of a cable-parallel robot demonstrate the robustness to the bias instability and the accuracy improvement of MEMS gyros. © 2022, CC BY.

关键词： Gyroscopes

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An Analysis of Haskell Parallel Programming Model in the HaLVM 4

An Analysis of Haskell Parallel Programming Model in the HaL...

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4th International Conference on Computing and Applied Informatics 2019, ICCAI 2019

作者： Cheon, Junseok Kim, Yeoneo Hur, Taekwang Byun, Sugwoo Woo, Gyun Dep. of Electrical and Computer Engineering Pusan National University Korea Republic of Dep. of Computer Science Kyungsung University Korea Republic of Smart Control Center of LG Electronics Korea Republic of

Recently, cloud computers are widely used due to the development of computing environment and network speed. Unikernel is considered to be an attractive operating system in the manycore environment that effectively provides cloud computers. HalVM is a unikernel coded in Haskell. In this paper, we analyze the performance of Haskell running in a unikernel, which has shown good parallel programming performance in a manycore environment. In this paper, we analyze the performance of Haskell running in a unikernel, while Haskell has shown good performance in a manycore environment. To do this, we select HaLVM as the target operating system and analyze the performance by testing various Haskell parallel programming models. Test result shows that HaLVM is scalable only on IVC. We also implement a library to make programming with IVC more convenient, and use this library to compare extensibility experiments. Our test shows HaLVM's scalability is 112.42% higher than Linux environment. © Published under licence by IOP Publishing Ltd.

关键词： computer operating systems

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SSAM: A Haskell Parallel Programming STM Based Simple Actor Model 4

SSAM: A Haskell Parallel Programming STM Based Simple Actor ...

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4th International Conference on Computing and Applied Informatics 2019, ICCAI 2019

作者： Kim, Y. Cheon, J. Hur, T. Byun, S. Woo, G. Dep. of Electrical and Computer Engineering Pusan National University Korea Republic of Dep. of Computer Science Kyungsung University Korea Republic of Smart Control Center of LG Electronics Korea Republic of

As computers with manycore architecture are being widely spread, parallel programming becomes a pending issue. While parallel programming has been a challenging issue, Haskell is known to be of the best available one. The immutability and declarative nature of Haskell suit well the need for parallelism. In our previous work, we implemented the SAM, a parallel programming model based on Haskell. In this paper, we propose the SSAM, an updated version of SAM, by solving the performance overhead of SAM socket. We discuss the architecture of SSAM and its major functions and show the 14% scalability upgrade in a performance test. © Published under licence by IOP Publishing Ltd.

关键词： Parallel programming

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Optimal sensors and actuators placement for large-scale switched systems

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International Journal of Dynamics and control 2019年第1期7卷 147-156页

作者： Seyed Sakha, Masoud Shaker, Hamid Reza Tahavori, Maryamsadat Department of Control Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Center for Energy Informatics University of Southern Denmark Odense Denmark

The problem of sensor and actuator placement is computationally expensive in particular in dealing with large-scale systems. This problem is even more computationally intensive in the case of switched systems. In this paper, we propose a new numerical approach for sensor and actuator placement for large-scale switched systems. We first introduce restricted genetic algorithm (RGA). RGA is an evolutionary algorithm which is developed specifically for sensors and actuator placement. We then use RGA to reduce the computational burden of the sensor and actuator placement for switched system. The proposed method uses the generalized gramians for switched systems which are called nice gramians. The nice gramians quantify the level of observability and reachability of switched systems. To the best of our knowledge, this paper presents the first results on sensor and actuator placement of switched systems. We show the effectiveness of the approach with the help of numerical examples. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Genetic algorithms

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Radiomics model based on computed tomography images for prediction of radiation-induced optic neuropathy following radiotherapy of brain and head and neck tumors

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Heliyon 2025年第1期11卷 e41409页

作者： Nafchi, Elham Raiesi Fadavi, Pedram Amiri, Sepideh Cheraghi, Susan Garousi, Maryam Nabavi, Mansoureh Daneshi, Iman Gomar, Marzieh Molaie, Malihe Nouraeinejad, Ali Department of Radiation Sciences Faculty of Allied Medicine Iran University of Medical Sciences Tehran Iran Department of Radiation Oncology School of Medicine Iran University of Medical Science Tehran Iran Department of Information Technology Faculty of Electrical and Computer Engineering University of Tehran Tehran Iran Radiation Biology Research Center Iran University of Medical Sciences Tehran Iran Radiation Oncology Research Center (RORC) Cancer Institute Tehran University of Medical Sciences Tehran Iran Department of Clinical Oncology Haft-e-Tir Hospital Iran University of Medical Science Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Optometry and Vision Science School of Rehabilitation Tehran University of Medical Science Tehran Iran

Purpose: We aimed to build a machine learning-based model to predict radiation-induced optic neuropathy in patients who had treated head and neck cancers with radiotherapy. Materials and methods: To measure radiation-induced optic neuropathy, the visual evoked potential values were obtained in both case and control groups and compared. Radiomics features were extracted from the area segmented which included the right and left optic nerves and chiasm. We integrated CT image features with dosimetric and clinical data subsequently, ranked 5 supervised ML models Bernoulli Naive Bayes, Decision Tree, Gradient Boosting Decision Trees, K-Nearest Neighbor, and Random Forest on 4 input datasets to predict radiation-induced visual complications classifiers by implementing 5-fold cross-validation. The F1 score, accuracy, sensitivity, specificity, and area under the ROC curve were compared to access prediction capability. Results: radiation-induced optic neuropathy affected 31 % of the patients. 856 radiomic characteristics were extracted and selected from each segmented area. Decision Tree and Random Forest with the highest AUC (97 % and 95 % respectively) among the five classifiers were the most powerful algorithms to predict radiation-induced optic neuropathy. Chiasm with higher sensitivity and precision was able to predict radiation-induced optic neuropathy better than right or left optic nerve or combination of all radiomic features. Conclusion: We found that combination of radiomic, dosimetric, and clinical factors can predict radiation-induced optic neuropathy after radiation treatment with high accuracy. To acquire more reliable results, it is recommended to conduct visual evoked potential tests before and after radiation therapy, with multiple follow-up courses and more patients. © 2024 The Authors

关键词： Computed tomography Machine learning Radiation-induced optic neuropathy Radiomics Visual evoked potential

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Navigation Learning Assessment Using EEG-based Multi-Time Scale Spatiotemporal Compound Model

TechRxiv

引用

TechRxiv 2023年

作者： Wang, Lingling Liu, Yixin Li, Yiqing Chen, Renxiang Liu, Xiaohong Fu, Li Wang, Yao Experimental Teaching Demonstration Center of Mechanical and Control Engineering Beihang University Beijing100191 China The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The School of Communication Science Beijing Language and Culture University Beijing100083 China Software Services Co. Ltd Beijing101100 China The School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China

This study presents a novel method to assess the learning effectiveness using Electroencephalography (EEG)-based Multi-Time Scale Spatiotemporal Compound Model (MTSC). Due to the evaluation of navigation learning, which is centered on the practice innovation ability development, it is difficult to assess the learning effectiveness in cultivating students’ ability objectively by questionnaire or other assessment methods. Research in the field of brain science has shown that innovation ability can be reflected from cognitive ability which can be embodied by EEG signal features. Therefore, a navigation learning assessment method was applied to detect the cognitive training traces by EEG-based MTSC. To verify the validity of the method, three navigation missions with increasing cognitive difficulty were designed, and 41 participants attended the experiment. The features of participants’ EEG signals were extracted and classified by MTSC. Comparing the results between experimental group and control group, it can be seen that MTSC can effectively distinguish the cognitive training traces of students in the navigation problem-solving process. In addition, MTSC achieved 89.2% accuracy and 0.883 F1-Score on features classification, which had an average improvement of 15.7% over other three traditional EEG classification models in accuracy. The results indicate that the navigation learning assessment method provides a useful evaluation approach for learning effectiveness using EEG-based MTSC. © 2023, CC BY.

关键词： Electroencephalography

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Discrete-time fractional-order dynamical networks minimum-energy state estimation

arXiv

引用

arXiv 2021年

作者： Chatterjee, Sarthak Alessandretti, Andrea Aguiar, A. Pedro Pequito, Sérgio The Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Magneti Marelli S.p.A. Italy The Department of Electrical and Computer Engineering Faculty of Engineering University of Porto Porto Portugal The Delft Center for Systems and Control Delft University of Technology Delft Netherlands

Fractional-order dynamical networks are increasingly being used to model and describe processes demonstrating long-term memory or complex interlaced dependencies amongst the spatial and temporal components of a wide variety of dynamical networks. Notable examples include networked control systems or neurophysiological networks which are created using electroencephalographic (EEG) or blood-oxygen-level-dependent (BOLD) data. As a result, the estimation of the states of fractional-order dynamical networks poses an important problem. To this effect, this paper addresses the problem of minimum-energy state estimation for discrete-time fractional-order dynamical networks (DT-FODN), where the state and output equations are affected by an additive noise that is considered to be deterministic, bounded, and unknown. Specifically, we derive the corresponding estimator and show that the resulting estimation error is exponentially input-to-state stable with respect to the disturbances and to a signal that is decreasing with the increase of the accuracy of the adopted approximation model. An illustrative example shows the effectiveness of the proposed method on real-world neurophysiological networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Neurophysiology

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Experimental Study on Neural Network-ARX and ARMAX Actuation Identification of a 3-DoF Delta Parallel Robot for Accurate Motion controller Design

Experimental Study on Neural Network-ARX and ARMAX Actuation...

引用

RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Saeed Rahimi Hasan Jalali Mohammad Reza Hairi Yazdi Ahmad Kalhor Mehdi Tale Masouleh Human and Robot Interaction Laboratory School of Mechanical Engineering University of Tehran Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Mechanical Engineering College of Engineering University of Tehran Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781665420952

This paper investigates the problem of actuator identification of a 3-DoF Delta parallel robot, by means of linear AutoRegressive Moving Average with eXogenous input (ARMAX) and nonlinear dynamic Neural Network AutoRegressive with eXogenous input (NN-ARX) methods. To this end, the ARMAX and NN-ARX approaches are used to develop a scheme which is capable of identifying a model for each actuator. Based on the ARMAX structure, an accurate model of the actuation system is derived. The model is then trained and tested using the data collected from a real robotic setup. Using a dynamic neural network capabilities, an identification and prediction scheme is designed for modeling the nonlinear dynamic behavior of the system. The NN-ARX is trained based on the collected data from the system, and the new trajectory data is used to validate both methods. By considering the results of experimental implementations, three servo motors are demonstrated to have different dynamical behavior which was expected to happen from the outset, due to uncertainty in fabrication of motors component and gearbox. In the identification and prediction stages, the Root Mean Square Errors (RMSE) index is used to validate and analyze the performance of each method using the validation data from new trajectories. In terms of predicting the output of the system, NN-ARX performed better than ARMAX with RMSE of 0.001441, compared to ARMAX with RMSE of 0.0886. Due to the high accuracy of the obtained models. thus they can be used in the design of motion controllers and modelling disturbances in the system.

关键词： Actuators Parallel robots Artificial neural networks Predictive models Mathematical models Trajectory Nonlinear dynamical systems

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