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2024 IEEE URUCON, URUCON 2024

作者： Dahri, Fida Hussain Laghari, Asif Ali Dahri, Nisar Ahmed Abro, Ghulam E Mustafa Estrela, Vania V. De Oliveira, G.G. Iano, Y. Chuma, Euclides L. Southeast University School of Computer Science and Engineering Nanjing China Shenyang Normal University Software College Shenyang China University Teknologi Malaysia Faculty of Educational Sciences and Technology Johor Bahru Malaysia King Fahd University of Petroleum and Minerals Interdisciplinary Research Centre for Aviation and Space Exploration Dhahran Saudi Arabia Department of Telecommunications Niteroi Brazil School of Electrical and Computer Engineering Campinas Brazil School of Technology Limeira Brazil

ISBN: (纸本)9798350355383

Unmanned aerial vehicles (UAVs) have boosted modern living. Tiny, frail high-density targets, low resolution, complicated backgrounds, noise, and poor real-time exposure performance have augmented due to UAV firms. Realtime recognition in high-altitude (HA) infrared thermal images is intricate. Our fresh OSTD-YOLOv8 is a multi-class target recognition tactic to tackle these issues and spot and classify objects on the HIT-UAV dataset. YOLOv8 is an effective advanced object detection model. Our OSTD-YOLOv8 model detects small objects in HA thermal images with 90% precision, 91.5% recall, 90.5% F1-score, 89.1% AP, and 98% mAP. Our unique approach delivers effective object recognition in difficult thermal imaging conditions. Consequently, this distinctive UAV-focused HA, thermal, target detection scheme can be used. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Disturbance rejection and performance enhancement of perturbed tri-stable energy harvesters by adaptive finite-time disturbance observer

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Acta Mechanica Sinica 2022年第8期38卷 132-148页

作者： Shitong Fang Naser Padar Mohammad Javad Mirzaei Shengxi Zhou Wei-Hsin Liao Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong Kong999077China Department of Electrical Engineering Amirkabir University of TechnologyTehran15875-4413Iran Faculty of Electrical and Computer Engineering University of TabrizTabriz516661-6471Iran Research&Development Institute in Shenzhen Northwestern Polytechnical UniversityShenzhen518057China

Tristable energy harvesters(TEHs)have been proposed to achieve broad frequency bandwidth and superior low-frequency energy harvesting ***,due to the coexistence of three potential wells and the sensitivity to system conditions and external disturbances,the desired high-amplitude inter-well oscillation in the TEHs may be replaced by the chaotic or intra-well oscillations with inferior energy ***,the chaos has an unpredictable trajectory and may cause system damages,lessen the structural durability as well as require a more complicated circuit for power ***,in this paper,we firstly propose an adaptive finite-time disturbance observer(AFTDO)for performance enhancement of TEHs by detecting the external disturbances that induce the chaos,and reject them for the recovery of the desired inter-well *** proposed AFTDO eliminates the need to know in advance the upper bounds of imposed perturbations in conventional observers by means of the proposed adaptive protocols,leading to the higher efficacy of *** mathematical model of the piezoelectric TEH system and the AFTDO is *** demonstrate the effectiveness of the AFTDO,a series of numerical simulations have been *** show that for both cases with sinusoidal and impulsive disturbances,the AFTDO can successfully track the trajectories of the disturbance signals with the adaptive gain,and reject the disturbance to enable the TEH to sustain the periodic inter-well oscillation with effective energy harvesting performance.

关键词： Tristable energy harvesters Finite-time disturbance observer Sinusoidal and impulsive disturbances Chaos Inter-well oscillation

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Sentiment analysis of Indonesian police chief using multi-level ensemble model

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Procedia computer Science 2023年 216卷 620-629页

作者： Wahyu Fadli Satrya Ria Aprilliyani Emny Harna Yossy Computer Science Department BINUS Online Learning Bina Nusantara University West Jakarta Jakarta and 11480 Indonesia Engineering Faculty Electrical Engineering Universitas Pertahanan Sentul Bogor and 16810 Indonesia

Sentiment analysis is a technique of analyzing text to classify its emotion into positive, negative, or neutral sentiments. The main purpose of this study is to use sentiment analysis to seek Indonesian opinions about the chief of the Indonesian National Police. The dataset obtained from Twitter contains opinions toward the police chief from 20th August until 30th August 2022. Sentiment analysis can be done by using machine learning. Unfortunately, each machine learning algorithm performs differently depending on the data. This performance led to the issue of confusion in picking the best machine learning model to perform sentiment analysis, especially for unseen data. Therefore, ensemble learning is needed to combine multiple machine learning models. Bagging-based and Boosting-based ensemble learning have different algorithms. In order to get a high-performance model, Bagging-based and Boosting-based ensemble models can be combined as one model. This work proposed a second-level ensemble model called a multi-level ensemble model for sentiment analysis. It combines Multinomial Naive Bayes, Boosting-based ensemble learning, and Bagging-based ensemble learning. The data is processed using CountVectorizer to convert text into numbers and oversampling to handle imbalanced data. The experimental results show that our proposed model has the best results in terms of accuracy, precision, and F1-score for testing set compared to individual models.

关键词： sentiment analysis multi-level ensemble oversampling

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USK-FEMO: A Face Emotion Dataset using Deep Learning for Effective Learning

USK-FEMO: A Face Emotion Dataset using Deep Learning for Eff...

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computer System, Information Technology, and electrical engineering (COSITE), International Conference on

作者： Muhajir Muhajir Maulisa Oktiana Kahlil Muchtar Maya Fitria Akhyar Akhyar Muhammad Dandy Pratama Chih-Yang Lin Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Electrical Electronic and Systems Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Selangor Malaysia Department of Mechanical Engineering National Central University Taoyuan City Taiwan

Emotions play an essential role in the learning process and have an impact on how the learning process is eventually carried out. Facial expressions can be used to visually identify a person’s emotions. Along with the advancement of computer vision and deep learning techniques, the study of human-computer interaction is increasingly focusing on the recognition of facial expressions. One of the main issues is the availability of sufficient datasets, especially for students. This study examined the deep learning architecture for face emotion classification. In addition, this research also introduces a new emotional dataset acquired from the junior high school student at SMP Negeri 1 Darul Imarah, Aceh Besar Regency, Indonesia. This dataset contains five classes that include the emotions of happiness, sadness, anger, surprise, and boredom. The dataset was then tested using the Mobile-Net architecture, the highest accuracy was achieved with a learning rate of 0.0001% of 88.492%. The dataset can be explored via the link https://***/USK-FEMO-DATASET/

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A Study on 1D-Point Cloud Using Through-Wall mm Wave Radar for Human Pose Estimation

A Study on 1D-Point Cloud Using Through-Wall mm Wave Radar f...

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International electrical engineering Congress (iEECON)

作者： Kritthee Phaisiri Chainarong Kittiyanpunya Nattakarn Shutimarrungson Akkarat Boonpoonga Prayoot Akkaraekthalin Kamol Kaemarungsi Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Mechatronics Engineering Faculty of Engineering Rajamangala University of Technology Rattanakosin Nakhon Pathom Thailand Department of Business Computer Mahasarakham Business School Mahasarakham University Maha Sarakham Thailand Department of Electrical and Computer Engineering Research Center of Innovation Digital and Electromagnetic Technology (iDEMT) Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathum Thani Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

This paper presents human pose estimation using millimeter-wave (mmWave) radar technology capable of penetrating obstacles. Operating within the frequency modulated continuous wave (FMCW) at 60 - 64 GHz, our radar system generates signals conducive to accurate human pose representation. Our data processing framework focuses on producing point cloud data, effectively capturing each human movement. Results are visualized through a 2D graph, with the z-axis representing time. Experimental data, collected from various activities conducted behind different walls, underwent thorough analysis, comparing point cloud positions with actual human postures. The outcomes affirm the effectiveness of mmWave radar systems, positioned behind obstacles, in accurately extracting human postures.

关键词： Point cloud compression electrical engineering Frequency modulation Pose estimation Data visualization Millimeter wave radar Data processing

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Day-Ahead Electricity Price Forecasting Using a Hybrid Deep Learning Model

SSRN

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SSRN 2023年

作者： Mubarak, Hamza Merza, Ahmed Mohammed Ahmad, Shameem Islam, Mohammad Zohurul Muyeen, S.M. Mannan, Mohammad Abdul Abdellatif, Abdallah Rasool, Zaid Ibrahim University of Malaya Malaysia Department of Biomedical engineering college of engineering University of Warith Al-Anbiyaa Karbala Iraq Department of Electrical and Electronics Engineering Faculty of Engineering American International University–Bangladesh Dhaka Bangladesh Department of Electrical Engineering Qatar University Doha2713 Qatar Department of Electrical Engineering Faculty of Engineering Universiti Malaya Kuala Lumpur50603 Malaysia Computer Techniques Engineering Department Al-Mustaqbal University college Hillah51001 Iraq

The electricity market's dynamics are significantly impacted by fluctuating prices, with participants trading based on these values. Accurate electricity price forecasting (EPF) enables energy suppliers to refine their bidding strategies, minimize transaction risks, and seize opportunities in the market, ultimately reflecting the true value of energy in transactions. Hence, this work proposes a deep learning model for electricity price forecasting one day ahead. The proposed model is based on a combination of Convolutional Neural Networks (CNN) and bi-directional long short-term memory networks (BiLSTM) in parallel with autoregressive (AR), which can be abbreviated as CNN-BiLSTM-AR. Integrating an AR model in parallel with a CNN-BiLSTM enhances the overall performance by capitalizing on the AR's proficiency in capturing short-term linear dependencies. At the same time, the CNN-BiLSTM effectively learns spatial and long-range temporal features. Further, the impact of integrating the hyperparameter optimization (HPO) methods, such as particle swarm optimization (PSO), genetic algorithm (GA), and random search (RS) are also investigated. In addition, two different European datasets, including the UK and German electricity markets, are employed to validate the proposed model and evaluate the model performance using performance metrics, such as root mean square error (RMSE) and mean absolute error (MAE). The results revealed that implementing the HPO methods with the proposed model enhances the model forecasting performance, where the PSO-CNN-BiLSTM-AR have attained the highest reduction in RMSE and MAE values by 16.7% and 23.46% for the German electricity market, respectively. © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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Identifiability and Solvability in Inverse Linear Quadratic Optimal control Problems

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Journal of Systems Science & Complexity 2021年第5期34卷 1840-1857页

作者： LI Yibei WAHLBERG Bo HU Xiaoming Optimization and Systems Theory Department of MathematicsKTH Royal Institute of TechnologySE-10044StockholmSweden Division of Decision and Control School of Electrical Engineering and Computer ScienceKTH Royal Institute of TechnologySE-10044StockholmSweden

In this paper, the inverse linear quadratic(LQ) problem over finite time-horizon is *** the output observations of a dynamic process, the goal is to recover the corresponding LQ cost function. Firstly, by considering the inverse problem as an identification problem, its model structure is shown to be strictly globally identifiable under the assumption of system invertibility. Next, in the noiseless case a necessary and sufficient condition is proposed for the solvability of a positive semidefinite weighting matrix and its unique solution is obtained with two proposed algorithms under the condition of persistent excitation. Furthermore, a residual optimization problem is also formulated to solve a best-fit approximate cost function from sub-optimal observations. Finally, numerical simulations are used to demonstrate the effectiveness of the proposed methods.

关键词： Inverse optimal control linear quadratic regulators model identifiability

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Low-Complexity control for a Class of Uncertain MIMO Nonlinear Systems under Generalized Time-Varying Output Constraints

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IEEE Transactions on Automatic control 2024年

作者： Mehdifar, Farhad Lindemann, Lars Bechlioulis, Charalampos P. Dimarogonas, Dimos V. The Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Computer Science University of Southern California Los AngelesCA United States The Department of Electrical and Computer Engineering University of Patras Patra Greece

This paper introduces a novel control framework to address the satisfaction of multiple time-varying output constraints in uncertain high-order MIMO nonlinear control systems. Unlike existing methods, which often assume that the constraints are always decoupled and feasible, our approach can handle coupled time-varying constraints even in the presence of potential infeasibilities. First, it is shown that satisfying multiple constraints essentially boils down to ensuring the positivity of a scalar variable, representing the signed distance from the boundary of the time-varying output-constrained set. To achieve this, a single consolidating constraint is designed that, when satisfied, guarantees convergence to and invariance of the time-varying output-constrained set within a user-defined finite time. Next, a novel robust and low-complexity feedback controller is proposed to ensure the satisfaction of the consolidating constraint. Additionally, we provide a mechanism for online modification of the consolidating constraint to find a least violating solution when the constraints become mutually infeasible for some time. Finally, a simulation study validates the proposed approach. ©1963-2012 IEEE.

关键词： MIMO systems

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Data Driven Level Set Fuzzy Modeling for Cryptocurrencies Price Forecasting 14th

Data Driven Level Set Fuzzy Modeling for Cryptocurrencies Pr...

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14th International Joint Conference on Computational Intelligence, IJCCI 2022

作者： Maciel, Leandro Ballini, Rosangela Gomide, Fernando Department of Business Administration Faculty of Economics Business and Accounting University of Sao Paulo Sao Paulo Brazil Department of Economic Theory Institute of Economics University of Campinas Sao Paulo Brazil Department of Computer Engineering and Automation School of Electrical and Computer Engineering University of Campinas Sao Paulo Brazil

ISBN: (纸本)9789897586118

The paper develops a data-driven fuzzy modeling procedure based on level set to forecast cryptocurrencies prices. Data-driven level set is a novel fuzzy modeling method that differs from linguistic and functional fuzzy models in how the fuzzy rules are built and processed. The level set-based model outputs the weighted average of output functions associated with the fuzzy rules. Output functions map the activation levels of the fuzzy rules directly in the model outputs. Computational experiments are done to evaluate the level set method to forecast the closing prices of Bitcoin, Ethereum, Litecoin and Ripple. Comparisons are made with ARIMA, ETS, MLP and naïve random walk. The results suggest that the random walk outperforms most methods addressed in this paper, but it is surpassed by the level set model for Ethereum. When performance is measured by the direction of price change, the level set-based fuzzy modeling performs best amongst the remaining methods. Copyright © 2022 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Forecasting

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A Monitoring Strategy of Multi-Robot Systems in Dynamic Environments: Coverage control and Density Estimation

A Monitoring Strategy of Multi-Robot Systems in Dynamic Envi...

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

作者： Samane Kaviri Ahmadreza Tahsiri Hamid D. Taghirad Department od Systems and Control Faculty of Electrical Engineering K.N. Toosi University of Technology Tehran Iran

A critical task in environmental monitoring is the reconstruction of the density function of an event over the mission region. In such tasks, Unmanned Aerial Vehicles (UAVs) are employed as spatially distributed mobile sensing agents. In this paper, we consider the cooperative coverage problem for a group of UAVs in the mission region with a time-varying density function. The density function is modeled by a Gaussian Mixture Model (GMM) with unknown parameters. By resorting to a consensus-based Expectation-Maximization (EM) approach, the GMM parameters are estimated based on the local observations of UAVs. Finally, the proposed strategy is applied to an offshore oil spill monitoring mission. The simulation results verify the theoretical results and demonstrate the competence of the proposed strategy for such applications.

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