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2023 International Conference on Unmanned Aircraft Systems, ICUAS 2023

作者： Braathen De Carvalho, Kevin De Oliveira, Iure Rosa L. Brandao, Alexandre S. Universidade Federal de Viçosa Nucleus of Specialization in Robotics Department of Electrical Engineering Graduate Program in Computer Science MG Viçosa36570-900 Brazil Federal University of Viçosa Department of Electrical Engineering MG Viçosa Brazil

ISBN: (纸本)9798350310375

Unmanned Aerial Vehicles (UAVs) are widely used in various applications, from inspection and surveillance to transportation and delivery. Navigating UAVs in complex 3D environments is a challenging task that requires robust and efficient decision-making algorithms. This paper presents a novel approach to UAV navigation in 3D environments using a Curriculum-based Deep Reinforcement Learning (DRL) approach. The proposed method utilizes a deep neural network to model the UAV's decision-making process and to learn a mapping from the state space to the action space. The learning process is guided by a reinforcement signal that reflects the performance of the UAV in terms of reaching its target while avoiding obstacles and with energy efficiency. Simulation results show that the proposed method has a positive trade off when compared to the baseline algorithm. The proposed method was able to perform well in environments with a state space size of 22 millions, allowing the usage in big environments or in maps with high resolution. The results demonstrate the potential of DRL for enabling UAVs to operate effectively in complex environments. © 2023 IEEE.

关键词： Reinforcement learning

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Finding optimal random boolean networks for reservoir computing

Finding optimal random boolean networks for reservoir comput...

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13th International Conference on the Simulation and Synthesis of Living Systems: Artificial Life 13, ALIFE 2012

作者： Snyder, David Goudarzi, Alireza Teuscher, Christof Department of Computer Science Portland State University Portland OR 97201 United States Systems Science Graduate Program Portland State University Portland OR 97201 United States Department of Electrical and Computer Engineering Portland State University Portland OR 97201 United States

ISBN: (纸本)9780262310505

Reservoir Computing (RC) is a computational model in which a trained readout layer interprets the dynamics of a component called a reservoir that is excited by external input stimuli. The reservoir is often constructed using homogeneous neural networks in which a neuron's in-degree distributions as well as its functions are uniform. RC lends itself to computing with physical and biological systems. However, most such systems are not homogeneous. In this paper, we use Random Boolean Networks (RBN) to build the reservoir. We explore the computational capabilities of such a RC device using the temporal parity task and the temporal density classification. We study the sufficient dynamics of RBNs using kernel quality and generalization rank measures. We verify findings by Lizier et al. (2008) that the critical connectivity of RBNs optimizes the balance between the high memory capacity of RBNs with (K) 2. We show that in a RBN-based RC system, the optimal connectivity for the parity task, a processing intensive task, and the density classification task, a memory intensive task, agree with Lizier et al.'s theoretical results. Our findings may contribute to the development of optimal selfassembled nanoelectronic computer architectures and biologically-inspired computing paradigms. © 2012 Massachusetts Institute of Technology.

关键词： computer architecture

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Improving Candle Direction Classification in Forex Market Using Support Vector Machine with Hyperparameters Tuning 7

Improving Candle Direction Classification in Forex Market Us...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Oetama, Raymond Sunardi Heryadi, Yaya Lukas Suparta, Wayan Computer Science Bina Nusantara University Jakarta Indonesia Binus Graduate Program Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Cognitive Engineering Research Group Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Institut Teknologi Nasional Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350345711

Foreign Exchange market is the world's largest daily currency turnover. Two of the popular currencies Euro and Pound sterling traded against the US Dollar. Since the Russia and Ukraine war started in February 2022, their exchange rates decrease to the lowest rate ever. Even though the general trend is bearish, several daily candles increase for some days making challenges for forex analysts. To solve this problem, classification is applied. The data is labeled downward and upward. By utilizing Linear Kernel and Radial Basis Function (RBF) Kernel-based Support Vector Machines (SVM), the candle direction can be classified and optimized by tuning the Hyperparameters. The accuracy of candle direction classifications are highly improved. After tuning, in general, classification using Linear Models can outperform RBF Models. The best accuracy found on the Pound sterling against US Dollar by using the Linear model is 98.11% and the accuracy becomes 100% on data testing at a ratio of 70:30. Whilst for the Euro against the US Dollar, the best accuracy found the same for both Linear and RBF models on a ratio of 80:20 at 97.53%. However, on data testing, it decreases to 94.51% for Linear Model and 93.41% using RBF Model. The implication of this study is SVM can successfully classify candle direction on pairs in the Forex Market that are affected by a big event that comes for such a long period as long as the hyperparameter is tuned. © 2022 IEEE.

关键词： Support vector machines

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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space 8

Multivariate Time-Series Deep Learning for Joint Prediction ...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Gunawan, Fergianto E. Budiman, Arief S. Pardamean, Bens Juana, Endang Romeli, Sugiarto Cenggoro, Tjeng W. Purwandari, Kartika Hidayat, Alam A. Redi, Anak A.N.P. Asrol, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia PT Impack Pratama Industri Jakarta14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99. © 2023 The Authors. Published by Elsevier B.V.

关键词： closed space deep learning humity prediction indoor temperature prediction

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DEEP LEARNING-BASED CHANNEL ESTIMATION FOR RIS-ASSISTED UAV-ENABLED WIRELESS COMMUNICATIONS SYSTEM

DEEP LEARNING-BASED CHANNEL ESTIMATION FOR RIS-ASSISTED UAV-...

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2023 International Conference on Innovation, Communication and engineering, ICICE 2023

作者： Tesfaw, Belayneh A. Lin, Hsin-Piao Tarekegn, Getaneh B. Juang, Rong-Terng Jeng, Shiann-Shiun Department of International Graduate Program in Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Institute of Aerospace and System Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Electrical and Computer Engineering National Yang-Ming Chiao Tung University Hsinchu30010 Taiwan Department of Electrical Engineering National Dong Hwa University Hualien Taiwan

ISBN: (纸本)9781839539954

Wireless communication via unmanned aerial vehicles (UAVs) has drawn a great deal of attention due to its flexibility in establishing line-of-sight (LoS) communications. However, in complex urban and dynamic environments, the movement of UAVs can be blocked by trees and high-rise buildings that obstruct directional paths. This problem can be effectively addressed by employing reconfigurable intelligent surfaces (RIS). To achieve this goal, accurate channel estimation in RIS-assisted UAV-enabled wireless communications is crucial. This paper proposes an accurate channel estimation model using long short-term memory (LSTM) for a multi-user RIS-assisted UAV-enabled wireless communications system. Simulation results showed that LSTM can effectively improve the channel estimation performance of RIS-assisted UAV-enabled wireless communications. © The Institution of engineering & Technology 2023.

关键词： Channel estimation

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System identification of magneto-rheological damper using group method of data handling (GMDH)

System identification of magneto-rheological damper using gr...

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2009 6th International Symposium on Mechatronics and its Applications, ISMA 2009

作者： Assaleh, Khaled Shanableh, Tamer Kheil, Yasmeen Abu Department of Electrical Engineering American University of Sharjah United Arab Emirates Department of Computer Science and Engineering American University of Sharjah United Arab Emirates Mechatronics Graduate Program American University of Sharjah United Arab Emirates

ISBN: (纸本)9781424434817

In this paper Group Method of Data Handling (GMDH) technique is implemented and applied to the system identification problem of a Magneto-Rheological (MR) damper. GMDH networks are used to approximate the forward and inverse dynamic behaviors of a MR damper. The difficulty of this identification problem stems from the inherent hysteretic and highly nonlinear dynamics of MR dampers. The GMDH-based solution exhibits comparable identification accuracy in both the forward and inverse models as compared to state-of-the-art more computationally extensive solutions. © 2009 IEEE.

关键词： Inverse problems

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Recurrence plots and convolutional neural networks applied to nonintrusive load monitoring

Recurrence plots and convolutional neural networks applied t...

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2020 IEEE Power and Energy Society General Meeting, PESGM 2020

作者： Cavalca, Diego L. Fernandes, Ricardo A. S. Federal University of São Carlos Graduate Program in Computer Science São Carlos Brazil Federal University of São Carlos Electrical Engineering Department São Carlos Brazil

ISBN: (纸本)9781728155081

The need to increasingly promote the efficient use of electricity for residential consumers motivated some researches to obtain solutions that allow the identification of loads for use in demand response programs and in the energy consumption disaggregation. Recently, the use of Convolutional Neural Networks for load identification has been common. However, it is necessary to transform the temporal signal into an image. In this sense, the present paper proposes a novel feature engineering stage, based on Recurrence Plots, to obtain the images for Nonintrusive Load Monitoring. In order to prove its effectiveness, the Recurrence Plots were compared to the Gramian Angular Difference Fields, which had already been applied in this context. For this purpose, it was used the REDD dataset, in which only the low frequency data measured in the main distribution panel were considered. Individual load measurements were only used for the labelling of the 90-seconds windows. The Convolutional Neural Network was trained and validated, considering two different strategies (multi-label and binary models), obtaining an F1-score of 66.05% and 72.65%, respectively. © 2020 IEEE.

关键词： Machine learning

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Fatigue and non-fatigue mathematical muscle models during functional electrical stimulation of paralyzed muscle

Fatigue and non-fatigue mathematical muscle models during fu...

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作者： Cai, Zhijun Bai, Er-Wei Shield, Richard K. Electrical and Computer Engineering University of Iowa Iowa City IA 52242 United States Graduate Program in Physical Therapy and Rehabilitation Science University of Iowa Iowa City IA 52242 United States

ISBN: (纸本)9783902661494

electrical muscle stimulation demonstrates potential for restoring functional movement and for preventing muscle atrophy after spinal cord injury (SCI). To optimize delivery of electrical stimulation protocols, an accurate and easy-to-implement mathematical skeletal muscle model is essential. The existing models are either accurate but complex, making them hard to implement, such as the Hill-Huxley-type model, or simple but inaccurate, such as second order model. In this paper, we propose a Wiener-Hammerstein system to model the paralyzed skeletal muscle dynamics under electrical stimulus conditions. The proposed model has substantial advantages in identification algorithm analysis and implementation including computational complexity and convergence. Experimental data sets were collected on soleus muscles under 15 Hz frequency stimulation for fourteen subjects with SCI. The simulation results show that the proposed model outperforms the Hill-Huxley-type model not only in peak force prediction, but also in fitting performance for force output of each individual stimulation train. © 2009 IFAC.

关键词： Muscle

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A Computational Primitive for Convolution based on Coupled Oscillator Arrays

A Computational Primitive for Convolution based on Coupled O...

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IEEE computer Society Annual Symposium on VLSI

作者： Donald M. Chiarulli Brandon Jennings Yan Fang Andrew Seel Steven P. Levitan Department of Computer Science Computer Engineering Graduate Program Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh PA

In this paper we present a new computational primitive for convolution using coupled oscillator arrays. It is based on a Degree of Match (DOM) operation for pairs of vectors of analog voltage encoded data. The convolution operation is synthesized from these DOM circuits and computes the precise mathematical convolution of the two vectors. We present an example circuit design for the DOM operator and SPICE simulations of the circuit behavior. A MATLAB model is curve fit to an inverted copy of this output and shown to be roughly equivalent to the square of the Euclidian distance between the vectors. Using a parameterized version of this model we conducted a MATLAB study to analyze the accuracy of the DOM and convolution operations under variations in oscillator array symmetry, locking range, and additive noise. We also show a small example of convolution with an edge enhancement filter.

关键词： Very large scale integration

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A Game Comparative Study:Object-Oriented Paradigm and Notification-Oriented Paradigm

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Journal of Software engineering and Applications 2012年第9期5卷 722-736页

作者： Jean M.Simao Danillo L.Belmonte Glauber Z.Valenca Márcio V.Batista Robson R.Linhares Roni F.Banaszewski Joao A.Fabro Cesar A.Tacla Paulo C.Stadzisz Adriano F.Ronszcka Graduate School in Electrical Engineering&Industrial Computer Science(CPGEI) Federal University of Technology-ParanáCu-ritibaBrazil Graduate School of Applied Computing(PPGCA) Federal University of Technology-ParanáCuritibaBrazil

This paper presents a new programming paradigm named Notification-Oriented Paradigm (NOP) and analyses the performance aspects of NOP programs by means of an experiment. NOP provides a new manner to conceive, structure, and execute software, which would allow better performance, causal-knowledge organization, and decoupling than standard solutions based upon usual paradigms. These paradigms are essentially Imperative Paradigm (IP) and Declarative Paradigm (DP). In short, DP solutions are considered easier to use than IP solutions due to the concept of high-level programming. However, they are considered slower in execution and less flexible in development. Anyway, both paradigms present similar drawbacks such as redundant causal-evaluation and strongly coupled entities, which decrease the software performance and the processing distribution feasibility. These problems exist due to an orientation to a monolithic inference mechanism based upon sequential evaluation by searching on passive computational entities. NOP proposes another way to structure software and make its inferences, which is based upon small, collaborative, and decoupled computational entities whose interaction happens through precise notifications. In this context, this paper presents a quantitative comparison between two equivalent implementations of a computer game simulator (Pacman simulator), one developed according to the principles of Object-Oriented Paradigm (OOP/IP) in C++ and other developed according to the principles of NOP. The results obtained from the experiments demonstrate, however, a quite lower performance of NOP implementation. This happened because NOP applications are still developed using a framework based on C++. Besides, the paper shows that optimizations in the NOP framework improve NOP program performance, thereby evidencing the necessity of developing a NOP language/compiler.

关键词： Notification Oriented Paradigm Notification Oriented Inference NOP and OOP Comparison

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