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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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Forecasting of Monkeypox Cases Using Optimized SARIMAX Based Model

Forecasting of Monkeypox Cases Using Optimized SARIMAX Based...

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Electronic engineering (ICEEM), International Conference on

作者： Mahmoud Elshabrawy Marwa M. Eid Abdelaziz A. Abdelhamid El-Sayed M. El-Kenawy Abdelhameed Ibrahim Dept. of Communications & Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Dep. of Computer Science Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Computer Engineering and Control Systems Dept. Faculty of Engineering University Mansoura Egypt

This study presents a dipper-throated-based ant colony optimization (DTACO) with the Seasonal Auto-Regressive Integrated Moving Average with eXogenous factor (SARIMAX) model (DTACO+SARIMAX) to forecast monkeypox cases. The work optimizes the SARIMAX model using grid search cross-validation and fine-tunes its hyperparameters using DTACO to improve prediction accuracy. The suggested model's consistency and accuracy are considerable compared to previous studies. Comparisons with state-of-the-art models validate the proposed model's predictions. DTACO+SARIMAX can be used to control disease and monitor monkeypox. Healthcare organizations and governments can better manage and track the pandemic's course by offering accurate predictions, reducing public panic, and enabling effective pandemic planning. The Analysis of Variance (ANOVA) and Wilcoxon signed-rank tests are conducted on the proposed DTACO-SARIMAX model and compared models.

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An actor-critic algorithm with policy gradients to solve the job shop scheduling problem using deep double recurrent agents

arXiv

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arXiv 2021年

作者： Monaci, Marta Agasucci, Valerio Grani, Giorgio Sapienza University of Rome Dep. of Computer Science Control and Management Engineering Rome Italy Sapienza University of Rome Dep. of Statistics Rome Italy

In this work, we present a method that applies Deep Reinforcement Learning, an approximate dynamic programming procedure using deep neural networks, to the job shop scheduling problem (JSSP). The aim is to show that a greedy-like heuristic trained on a subset of problems, can effectively generalize to some extent to unseen instances, and be competitive compared to other methods. We model the JSSP as a Markov Decision Process and we exploit the efficacy of reinforcement learning to solve the problem. We adopt an actor-critic scheme based on policy gradients, specifically the Proximal Policy Gradient method, where the action taken by the agent is influenced by policy considerations on the state-value function. The procedures take into account the challenging nature of JSSP, where the state and the action space change for every instance and after each decision. To tackle this variability, we introduced a novel model based on two incident Long-Short Term Memory networks, followed by an encoding model, different in structure for both the actor and the critic. Experiments show the algorithm reaches good solutions in a short time, proving that is possible to generate new greedy heuristics just from learning-based methodologies. We compared our algorithms against several established heuristics, an adaptive method, a commercial solver based on branch and cut, and another approach based on Deep Reinforcement Learning, proving the validity of the proposed method in terms of time and makespan. The model can generalize, to some extent, to larger problems originating from a different *** Codes 90-08 © 2021, CC BY.

关键词： Reinforcement learning

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Assessment and recognition in technical massive open on-line courses with and without on-line laboratories

Assessment and recognition in technical massive open on-line...

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IEEE Education engineering (EDUCON)

作者： Sergio Martin Manuel Castro Elio Sancristobal Gabriel Diaz Felix Garcia-Loro Clara Perez Blanca Quintana Rosario Gil Pedro Plaza Africa Lopez German Carro Antonio Robles Llanos Tobarra Miguel Rodriguez-Artacho Jose A. Ruiperez-Valiente Electrical and Computer Engineering Department Universidad Nacional de Educación a Distancia Madrid Spain Dep. Sistemas de Comunicación y Control Universidad Nacional de Educación a Distancia Madrid Spain Dep. Lenguajes y Sistemas Informáticos Universidad Nacional de Educación a Distancia Madrid Spain Teaching Systems Lab Masschusetts Institute of Technology USA

This paper describes the organization and results of several MOOCs delivered about technical topics (i.e., digital electronics, VHDL design on FPGAs, open education and OERs repositories and the use of STEAM technologies to encourage diversity and inclusion), where online laboratories have been used in some of them. An analysis of the enrollment, students participating on tasks or quizzes, drop-out rate and certifications requests have been done.

关键词： VHDL Laboratories Organizations Task analysis engineering education Field programmable gate arrays Certification

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Neuromorphic Sensors with Visible Light Communications 4

Neuromorphic Sensors with Visible Light Communications

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4th West Asian Symposium on Optical and Millimeter-Wave Wireless Communications, WASOWC 2022

作者： Hulea, Mircea Uleru, George-Iulian Younus, Othman Isam Rajbhandari, Sujan Ghassemlooy, Zabih Fac. of Automatic Control and Computer Engineering Gheorghe Asachi Technical University of Iasi Iasi Iasi Romania Optical Communications Research Group Northumbria University Dep. of Physics and Electrical Eng. Newcastle upon Tyne United Kingdom Dsp Centre of Excellence Bangor University Bangor United Kingdom Optical Communications Research Group Northumbria University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9781665409131

Spiking neural networks (SNN) can control single-joint robotic arms' precise rotation and force when shape memory alloy (SMA) actuators are used. SNN receives feedback from neuromorphic sensors for controlling anthropomorphic fingers, which typically respond to the flexion angle and the force applied to the fingertips. The robotic fingers and hands are in relative motion with the robot's body, which typically includes the main unit for the limb's motion control. An elegant method to implement the connection between the main control unit and the neuromorphic sensors on limbs is to use visible light communication technology. This work presents a system that uses the recently introduced optical axons to connect the neuromorphic force sensors placed on fingers and the main electronic SNN that controls the finger's motion. The SNN behaviour is evaluated comparatively with and without optical axons when the hand moves relatively to the neural control unit (NCU), which regulates the finger's force. The expected results show that despite small oscillations of the finger force during steady-state, the robotic hand can hold on to an object while moving in the vicinity of NCU. © 2022 IEEE.

关键词： Robotic arms

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Decoupled feedforward-feedback periodic event-triggered control for disturbance rejection ⁎

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IFAC-PapersOnLine 2020年第2期53卷 2708-2713页

作者： Ernesto Aranda-Escolástico María Guinaldo José Luis Guzmén Sebastián Dormido Dep. of Software and Systems Engineering UNED Dep. of Computer Sciences and Automatic Control UNED Dep. of Informatics ceiA3 CIESOL University of Almería

In this paper, feedforward and feedback controllers are studied considering decoupled periodic event-triggering mechanisms for output and disturbance sensors. Stability and robustness conditions for linear systems are obtained considering transportation delays and actuator saturation following the Lyapunov-Krasovskii procedure. A numerical example shows that the proposed control strategy reduces the communication between sensors and controller significantly, while the system performance is not deteriorated.

关键词： Event-triggered control feedforward control Lyapunov stability time delay disturbance rejection saturation

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Learning Based Model Predictive control for Quadcopters with Dual Gaussian Process

arXiv

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arXiv 2021年

作者： Liu, Yuhan Tóth, Roland The Harbin Institute of Technology Department of Control Science and Engineering Harbin China The Eindhoven University of Tech. Dep. of Electrical Eng. Control Systems Group Eindhoven Netherlands The Institute for Computer Science and Control Budapest Hungary

An important issue in quadcopter control is that an accurate dynamic model of the system is nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian process (GP) based estimation is an effective tool to learn unknown dynamics from input/output data. However, conventional GP-based control methods often ignore the computational cost associated with accumulating data during the operation of the system and how to handle forgetting in continuous adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based model predictive control strategy that improves the performance of a quadcopter during trajectory tracking. The bio-inspired DGP structure is a combination of a long-term GP and a short-term GP, where the long-term GP is used to keep the learned knowledge in memory and the short-term GP is employed to rapidly compensate unknown dynamics during online operation. Furthermore, a novel recursive online update strategy for the short-term GP is proposed to successively improve the learnt model during online operation in an efficient way. Numerical simulations are used to demonstrate the effectiveness of the proposed strategy. © 2021, CC BY-NC-SA.

关键词： Model predictive control

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Toward HAZOP 4.0 Approach for Managing the Complexities of the Hazard and Operability of an Industrial Polymerization Reactor

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IFAC-PapersOnLine 2020年第2期53卷 13593-13600页

作者： R. Mokhtarname A.A. Safavi L. Urbas F. Salimi M.M. Zerafat N. Harasi Advanced Control Laboratory School of Electrical and Computer Engineering Shiraz University Shiraz Iran Mechanical Science and Eng. Dep. Technische Universität Dresden Dresden Germany ADEPP Academy France Nano-chemical Eng. Dep. Shiraz University Shiraz Iran

Conventional HAZOP is a collaborative and multidisciplinary activity to identify the hazards associated to operability of the chemical processes. Then the consequences and required safeguards of each potential deviation or failure are assessed qualitatively one by one. It is performed with the assumption of "one failure at the time" for the process parameters. Furthermore, process complexities such as multiple failures, domino effects, time and amplitude dep.ndent deviations etc. are avoided to keep the HAZOP brainstorming sessions systematic and effective. Therefore, the quality and outcomes of HAZOP study are relatively subjective and dep.nd on the experience and competency of the HAZOP team. This simplified approach cannot fully cover the hazard identification and risk assessment of the complex processes such as polymerization. Furthermore, the incident investigations show that almost all major accidents have occurred due to multiple failures or domino effects. This paper aims at developing a practical methodology in the context of "Industry 4.0" and particularly illustrate how dynamic simulation liberates the HAZOP team from the simplification assumptions such as one failure at the time or neglecting the domino effects during the lifecycle of the complex processes. An industrial styrene bulk free radical polymerization process has been chosen as the case study to dep.ct the applicability of the proposed method. In continuation of this research, the dynamic simulation integrated with Artificial Intelligence (AI) algorithms and Multivariable Process Monitoring (MPM) together with virtual collaboration tools will be invoked towards a more practical and effective HAZOP 4.0 platform. Such a platform can be the foundation of the further Process Safety Management (PSM) elements such as "Operating manuals", "Training and Competency management", "Condition monitoring and predictive maintenance", "Management of Change", "Pre-Start-up Safety Review", etc.

关键词： Dynamic HAZOP Dynamic Simulation Polymerization Plant Process Complexity HAZOP 4.0

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An analysis of lockergoga ransomware

An analysis of lockergoga ransomware

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2019 IEEE East-West Design and Test Symposium, EWDTS 2019

作者： Adamov, Alexander Carlsson, Anders Surmacz, Tomasz NioGuard Security Lab Kharkiv National University of Radio Electronics Kharkiv Ukraine Dep. of Computer Science Blekinge Institute of Technology Karlskrona Sweden Institute of Computer Engineering Control and Robotics Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9781728110035

This paper contains an analysis of the LockerGoga ransomware that was used in the range of targeted cyberattacks in the first half of 2019 against Norsk Hydra-A world top 5 aluminum manufacturer, as well as the US chemical enterprises Hexion, and Momentive-Those companies are only the tip of the iceberg that reported the attack to the public. The ransomware was executed by attackers from inside a corporate network to encrypt the data on enterprise servers and, thus, taking down the information control systems. The intruders asked for a ransom to release a master key and decryption tool that can be used to decrypt the affected files. The purpose of the analysis is to find out tactics and techniques used by the LockerGoga ransomware during the cryptolocker attack as well as an encryption model to answer the question if the encrypted files can be decrypted with or without paying a ransom. The scientific novelty of the paper lies in an analysis methodology that is based on various reverse engineering techniques such as multi-process debugging and using open source code of a cryptographic library to find out a ransomware encryption model. © 2019 IEEE.

关键词： Reverse engineering

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SSAM: A Haskell Parallel Programming STM Based Simple Actor Model

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Journal of Physics: Conference Series 2020年第1期1566卷

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

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