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Two-Operation Buffering based on Dynamic Optical RAM 17

Two-Operation Buffering based on Dynamic Optical RAM

17th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer engineering, TCSET 2024

作者： Semenov, Andriy Tsyrulnyk, Serhii Baraban, Serhii Semenova, Olena Rudyk, Andrii Khloba, Anton Vinnytsia National Technical University Faculty of Information Electronic Systems Vinnytsia Ukraine Vinnytsia National Agrarian University Department of Computer Sciences and Digital Economy Vinnytsia Ukraine Poznan University of Technology Faculty of Computing and Telecommunications Poznan Poland National University of Water and Environmental Engineering Department of Automation Electrical Engineering and Computer-Integrated Technologies Rivne Ukraine

ISBN: (纸本)9798331520564

The authors of this paper propose a block diagram for a dual-operating buffer on a dynamic optical RAM. The buffer consists of two dynamic optical RAMs, each controlled separately to allow for simultaneous writing and reading of data, thereby improving performance. This setup enables data to be read from the buffer while new data is being written to it. Implementing a dual-operating buffer on a dynamic optical RAM has the potential to enhance the performance of high-speed data transmission in telecommunications networks. The authors conducted numerical calculations to compare failure probabilities between single-operation and dual-operation buffers on dynamic optical RAM. The results indicated that using a dual-operating buffer significantly reduces the probability of failure with the same buffer memory capacity compared to a single-operating buffer. Additionally, the authors analyzed the effectiveness of the proposed dual-operating buffer. The analysis confirmed that exploiting dynamic optical memory for buffering enhances data transfer speed and efficiency due to the inherent properties of optical data transfer, such as high bandwidth and low latency. © 2024 IEEE.

关键词： Dynamic random access storage

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Improved Q-Learning Algorithm for Path Planning of an Automated Guided Vehicle (AGV) 5

Improved Q-Learning Algorithm for Path Planning of an Automa...

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5th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2023

作者： Guo, Hao Tan, Min Keng Lim, Kit Guan Ee Chuo, Helen Sin Yang, Baojian Kin Teo, Kenneth Tze Universiti Malaysia Sabah Modelling Simulation & Computing Laboratory Faculty of Engineering Kota Kinabalu Malaysia Baotou Light Industry Vocational and Technical College Department of Automation Baotou City China Wuyi University Department of Intelligent Manufacturing Jiangmen China

ISBN: (纸本)9798350304152

With the development of modern industry 4.0, intelligent path planning is an essential research direction of schedule systems for an automated guided vehicle (AGV), which has been widely used in logistics distribution centers of enterprises. This work implements the improved Q-learning algorithm to solve the typical obstacle avoidance problems in path planning. Specifically, the conventional Q-learning algorithm has shortcomings including low operational efficiency and slow learning speed. The improved Q-learning algorithm is successively proposed by adding a learning process based on the original Q-learning algorithm, which enables AGV to find obstacles and target locations within the shortest time, thus improving the efficiency of path planning. Finally, the simulation experiments are carried out in the grid environment with MATLAB. In comparison to the conventional Q-learning algorithm, the improved Q-learning algorithm has faster convergence and higher learning efficiency, improved by 20%. © 2023 IEEE.

关键词： Motion planning

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Novel Approach for Passive Mixing in Microfluidics Utilizing Porous PDMS Sponge 11

Novel Approach for Passive Mixing in Microfluidics Utilizing...

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11th International Scientific Conference on Computer Science, COMSCI 2023

作者： Grigorov, Emil Kirov, Boris Denev, Jordan A. Galabov, Vassil Technical University of Sofia German Engineering Education and Industrial Management Sofia Bulgaria Technical University of Sofia Department Industrial Automation Sofia1756 Bulgaria Karlsruhe Institute of Technology Steinbuch Centre for Computing Karlsruhe Germany

ISBN: (纸本)9798350325256

Mixing in microfluidic devices has been always a significant challenge due to the small channel dimensions and the strongly laminar flow character of the fluids. However, mixing is an essential process in various biological, chemical and pharma processes. This paper introduces a new approach to passive mixing in microfluidic devices, utilizing a quick and cheap way with a sugar cube as prototype to produce a) porous medium. The authors present experimental and numerical analyses of the method, showing that the efficiency of mixing decreases as the volume flow rates of the fluids increase for flows studied with Re in the range 0,028-0,26. The largest porous mediumtested achieved a maximum mixing efficiency of 98%, suggesting that larger geometries may yield even higher efficiencies. A decrease in mixing efficiency along the length of the sponge with increasing volume flow rates was also observed (experimentally and numerically), possibly due to increased backflow. Despite the complexity of the Polydimethylsiloxan (PDMS) sponge, numerical analysis indicates a linear relationship between pressure drop and Reynolds number. © 2023 IEEE.

关键词： Microfluidics

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Next-Gen IoT: 5G Realities and 6G Possibilities 15

Next-Gen IoT: 5G Realities and 6G Possibilities

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Ticku, Amrita Sidana, Sagar Sinha, Anurag Sathesh, S. Uniyal, Manas Kumar, Biresh Sinha, Raj Shekhar Raj, Petheru Al-Khayyat, Ahmed Sikarwar, Mansi Bharati Vidyapeeth's College of Engineering Department of computer science and engineering India Maharshi Dayanand University Department of computer science and engineering Haryana Rohtak India School of Computing and information Science Ignou New Delhi India Kongu Engineering College Department of Electronics and Communication Engineering Tamil Nadu Perundurai India Graphic Era Hill University Department of computer science and engineering India Amity University Department of computer science and It Jharkhand India Reliance Jio Platforms Ltd Bangalore India Islamic University Department of computer science and engineering Iraq Indira Gandhi Technical University For Women Department Of Mechanical And Automation India

ISBN: (纸本)9798350370249

In a world where technology reigns supreme, the Internet of Things has taken center stage, revolutionizing our lives in countless ways. Augmented reality, crystal-clear video streaming, self-driving cars, intelligent environments, and advanced healthcare are just a few examples of the IoT's widespread impact. With these groundbreaking ideas in mind, the IoT demands faster data rates, wider bandwidth, increased capacity, swift data transmission, and minimal delays. Addressing these requirements head-on, this paper introduces 5G as the leading-edge technology capable of meeting these challenges. The fifth-generation cellular networks, known as 5G, offer a host of vital features that facilitate the widespread adoption of IoT. These features include device-to-device communication, where devices can directly exchange information without relying on a central server, coordinated multipoint processing to enhance network efficiency, massive machine type communication that enables seamless connectivity for countless IoT devices, ultra-reliable low latency communication to ensure critical data is transmitted swiftly and reliably, and extreme mobile broadband to support high-speed, uninterrupted connectivity on the go. It has paved ways for innovative wireless architecture and intelligent services and 6 G which will be the next step in this evolutionary cycle of wireless networks is expected to amplify this development to new and unimaginable levels. This article gives an extensive overview of the IoT technology, outlining its statistical and architectural trends, application scenarios, challenges and its potential. The research also offers a thorough and in-depth analysis of the current 5G-IoT environment. The use cases for 5 G in IoT systems are also emphasized. As a result, we discuss some of the key 5G IoT research directions and trends. After which we highlight the challenging areas of 5G-IoT and where special emphasis will be a necessity in paving the way for 6 G enabled

关键词： 6g Iot NEXT GEN Pervasive computing Smart City

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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Computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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An Energy Sharing Mechanism Considering Network Constraints and Market Power Limitation

An Energy Sharing Mechanism Considering Network Constraints ...

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IEEE General Meeting Power& Energy Society

作者： Yue Chen Changhong Zhao Steven H. Low Adam Wierman Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Department of Information Engineering The Chinese University of Hong Kong Computing and Mathematical Sciences Department California Institute of Technology

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

As the number of prosumers with distributed energy resources (DERs) grows, the conventional centralized operation scheme may suffer from conflicting interests, privacy concerns, and incentive inadequacy. In this paper, we propose an energy sharing mechanism to address the above challenges. It takes into account network constraints and fairness among prosumers. In the proposed energy sharing market, all prosumers play a generalized Nash game. The market equilibrium is proved to have nice features in a large market or when it is a variational equilibrium. To deal with the possible market failure, inefficiency, or instability in general cases, we introduce a price regulation policy to avoid market power exploitation. The improved energy sharing mechanism with price regulation can guarantee the existence and uniqueness of a socially near-optimal market equilibrium. Some advantageous properties are proved, such as the prosumer’s individual rationality, a sharing price structure similar to the locational marginal price, and the tendency towards social optimum with an increasing number of prosumers. For implementation, a practical bidding algorithm is developed with a convergence condition. Experimental results validate the theoretical outcomes and show the practicability of our model and method.

关键词： Privacy Games Regulation Distributed power generation Convergence

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Swapped goal-conditioned offline reinforcement learning

arXiv

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arXiv 2023年

作者： Yang, Wenyan Wang, Huiling Cai, Dingding Pajarinen, Joni Kämäräinen, Joni-Kristian Computing Sciences Tampere University Finland Department of Electrical Engineering and Automation Aalto University Finland

Offline goal-conditioned reinforcement learning (GCRL) can be challenging due to overfitting to the given dataset. To generalize agent’s skills outside the given dataset, we propose a goal-swapping procedure that generates additional trajectories. To alleviate the problem of noise and extrapolation errors, we present a general offline reinforcement learning method called deterministic Q-advantage policy gradient (DQAPG). In the experiments, DQAPG outperforms state-of-the-art goal-conditioned offline RL methods in a wide range of benchmark tasks, and goal-swapping further improves the test results. It is noteworthy, that the proposed method obtains good performance on the challenging dexterous in-hand manipulation tasks for which the prior methods failed. © 2023, CC BY-NC-SA.

关键词： Reinforcement learning

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PRIORITIZED OFFLINE GOAL-SWAPPING EXPERIENCE REPLAY

arXiv

引用

arXiv 2023年

作者： Yang, Wenyan Pajarinen, Joni Cai, Dingding Kämäräinen, Joni-Kristian Computing Sciences Tampere University Finland Department of Electrical Engineering and Automation Aalto University Finland

In goal-conditioned offline reinforcement learning, an agent learns from previously collected data to go to an arbitrary goal. Since the offline data only contains a finite number of trajectories a main challenge is how to generate more data. Goal-swapping generates additional data by switching trajectory goals but while doing so produces a large number of invalid trajectories. To address this issue we propose prioritized goal-swapping experience replay (PGSER). PGSER uses a pre-trained Q function to assign higher priority weights to goal-swapped transitions that allow reaching the goal. In experiments, PGSER significantly improves over baselines in a wide range of benchmark tasks. © 2023, CC BY-NC-SA.

关键词： Trajectories

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HDCBAN: Hybrid Neural Network for Network Intrusion Detection System

HDCBAN: Hybrid Neural Network for Network Intrusion Detectio...

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International Conference on Computers, Communications, and Systems (ICCCS)

作者： Bo Xie Mingdi Xu Chaoyang Jin Feng Cui Zhengxiao Li Haipeng Fan Computing Environment Department Wuhan Institute of Digital Engineering Wuhan China School of Automation China University of Geosciences Wuhan China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

Intrusion Detection Systems (IDS) play a crucial role in safeguarding network security perimeters by monitoring network traffic and system behavior in real time. The advent of deep learning, particularly through the application of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, has significantly enhanced IDS's ability to detect network threats by leveraging autonomous learning and generalization capabilities. However, deep learning-based IDSs encounter challenges, including the necessity for extensive data labeling and the computational resources required, which pose obstacles to their widespread adoption and effectiveness. Nonetheless, deep learning-based IDS encounter challenges, including the requirement for extensive labeled datasets and significant computational resources. This paper proposes a novel hybrid IDS framework — HDCBAN that integrates deep convolutional neural networks, bidirectional LSTM networks, and the AlexNet model to efficiently predict and classify malicious network activities. The HDCBAN model employs deep CNNs to capture local features through convolution, bidirectional LSTMs to seize temporal features for enhanced performance and prediction capabilities, and AlexNet to augment classification efficacy through feature extraction. The effectiveness of this hybrid approach was assessed using real-world datasets, including the publicly available CSE-CIC-IDS 2017, CSE-CIC-IDS 2018, and NSL-KDD datasets, with preprocessing to optimize model performance. Experimental results, validated through 10-fold cross-validation, reveal that our model achieves a malicious attack detection rate and accuracy of up to 99.88% for CSE-CIC-IDS and 99.93% for NSL-KDD, thereby outperforming existing models in detection rate, accuracy, and reducing false positives.

关键词： Training Accuracy Computational modeling Scalability Training data Telecommunication traffic Predictive models

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Control Strategy based on Frequency Response for a Single-Phase Full- Bridge Series Active Filter 16

Control Strategy based on Frequency Response for a Single-Ph...

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16th Brazilian Power Electronics Conference, COBEP 2021

作者： Curi Busarello, Tiago Davi Automation and Computing Engineering Federal University of Santa Catarina Department of Control Blumenau Brazil

ISBN: (纸本)9781665421294

Series active filters are a mature technology and well covered in the literature. However, they are usually designed for three-phase systems employing control strategies in dq-rotating reference frame. As a result, proportional-integrator controllers are used. However, they are not suited when the system has abnormal conditions. Moreover, with the high penetration of distribution generators, single-phase distribution voltage is being deteriorated. This paper presents a control strategy based on frequency response for a single-phase full bridge series active filter. The series active filter is controlled in voltage mode through a digital lead-lag controller. The controller parameters are obtained by frequency response technique using the k-factor methodology and later discretized via Backward-Euler approximation. The series active filter is tested in a system with nonlinear loads, distortions at the distribution voltage and under disturbance at the DC-link. The proposal was verified under simulation results and under Hardware-in-Loop Typhoon HIL402 with external microcontroller TMS320F28335. Results showed the effectiveness of the proposed control strategy. © 2021 IEEE.

关键词： Active filters

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