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IEEE International Conference on Cluster Computing Workshops and Posters (CLUSTER WORKSHOPS)

作者： Kaito Kitazume Norihisa Fujita Ryohei Kobayashi Taisuke Boku Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Center for Computational Sciences University of Tsukuba

In recent years, Field-Programmable Gate Arrays (FPGAs) are gaining attention as computational acceleration devices in the field of high-performance computing. By implementing specialized circuits that can be customiz... 详细信息

ISBN: (数字)9798350383454

ISBN: (纸本)9798350383461

关键词： Conferences High performance computing Circuits Cluster computing Low latency communication Field programmable gate arrays

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Directed Machine Learning Approaches for Determining Alzheimer Disease

Directed Machine Learning Approaches for Determining Alzheim...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Ansh Vashist Tarun Jain Vijay Shankar Sharma Department of Computer Science and Engineering Manipal University Jaipur Department of IOT & Intelligent Systems Manipal University Jaipur

Alzheimer's is a neurogenic disease which progress into neurological disorder that primarily affects cognitive function and memory. It's a Neurodegenerative (ND) disease, characterized by the gradual deterioration of cognitive function, memory, thinking, and behaviour. There are two most common diseases among neurodegenerative diseases: (a) Alzheimer's Disease and (b) Parkinson's Disease. We used 12 classifiers on the given dataset on UC Irvine Machine Learning Repository. The machine learning algorithms were engaged to identify Alzheimer Disease. Our research results showed that the XGB Model is the one that shows the best accuracy, of 100%, of all the 12 classifiers.

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Delay Minimization of Task Offloading in O-RAN Architecture

Delay Minimization of Task Offloading in O-RAN Architecture

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IEEE Future Networks World Forum (FNWF)

作者： Nikos A. Mitsiou Sotiris A. Tegos Thomas Lagkas Panagiotis Sarigiannidis George K. Karagiannidis Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Department of Computer Science Democritus University of Thrace Kavala Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Artificial Intelligence & Cyber Systems Research Center Lebanese American University (LAU) Lebanon

ISBN: (数字)9798350379495

ISBN: (纸本)9798350379501

In this paper, we propose a heuristic algorithm for minimizing the delay of task offloading within the open radio access network (O-RAN) architecture. We model the O-RAN architecture using a directed graph connecting its various components. Assuming that frequency-division multiplexing (FDM) is used between all users sharing the same communication link, and applying the M/M/1 queueing model at the O-RAN central units (O-CUs), we update the graph weights and design a heuristic algorithm to provide delay-efficient routing for all users. Numerical results demonstrate the performance of the algorithm and provide insights into the O-RAN architecture.

关键词： Heuristic algorithms Scalability Directed graphs Open RAN Routing Minimization Delays Numerical models Frequency division multiplexing Virtualization

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Facial Emotion Recognition and Detection Using Convolutional Neural Networks 3

Facial Emotion Recognition and Detection Using Convolutional...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Dalal, Renu Khari, Manju Pandey, Priyank Jatana, Samanvay Joshi, Vijay Usar Guru Gobind Singh Indraprastha University East Delhi Campus Surajmal Vihar New Delhi India Jawaharlal Nehru University Department of Computer and Systems Science New Delhi India Graphic Era Deemed to Be University Department of Computer & Engineering Dehradun India Maharaja Surajmal Institute of Technology Ggsipu Department of Information Technology Janakpuri Delhi India

ISBN: (纸本)9798350319125

Facial emotion recognition is now becoming a hot topic in previous years as it helps in human computer interaction. As human emotions can be detected, there are various studies on it in the computer vision field. Humans can easily recognize the type of emotion from someone's facial expressions but it's tough for a computer machine. FER can be used in vast areas for applications such as education, social media, healthcare, Al based application, etc. In this research paper, the Convolutional Neural Network (CNN) technique has been built. The methodology is mainly divided into two steps: Haar cascade features from the face are extracted by using Viola Jones algorithm leading to facial identification and then facial emotion is recognised by using the help of neural network. This technique is built on conventional neural networks (CNN) and it has used FER2013 and CK+ dataset for detection of human emotions. This model consists of 3 completed connected layers and six convolutional layers. On the basis of our experimental results, this model has a validation accuracy of 68.26% with FER 2013 dataset and 91.58% with CK+ dataset. © 2023 IEEE.

关键词： CNN Deep Learning Facial Emotion Recognition Human computer Interaction

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Multimodal Deep Learning to Differentiate Tumor Recurrence from Treatment Effect in Human Glioblastoma

Multimodal Deep Learning to Differentiate Tumor Recurrence f...

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IEEE International Symposium on Biomedical Imaging

作者： Tonmoy Hossain Zoraiz Qureshi Nivetha Jayakumar Thomas Eluvathingal Muttikkal Sohil Patel David Schiff Miaomiao Zhang Bijoy Kundu Departments of Computer Science University of Virginia USA Radiology and Medical Imaging University of Virginia USA Neurology University of Virginia USA Electrical and Computer Engineering University of Virginia USA Biomedical Engineering University of Virginia USA

Differentiating tumor progression (TP) or recurrence from treatment-related necrosis (TN) is critical for clinical management decisions in glioblastoma (GBM). Dynamic FDG PET (dPET), an advance from traditional static FDG PET, may prove advantageous in clinical staging. dPET includes novel methods of a model-corrected blood input function that accounts for partial volume averaging to compute parametric maps that reveal kinetic information. In a preliminary study, a convolution neural network (CNN) was trained to predict classification accuracy between TP and TN for 35 brain tumors from 26 subjects in the PET-MR image space. 3D parametric PET Ki (from dPET), traditional static PET standardized uptake values (SUV), and also the brain tumor MR voxels formed the input for the CNN. The average test accuracy across all leave-one-out cross-validation iterations adjusting for class weights was 0.56 using only the MR, 0.65 using only the SUV, and 0.71 using only the Ki voxels. Combining SUV and MR voxels increased the test accuracy to 0.62. On the other hand, MR and Ki voxels increased the test accuracy to 0.74. Thus, dPET features alone or with MR features in deep learning models would enhance prediction accuracy in differentiating TP vs TN in GBM.

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IoT-Based Smart Beehive Monitoring System

IoT-Based Smart Beehive Monitoring System

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Advancements in Computing (ICAC), International Conference on

作者： Nisal Liyanage Chamindra Attanayaka Tarinya Perera Daniru Neilkumara Isuru Sri Bandara Lakshman Chandrasiri Department of Computer Science and Data Science NSBM Green University Town Homagama Sri Lanka Department of Computer Security and Network Systems NSBM Green University Town Homagama Sri Lanka Department of Electrical Electronic and Systems Engineering NSBM Green University Town Homagama Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

In recent years, there has been an alarming decline in bee populations both globally and in Sri Lanka, severely impacting ecosystems and agriculture worldwide. Traditional methods in Sri Lankan beekeeping are often time-consuming and less efficient, making hive management and health assessment challenging. This paper introduces a smart IoT-based beehive monitoring system tailored to the Sri Lankan beekeeping industry. The system integrates various sensors to measure key hive parameters, such as temperature, humidity, and weight, offering beekeepers a user-friendly web interface for real-time monitoring of hive conditions. It also provides insights into hive management aspects, including feeding needs, optimal harvesting times, and abnormal temperature detection within the hive. This paper highlights the initial steps towards implementing a robust IoT-based beehive monitoring system, acknowledging its current limitations. By addressing these limitations in the future, the authors aim to develop a more effective and efficient monitoring method for Sri Lankan beekeepers, saving time and enhancing the productivity and health of bee colonies.

关键词： Temperature sensors Temperature measurement Weight measurement Productivity Humidity measurement Humidity Sensor systems Real-time systems Monitoring Intelligent sensors

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Parameter fault estimation in distributed heating/cooling systems 22

Parameter fault estimation in distributed heating/cooling sy...

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Proceedings of the 7th International Conference on Sustainable Information engineering and Technology

作者： Wijaya Kurniawan Katalin M. Hangos Lorinc Marton Faculty of Computer Science University of Brawijaya Indonesia) and Departement of Electrical Engineering and Information Systems University of Pannonia Hungary Departement of Electrical Engineering and Information Systems University of Pannonia Hungary and Institute for Computer Science and Control Hungary Department of Electrical Engineering Sapientia Hungarian University of Transylvania Romania)

ISBN: (纸本)9781450397117

In this paper, the problem of fault estimation and localization in the connecting dynamic elements of distributed heating and cooling systems are treated. The fault represents the physical parameter change related to the heat transfer between the system and the external environment. First, based on the bi-linear dynamic state space model of the system in the presence of a fault, structural observability analysis using Signed Directed Graph (SDG) has been performed to investigate the sensor placement problem. Then, a nonlinear observer with a parameter adaptation algorithm was proposed for fault estimation. The simulation results show that it can successfully detect and estimate the fault. Fault localization along the length of the element has also been attempted, but it has been found that the localization cannot be performed using practically changeable input variables. Frequency domain analysis is presented to discuss this phenomenon.

关键词： Nonlinear Observer Heating/cooling system Fault diagnosis Structural observability Parameter estimation

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Learning Feasibility Constraints for Control Barrier Functions

Learning Feasibility Constraints for Control Barrier Functio...

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European Control Conference (ECC)

作者： Wei Xiao Christos G. Cassandras Calin A. Belta Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology Division of Systems Engineering and Center for Information and Systems Engineering Boston University Brookline MA USA

It has been shown that optimizing quadratic costs while stabilizing affine control systems to desired (sets of) states subject to state and control constraints can be reduced to a sequence of Quadratic Programs (QPs) by using Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). In this paper, we employ machine learning techniques to ensure the feasibility of these QPs, which is a challenging problem, especially for high relative degree constraints where High Order CBFs (HOCBFs) are required. To this end, we propose a sampling-based learning approach to learn a new feasibility constraint for CBFs; this constraint is then enforced by another HOCBF added to the QPs. The accuracy of the learned feasibility constraint is recursively improved by a recurrent training algorithm. We demonstrate the advantages of the proposed learning approach to constrained optimal control problems with specific focus on a robot control problem and on autonomous driving in an unknown environment.

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Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial Group Scheduling Problem

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IEEE/CAA Journal of Automatica Sinica 2021年第6期8卷 1199-1209页

作者： Ziyan Zhao Shixin Liu MengChu Zhou Abdullah Abusorrah State Key Laboratory of Synthetical Automation for Process Industries and the College of Information Science and Engineering Northeastern UniversityShenyang 110819China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah 21481Saudi Arabia Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia

Group scheduling problems have attracted much attention owing to their many practical *** work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time,release time,and due *** is originated from an important industrial process,i.e.,wire rod and bar rolling process in steel production *** objective functions,i.e.,the number of late jobs and total setup time,are minimized.A mixed integer linear program is established to describe the *** obtain its Pareto solutions,we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods,i.e.,an insertion-based local search and an iterated greedy *** computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its *** high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems.

关键词： Insertion-based local search iterated greedy algorithm machine learning memetic algorithm nondominated sorting genetic algorithm II(NSGA-II) production scheduling

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Performance Upper Bound of the Grover-Mixer Quantum Alternating Operator Ansatz

arXiv

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arXiv 2024年

作者： Xie, Ningyi Xu, Jiahua Chen, Tiejin Saito, Yoshiyuki Asai, Nobuyoshi Cai, Dongsheng Graduate School of Science and Technology University of Tsukuba Japan School of Computing and Augmented Intelligence Arizona State University United States Graduate School of Computer Science and Engineering University of Aizu Japan School of Computer Science and Engineering University of Aizu University of Aizu Japan Faculty of Engineering Information and Systems University of Tsukuba Japan

The Quantum Alternating Operator Ansatz (QAOA) represents a branch of quantum algorithms designed for solving combinatorial optimization problems. A specific variant, the Grover-Mixer Quantum Alternating Operator Ansatz (GM-QAOA), ensures uniform amplitude across states that share equivalent objective values. This property makes the algorithm independent of the problem structure, focusing instead on the distribution of objective values within the problem. In this work, we prove the probability upper bound for measuring a computational basis state from a GM-QAOA circuit with a given depth, which is a critical factor in QAOA cost. From this, we derive the upper bounds for the probability of sampling an optimal solution and for the approximation ratio of maximum optimization problems, based on the objective value distribution. Using numerical analysis, we link the distribution to the problem size and build the regression models that relate the problem size, QAOA depth, and performance upper bound. Our results suggest that the GM-QAOA provides a quadratic enhancement in sampling probability and requires circuit depth that scales exponentially with problem size to maintain consistent performance. © 2024, CC BY.

关键词： Mixers (machinery)

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