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Quantum Computing and engineering (QCE), IEEE International Conference on

作者： Ward van der Schoot Robert Wezeman Niels Neumann Frank Phillipson Rob Kooij The Netherlands Organisation for Applied Scientific Research The Hague The Netherlands Maastricht University School of Business and Economics Maastricht The Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Evaluating the performance of quantum devices is an important step towards scaling quantum devices and eventually using them in practice. The great number of available quantum metrics and the different hardware technologies used to develop quantum computers complicate this evaluation. In addition, different computational paradigms implement quantum operations in different ways. A prominent quantum metric is given by the Q-score metric of Atos. This metric was originally introduced as a standalone way to benchmark devices using the Max-Cut problem. In this work, we show that the Q-score defines a framework of quantum metrics, which allows benchmarking using different problems, user settings and solvers. To showcase the applicability of the framework, we showcase a second Q-score in this framework, called the Q-score Max-Clique. This yields, to our knowledge, the first application-level metric capable of natively comparing three different paradigms of quantum computing. This metric is evaluated on these computational quantum paradigms – quantum annealing, gate-based quantum computing, and photonic quantum computing – and the results are compared to those obtained by classical solvers.

关键词： Measurement Performance evaluation computers Quantum computing Annealing Benchmark testing Logic gates Quantum annealing Hardware Photonics

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Beyond Kalman Filters: Deep Learning-Based Filters for Improved Object Tracking

arXiv

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arXiv 2024年

作者： Adžemović, Momir Tadić, Predrag Petrović, Andrija Nikolić, Mladen Department of Computer Science Faculty of Mathematics University of Belgrade Belgrade Serbia Department of Signals and Systems School of Electrical Engineering University of Belgrade Belgrade Serbia Center for Bussiness Decision Making Faculty of Organizational sciences University of Belgrade Belgrade Serbia

Traditional tracking-by-detection systems typically employ Kalman filters (KF) for state estimation. However, the KF requires domain-specific design choices and it is ill-suited to handling non-linear motion patterns. To address these limitations, we propose two innovative data-driven filtering methods. Our first method employs a Bayesian filter with a trainable motion model to predict an object’s future location and combines its predictions with observations gained from an object detector to enhance bounding box prediction accuracy. Moreover, it dispenses with most domain-specific design choices characteristic of the KF. The second method, an end-to-end trainable filter, goes a step further by learning to correct detector errors, further minimizing the need for domain expertise. Additionally, we introduce a range of motion model architectures based on Recurrent Neural Networks, Neural Ordinary Differential Equations, and Conditional Neural Processes, that are combined with the proposed filtering methods. Our extensive evaluation across multiple datasets demonstrates that our proposed filters outperform the traditional KF in object tracking, especially in the case of non-linear motion patterns—the use case our filters are best suited to. We also conduct noise robustness analysis of our filters with convincing positive results. We further propose a new cost function for associating observations with tracks. Our tracker, which incorporates this new association cost with our proposed filters, outperforms the conventional SORT method and other motion-based trackers in multi-object tracking according to multiple metrics on motion-rich DanceTrack and SportsMOT datasets. © 2024, CC BY.

关键词： Kalman filters

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IRKO:An Improved Runge-Kutta Optimization Algorithm for Global Optimization Problems

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computers, Materials & Continua 2022年第3期70卷 4803-4827页

作者： R.Manjula Devi M.Premkumar Pradeep Jangir Mohamed Abdelghany Elkotb Rajvikram Madurai Elavarasan Kottakkaran Sooppy Nisar Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060Tamil NaduIndia Department of Electrical&Electronics Engineering Dayananda Sagar College of EngineeringBengaluru560078KarnatakaIndia Rajasthan Rajya Vidyut Prasaran Nigam Sikar332025RajasthanIndia Mechanical Engineering Department College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Mechanical Engineering Department Faculty of EngineeringKafrelsheikh UniversityEgypt Clean and Resilient Energy Systems(CARES)Laboratory Texas A&MUniversityGalveston77553TXUSA College of Arts and Sciences Prince Sattam bin Abdulaziz UniversityWadi Aldawaser11991Saudi Arabia

Optimization is a key technique for maximizing or minimizing functions and achieving optimal cost,gains,energy,mass,and so *** order to solve optimization problems,metaheuristic algorithms are *** of these techniques are influenced by collective knowledge and natural *** is no such thing as the best or worst algorithm;instead,there are more effective algorithms for certain ***,in this paper,a new improved variant of a recently proposed metaphorless Runge-Kutta Optimization(RKO)algorithm,called Improved Runge-Kutta Optimization(IRKO)algorithm,is suggested for solving optimization *** IRKO is formulated using the basic RKO and local escaping operator to enhance the diversification and intensification capability of the basic RKO *** performance of the proposed IRKO algorithm is validated on 23 standard benchmark functions and three engineering constrained optimization *** outcomes of IRKO are compared with seven state-of-the-art algorithms,including the basic RKO *** to other algorithms,the recommended IRKO algorithm is superior in discovering the optimal results for all selected optimization *** runtime of IRKO is less than 0.5 s for most of the 23 benchmark problems and stands first for most of the selected problems,including real-world optimization problems.

关键词： engineering design global optimization local escaping operator metaheuristics Runge-Kutta optimization algorithm

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Enhancing photovoltaic system performance using advanced gradient boosting methods

Enhancing photovoltaic system performance using advanced gra...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Fouzi Harrou Abdelhakim Dorbane Ying Sun Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Computer Thuwal Saudi Arabia Smart Structures Laboratory (SSL) Faculty of Science and Technology University of Ain Temouchent Ain Temouchent Algeria

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

Photovoltaic (PV) systems are indispensable elements in clean energy production. Predicting PV power is crucial for optimizing system performance and ensuring grid stability. This paper investigates the performance of gradient boosting approaches, XGBoost and Catboost, in predicting PV power. Datasets recorded each minute, including weather data and power output from two distinct PV systems in Brisbane, Australia, are used in this study for evaluation. Results show that the XGBoost slightly outperformed Catboost and achieved good prediction performance. Also, the XGBoost output was interpreted using the Shapley additive interpretation (SHAP) method to identify the important factor impacting PV power prediction. Results confirm that solar radiation is the most important factor influencing the performance of photovoltaic systems, and the other weather variables have a weaker impact. These findings contribute valuable insights for enhancing the efficiency and reliability of PV systems in renewable energy applications.

关键词： Photovoltaic systems Accuracy System performance Noise reduction Noise Predictive models Boosting

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A Deep Learning Approach Towards Student Performance Prediction in Online Courses: Challenges Based on a Global Perspective

A Deep Learning Approach Towards Student Performance Predict...

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Information Technology (ACIT)

作者： Abdallah Moubayed MohammadNoor Injadat Nouh Alhindawi Ghassan Samara Sara Abuasal Raed Alazaidah School of Computing and Augmented Intelligence Arizona State University Gilbert Arizona USA Electrical and Computer Engineering Department Lebanese American University Byblos Lebanon Electrical and Computer Engineering Department Western University London Ontario Canada Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Zarqa Jordan Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Software Engineering Department Faculty of Information Technology Zarqa University Zarqa Jordan

Analyzing and evaluating students' progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students' performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset.

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Deep Learning and Machine Learning Techniques for Automated PET/CT Segmentation and Survival Prediction in Head and Neck Cancer 3rd

Deep Learning and Machine Learning Techniques for Automated ...

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3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2022

作者： Salmanpour, Mohammad R. Hajianfar, Ghasem Hosseinzadeh, Mahdi Rezaeijo, Seyed Masoud Hosseini, Mohammad Mehdi Kalatehjari, Ehsanhosein Harimi, Ali Rahmim, Arman Department of Physics and Astronomy University of British Columbia VancouverBC Canada Department of Integrative Oncology BC Cancer Research Institute VancouverBC Canada VancouverBC Canada Rajaie Cardiovascular Medical and Research Center Iran University of Medical Science Tehran Iran Department of Electrical and Computer Engineering University of Tarbiat Modares Tehran Iran Department of Medical Physics Faculty of Medicine Ahvaz Jundishapur University of Medical Sciences Ahvaz Iran Department of Computer Engineering Islamic Azad University Shahrood Branch Shahrood Iran Department of Electrical Engineering Islamic Azad University Shahrood Branch Shahrood Iran

ISBN: (纸本)9783031274190

Background: Accurate prognostic stratification as well as segmentation of Head-and-Neck Squamous-Cell-Carcinoma (HNSCC) patients can be an important clinical reference when designing therapeutic strategies. We set to enable automated segmentation of tumors and prediction of recurrence-free survival (RFS) using advanced deep learning techniques and Hybrid Machine Learning Systems (HMLSs). Method: In this work, 883 subjects were extracted from HECKTOR-Challenge: ~60% of the total subjects were considered for the training and validation procedure, and the remaining subjects for external testing were employed to validate our models. PET images were registered to CT. First, a weighted fusion technique was employed to combine PET and CT information. We also employed Cascade-Net to enable automated segmentation of HNSCC tumors. Moreover, we extracted deep learning features (DF) via a 3D auto-encoder algorithm from PET and the fused image. Subsequently, we employed an HMLS including a feature selection algorithm such as Mutual Information (MI) linked with a survival prediction algorithm such as Random Survival Forest (RSF) optimized by 5-fold cross-validation and grid search. The dataset with DFs was normalized by the z-score technique. Moreover, dice score and c-Index were reported to evaluate the segmentation and prediction models, respectively. Result: For segmentation, the weighted fusion technique followed by the Cascade-Net segmentation algorithm resulted in a validation dice score of 72%. External testing of 71% confirmed our findings. DFs extracted from sole PET and MI followed by RSF enabled us to receive a validation c-index of 66% for RFS prediction. The external testing of 59% confirmed our finding. Conclusion: We demonstrated that using the fusion technique followed by an appropriate automated segmentation technique provides good performance. Moreover, employing DFs extracted from sole PET and HMLS, including MI linked with RSF, enables us to perform the approp

关键词： Tumors

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Real-Time Obstacle Detection and Localization for USV Using Stereo Vision and Edge Computing

Real-Time Obstacle Detection and Localization for USV Using ...

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Artificial Intelligence of Things and Systems (AIoTSys), International Conference on

作者： Di Wang Haiming Chen MD Asif Hasan Weihao Zhang Yuqing Hu Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo Zhejiang China Information Center Ningbo Polytechnic Ningbo Zhejiang China Ningbo Industrial Internet Research Institute Ningbo Zhejiang China

ISBN: (数字)9798331507886

ISBN: (纸本)9798331507893

Unmanned Surface Vessel (USV) is widely used in various fields, and obstacle avoidance is a key technology for USV. Accurately detecting the position and type of obstacles is crucial for obstacle avoidance. Active sensors can accurately detect the position of obstacles but cannot distinguish their types, necessitating the use of vision sensors. Therefore, this paper employs stereo vision and proposes a stereo obstacle detection and localization method, based on SGBM and YOLOv5. The SGBM algorithm generates depth information, while the YOLOv5 algorithm detects the position of obstacles in the image. To address the issue of errors in measuring the continuous position of obstacles, this paper uses a Kalman filter to smooth the trajectory. Experimental results show that the proposed method improves obstacle localization accuracy by approximately 38% compared to raw measurements. Besides, considering the energy constraints and computing capabilities of lightweight USV, which cannot meet the demands of processing vision tasks, this paper introduces an edge-device collaboration architecture, by offloading the USV computing tasks to edge nodes, thereby extending the computing capacity and energy efficiency of the USV. Experimental results indicate that the proposed architecture meets the real-time obstacle avoidance requirements of USV.

关键词： Location awareness YOLO Accuracy Image edge detection computer architecture Approximation algorithms Stereo vision Trajectory Collision avoidance Underactuated surface vessels

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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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Real-Time Modeling of Biogas Production and Quality with ANFIS

SSRN

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SSRN 2024年

作者： Doğan, Ferdi Oyucu, Saadin Fardrazm, Reza Jalali, Arman Unsal, Derya Betul Aksoz, Ahmet Abdi, Reza Golestan, Saeed Department of Computer Engineering Adıyaman University Adıyaman02040 Turkey Department of Biosystems Engineering Faculty of Agriculture University of Tabriz Tabriz Iran Department of Electrical and Electronics Engineering Cumhuriyet University Sivas Turkey Mobilers Research Team Cumhuriyet University Sivas Turkey Aalborg University The Faculty of Engineering and Science Aalborg Denmark

Biogas is an essential type of sustainable energy that can replace or be combined with the supply of natural gas. Biogas is generated through anaerobic digestion, a biological process in which microorganisms break down organic matter without the presence of oxygen. This process produces a mixture of gases, including methane, carbon dioxide, and small amounts of other gases. This study focused on addressing a research gap in the literature, namely predicting biogas production for smart grid utilization using real-time data from cow manure under various stirring intensities and temperatures. A full-scale (400 L) batch reactor operated under mesophilic conditions was utilized and an Adaptive Neuro-Fuzzy Inference System (ANFIS) was employed to model biogas production from anaerobic digestion. Residence time, temperature, and stirring intensity were the input parameters, while daily biogas production and methane percentage were the outputs. The coefficient of determination (R²) for biogas production and methane percentage was 0.978 and 0.9978, respectively, indicating high model accuracy. The study also found that moderate stirring at 100 RPM and 40°C yielded the highest biogas production, whereas higher stirring intensity at 150 RPM resulted in lower biogas production due to increased energy consumption, reducing overall efficiency. © 2024, The Authors. All rights reserved.

关键词： Fuzzy inference

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Evaluation of Preoperative Microvascular Invasion in Hepatocellular Carcinoma Through Multidimensional Parameter Combination Modeling Based on Gd-EOB-DTPA MRI

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Journal of Clinical and Translational Hepatology 2023年第2期11卷 350-359页

作者： Han-Dan Zhang Xiao-Ming Li Yu-Han Zhang Fang Hu Liang Tan Fang Wang Yang Jing Da-Jing Guo Yang Xu Xian-Ling Hu Chen Liu Jian Wang Department of Radiology Southwest HospitalThird Military Medical University(Army Military Medical University)ChongqingChina Department of Neurosurgery Third Military Medical University(Army Military Medical University)ChongqingChina Department of Electrical and Computer Engineering Faculty of Science and TechnologyUniversity of MacaoMacaoChina Department of Market Huiying Medical Technology Co.LtdBeijingChina Department of Radiology The Second Affiliated Hospital of Chongqing Medical UniversityChongqingChina Communication Sergeant School Army Engineering University of PLAChongqingChina

Background and Aims:The study established and compared the efficacy of the clinicoradiological model,radiomics model and clinicoradiological-radiomics hybrid model in predicting the microvascular invasion(MVI)of hepatocellular carcinoma(HCC)using gadolinium ethoxybenzyl diethylene triaminepentaacetic acid(Gd-EOB-DTPA)enhanced ***:This was a study that enrolled 602 HCC patients from two *** absolute shrinkage and selection operator(Lasso)method was used to screen for the most important clinicoradiological and radiomics features that predict MVI *** machine learning algorithms were used to establish the clinicoradiological,radiomics,and clinicoradiological-radiomics hybrid *** under the curve(AUC)of receiver operating characteristic(ROC)curves and Delong’s test were used to compare and quantify the predictive performance of the ***:The AUCs of the clinicoradiological model in training and validation cohorts were 0.793 and 0.701,*** radiomics signature of arterial phase(AP)images alone achieved satisfying predictive efficacy for MVI,with AUCs of 0.671 and 0.643 in training and validation cohort,*** combination of clinicoradiological factors and fusion radiomics signature of AP and VP images achieved AUCs of 0.824 and 0.801 in training and validation cohorts,0.812 and 0.805 in prospective validation and external validation cohorts,*** hybrid model provided the best prediction *** results of the Delong test revealed that there were statistically significant differences among the clinicoradiological-radiomics hybrid model,clinicoradiological model,and radiomics model(p<0.05).Conclusions:The combination of clinicoradiological factors and fusion radiomics signature of AP and VP images based on Gd-EOB-DTPA-enhanced MRI can effectively predict MVI.

关键词： Hepatocellular carcinoma Microvascular invasion Radiomics GdEOB-DTPA

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