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2022 International Conference of Yearly Reports on Informatics, Mathematics, and Engineering, ICYRIME 2022

作者： De Magistris, Giorgio Iacobelli, Emanuele Brociek, Rafal Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Department of Mathematics Applications and Methods for Artificial Intelligence Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

The ongoing COVID-19 pandemic has highlighted the importance of wearing face masks as a preventive measure to reduce the spread of the virus. In medical settings, such as hospitals and clinics, healthcare professionals and patients are required to wear surgical masks for infection control. However, the use of masks can hinder facial recognition technology, which is commonly used for identity verification and security purposes. In this paper, we propose a convolutional neural network (CNN) based approach to detect faces covered by surgical masks in medical settings. We evaluated the proposed CNN model on a test set comprising of masked and unmasked faces. The results showed that our model achieved an accuracy of over 96% in detecting masked faces. Furthermore, our model demonstrated robustness to different mask types and fit variations commonly encountered in medical settings. Our approaches reaches state of the art results in terms of accuracy and generalization. © 2022 Copyright for this paper by its authors.

关键词： COVID-19

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A Semi-supervised Modeling Method for Dioxin Emission Prediction Based on Random Forest 5

A Semi-supervised Modeling Method for Dioxin Emission Predic...

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5th Asian Conference on Artificial Intelligence technology, ACAIT 2021

作者： Xu, Wen Tang, Jian Xia, Heng Zhang, Jian Yu, Wen Qiao, JunFei Beijing University of Technology Faculty of Information Technology Beijing100124 China Nanjing University of Information Science Technology School of Computer and Software 210044 China CINVESTAV-IPN National Polytechnic Institute Departamento de Control Automatico Mexico City07360 Mexico

ISBN: (纸本)9781665426305

Dioxin (DXN) is a kind of pollutants with cumulative effects in the municipal solid waste incineration (MSWI) process. Its emission concentration is difficult to detect online and in real-time, which restricts the operational optimization of the MSWI process. At the same time, it is difficult to meet actual needs through traditional supervised modeling methods because of the high time and economic cost of directly measuring DXN. Therefore, a DXN emission prediction model based on semi-supervised random forest (SSRF) is established to make full use of the unlabeled data obtained in the actual industrial process. First, the training subsets are acquired through randomly sampling the labeled data. Second, the training subsets are utilized to build multiple random forest (RF) models and pseudo-label the unlabeled data. Finally, the mixed samples composed of pseudo-labeled data and labeled data are used to train an RF model for predicting the DXN emission concentration. The proposed method is verified by the actual DXN dataset. © 2021 IEEE.

关键词： Decision trees

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Smart Contract Service Optimization in Blockchain-Cloud Collaborative Computing 24

Smart Contract Service Optimization in Blockchain-Cloud Coll...

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24th IEEE International Conference on Mobile Data Management, MDM 2023

作者： Wan, Ji Hu, Kai Li, Jie Su, Hao Wu, Qingshun Li, Mingyuan Feng, Libo Pan, Yan Beihang University State Key Laboratory of Software Development Environment Beijing China Beihang Yunnan Innovation Institute Yunnan Key Laboratory of Blockchain Application Technology Kunming China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou China The First Affiliated Hospital of Zhengzhou University Zhengzhou China Yunnan University National Pilot School of Software Kunming China China Industrial Control Systems Cyber Emergency Response Team Institute of Software Beijing China

ISBN: (纸本)9798350341010

Smart contract is a trusted service provided on the blockchain, while cloud service is a traditional service mode with a large number of resources. The combination of the blockchain and cloud service is of great significance to the trusted expansion of services and the access to services inside and outside the blockchain. In this paper, we study the smart contract extension service in blockchain-cloud collaborative computing. The service module decoupling method of smart contract is proposed, and the parallel execution algorithm of smart contract service is designed, which improves the execution efficiency of smart contract service. Finally, this paper designs a secure data interaction method of smart contract and cloud service, which helps to maintain the data consistency between cloud computing and blockchain. The experimental results show that the proposed method can save at most 42.13% of the running time, and it can promote the data consistency between the cloud service and the blockchain. © 2023 IEEE.

关键词： Smart contract

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A Loop Optimization Method for Dataflow Architecture

A Loop Optimization Method for Dataflow Architecture

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Zhihua Fan Wenming Li Tianyu Liu Shengzhong Tang Zhen Wang Xuejun An Xiaochun Ye Dongrui Fan State Key Lab of Processors Institute of Computing Technology CAS Beijing China School of Computer and Control Engineering UCAS Beijing Beijing China

Dataflow architecture is a promising parallel computing platform that provides high performance, high efficiency and flexibility. Since the execution of a loop is usually the most time-consuming in a program, extracting sufficient loop-level parallelism is an essential task of dataflow architecture. However, existing methods often result in low execution performance and poor hardware utilization due to the imbalance among dataflow graph nodes and the non-preemptive mechanism. To tackle these problems, this paper makes three contributions: 1) A highly effective dataflow graph balancing method is developed to improve the utilization. 2) An enhanced dataflow execution model is proposed, which adapts a novel preemptive mechanism to further improve the hardware utilization and performance. 3) A decoupled dataflow architecture is designed to efficiently support our proposed dataflow execution model. Experiment shows that our methods can achieve an average of 2.61 ×performance improvement and 2.5 ×utilization improvement compared to the state-of-the-art methods with acceptable overhead.

关键词： Adaptation models Costs Optimization methods computer architecture Hazards Hardware Task analysis

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Attack Impact Evaluation for Stochastic control Systems through Alarm Flag State Augmentation

arXiv

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arXiv 2023年

作者： Sasahara, Hampei Tanaka, Takashi Sandberg, Henrik The Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo152-8552 Japan The Department of Aerospace Engineering and Engineering Mechanics Cockrell School of Engineering The University of Texas AustinTX78712 United States The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden

This note addresses the problem of evaluating the impact of an attack on discrete-time nonlinear stochastic control systems. The problem is formulated as an optimal control problem with a joint chance constraint that forces the adversary to avoid detection throughout a given time period. Due to the joint constraint, the optimal control policy depends not only on the current state, but also on the entire history, leading to an explosion of the search space and making the problem generally intractable. However, we discover that the current state and whether an alarm has been triggered, or not, is sufficient for specifying the optimal decision at each time step. This information, which we refer to as the alarm flag, can be added to the state space to create an equivalent optimal control problem that can be solved with existing numerical approaches using a Markov policy. Additionally, we note that the formulation results in a policy that does not avoid detection once an alarm has been triggered. We extend the formulation to handle multi-alarm avoidance policies for more reasonable attack impact evaluations, and show that the idea of augmenting the state space with an alarm flag is valid in this extended formulation as well. © 2023, CC BY-NC-ND.

关键词： Stochastic systems

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Stochastic Approximation for Identification of Non-Linear Differential-Algebraic Equations with Process Disturbances

Stochastic Approximation for Identification of Non-Linear Di...

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IEEE Conference on Decision and control

作者： Robert Bereza Oscar Eriksson Mohamed R.-H. Abdalmoaty David Broman Håkan Hjalmarsson Division of Decision and Control Systems EECS and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Division of Software and Computer Systems EECS and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Division of Systems and Control Uppsala University Uppsala Sweden

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Differential-algebraic equations, commonly used to model physical systems, are the basis for many equation-based object-oriented modeling languages. When systems described by such equations are influenced by unknown process disturbances, estimating unknown parameters from experimental data becomes difficult. This is because of problems with the existence of well-defined solutions and the computational tractability of estimators. In this paper, we propose a way to minimize a cost function—whose minimizer is a consistent estimator of the true parameters—using stochastic gradient descent. This approach scales significantly better with the number of unknown parameters than other currently available methods for the same type of problem. The performance of the method is demonstrated through a simulation study with three unknown parameters. The experiments show a significantly reduced variance of the estimator, compared to an output error method neglecting the influence of process disturbances, as well as an ability to reduce the estimation bias of parameters that the output error method particularly struggles with.

关键词： Monte Carlo methods Costs Object oriented modeling Computational modeling Stochastic processes Process control Estimation

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Few-shot Object Detection Algorithm Based on YOLOv8-Mamba-Like Linear Attention

Few-shot Object Detection Algorithm Based on YOLOv8-Mamba-Li...

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International Conference on Communication Software and Networks, ICCSN

作者： Hongliang Wang Kaizhu Wang Binglong Ji Changyi Deng Liaoning Province Human- Computer Interaction System Engineering Research Center Based on DigitTwin Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China Industrial Control Systems Cyber Emergency Response Team Institute of Software China Beijing China

ISBN: (数字)9798331506322

ISBN: (纸本)9798331506339

In the production and assembly process, the accuracy of detection is usually low due to limited resources and small quantities. In this paper, we propose a few-shot learning method for object detection based on the combination of YOLOv8 and Mamba-Like Linear Attention (MLLA) module. This method utilizes a high-pixel camera to capture the shape and size of the workpiece. By consistency-based mutual augmentation structure for few-shot samples data augmentation. By introducing the efficient and lightweight MLLA module, the model’s speed and accuracy in object detection and segmentation tasks on mobile devices are significantly improved. Specifically, MLLA mainly integrates the two key elements of “forget gate” and “block design” in Mamba, and MLLA replaces forget gate by using position coding (RoPE) to provide necessary location information while maintaining parallel computation and fast inference speed. This makes MLLA improves the accuracy of detection and segmentation recognition. Experimental results show that compared to the original YOLOv8 model, the model with the MLLA module exhibits improvements in segmentation precision and recognition accuracy. This method demonstrates high accuracy and robustness in workpiece recognition, making it suitable for real-time industrial applications.

关键词： Accuracy Shape Computational modeling Object detection Production Logic gates Feature extraction Robustness Software Real-time systems

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Individually-guided Evolutionary Algorithm for Solving Multi-task Optimization Problems

Individually-guided Evolutionary Algorithm for Solving Multi...

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IEEE International Conference on Networking, Sensing and control

作者： XiaoLing Wang Qi Kang MengChu Zhou Department of Control Science and Engineering Tongji University Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark USA

ISBN: (纸本)9781665472449

Multi-task optimization (MTO) is a novel emerging evolutionary computation paradigm that is used for solving multiple optimization tasks concurrently. Most MTO algorithms limit each individual to one task, and thus weaken the performance of information exchange. To address this issue and improve the efficiency of knowledge transfer, this work proposes an efficient MTO framework named individually-guided multi-task optimization (IMTO). It divides evolutions into vertical and horizontal ones. To further improve the efficiency of knowledge transfer, a partial individuals' learning scheme is used to choose suitable individuals to learn from other tasks. Experimental results show its superior advantages over the multifactorial evolutionary algorithm and its variants.

关键词： Focusing Evolutionary computation Multitasking Sensors Task analysis Optimization Knowledge transfer

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Investigating Role of Renewable Resources Including Wind Turbines in Energy Hub by Considering Simultaneous Demand for Electricity, Heating and Cooling

Investigating Role of Renewable Resources Including Wind Tur...

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Iranian Conference on Renewable Energy and Distributed Generation (ICREDG)

作者： Daryoush Tavangar Rizi Mohammad Hassan Nazari Ali Maleki Seyed Hossein Hosseinian Maryam Fani Gevork B. Gharehpetian Department of Physics and Energy Engineering Amirkabir University of Technology Tehran Iran Smart Control Systems Department Niroo Research Institute (NRI) Tehran Iran School of Electrical Engineering & Computer Science Washington State University Pullman Washington Department of Electrical Engineering Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798350361322

ISBN: (纸本)9798350361339

As a crucial component of multi-energy systems (MES), the energy hub significantly enhances their performance and reliability. In the energy system, the utilization of renewable energy sources (RES) can presents a significant optimization problem, capable of substantially reducing environmental pollution and lowering energy costs for users. This paper presents an optimal load dispatch model incorporating a collection of wind turbines, aimed at reducing the overall cost of operating an energy hub. It is also proposes a hub structure based on wind, natural gas, and electricity as a combined heat and power system, utilizing converters and energy storage mechanisms to obtain electricity, thermal, and cooling energy. To accomplish this, two situations are executed in the network, aiming to minimize costs by applying a problem-solving approach to the installation of wind turbines at an energy hub. After implementing energy hub management in the optimal mode, the system’s operating costs are reduced by $16 \%$, demonstrating its advantage.

关键词： Renewable energy sources Costs Electricity Wind turbines Problem-solving Trigeneration Optimization

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Leakage Localization in Water Distribution Networks: A Model-Based Approach

Leakage Localization in Water Distribution Networks: A Model...

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European control Conference (ECC)

作者： Ludvig Lindströ m Sebin Gracy Sindri Magnú sson Henrik Sandberg Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and Digital Futures Stockholm Sweden Department of Computer and Systems Sciences Stockholm University

ISBN: (数字)9783907144077

ISBN: (纸本)9781665497336

The paper studies the problem of leakage localization in water distribution networks. For the case of a single pipe that suffers from a single leak, by taking recourse to pressure and flow measurements, and assuming those are noiseless, we provide a closed-form expression for leak localization, leak exponent and leak constant. For the aforementioned setting, but with noisy pressure and flow measurements, an expression for estimating the location of the leak is provided. Finally, assuming the existence of a single leak, for a network comprising of more than one pipe and assuming that the network has a tree structure, we provide a systematic procedure for determining the leak location, the leak exponent, and the leak constant.

关键词： Location awareness Knowledge engineering Closed-form solutions Systematics Network topology Distribution networks Topology

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