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Toward a Normative Approach for Resilient Multiagent systems: A Summary 24

Toward a Normative Approach for Resilient Multiagent Systems...

引用

Proceedings of the 23rd international conference on Autonomous Agents and Multiagent systems

作者： Geeta Mahala Ozgur Kafali Hoa Khanh Dam Aditya Ghose Munindar P. Singh University of Wollongong Wollongong Australia University of Kent Canterbury United Kingdom Computer Science North Carolina State university Raleigh NC USA

ISBN: (纸本)9798400704864

We model a multiagent system (MAS) in socio-technical terms, combining a social layer consisting of norms with a technical layer consisting of actions that the agents execute. We express stakeholder needs to ensure that a MAS demonstrates resilience, allowing it to recover effectively from failures within a brief timeframe. This extended abstract presents a framework that computes probabilistic and temporal guarantees on whether the underlying requirements are met or, if failed, recovered. An important contribution of the framework is that it shows how the social and technical layers can be modeled jointly to enable the construction of resilient systems of autonomous agents. This paper facilitates specification refinement through methodological guidelines, emphasizing joint modeling of social and technical layers. We demonstrate our framework using a manufacturing scenario with competing public, industrial, and environmental requirements. This is an extended abstract of our JAAMAS paper available online[11].

关键词： formal verification norms resilience

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joint design of the transmit and receive beamforming via non-iterative optimization for MIMO radar

Joint design of the transmit and receive beamforming via non...

引用

IET international Radar conference (IRC 2023)

作者： Yujia Li Xiaolin Du Mengjiao Tang Guolong Cui Yao Rong Computer and Control Engineering Yantai University Yantai People's Republic of China Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Kunming People's Republic of China Information and Communication Engineering University of Electronic Science and Technology of China Chengdu People's Republic of China

ISBN: (数字)9781837240982

The joint design of the transmit and receive beamforming for multiple-input multiple-output (MIMO) radar systems is considered in the background of signal dependent interference. An optimization model using signal-to-interference-plus-noise-ratio (SINR) as a performance metric is designed under similarity cone, power, and energy constraints. An algorithm for simultaneous updating of transmit and receive (T-R) beamforming is presented, that bypasses the conventional T-R iteration and initialization, thus avoiding premature termination of the iteration or poor initialization. Simulation experiments verify the effectiveness of the method.

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Neural Network Software to Process Medical Data modeling 3

Neural Network Software to Process Medical Data Modeling

引用

3rd international conference on Control systems, Mathematical modeling, Automation and Energy Efficiency, SUMMA 2021

作者： Emelyanov, Egor Polyakov, Maxim Volgograd State University Depart. of Information Systems and Computer Modeling Volgograd Russia

ISBN: (纸本)9781665439817

Microwave radiometry enables measuring the temperature in biological tissues at a depth of several centimeters. Temperature is a crucial source of data on person's physical condition. The paper is devoted to developing an approach to ensure early breast cancer diagnosis by mapping temperature using machine learning techniques, using convolutional neural networks in particular. The novelty of our approach lies in studying the images drawn up based on temperature data obtained from local measurement points, with different color schemes used. Interpolation algorithms were used to improve the quality of the images obtained. The originality of the approach lies in the fact that, in addition to measuring real patients, computer modeling of mammary gland thermal fields is also used. We found that the use of extremely-Accurate neural networks to diagnose breast cancers is an effective and viable approach. Even if few learning samples (192 examples) are available, our proposed algorithm shows roughly 80% high accuracy adjusted to the neural network topology and the color scheme applied. We also demonstrate the dependence of diagnosis quality on the number of source images used while training and testing. We have concluded that the combined use of the left and right mammary gland thermograms greatly improves diagnosis efficiency when using graphical image machine learning. © 2021 IEEE.

关键词： Diagnosis

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A Design of Hybrid Model and Bayesian Neural Networks for Smart Grid Stability Prediction

A Design of Hybrid Model and Bayesian Neural Networks for Sm...

引用

Smart and Sustainable Developments in Electrical Engineering (SSDEE), IEEE international conference on

作者： Shreyas Rajendra Hole Vinothkumar Kolluru Shreekant Salotagi Yagnesh Challagundla Sudeep Mungara Sriramkumar R Computer Science & Engineering AI & ML Dayananda Sagar University Bangalore India Department of data science Stevens Institute of technology Hoboken NJ U.S.A. Computer Science & Engineering Dayananda Sagar University Bagalore India Department of Computer science Herbert Wertheim College of Engineering University of Florida Gainesville Florida USA Masters in Information Systems Stevens Institute of Technology Hoboken NJ U.S.A.

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

The latest evolution in power systems, is ‘smart grids'that offers real time monitoring, control features as well as effective management of renewable energy sources. Nonetheless, with the increase in the system complexity, the chances for insta- bility also increase and thus it is crucial to forecast the stability accurately for the safety of the grid. Weather it is in terms of Gradient Boosting Tree Algorithm or any other traditional algorithm, these machine learning models are successful in achieving the objectives of the task but they lack a forthcoming metric to assist in high stake situations, uncertainty quantification. The paper is devoted to the analysis of the reliability of smart grid systems based on Bayesian Neural Networks (BNN) taking into account both the reliability of predictive modeling and forecasting uncertainty. The smart grid stability augmented dataset was used to benchmark the efficacy of using BNNs or BNNs combined hybrid models, one with XGBoost, for feature extraction and the other with LightGBM. The present study shows that the BNN used with the above mentioned smart grid system achieved an accuracy of 92% with precision of 0.89, recall of 0.91 and F1 of 0.90. These metrics are appending with the help of the additional features of BNN where they are capable of addressing the key questions around uncertainty and thus the metrics confirming the predictions are quite strong in terms of real-time grid stability evaluation. This article encapsulates a detailed study of BNN architecture, the role of uncertainty in machine learning models and the natural application of such models in the design of dependable and fault-tolerant smart grids.

关键词： measurement Renewable energy sources Uncertainty Neural networks Power system stability Predictive models Stability analysis Real-time systems Smart grids Bayes methods

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Sparse Channel Representation and Estimation in Near Field Communications

Sparse Channel Representation and Estimation in Near Field C...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xing Zhang Haiyang Zhang Yonina C. Eldar School of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China Faculty of Math and Computer Science Weizmann Institute of Science Israel

The employment of extremely large antenna arrays and high-frequency signaling makes future communication systems likely to operate in the near-field region, where the conventional planar wave assumption is invalid. Instead, a spherical wave assumption which provides both the user angle and distance information is more accurate. Spherical wave-based channel representation and estimation is still under investigation. In this work, we propose a distance-parameterized angular-domain sparse model to represent the near-field channel, followed by a joint dictionary learning and sparse recovery based channel estimation algorithm. The proposed sparse representation model overcomes challenges such as the storage burden and high dictionary coherence that arise in the existing polar-domain method. Simulations in multi-user communication scenarios support the superiority of the proposed near-field channel sparse representation and estimation over the polar-domain method in channel estimation error and pave the way to efficient and accurate modeling in the near-field regime.

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An Iterative Variable Step Error Compensation Approach for Enhancing Robot Trajectory Accuracy

An Iterative Variable Step Error Compensation Approach for E...

引用

Automation, Robotics and computer Engineering (ICARCE), international conference on

作者： Chengzhi Wang Sikai Zhao Tianjiao Zheng Hegao Cai Jie Zhao Yanhe Zhu State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

In this paper, an iterative error compensation approach is analyzed and designed to improve trajectory accuracy of industrial robots. Advanced offline programming and robotic machining tasks require high trajectory accuracy of industrial robots, which is significantly affected by parameter inaccuracy, stiffness deformation, backlash, and so on. Such complicated coupling makes it impossible to model each factor separately, thus data-driven direct error compensation is the major solution to improve trajectory accuracy. Previous methods utilize external measurement equipment to obtain tracking error and rely on the error consistency assumption to compensate for the error by modifying the trajectory commands directly. However, such consistency assumption does not hold. Even a minor incremental modification of the initial commands can lead to unexpected influence on the robot's tracking performance, owning to unaccounted factors such as the robot's dynamic stiffness and natural frequency. This paper decomposes trajectory tracking errors based on servo encoder feedback and external laser tracker, and proposes a variable step error compensation scheme to improve tracking performance. Physical experiments are conducted on a standard 6 degree-of-freedom industrial robot to validate the effectiveness of the proposed method. The results of various error compensation approaches are analyzed to draw a possible error correlation model, in order to account for the error inconsistency in the compensation process.

关键词： Accuracy Service robots Trajectory tracking measurement uncertainty Error compensation Industrial robots Laser modes Trajectory Iterative methods Load modeling

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Design of A Tidal Volume measurement Device for Respiration Tracking of Radiosurgrical Robots 6

Design of A Tidal Volume Measurement Device for Respiration ...

引用

6th IEEE international conference on Advanced Robotics and Mechatronics (ICARM)

作者： Wang, Jiateng Li, Bo Yu, Shumei Sun, Rongchuan Sun, Lining Soochow Univ Sch Mech & Elect Engn Suzhou Peoples R China

ISBN: (纸本)9780738133645

In radiotherapy of tumor, position changes of the tumor due to the respiratory movement of the patient will affect the accuracy of treatment. Therefore, the study of respiratory process is important for respiration tracking. In order to solve the problem of the difference between expiratory and inspiratory processes, a real-time tidal volume measuring device was proposed to measure the expiratory and inspiratory processes independently. Compared with the existing tidal volume collection devices, this design avoids the tidal volume measurement error caused by the difference between expiratory process and inspiratory process. Besides, the segmented tidal volume measurement is potentially feasible for the establishment of respiration tracking models. Several experiment results have validated the effectiveness and superiority of the proposed tidal volume measurement device.

关键词： Solid modeling Mechatronics Tracking Volume measurement conferences Valves Real-time systems

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Paddy Leaf Disease Detection Using Fine-tuned EfficientNetB4 Convolutional Neural Network

Paddy Leaf Disease Detection Using Fine-tuned EfficientNetB4...

引用

international conference on Computing and Networking Technology (ICCNT)

作者： Thota Siddartha Nallamilli Eswar Venkata Reddiar Pilla Veera Satya Sai Vikranth Teerdhala Kumar Rimjhim Padam Singh Priyanka Kumar Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Department of Computer Science University of Texas at San Antonio Texas USA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Agriculture remains the backbone of many countries since it acts as the main source of income and food. In this regard, different food crops are significant in the global economy, where rice occupies a very central position as one of the most important staple foods in many Asian countries. However, rice cultivation encounters various diseases at the different stage affecting either the quality or the rate of growth in the crop. Diagnosis of these diseases can be tiresome by applying traditional methods and use of naked eyes because symptoms of most diseases are often related. Therefore, the use of automated systems and the integration of such systems adds more realization to disease detection, hence informing the farmer to take appropriate measures on time to minimize crop or plant losses in order to yield quality produce. In the last several years different approaches utilizing image processing, machine learning, and deep learning algorithms have been investigated by researchers to create effective automatic systems. Therefore, this paper aims at optimizing several of the existing highly accurate deep learning-based state-of-art Convolutional neural networks for the early detection of different rice crop diseases, also known as paddy leaf diseases. This work also pointed out a comparative assessment of the utilized techniques through precision, accuracy, recall, and F1-score metrics with a fine-tuned EfficientNetB4 model with an optimum F1-score of approximately 99.7%.

关键词： Deep learning Accuracy Machine learning algorithms Computational modeling Crops Agriculture Loss measurement Time measurement Convolutional neural networks Diseases

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Detection of Ladder Logic Bombs in PLC Control Programs: an Architecture based on Formal Verification

Detection of Ladder Logic Bombs in PLC Control Programs: an ...

引用

Industrial Cyber-Physical systems (ICPS)

作者： Antonio Iacobelli Lorenzo Rinieri Andrea Melis Amir Al Sadi Marco Prandini Franco Callegati Department of Computer Science and Engineering Università di Bologna Bologna Italy

ISBN: (数字)9798350363012

ISBN: (纸本)9798350363029

Industrial Control systems (ICS) are responsible for the operations of production plants and critical infrastructures such as nuclear plants and smart grids. Since failures in such systems may harm people, damage properties, and affect the environment, security (in addition to safety) concerns are of paramount importance. In this work, we present a formal verification architecture for the detection of Ladder Logic Bombs, a class of attacks aimed at disrupting the normal operation of Programmable Logic controllers (PLCs). We built a dataset of PLC programs modeling the control logic of a physical system, containing thirty malicious and thirty legitimate samples, then we tested the proposed framework showing that it is able to detect LLBs with very high values of accuracy and precision.

关键词： measurement Weapons Programmable logic devices Production Mathematical models Smart grids Safety

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An Uncertainty Decision Model for Partial Observable Agents by Dynamically Sharing Group Utilities

An Uncertainty Decision Model for Partial Observable Agents ...

引用

6th international conference on Intelligent Computing and Signal Processing (ICSP)

作者： Shengqi Xia Shangyan Zhang Yang Xu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

A decision-making model for intelligent agents is proposed in this study, emphasizing the maximization of group utility to address the challenges of multi-agent decision-making in dynamic and partially observable environments. The model functions on the principle of agents collectively forming utility, thereby mitigating limitations arising from incomplete observations through shared utilities. By prioritizing the enhancement of group utility, the model facilitates the establishment of joint behavior and action plans for the agents. A representative experiment is conducted within the context of Pac-Man gameplay, where the ghosts aim to encircle the Pac-Man. Through controlled experiments, the cooperative decision-making capability of the model is assessed, revealing a significant improvement in the performance of the multi-agent system. The findings demonstrate that adopting a decision-making approach centered on group utility diminishes the constraints arising from individual agent observations, particularly within extensive search spaces. Consequently, the model proves beneficial in aiding multi-agent systems in efficiently accomplishing their designated tasks.

关键词： Uncertainty Computational modeling Decision making Information sharing Collaboration Games Aerospace electronics Signal processing Intelligent agents Multi-agent systems

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