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Interval Type-2 Fuzzy Model for Intelligent Fire Intensity Detection Algorithm with Decision Making in Low-Power Devices

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Intelligent Automation & Soft Computing 2023年第10期38卷 57-81页

作者： Emmanuel Lule Chomora Mikeka Alexander Ngenzi Didacienne Mukanyiligira African Center of Excellence in Internet of Things(ACEIoT) College of Science&TechnologyUniversity of RwandaNyarugenge-Kigali3900Rwanda Department of Computer Science College of Computing&Information SciencesMakerere UniversityKampala7062Uganda Directorate of Science Technology&Innovation(DSTI)Ministry of EducationMalawiLilongwe328Malawi Department of Computer&Software Engineering College of Science&TechnologyUniversity of RwandaNyarugenge-Kigali3900Rwanda National Council for Science&Technology(NCST) Government of RwandaKigali2285Rwanda

Local markets in East Africa have been destroyed by raging fires,leading to the loss of life and property in the nearby *** circuits,arson,and neglected charcoal stoves are the major causes of these *** methods,i.e.,satellites,are expensive to maintain and cause unnecessary ***,unit-smoke detectors are highly prone to false *** this paper,an Interval Type-2 TSK fuzzy model for an intelligent lightweight fire intensity detection algorithm with decision-making in low-power devices is proposed using a sparse inference rules approach.A free open–source MATLAB/Simulink fuzzy toolbox integrated into MATLAB 2018a is used to investigate the performance of the Interval Type-2 fuzzy *** crisp input parameters,namely:FIT and FIG��are *** show that the Interval Type-2 model achieved an accuracy value of FIO�=98.2%,MAE=1.3010,MSE=1.6938 and RMSE=1.3015 using regression *** study shall assist the firefighting personnel in fully understanding and mitigating the current level of fire *** a result,the proposed solution can be fully implemented in low-cost,low-power fire detection systems to monitor the state of fire with improved accuracy and reduced false *** informed decision-making in low-cost fire detection devices,early warning notifications can be provided to aid in the rapid evacuation of people,thereby improving fire safety surveillance,management,and protection for the market community.

关键词： Interval type-2(IT2)fuzzy systems mean absolute error(MAE) mean square error(MSE) root mean square

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Edge-assisted ML-aided Uncertainty-aware Vehicle Collision Avoidance at Urban Intersections

arXiv

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arXiv 2024年

作者： Selvaraj, Dinesh Cyril Vitale, Christian Panayiotou, Tania Kolios, Panayiotis Chiasserini, Carla Fabiana Ellinas, Georgios CARS@Polito Politecnico di Torino Torino Italy The KIOS Research Innovation Center of Excellence The Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

Intersection crossing represents one of the most dangerous sections of the road infrastructure and Connected Vehicles (CVs) can serve as a revolutionary solution to the problem. In this work, we present a novel framework that detects preemptively collisions at urban crossroads, exploiting the Multiaccess Edge Computing (MEC) platform of 5G networks. At the MEC, an Intersection Manager (IM) collects information from both vehicles and the road infrastructure to create a holistic view of the area of interest. Based on the historical data collected, the IM leverages the capabilities of an encoder-decoder recurrent neural network to predict, with high accuracy, the future vehicles’ trajectories. As, however, accuracy is not a sufficient measure of how much we can trust a model, trajectory predictions are additionally associated with a measure of uncertainty towards confident collision forecasting and avoidance. Hence, contrary to any other approach in the state of the art, an uncertainty-aware collision prediction framework is developed that is shown to detect well in advance (and with high reliability) if two vehicles are on a collision course. Subsequently, collision detection triggers a number of alarms that signal the colliding vehicles to brake. Under real-world settings, thanks to the preemptive capabilities of the proposed approach, all the simulated imminent dangers are averted. © 2024, CC BY.

关键词： Recurrent neural networks

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Multi-Microgrid Based Coordinated Consensus Control for Demand Energy Management

Multi-Microgrid Based Coordinated Consensus Control for Dema...

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International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Don Gamage Chathura Wanigasekara Abhisek Ukil Akshya Swain Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand German Aerospace Center (DLR) Institute for the Protection of Maritime Infrastructures Bremerhaven Germany

ISBN: (数字)9798350372786

ISBN: (纸本)9798350372793

We propose a coordinated consensus-based approach for managing sudden peak demands in a multi-microgrid system (MMGS). Each micro-grid is equipped with a battery which will create a DC link. We introduce a mesh system that employs consensus-based coordination with droop coefficients to facilitate power sharing among neighbouring microgrids, ensuring load demand fulfilment while regulating frequency and voltage at the point of common coupling (PCC). The overall system comprises five interconnected microgrids forming a undirected communication graph. Furthermore, We analyze the system’s stability using mathematical techniques derived from graph theory. Simulation of the entire system is conducted in MATLAB/Simulink virtual environment. The simulation results demonstrate various operational modes and are compared with those obtained using conventional control methods.

关键词： Reactive power Simulation Virtual environments Microgrids Power system stability Probabilistic logic Stability analysis

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New Insights on Molecular Communication in Nano Communication Networks and Their applications 1

New Insights on Molecular Communication in Nano Communicatio...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Koshy, Akshay Mammen Jeerapan, Itthipon Manjunatha, C. Department of Computer Science Engineering RV College of Engineering Bengaluru560059 India Division of Physical Science Center of Excellence for Trace Analysis and Biosensor Prince of Songkla University Songkhla Hat Yai90110 Thailand Department of Chemistry RV College of Engineering Bengaluru560059 India Visvesvaraya Technological University Belagavi590018 India

ISBN: (纸本)9781607685395

The advancement in nanotechnology has carved out a way to establish a powerful base to fall on for implementation of nanoscale machines having the capability to carry out significant yet trivial tasks. This impetus has led the researchers to continually bring up various solutions for implementing and executing nanoscale communications taking into consideration of biological as well as electromagnetic communication. The primary aims are expanding the abilities of nanoscale devices to integrated multiple composite tasks by reciprocated coordination, which can be carried out through this communication type. This paper describes the current research developments in nano communication networks, focusing on molecular communication (MC). MC makes use of molecules as an information transporter between nanoscale machines. Molecules help in enciphering messages to send between a sender and a receiver. MC has, thereby, become an inseparable part of the IONT (Internet of Nano-Things) as a communication model among nanoscale machines. This paper also gives a description of the design of MC systems and various channelling models used to address the various challenges and opportunities in this new upcoming and developing field. In addition, this review article is also inclusive of an investigative survey on the recent developments in this field. © The Electrochemical Society

关键词： Biochemical Communication Molecular Communication Molecular Propagation Systems Nano Networks Nanomachines

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A Survey on Anomaly Detection with Few-Shot Learning 8th

A Survey on Anomaly Detection with Few-Shot Learning

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8th International Conference on Cognitive Computing, ICCC 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Chen, Junyang Wang, Changbo Hong, Yifan Mi, Rui Zhang, Liang-Jie Wu, Yirui Wang, Huan Zhou, Yue College of Computer Science and Software Engineering Shenzhen University Shenzhen China Center of Health Administration and Development Studies Hubei University of Medicine Shiyan China College of Informatics Huazhong Agricultural University Wuhan China Hohai University Nanjing China Department of Obstetrics and Gynecology Tianyou Hospital Affiliated to Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783031779534

The primary objective of anomaly detection is to identify abnormal or unusual patterns within a dataset, where the number of normal samples typically exceeds that of abnormal samples. Due to the scarcity of labeled abnormal samples, traditional methods face challenges when dealing with anomaly detection. To overcome these limitations, few-shot learning has emerged as a promising solution. By leveraging a limited number of labeled anomaly samples, few-shot learning enables the construction of models that enhance anomaly detection performance and generalization. This paper provides a comprehensive investigation of anomaly detection, covering its definition, fundamental principles, methods, and challenges. Furthermore, it introduces few-shot learning as a solution and explores its principles, applications, and technical categorization, including meta-learning, transfer learning, generative models, prototypical learning, and siamese networks. The paper explores the utilization of few-shot learning in anomaly detection across diverse data types This paper delves into significance across different domains. Additionally, it addresses the challenges faced by few-shot learning in the field of anomaly detection and proposes future directions for development. This comprehensive analysis aims to provide profound insights and guidance for prospective research and application in anomaly detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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A Toolbox for Supporting Research on AI inWater Distribution Networks

arXiv

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arXiv 2024年

作者： Artelt, André Kyriakou, Marios S. Vrachimis, Stelios G. Eliades, Demetrios G. Hammer, Barbara Polycarpou, Marios M. Faculty of Technology Bielefeld University Germany KIOS Research and Innovation Center of Excellence University of Cyprus Cyprus Department of Electrical and Computer Engineering University of Cyprus Cyprus

Drinking water is a vital resource for humanity, and thus,Water Distribution Networks (WDNs) are considered critical infrastructures in modern societies. The operation of WDNs is subject to diverse challenges such as water leakages and contamination, cyber/physical attacks, high energy consumption during pump operation, etc. With model-based methods reaching their limits due to various uncertainty sources, AI methods offer promising solutions to those challenges. In this work, we introduce a Python toolbox for complex scenario modeling & generation such that AI researchers can easily access challenging problems from the drinking water domain. Besides providing a high-level interface for the easy generation of hydraulic and water quality scenario data, it also provides easy access to popular event detection benchmarks and an environment for developing control algorithms. © 2024, CC BY.

关键词： Potable water

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ESPiM: Eye-Strain Probation Model, An Eye-Tracking Analysis Measure for Digital Displays

arXiv

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arXiv 2023年

作者： Parisay, Mohsen Haghbin, Negar Poullis, Charalambos Kersten-Oertel, Marta Department of Computer Science and Software Engineering Immersive and Creative Technologies Lab Canada PERFORM Center Concordia University Montreal Canada

Eye-strain is a common issue among computer users due to the prolonged periods they spend working in front of digital displays. This can lead to vision problems, such as irritation and tiredness of the eyes and headaches. We propose the Eye-Strain Probation Model (ESPiM), a computational model based on eye-tracking data that measures eye-strain on digital displays based on the spatial properties of the user interface and display area for a required period of time. As well as measuring eye-strain, ESPiM can be applied to compare (a) different user interface designs, (b) different display devices, and (c) different interaction techniques. Two user studies were conducted to evaluate the effectiveness of ESPiM. The first was conducted in the form of an in-person study with an infrared eye-tracking sensor with 32 participants. The second was conducted in the form of an online study with a video-based eye-tracking technique via webcams on users’ computers with 13 participants. Our analysis showed significantly different eye-strain patterns based on the video gameplay frequency of participants. Further, we found distinctive patterns among users on a regular 9-to-5 routine versus those with more flexible work hours in terms of (a) error rates and (b) reported eye-strain symptoms. © 2023, CC BY.

关键词： Display devices

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Staggered School Schedules for the morning commute problem - An MFD-based Optimization Approach

Staggered School Schedules for the morning commute problem -...

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Mediterranean Conference on Control and Automation (MED)

作者： Antonios Georgantas Charalambos Menelaou Stelios Timotheou Christos G. Panayiotou KIOS Research and Innovation Center of Excellence and the Department of Electrical and Computer Engineering University of Cyprus 2109 Panepistimiou 1 Avenue Cyprus

This paper deals with the morning commute problem when two classes of commuters co-exist in an urban transportation network. While each school starts at the same time, traffic flow enters the network simultaneously, leading to the formation of a peak demand value that the network cannot fully accommodate. The task becomes more challenging when commuters head to their workplace after reaching their respective school. To tackle this issue, we propose the School Demand Allocation Paradigm (SDAP), which allows schools to have different starting times. Subsequently, we deploy an optimization framework, the target of which is to determine the optimal pair of the shifted school start time for each school that maintains operation under free-flow conditions. We utilize a macroscopic MFD-based traffic model, which can account for the coupling of the classes mentioned above. The effectiveness of the proposed approach is verified through macroscopic simulations.

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NON-INTRUSIVE WATER USAGE CLASSIFICATION CONSIDERING LIMITED TRAINING DATA

arXiv

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arXiv 2023年

作者： Pavlou, Pavlos Vrachimis, Stelios Eliades, Demetrios G. Polycarpou, Marios M. Kios Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Cyprus

Smart metering of domestic water consumption to continuously monitor the usage of different appliances has been shown to have an impact on people's behavior towards water conservation. However, the installation of multiple sensors to monitor each appliance currently has a high initial cost and as a result, monitoring consumption from different appliances using sensors is not cost-effective. To address this challenge, studies have focused on analyzing measurements of the total domestic consumption using Machine Learning (ML) methods, to disaggregate water usage into each appliance. Identifying which appliances are in use through ML is challenging since their operation may be overlapping, while specific appliances may operate with intermittent flow, making individual consumption events hard to distinguish. Moreover, ML approaches require large amounts of labeled input data to train their models, which are typically not available for a single household, while usage characteristics may vary in different regions. In this work, we initially propose a data model that generates synthetic time series based on regional water usage characteristics and resolution to overcome the need for a large training dataset with real labeled data. The method requires a small number of real labeled data from the studied region. Following this, we propose a new algorithm for classifying single and overlapping household water usage events, using the total domestic consumption measurements. The classification procedure is described below: 1) During the offline feature learning stage, a dataset of labeled data corresponding to water-use profile signals is analyzed to some predefined features, such as event volume, event duration, event flow peak, and event signature, to extract its statistical properties, 2) The event classification stage monitors the provided measurement time-series for events between zero-flow intervals. The identified events are then classified using Dynamic Time Wrapping an

关键词： Classification (of information)

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Improving Customer Experience in Call centers with Intelligent Customer-Agent Pairing

arXiv

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arXiv 2023年

作者： Filippou, Stylianos Tsiartas, Andreas Hadjineophytou, Petros Christofides, Spyros Malialis, Kleanthis Panayiotou, Christos G. Kios Research and Innovation Center of Excellence University of Cyprus Cyprus Department of Electrical and Computer Engineering University of Cyprus Cyprus Cyprus

Customer experience plays a critical role for a profitable organisation or company. A satisfied customer for a company corresponds to higher rates of customer retention, and better representation in the market. One way to improve customer experience is to optimize the functionality of its call center. In this work, we have collaborated with the largest provider of telecommunications and Internet access in the country, and we formulate the customer-agent pairing problem as a machine learning problem. The proposed learning-based method causes a significant improvement in performance of about 215% compared to a rule-based method. © 2023, CC BY-SA.

关键词： Machine learning

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