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IEEE General Meeting Power& Energy Society

作者： Lysandros Tziovani Lenos Hadjidemetriou Stelios Timotheou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

Energy storage systems (ESSs) are increasingly used in power system optimization by deriving different ESS mathematical models. The most widely-used model is the piecewise linear ESS model which utilizes non-convex constraints to represent the ESS power losses, resulting in challenging optimization problems. To reduce the problem complexity, convex relaxation models are often derived but may compromise the solution quality of the underlying problems. This work investigates the exact and relaxed versions of three different mathematical representations of the piecewise linear ESS model, in terms of their solution quality and execution efficiency. Towards this direction, the three ESS models are incorporated into the unit commitment problem which often violates the ESS relaxation exactness under ramping constraints. Simulation results present (a) the execution times of the exact and relaxed ESS models and (b) the optimality gap of the relaxed models when the relaxation exactness is violated.

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A Blockchain based Framework for Verifying Drug Authenticity

A Blockchain based Framework for Verifying Drug Authenticity

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Information Technology (ACIT)

作者： Soomaiya Hamid Narmeen Zakaria Bawany Kiran Fatima Department of Computer Science and Software Engineering Center for Computing Research Jinnah University for Women Karachi Pakistan

Counterfeit drugs are fake medicines that are potentially harmful for health. Safeguarding the integrity of pharmaceutical distribution plays a crucial role in preventing the circulation of counterfeit drugs. Traditional drug supply chain systems have no effective mechanism to ensure the traceability and integrity of the drug supply chain process. Various ICT-based solutions have been adopted previously and numerous regulations were passed by drug regulatory authorities to ensure interoperable and traceable drug distribution process. However, these solutions could not guarantee a tamper-proof history of supply chain process. Blockchain technology has gained traction in many diverse fields including real-estate, identity management, digital voting, healthcare, etc. Therefore, we have proposed a decentralized framework to insure the authenticity and traceability of the drugs. Moreover, this study presents an overview of how blockchain technology can be employed to counter the fake medicines circulation by embedding the traceability in drug supply chain process. Comparison of various blockchain-based solutions and open research challenges in this regard are also presented.

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Market Order Prequalification for TSO-DSO Coordination

Market Order Prequalification for TSO-DSO Coordination

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Power Tech Conference

作者： Ioannis Papayiannis Markos Asprou Lenos Hadjidemetriou Stelios Timotheou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

The increasing deployment of Distributed Energy Resources (DERs) creates an urgent need for active distribution grid management and effective coordination between the Transmission System Operator (TSO) and Distribution System Operator (DSO). Several TSO-DSO coordination models have been proposed in the literature. Two of these models, the Centralized Market and the Local Market model, require a market order prequalification stage. Prequalification is essential to ensure the distribution grid thermal limits are not violated when DER orders are activated in the central market (operated by the TSO). In this study, a market order prequalification scheme, applicable to both coordination models, is proposed to create a new set of market orders, ensuring that the distribution lines will not get congested under any order activation scenario. The effectiveness of the proposed prequalification scheme and the performance of the two coordination models are evaluated through a case study using a test system comprising the IEEE 9-bus system (transmission) and the IEEE 33-bus test feeder (distribution).

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Sensor Selection for High-dimensional Swarm Systems Based on Observability Analysis

Sensor Selection for High-dimensional Swarm Systems Based on...

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Mediterranean Conference on Control and Automation (MED)

作者： Qingkai Meng Marios M. Polycarpou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

The location selection of sensors in large-scale swarm systems is a prerequisite for further design of mechanisms to monitor the system states. This paper considers the required number and location of the sensors in a large-scale swarm system so that the observability of the overall system is satisfied. Firstly, by extending observability theory for swarm systems, some necessary and/or sufficient observability conditions related to the node-dynamics, network topology, coupling mode and measured outputs are obtained. Secondly, based on the above observability conditions, an algorithm for deciding how many and where to place the sensors is designed, which can be implemented in a polynomial complexity time. Finally, an unmanned aerial vehicle (UAV) swarm system is employed to verify the effectiveness of the theoretical results.

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Deep Learning for Violence Detection in Surveillance: The Role of Transfer Learning and Pre-Trained Models

Deep Learning for Violence Detection in Surveillance: The Ro...

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Information Technology (ACIT)

作者： Anum Ilyas Surayya Obaid Narmeen Zakaria Bawany Department of Computer Science and Software Engineering Center for Computing Research Jinah University for Women Karachi Pakistan

In recent years, the use of CCTV footage for proactive crime prevention has surged, particularly in public places like airports, train stations, and malls. However, the efficacy of these surveillance systems becomes questionable for their substantial reliance on human resources which may lead to erroneous or delayed responses. This research takes a comprehensive approach for violence detection which was previously limited to binary classification, by categorizing violence into four distinct classes: abuse, arson, assault, and fight. It employs transfer learning combined with computer vision techniques for violence detection and classification in video footages. The study compares the performance of four pre-trained neural networks, namely DenseNet121, VGG16, MobileNet, and Xception. The dataset created for this research is compiled from three datasets available on Kaggle. The results reveal that the Xception model performed comparatively better achieving the highest AUC score of 98.39%, while the VGG16 model attained the lowest AUC score of 96%. In addition to the AUC score, precision, recall, and fl-score are employed as performance metrics. Transfer learning with convolutional neural networks (CNN) significantly reduced computational requirements and time. Automating the detection and categorization of violent behavior through the employed approach has the potential to reduce the risk of fatalities and injuries in public areas. It also improves the speed and accuracy of threat detection, enabling swift preventive actions by authorities.

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A Multi-Task Learning Framework for Drone State Identification and Trajectory Prediction

A Multi-Task Learning Framework for Drone State Identificati...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Antreas Palamas Nicolas Souli Tania Panayiotou Panayiotis Kolios Georgios Ellinas Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence (KIOS CoE) University of Cyprus

The rise of unmanned aerial vehicle (UAV) operations, as well as the vulnerability of the UAVs' sensors, has led to the need for proper monitoring systems for detecting any abnormal behavior of the UAV. This work addresses this problem by proposing an innovative multi-task learning framework (MLF-ST) for UAV state identification and trajectory prediction, that aims to optimize the performance of both tasks simultaneously. A deep neural network with shared layers to extract features from the input data is employed, utilizing drone sensor measurements and historical trajectory information. Moreover, a novel loss function is proposed that combines the two objectives, encouraging the network to jointly learn the features that are most useful for both tasks. The proposed MLF-ST framework is evaluated on a large dataset of UAV flights, illustrating that it is able to outperform various state-of-the-art baseline techniques in terms of both state identification and trajectory prediction. The evaluation of the proposed framework, using real-world data, demonstrates that it can enable applications such as UAV-based surveillance and monitoring, while also improving the safety and efficiency of UAV operations.

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Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

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computers, Materials & Continua 2022年第9期72卷 5117-5134页

作者： Hiam Alquran Wan Azani Mustafa Isam Abu Qasmieh Yasmeen Mohd Yacob Mohammed Alsalatie Yazan Al-Issa Ali Mohammad Alqudah Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Faculty of Electrical Engineering Technology Campus Pauh PutraUniversiti Malaysia Perlis02000ArauPerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia Perlis(UniMAP)02000ArauPerlisMalaysia The Institute of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Department of Computer Engineering Yarmouk UniversityIrbid21163Jordan

Cervical cancer is screened by pap smear methodology for detection and classification *** smear images of the cervical region are employed to detect and classify the abnormality of cervical *** this paper,we proposed the first system that it ables to classify the pap smear images into a seven classes *** smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images *** features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine(SVM)*** success of this proposed system in distinguishing between the levels of normal cases with 100%accuracy and 100%*** top of that,it can distinguish between normal and abnormal cases with an accuracy of 100%.The high level of abnormality is then studied and classified with a high *** the other hand,the low level of abnormality is studied separately and classified into two classes,mild and moderate dysplasia,with∼92%*** proposed system is a built-in cascading manner with five models of polynomial(SVM)*** overall accuracy in training for all cases is 100%,while the overall test for all seven classes is around 92%in the test phase and overall accuracy reaches 97.3%.The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer,which may lead to an increase in the survival rate in women.

关键词： Classification deep learning machine learning pap smear images resnet101 support vector machines

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Machine Learning for Real-Time Anomaly Detection in Optical Networks

Machine Learning for Real-Time Anomaly Detection in Optical ...

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International Conference on Transparent Optical Networks

作者： Sadananda Behera Tania Panayiotou Georgios Ellinas Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

This work proposes a real-time anomaly detection scheme that leverages the multi-step ahead prediction capabilities of encoder-decoder (ED) deep learning models with recurrent units. Specifically, an encoder-decoder is used to model soft-failure evolution over a long future horizon (i.e., for several days ahead) by analyzing past quality-of-transmission (QoT) observations. This information is subsequently used for real-time anomaly detection (e.g., of attack incidents), as the knowledge of how the QoT is expected to evolve allows capturing unexpected network behavior. Specifically, for anomaly detection, a statistical hypothesis testing scheme is used, alleviating the limitations of supervised (SL) and unsupervised learning (UL) schemes, usually applied for this purpose. Indicatively, the proposed scheme eliminates the need for labeled anomalies, required when SL is applied, and the need for on-line analyzing entire datasets to identify abnormal instances (i.e., UL). Overall, it is shown that by utilizing QoT evolution information, the proposed approach can effectively detect abnormal deviations in real-time. Importantly, it is shown that the information concerning soft-failure evolution (i.e., QoT predictions) is essential to accurately detect anomalies.

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Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation

Autoencoder-based Anomaly Detection in Streaming Data with I...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jin Li Kleanthis Malialis Marios M. Polycarpou KIOS Research and Innovation Center of Excellence Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

In our digital universe nowadays, enormous amount of data are produced in a streaming manner in a variety of application areas. These data are often unlabelled. In this case, identifying infrequent events, such as anomalies, poses a great challenge. This problem becomes even more difficult in non-stationary environments, which can cause deterioration of the predictive performance of a model. To address the above challenges, the paper proposes an autoencoder-based incremen-tal learning method with drift detection (strAEm++DD). Our proposed method strAEm++DD leverages on the advantages of both incremental learning and drift detection. We conduct an experimental study using real-world and synthetic datasets with severe or extreme class imbalance, and provide an empirical analysis of strAEm++DD. We further conduct a comparative study, showing that the proposed method significantly outper-forms existing baseline and advanced methods.

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Attitude Consensus Control with Disturbance Rejection for Incompletely Cooperative Multi-agent Systems on $SO(3)$

Attitude Consensus Control with Disturbance Rejection for In...

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IEEE Conference on Decision and Control

作者： Qingkai Meng Andreas Kasis Marios M. Polycarpou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

The three-dimensional orthogonal matrix group $SO(3)$ offers global, unique, and nonsingular attitude representation of the rotational motion. This paper addresses the attitude consensus problem with disturbance rejection for multi-agent systems consisting of incompletely cooperative agents evolving on $SO(3)$ . Firstly, we establish an individual cost functional that evaluates the agent consensus aim using the natural Riemannian metric on $SO(3)$ , and formulate the considered consensus problem as a differential game. Secondly, a baseline consensus protocol is designed following the inverse optimal control procedure. In addition, a finite-time disturbance observer on $SO(3)$ is developed based on the non-singular terminal sliding mode technique, which is used for estimating and compensating for disturbances. The effectiveness of the proposed schemes is verified with simulations on a 4-vehicle formation setting.

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