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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Solomatin, Alexander Federal Research Center 'Computer Science and Control' of Ras Department of Mathematical Methods of Regional Programming Moscow Russia

ISBN: (纸本)9798350337907

The principle of multiplicity is introduced for the components of the investment planning process. Some algorithms are given that ensure the process of investment planning at the regional level: the formation of integrated assessments of investment projects, automated generation of projects, multicriteria evaluation and selection of projects, taking into account the mutual influence of projects, optimizing the composition of projects, and taking into account financing restrictions. The features of the program system intended for the formation of plans for the territorial and sectoral development of the region are considered. © 2023 IEEE.

关键词： Regional planning

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RES-EV: Identifying EV-Households under High AC Load Using a Residual-Based Model 8

RES-EV: Identifying EV-Households under High AC Load Using a...

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8th IEEE International Energy Conference, ENERGYCON 2024

作者： Aly, Hussein Al-Ali, Abdulaziz Al-Ali, Abdulla Malluhi, Qutaibah College of Engineering Department of Computer Science and Engineering Qatar University Doha Qatar Kindi Center for Computing Research Qatar University Doha Qatar

ISBN: (纸本)9798350382150

This paper introduces a novel residual-based model to identify households with Battery Electric Vehicles (EVs) under high Air Conditioning (AC) load. The considerable energy demands of AC units can obscure charging events for EVs. In this work we propose a residual based model which leverages the distinctive characteristics of EV charging patterns, marked by unpredictable spikes in energy consumption, and the more predictable nature of AC load. Our proposed approach involves training a lightweight forecasting model to predict overall household consumption and utilizes the residuals of this model for identifying household with EVs. The residual-based model, ResEV-AR, demonstrated a substantial advantage in F1 score (5.8% and 7.32%) compared to state-of-the-art models such as EVS and KBF, respectively. Additionally, a simpler residual model, ResEV-SRM, exhibited a 3.5% F1 score advantage over EVS, coupled with an impressive 11-fold reduction in computation time. ©2024 IEEE.

关键词： Energy utilization

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Improving Energy Theft Detection through Time Series Segmentation and Ensemble Learning 50

Improving Energy Theft Detection through Time Series Segment...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Altamimi, Emran Al-Ali, Abdulaziz Al-Ali, Abdulla Malluhi, Qutaibah College of Engineering Department of Computer Science and Engineering Qatar University Doha Qatar Kindi Center for Computing Research Qatar University Doha Qatar

ISBN: (纸本)9781665464543

Non-technical loss and energy theft detection are crucial for improving the stability and reducing financial losses in smart grid and power grid utilities. Recently, the availability of massive datasets has improved detection capabilities using sophisticated techniques like deep neural networks. However, training models on extensive feature sets, such as multi-year data, can lead to confusion due to varied behavioral changes in electricity consumption. To address this, we propose a reformulation of the energy theft detection problem by segmenting the time series data and training individual models on each segment. These models’ anomaly scores are then aggregated to produce a final classification. Our framework has shown significant improvement, elevating the F1 score from 0.6 to 0.74, outperforming recent state-of-the-art techniques on the SGCC dataset, the only publicly available dataset labeled for energy theft. © 2024 IEEE.

关键词： Adversarial machine learning

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Battery-Induced Load Hiding and Its Utility Consequences 4

Battery-Induced Load Hiding and Its Utility Consequences

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Aly, Hussein Altamimi, Emran Al-Ali, Abdulaziz Al-Ali, Abdulla Malluhi, Qutaibah Qatar University College of Engineering Department of Computer Science and Engineering Doha Qatar Qatar University Kindi Center for Computing Research Doha Qatar

ISBN: (纸本)9798350306262

The introduction of smart grids allows utility providers to collect detailed data about consumers, which can be utilized to enhance grid efficiency and reliability. However, this data collection also raises privacy concerns. To protect user privacy, some studies suggest using battery-based load hiding. Nevertheless, the impact of widespread adoption of this approach on utility providers remain unclear. Our paper seeks to evaluate the effects of battery-based load hiding on two critical operations: user profiling and anomaly detection. Our findings reveal that the inclusion of battery users in datasets can diminish the quality of conclusions drawn from these data. This can result in a decrease in the Area Under the Curve (AUC) by more than 10% when attempting to profile users within single-occupant and multiple-occupant households. Furthermore, our experiments demonstrate that battery-based load hiding not only conceals information about users employing the batteries but can also lead to an increased rate of false positives for other non-battery users (from 0.15 to 0.37) within the system. To mitigate these adverse effects, our study assessed various mitigation strategies. In the context of user profiling, our experiment demonstrated that identifying and removing battery users from the analytical dataset using unsupervised detection methods can effectively lessen the impact of battery users. For anomaly detection, our experiment revealed that creating separate classification models for battery and non-battery users can significantly reduce the adverse influence of battery users on the detection performance. © 2024 IEEE.

关键词： Smart power grids

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Implementation of computer vision of Jakarta Weather Image Categorization Using ResNet 8

Implementation of Computer Vision of Jakarta Weather Image C...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Purwandari, Kartika Sigalingging, Join W.C. Hidayat, Alam Ahmad Cenggoro, Tjeng Wawan Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta10720 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

At the moment, weather data is crucial for supporting neighborhood activities. The economy and trade are both centered in Jakarta, which is also Indonesia's capital. Therefore, it is crucial to have access to weather information so that these activities don't get disrupted, which would then hinder commercial and trade activity. Social media has been a very popular tool for spreading information recently. Particularly on Instagram, where users favor taking images and sharing the information they encounter. @jktinfo is the Instagram account that posts information about the situation in Jakarta and the area, including the current weather. The @jktinfo account is utilized in this project to gather data. Utilizing a variety of techniques, the collected photographs of sunny, cloudy, and wet situations were. © 2023 The Authors. Published by Elsevier B.V.

关键词： computer vision data augmentation deep transfer learning residual network weather image categorization

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Performance comparison of different algorithms for land surface temperature (LST) retrieval from Landsat-8 and the use of deep neural network regression to enhance the LST result (Case Study: Dallas, Texas and surrounding areas) 8

Performance comparison of different algorithms for land surf...

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Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications VIII 2024

作者： Ayudyanti, Amalia Gita Chen, Ying-Nong Jamaluddin, Ilham Center for Space and Remote Sensing Research National Central University Taiwan Department of Computer Science and Information Engineering National Central University Taiwan

ISBN: (数字)9781510682757

ISBN: (纸本)9781510682740

Land Surface Temperature (LST) is important data for various fields, especially for monitoring global warming. Landsat-8 satellite imagery provides thermal data with a spatial resolution of 30m (resampled from 100m) as the main data for LST retrieval. This research aims to compare several LST retrieval algorithms such as LST retrieval using band 10, Single Channel Model (SCM), Qin's Split-Window Algorithm (Q-SWA), Sobrino's Split-Window Algorithm (S-SWA), and LST from the analysis ready data of Landsat 8 Level-2. The study focuses on Dallas, Texas, and surrounding areas in March 2023. We collected air temperature data from 20 U.S. Environmental Protection Agency (EPA) stations for indirect validation. Based on the results, the Q-SWA method outperformed other retrieval algorithms with mean absolute error (MAE) and root mean squared error (RMSE) were 0.683°C and 0.842°C, respectively. Given that Landsat-8's thermal band data is resampled from 100m to 30m, this research also explores enhancing the retrieved LST using deep neural network regression (DNNR). The best retrieved LST from the Q-SWA method served as the target data while several bands and spectral indices from Landsat-8 were used as the input for DNNR model. Due to the large scale of data, we randomly selected 10 million pixels and divided into 80% of training and 20% of testing data. The DNNR model achieved MAE of 1.022°C on the testing dataset. The enhanced LST from the DNNR model was also validated with the same air temperature validation data and achieved the MAE score of 1.037°C. Based on the visual comparison result, the DNNR model successfully enhanced the retrieved LST by providing more detailed results at the same 30m resolution and showing promising performance based on error metrics. This finding suggests the potential for using deep learning regression in LST downscaling to achieve better spatial resolution. © 2024 SPIE.

关键词： Landsat

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An Efficient Design of Multiplier for Using in Nano-Scale IoT Systems Using Atomic Silicon

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IEEE Internet of Things Journal 2023年第16期10卷 14908-14909页

作者： Ahmadpour, Seyed-Sajad Heidari, Arash Navimpour, Nima Jafari Asadi, Mohammad-Ali Yalcin, Senay Kadir Has University Department of Computer Engineering Istanbul34083 Turkey Islamic Azad University Tabriz Branch Department of Computer Engineering Tabriz*** Iran Kadir Has University Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul34083 Turkey National Yunlin University of Science and Technology Future Technology Research Center Douliou64002 Taiwan Nisantasi University Department of Computer Engineering Istanbul34485 Turkey

Because of recent technological developments, such as Internet of Things (IoT) devices, power consumption has become a major issue. Atomic silicon quantum dot (ASiQD) is one of the most impressive technologies for developing low-power processing circuits, which are critical for efficient transmission and power management in micro IoT devices. On the other hand, multipliers are essential computational circuits used in a wide range of digital circuits. Therefore, the multiplier design with a low occupied area and low energy consumption is the most critical expected goal in designing any micro IoT circuits. This article introduces a low-power atomic silicon-based multiplier circuit for effective power management in the micro IoT. Based on this design, a 4× 4 -bit multiplier array with low power consumption and size is presented. The suggested circuit is also designed and validated using the SiQAD simulation tool. The proposed ASiQD-based circuit significantly reduces energy consumption and area consumed in the micro IoT compared to most recent designs. © 2014 IEEE.

关键词： Semiconductor quantum dots

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Ground-Glass Lung Nodules Recognition Based on CatBoost Feature Selection and Stacking Ensemble Learning

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Journal of Shanghai Jiaotong University (science) 2024年 1-10页

作者： Miao, Jun Chang, Yiru Chen, Chen Zhang, Maoxuan Liu, Yan Qi, Honggang Guo, Zhijun Xu, Qian School of Computer Science Beijing Information Science and Technology University Beijing100101 China Pathological Information Engineering Technology Center Jinan Supercomputing Technology Research Institute Jinan250100 China School of Computer Science and Technology University of the Chinese Academy of Sciences Beijing100049 China Department of Radiology Tianjin Kanghui Hospital Tianjin300385 China Department of Radiology Huabei Petroleum General Hospital Hebei Renqiu062550 China

Aimed at the issue of high feature dimensionality, excessive data redundancy, and low recognition accuracy of using single classifiers on ground-glass lung nodule recognition, a recognition method based on CatBoost feature selection and Stacking ensemble learning was proposed. First, the method uses a feature selection algorithm to filter important features and remove features with less impact, achieving the effect of data dimensionality reduction. Second, random forests classifier, decision trees, K-nearest neighbor classifier, and light gradient boosting machine were used as base classifiers, and support vector machine was used as meta classifier to fuse and construct the ensemble learning model. This measure increases the accuracy of the classification model while maintaining the diversity of the base classifiers. The experimental results show that the recognition accuracy of the proposed method reaches 94.375%. Compared to the random forest algorithm with the best performance among single classifiers, the accuracy of the proposed method is increased by 1.875%. Compared to the recent deep learning methods (ResNet+GBM+Attention and MVCSNet) on ground-glass pulmonary nodule recognition, the proposed method’s performance is also better or comparative. Experiments show that the proposed model can effectively select features and make recognition on ground-glass pulmonary nodules. © Shanghai Jiao Tong University 2024.

关键词： Feature Selection

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Integrating Chatbot Functionality in a Patient Summary Based Healthcare System

Integrating Chatbot Functionality in a Patient Summary Based...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Vasili, Aliki Schiza, Eirini Schizas, Christos N. Pattichis, Constantinos S. Department of Computer Science and Biomedical Engineering Research Center University of Cyprus Cyprus CYENS Centre of Excellence Cyprus National eHealth Authority Cyprus

ISBN: (纸本)9781643685335

The integration of chatbots in healthcare has gained attention due to their potential to enhance patient engagement and satisfaction. This paper presents a healthcare chatbot providing comprehensive access to patient summaries, aligned with the European Patient Summary. Leveraging Natural Language Processing (NLP) capabilities, our chatbot employs intent classification using the fine-tuned bioBERT model to categorize user queries effectively and extract relevant information from the patient summary stored in a database. We detail our methodology, which involves dataset creation, hyperparameter tuning, and model evaluation. Results demonstrate the effectiveness of our approach, with the trained model achieving high precision, recall, and F1 score across intent classes. Our study underscores the potential of emerging NLP techniques in patient interaction and healthcare delivery, covering the way for smarter, user-friendly companions. © 2024 The Authors.

关键词： Electronic health record

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Deep Reinforcement Learning for AoI-aware Transmission Control in Energy-Harvesting Grant-Free NOMA IoT Networks 11

Deep Reinforcement Learning for AoI-aware Transmission Contr...

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11th International Symposium on Telecommunication, IST 2024

作者： Sheikhi, Marzieh Hakami, Vesal Zarrabi, Houman Iran University of Science and Technology School of Computer Engineering Tehran Iran Iran Telecommunication Research Center Department of Communication Technologies Tehran Iran

ISBN: (纸本)9798350356250

The advent of Internet of Things (IoT) networks has revolutionized various real-time applications, such as healthcare, smart cities, and remote automation, by enabling seamless data exchange and real-time monitoring and control. Real-time applications require fresh data, which is quantified by a novel metric, Age of Information (AoI), defined as the time elapsed since the generation time of last successfully received data. Additionally, due to energy limitations in most IoT scenarios, the trade-off between age and energy becomes an important challenge in these networks. Motivated by the need to optimize both data freshness and energy efficiency, this paper considers a real-time scenario in energy-harvesting (EH) IoT networks that operate based on grant-free (GF) Non-Orthogonal Multiple Access (NOMA) mechanism. Given the foresighted nature of the transmission control problem due to the stochastic dynamics of the AoI and energy charging processes, we introduce a deep reinforcement learning (DRL) framework that dynamically manages transmission decisions and resource block allocation, minimizing the average sum AoI across IoT devices, while considering energy feasibility. Through extensive simulations, we demonstrate that our proposed DRL-based approach significantly outperforms existing methods in terms of expected cumulative cost, showcasing its potential for enhancing the performance of future IoT networks. © 2024 IEEE.

关键词： Resource allocation

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