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A Study on Modeling of Activated Sludge Process in Wastewater Treatment System Utilizing XAI(eXplainable AI)

Transactions of the Korean Institute of electrical Engineers 2023年第2期72卷 263-269页

作者： Nahm, Eui-Seok Dept. of AI Computer Engineering Far East University Korea Republic of

In this paper, to solve the problem of model-based control due to data reliability in the sewage treatment system active sludge process, an explanable artificial intelligence (XAI, eXplainable AI) was applied to implement neural network model for dissolved oxygen in an active sludge process. By applying four reliable items of effluent water quality to explainable artificial intelligence techniques, the input water quality items of the model were determined. Among the explainable artificial intelligence techniques, in the technique using connection strength, the concept of GAN was used to divide it into positive and negative numbers and compete with each other. As a result, the performance of the model was maintained even though the input variables of the model were reduced from 6 to 5. The learning error and evaluation error of the final model were 0.13 and 0.20, respectively. In the case of the learning model, the error increased slightly in the evaluation model compared to the conventional method with 6 input variables, but this difference is almost meaningless in terms of the actual value of DO, so the proposed technique is much more efficient compared to reducing the input variables from 6 to 5. Copyright © The Korean Institute of electrical Engineers.

关键词： Neural network models

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Bridging the Modality Gap: Generative Adversarial Networks for T1-T2 MRI Image Translation 15

Bridging the Modality Gap: Generative Adversarial Networks f...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pandey, Mayank Kunda, Naga Sai Shreya Pranave, K.C. Singh, Tripty Nair, Rekha R. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Alliance University Alliance College of Engineering and Design Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

CycleGAN, a significant deep learning model, has transformed medical imaging by providing a remedy for translating MRI images between different modalities. Its ability to transform T1-weighted to T2-weighted images is crucial in healthcare environments where obtaining multiple scans can be time-consuming and burdensome for patients. This can be achieved by utilizing CycleGAN, which generates images that contains vital information that will help the healthcare professionals to diagnose the patient without exposing them to further radiations. This unleashes an effective and safer way of diagnosing patients. The adaptability of CycleGAN also allows scalability and the ability to customize it for different types of medical images, where scanning can be achieved with less exposure to radiation, without loosing or compromising any important information. © 2024 IEEE.

关键词： Discriminator GAN Generator Magnetic Resonance Image T1 T2

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Design and Characterization of a Ka-Band Parasitic Patch Antenna for 5G Applications 9

Design and Characterization of a Ka-Band Parasitic Patch Ant...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Kouser, Kahkashan Hannan, Mohammad Shafi, Faizan Aknan, Mohammad Dept. of Computer Science & Engineering Bihar Motihari India Rajiv Gandhi Institute of Petroleum Technology Dept. of Electrical and Electronics Engineering Uttar Pradesh Amethi India Indian Institute of Technology Dept. of Electrical Electronics and Communication Engineering Dharwad India

ISBN: (纸本)9798350377972

He design of parasitic patch antennas for high-frequency applications, such as 5G millimeter-wave (mm-wave) communication, presents several challenges, including achieving high gain, wide bandwidth, and efficient impedance matching within a compact structure. Recent techniques, such as the use of parasitic elements and stepped impedance feedlines, have shown promise in enhancing antenna performance. However, these methods still face limitations related to fabrication complexity, signal degradation, and reliability under varying conditions. This work introduces a circular patch antenna with a parasitic reflector element specifically designed for Ka-band applications at 28 GHz. The proposed design addresses these challenges by optimizing the placement of parasitic elements to achieve high gain, a wide bandwidth of 2 GHz, and stable VSWR performance across the operating range. The single-layer, single-side etching structure further simplifies fabrication, making it suitable for compact wireless *** design of parasitic patch antennas for high-frequency applications, such as 5G millimeter-wave (mm-wave) communication, presents several challenges, including achieving high gain, wide bandwidth, and efficient impedance matching within a compact structure. Recent techniques, such as the use of parasitic elements and stepped impedance feedlines, have shown promise in enhancing antenna performance. However, these methods still face limitations related to fabrication complexity, signal degradation, and reliability under varying conditions. This work introduces a circular patch antenna with a parasitic reflector element specifically designed for Ka-band applications at 28 GHz. The proposed design addresses these challenges by optimizing the placement of parasitic elements to achieve high gain, a wide bandwidth of 2 GHz, and stable VSWR performance across the operating range. The single-layer, single-side etching structure further simplifies fabrication, making it suitable

关键词： Slot antennas

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Classification of Depression, Anxiety and Stress from Handwriting and Drawing with Stacking Models 8

Classification of Depression, Anxiety and Stress from Handwr...

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8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024

作者： Bayrak, Semra Golgiyaz, Sedat Aykut, Murat Karadeniz Technical University Graduate School of Natural and Applied Science Dept. of Computer Engineering Trabzon61080 Turkey Bingöl University Dept. of Computer Engineering Bingöl Turkey Karadeniz Technical University Dept. of Computer Engineering Trabzon Turkey

ISBN: (纸本)9798331540104

Recently, emotion analysis from online handwriting and drawings has become an important research topic. This study aims to determine the optimal model combination using dynamic and statistical features obtained from handwriting and drawings for detecting emotional states. The feature vectors are derived from physical (kinematic), statistical and signal processing (spectral, cepstral and frequency domain) analyses. To address the class imbalance problem, ADASYN (Adaptive Synthetic Sampling) and Tomek Links methods were applied together. In this way, the number of samples of the minority class has been increased, while over-sampled classes have been reduced. Dimensionality reduction was realized using PCA, and Gradient Boosting Classifier (GBC) feature selection. After determining the fewest and most effective features, the samples were classified using the two-level stacking ensemble method. eXtreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), GBC, Random Forest (RF) methods are selected for base and meta models. Model and hyperparameter optimization was carried out by Optuna frameork. The performance of the models is evaluated through experiments in publicly available EMOTion recognition from HAndWriting and draWing (EMOTHAW) database. The aim was to determine the emotional states of depression, anxiety and stress with the data obtained from 7 different drawing/writing tasks of 129 people. The results obtained from accuracy, f1-score, precision and recall metrics show that the model provides remarkable performance. © 2024 IEEE.

关键词： Dimensionality reduction

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A Study on Energy Saving Method in Wastewater Process Control System

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Transactions of the Korean Institute of electrical Engineers 2023年第11期72卷 1485-1490页

作者： Nahm, Eui-Seok Dept. of AI Computer Engineering Far East University Korea Republic of

In this paper, an energy saving method for wastewater treatment biological process is proposed. In the wastewater treatment biological process, the effect on the output according to the change in input takes 4 to 6 hours due to the characteristics of the biological reaction. However, existing studies did not take this into consideration and implemented a prediction system for input versus output using data from the same time. In order to solve this problem, this paper 1) generates input/output data considering the biological reaction time of output by input, 2) builds an artificial neural network prediction model using this data, and 3) determines the minimum water quality from this model to achieve the target water quality. The blower was controlled by calculating the blowing volume. We proposed a mathematical method that considers the biological response time of output by input, and in this case, a method of implementing an artificial neural network model and a method of controlling actual airflow volume were proposed. As a result, it was possible to reduce the power consumption that occurred when trying to achieve the target water quality by unconditionally increasing the existing DO. Copyright © The Korean Institute of electrical Engineers.

关键词： Water quality

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Energy-Based Controller Design for Decentralized AC Inverter-Base Micro-grids

Energy-Based Controller Design for Decentralized AC Inverter...

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2023 IEEE International Conference on Control, Electronics and computer Technology, ICCECT 2023

作者： Bozorgmehrian, Mohsen Attarsharghi, Samareh Ramezani, Amin Tarbiat Modares University Dept. of Electrical and Computer Engineering Tehran Iran University of Doha for Science and Technology Dept. of Electrical Engineering Doha Qatar

ISBN: (纸本)9798350399028

Micro-grids, which utilize photo-voltaic (PV) cells, wind turbines, and batteries, are gaining widespread adoption as a viable solution for renewable and sustainable energy infrastructures. However, ensuring the reliability and stability of these power grids is critical, and several control strategies have been developed to achieve these goals. In this study, we have modeled the decentralized behavior of microgrids using the port-Hamiltonian formulation and a PI controller to control the inverter voltage and output power. To demonstrate the effectiveness of our control method, we have conducted simulations that consider both maximum and worst-case system fluctuations. © 2023 IEEE.

关键词： Hamiltonians

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Design and Fabrication of an IoT based Air Purifier using HEPA Filter 11

Design and Fabrication of an IoT based Air Purifier using HE...

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11th International Conference on Internet of Everything, Microwave engineering, Communication and Networks, IEMECON 2023

作者： Choudhary, Arpita Saini, Lakhan Ahmad, Afaq Banerjee, Hriday Gazi, Firoj University of Engineering and Management Jaipur Dept. of Computer Science Jaipur India UEM Jaipur Dept. of Computer Science Jaipur India University of Alberta Dept. of Computer Science Edmonton Canada

ISBN: (纸本)9781665475129

Air pollution has become one of the major problems for human health across the globe. Indoor air pollution poses more risk than outdoor air pollution, since the human body is more exposed to the inside. It refers to the contamination of indoor air that causes harmful health problems. To purify the polluted air, the air purifier is a main necessity. In this Internet of Things (IoT) project, we have created an air purifier that purifies the indoor air by passing it through the High Efficiency Particulate Air (HEPA) filter and Ultraviolet light. The microcontroller used is the NodeMCU based on the ESP8266 WiFi enabled chip. Two MQ135 air quality sensors used to monitor the change and calculate the efficiency of the purified air. IoT customization eases the manual work of changing the purifier manually every time by providing a platform that operates on the mobile phone via Wi-Fi where the user can operate it accordingly. It provides an equivalent grade of services at a very moderate cost that is affordable even for the average person and can provide real time analytics. The proposed system is capable for providing hands-on practical operations, which enables more exciting possibilities in IoT based automation systems. © 2023 IEEE.

关键词： Internet of things

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Analyzing File Access Characteristics for Deep Learning Workloads on Mobile Devices 5

Analyzing File Access Characteristics for Deep Learning Work...

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5th International Conference on Advances in electrical engineering and computer Applications, AEECA 2024

作者： Lee, Jeongha Lim, Soojung Bahn, Hyokyung Ewha University Dept. of Computer Engineering Seoul Korea Republic of Ewha University Dept. of Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350355253

Recent advances in artificial intelligence technologies have led to a significant increase in deep learning workloads on mobile devices. Given the limited resources of smartphones, much of the research in mobile deep learning has concentrated on offloading these workloads to edge or cloud servers. While computing resources are crucial, storage I/O remains a critical performance bottleneck for mobile devices, yet the file access characteristics of deep learning have not been thoroughly explored. This paper investigates the file access traces of deep learning workloads on mobile devices, comparing them to traditional workloads. The main findings include: 1) Write access constitutes 48-94% of total file accesses, aligning with conventional mobile apps but contrasting with most desktop applications;2) Write access in mobile deep learning workloads exhibits repetitive long-loop patterns, offering insights for enhancing file cache performance;3) Despite its prevalence, write access demonstrates low access skewness;4) Frequency of file accesses proves more informative than recency in predicting re-access likelihood. The insights from this study are expected to guide the efficient management of future smartphone systems by addressing the unique file access dynamics of deep learning. © 2024 IEEE.

关键词： Smartphones

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SLO and Cost-Driven Container Autoscaling on Kubernetes Clusters 15

SLO and Cost-Driven Container Autoscaling on Kubernetes Clus...

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15th International Conference on Cloud Computing and Services science, CLOSER 2025

作者： Marchese, Angelo Tomarchio, Orazio Dept. of Electrical Electronic and Computer Engineering University of Catania Catania Italy

ISBN: (纸本)9789897587474

Modern web services must meet critical non-functional requirements such as availability, responsiveness, scalability, and reliability, which are formalized through Service Level Agreements (SLAs). These agreements specify Service Level Objectives (SLOs), which define performance targets like uptime, latency, and throughput, essential for ensuring consistent service quality. Failure to meet SLOs can result in penalties and reputational damage. Service providers also face the challenge of avoiding over-provisioning resources, as this leads to unnecessary costs and inefficient resource use. To address this, autoscaling mechanisms dynamically adjust the number of service replicas to match user demand. However, traditional autoscaling solutions typically rely on low-level metrics (e.g., CPU or memory usage), making it difficult for providers to optimize both SLOs and infrastructure costs. This paper proposes an enhanced autoscaling methodology for containerized workloads in Kubernetes clusters, integrating SLOs with a cost-driven autoscaling policy. This approach overcomes the limitations of conventional autoscaling by making more efficient decisions that balance service-level requirements with operational costs, offering a comprehensive solution for managing containerized applications and their infrastructure in Kubernetes environments. The results, obtained by evaluating a prototype of our system in a testbed environment, show significant advantages over the vanilla Kubernetes Horizontal Pod Autoscaler. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Cluster computing

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An Overview of Machine Learning Applications in Blockchain for Green Energy Exchanges 33

An Overview of Machine Learning Applications in Blockchain f...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Nemakonde, Pfano Nemangwele, Fhulufhelo Ratshitanga, Mukovhe University of Venda Faculty Science Engineering and Agriculture Dept. of Physics Thohoyandou South Africa Cape Peninsula University of Technology Dept. of Electrical Electronic and Computer Engineering Bellville South Africa

ISBN: (纸本)9798331535162

There is an increasing demand for affordable, decentralised and distributed electricity, which is one of the key motivating factors that has incentivised this review article's writing. Industrial 4.0 has been a conduit through which the conventional grids shift towards a self-autonomous system, and blockchain platforms are emerging as viable solutions to facilitate that paradigm. With the advent of peer-to-peer (P2P) energy trading, community-based energy exchanges have significantly contributed to reduced grid dependency. Machine learning (ML) is being implemented in predictive analytics for energy forecasting (EF), thereby aiding both distribution network operators (DNOs) and aggregators to precisely and predictably monitor, optimise and stabilise grid operations through smart agents. The main aim of this review paper is to provide a detailed analysis of the existing research and implementation approaches on blockchain-based P2P energy trading and a comparative overview of how ML applications aid predictive analytics in community-based energy exchanges. © 2025 IEEE.

关键词： Predictive analytics

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