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International Conference on Data and Software engineering (ICODSE)

作者： Anteng Widodo Adian Fatchur Rochim Budi Warsito Doctoral Program of Information System School of Postgraduated Studies Diponegoro University Semarang Indonesia Department of Information System Faculty of Engineering Muria Kudus University Kudus Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia Department of Statistics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

Low-Rate Denial of Service (LDoS) attacks, an emerging breed of DoS attacks, present a formidable challenge in terms of their detectability. Within the realm of network security, these attacks cast a substantial shadow over the availability and integrity of services. This study seeks to delve into the intricate mechanics of LDoS attacks and explore the landscape of developed detection methodologies. Employing bibliometric analysis, we employ a multifaceted approach to gather research insights pertaining to LDoS attack detection. This approach encompasses the examination of evolving research trends, scrutiny of widely cited scientific publications within the same domain, identification of prolific researchers shaping the field, and a judicious selection of reputable publishers for dependable information acquisition. Furthermore, our study undertakes a meticulous categorization and classification of previously proposed LDoS detection methods documented in well-established scientific literature. This endeavor not only provides a more profound comprehension of the evolutionary trajectory and thematic thrusts within LDoS attack detection research, but it also facilitates the identification of gaps and directions for future exploration. The culmination of these analytical efforts culminates in a comprehensive framework. This contribution manifests through structured categorization, classification, and a forward-looking research roadmap, which collectively serve as valuable tools for both current and aspiring researchers engaged in LDoS attack detection. In summary, this research not only delves into the complexities of LDoS attacks and their mechanisms but also augments the current understanding of detection capabilities. Through its nuanced contributions, this paper extends a helpful guide to the dynamic realm of LDoS attack detection, thus catalyzing advancements and insights for the future.

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Optimisation-based sampling time selection for modelling the three interacting tanks

Optimisation-based sampling time selection for modelling the...

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International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA)

作者： Helmy Rahadian Azka Muji Burohman Augie Widyotriatmo Endra Joelianto Engineering Physics Doctoral Program Institut Teknologi Bandung Bandung Indonesia Electrical Engineering Study Program Universitas Dian Nuswantoro Semarang Indonesia Research Center for Photonics National Research and Innovation Agency (BRIN) Tangerang Selatan Indonesia Instrumentation and Control Research Group Institut Teknologi Bandung Bandung Indonesia Institut Teknologi Bandung University Center of Excellence AI on Vision NLP & Big Data Analytics Bandung Indonesia

This paper considers parameters and sampling time identification of a three interacting tanks system. A novel tailor-made model is introduced to deal with the real plant in the laboratory. Experimental data are then exploited to perform parameter identification via particle swarm optimisation (PSO) method. In addition to the system parameters, we propose to simultaneously optimize the sampling selection in the optimisation process. Even though several well known sampling time selection are established, a more precise selection is required. It is shown in this paper that a meta-heuristic optimisation method (PSO) can be an efficient procedure for this problem. Moreover, three different error measures for performing optimisation are also discussed.

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Efficient Removal of Amoxicillin, Ciprofloxacin, and Tetracycline from Aqueous Solution Utilizing Cu-Doped Bi2O3 Material

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SSRN 2023年

作者： Sa’adah, Fatkhiyatus Sutanto, Heri Hadiyanto, H. Alkian, Ilham Doctoral Program of Environmental Science School of Postgraduate Diponegoro University Semarang50275 Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang50275 Indonesia Department of Chemical Engineering Faculty of Engineering Diponegoro University Semarang50275 Indonesia Diponegoro University Semarang50275 Indonesia

This study investigates the effectiveness of the Cu-doped Bi2O3 method for removing antibiotics. Cu at varying concentrations of 0%, 2%, 4%, 6%, and 8% was used to synthesize Bi2O3 material successfully. The optimal results were obtained by adding 4% Cu to the Bi2O3 matrix. Cu-doped Bi2O3 was characterized by an energy band gap of 2.32 eV, a crystal size of 37.04 nm, and α-Bi2O3 and Rouaite phases. Material containing Cu-doped Bi2O3 was used to degrade antibiotics such as amoxicillin (AMX), ciprofloxacin (CIP), and tetracycline (TC). The various removal techniques investigated include adsorption, photolysis, and photocatalysis. The photocatalytic procedure yielded the highest results in terms of degradation efficiency. The removal efficiency of each antibiotic using the photocatalytic method varies, with AMX at 52.06%, CIP at 61.72%, and TC at 69.44%. Cu-doped Bi2O3 degraded TC-type antibiotics more rapidly, with a reaction rate constant of 0.0065 minutes-1, according to the results of the reaction rate constants. The high removal efficiency and rapid reaction rate indicate that Cu-doped Bi2O3 is an effective antibiotic removal agent, especially for tetracycline-type antibiotics. © 2023, The Authors. All rights reserved.

关键词： Antibiotics

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Convolutional Neural Network (CNN) Algorithm Development for Quality Measurement in Tire Product Classification: A Systematic Literature Review

Convolutional Neural Network (CNN) Algorithm Development for...

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International Conference on Informatics and Computing (ICIC)

作者： Armando Tirta Dwilaga Lintang Yuniar Banowosari Sudaryanto Aini Suri Talita Department of Information Technology Doctoral Program Gunadarma Information Technology University West Java Indonesia Department of Informatics Gunadarma University West Java Indonesia Department of Industrial Engineering and Management Gunadarma University West Java Indonesia Department of Information System Management Gunadarma University West Java Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Convolutional Neural Networks (CNN) have shown potential in tire defect classification, but challenges like image quality variations and model limitations persist. This Systematic Literature Review (SLR) employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology and a qualitative approach. It uses the Population, Intervention, Comparison, Outcome, and Context (PICOC) framework to analyze 30 articles from Google Scholar, published between 2018 and 2024 in Q1-Q4 journals. The review examines article distribution by publisher, evaluates the impact of key pre-training parameters (such as data split ratio, dropout rate, optimization algorithm, learning rate, and pre-trained models on CNN performance), and identifies challenges in defect classification. MDPI (Multidisciplinary Digital Publishing Institute) is the most frequent publisher, with the highest number of publications occurring in 2021. AlexNet and VGG16 are the most common models, and an 80:20 data split ratio, 0.5 dropout rate, and a 0.001 learning rate are frequently used. The Adam (Adaptive Momentum) optimization algorithm is widely adopted, but challenges remain in detecting small defects. Further improvements in CNN models, lighting techniques, and larger datasets are needed to enhance defect detection. This review highlights current trends and future research opportunities in tire quality measurement.

关键词： Image quality Adaptation models Current measurement Lighting Tires Data models Classification algorithms Convolutional neural networks Optimization Systematic literature review

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Advanced Data Instrumentation for Stunting Analysis in Physical-Cyber Architecture

Advanced Data Instrumentation for Stunting Analysis in Physi...

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International Conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Daniel Nugraha M. Riza Nurtam Hartuti Mistialustina Didin Saepudin Welly Surjono Ary Setijadi Prihatmanto Doctoral Program of School of Electrical Engineering and Infomatics Institut Teknologi Bandung Bandung Indonesia Universitas Widya Dharma Pontianak Kalimantan Barat Indonesia Electrical Engineering Department Faculty of Engineering Universitas Sangga Buana Bandung Indonesia Universitas Sangga Buana Bandung Indonesia School of Electical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

In Indonesia, stunting is a serious problem that has drawn a lot of attention from the government. This study offers a novel strategy for preventing stunting by combining the 5-Desk Model used in integrated health posts (Posyandu) with the Cyber-Physical System (CPS) architecture. Chronic malnutrition and childhood diseases can cause stunting, which can result in long-term health issues and cognitive deficits. Despite advancements, it still poses a serious threat to almost every Indonesian province. For the purpose of gathering data in Posyandu, such as registration, growth measurement, recording, counseling, and health services, the 5-Desk Model provides an organized framework. This promotes the well being of the community and gives healthcare professionals the ability to thoroughly monitor important health indicators. This strategy is improved with CPS integration, which results in a four-layered, comprehensive system that includes gamification, business intelligence, information systems, and instrumentation. To maximize efforts to avoid stunting, this architecture facilitates accurate monitoring, real-time data collecting, data analysis, and stakeholder communication. This study imagines a data-driven future for Indonesian stunting prevention, where cooperation, technology, and data are combined to address this pressing problem and safeguard the future prospects and wellbeing of Indonesia’s youth. Implementing this goal and expanding stunting prevention initiatives will be the main goals of future research.

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Cannabinoid Modulation of Monoamine Levels in Mouse Brain:Unveiling Neurochemical Dynamics through an Innovative High‑performance Liquid Chromatography‑Fluorescence Detection Bioanalysis

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Journal of Analysis and Testing 2024年第3期8卷 300-314页

作者： Fonseca Carla Cunha Susana Ferreira Ricardo Lapo Pais Mariana Goncalves Joana Camins Antoni Ettcheto Miren Barbosa Rui Fortuna Ana Laboratory of Pharmacology Faculty of PharmacyUniversity of CoimbraPolo das Ciencias da SaudeAzinhaga de Santa CombaCoimbra 3000‑548Portugal CIBIT/ICNAS-Coimbra Institute for Biomedical Imaging and Translational Research University of CoimbraCoimbra 3000‑548Portugal Department of Pharmacology Toxicology and Therapeutic ChemistryFaculty of Pharmacy and Food ScienceUniversitat de BarcelonaBarcelona 08028Spain Institute of Neurosciences Universitat de BarcelonaBarcelona 08035Spain Faculty of Sciences and Technology Doctoral Program in Biomedical EngineeringUniversity of CoimbraCoimbra 3004‑516Portugal Biomedical Research Networking Centre in Neurodegenerative Diseases(CIBERNED) Carlos Ⅲ Health InstituteMadrid 28031Spain Institut d’Investigacio Sanitaria Pere Virgili(IISPV) Reus 43204Spain CNC-Center for Neuroscience and Cell Biology University of CoimbraCoimbra 3004-504Portugal

Homeostasis of monoaminergic pathways is compromised in aging and neurodegenerative/neurological diseases such as Alzheimer’s disease and *** the other hand,their modulation has also been linked to the mechanism of action of several ***,monitoring the levels of noradrenaline(NA),adrenaline(AD),dopamine(DA),and serotonin(5-HT),as well as those of tryptophan(TRYP),the precursor of 5-HT,and DA metabolites,namely 3,4-dihydroxyphenylacetic acid(DOPAC)and homovanillic acid(HVA),is fundamental for assessing disease severity and *** work aimed to develop and validate the first High-Performance Liquid Chromatography(HPLC)coupled with fluorescence detector(FLD)method that simultaneously and accurately quantifies NA,AD,DA,DOPAC,HVA,5-HT and TRYP in mouse brain and prefrontal cortex(PFC)*** sample preparation by protein precipitation was required to extract the *** curves were plotted using the background subtraction approach to reduce the interference of the endogenous *** and inter-day accuracy and precision were within the ranges defined by ICH(The International Council for Harmonisation)guideline for bioanalytical method *** validation,the impact of cannabidiol(CBD),cannabigerol(CBG),and cannabidivarin(CBDV)was explored in mice brains post-administration,revealing significant alterations in specific neurotransmitter levels upon cannabinoid exposure and shedding light on the complex modulation of neurochemical dynamics by *** research highlights the fit-for-purpose of the HPLC-FLD method and provides insights into potential mechanisms underlying phytocannabinoid actions in the central nervous system(CNS).

关键词： Neurotransmitter HPLC Mice Brain Prefrontal cortex Cannabinoid

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Identification of subsurface soil layers in Sutami Dam area and its surroundings using magnetic methods 2

Identification of subsurface soil layers in Sutami Dam area ...

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2nd International Conference and Exhibition on Sciences and Technology, ICEST 2020

作者： Sutasoma, M. Susilo, A. Sunaryo, S. Suryo, E.A. Cahyo, R.H.D. Doctoral Program of Physics Faculty of Science Brawijaya University Jl. Veteran Jawa Timur Malang Indonesia Geophysics Engineering Faculty of Science Brawijaya University Jl. Veteran Jawa Timur Malang Indonesia Civil Engineering Faculty of Engineering Brawijaya University Jl. Veteran Jawa Timur Malang Indonesia Master Program of Physics Faculty of Science Brawijaya University Jl. Veteran Jawa Timur Malang Indonesia

The research was conducted to determine the subsurface layer type in the Sutami Dam area using magnetic methods. This study aims to determine the subsurface soil type in the area around the Sutami Dam. The instrument used in data collection was the Proton Precession Magnetometer (PPM) Scientrex Model G-8. The data collection area covers the Kromengan region (112.494oE;8.129oS) to the Sukorame area (112.358oE;-8.213oS). The area covers the Sutami Dam area. The research area is 15 km x 15 km or with an area of 225 KM with a space between points 300 m. From the data acquisition, it was found that the number of data collection points was 1372 points. The types of subsurface layers based on the 2,5 D Talwani modeling are soil, pumice, limestone, sandy tuff, sandstone, andesite, and lava. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Lime

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Determining key indicators for the assessment of sustainable development in Spanish cities under a multi-criteria approach

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Cleaner Production Letters 2023年 5卷

作者： Rebolledo-Leiva, Ricardo Vásquez-Ibarra, Leonardo Feijoo, Gumersindo Moreira, María Teresa González-García, Sara CRETUS. Department of Chemical Engineering School of Engineering. Universidade de Santiago de Compostela Santiago de Compostela 15782 Spain Doctoral Program in Engineering Systems Faculty of Engineering Campus Curicó. Universidad de Talca Camino a Los Niches km 1 Curicó Chile

In the second half of the 20th century, the behaviour of the world's population has undergone drastic changes: it has grown very rapidly, become longer-lived and moved preferably to urban areas. Migration of populations to cities has a special focus because it not only generates socio-economic development, but also causes different economic, social, and environmental issues. How to assess the sustainable development of cities and identify the most representative aspects of sustainability dimensions has become an essential challenge for effective planning and government policies. However, the selection of indicators has scarcely been addressed when Data Envelopment Analysis (DEA) has been used for sustainability assessment. This paper aims to identify the key indicators to determine the eco-efficient performance towards a sustainable development of a set of cities. For this purpose, the joint use of a variable selection method and a DEA model is proposed and applied to a set of 31 Spanish cities. The results obtained identified five social (population percentage at risk of poverty, number of registered gender violence cases, population density), five economic (average household income, average rental price per m², gross domestic product per capita, public/private vehicle ratio, indebtedness), and three environmental (ozone, NO₂, PM₁₀) indicators for the assessment. Moreover, 14 Spanish cities were considered non-eco-efficient, and targets to achieve eco-efficient levels were proposed. Policy makers could focus primarily on the social and economic aspects of non-eco-efficient cities, paying attention, for example, to the risk of poverty of the population, indebtedness of local governments, average rental price and NO₂ emissions. © 2023 The Authors

关键词： Data envelopment analysis Sustainability indicators Sustainable cities Variable selection method

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Flood hazard assessment in Kuala Langsa village, Langsa city, Aceh Province-Indonesia

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Case Studies in Chemical and Environmental engineering 2024年 10卷

作者： Isma, Faiz Kusuma, M.S. Badri Nugroho, E Oktariyanto Adityawan, M Bagus Civil Engineering Doctoral Program Faculty of Civil and Environmental Engineering Institut Teknologi Bandung Water Resources Engineering Research Group Faculty of Civil and Environmental Engineering Institut Teknologi Bandung Center for Water Resources Development Faculty of Civil and Environmental Engineering Institut Teknologi Bandung School of Water Resources Engineering and Management Faculty of Civil and Environmental Engineering Institut Teknologi Bandung Department of Civil Engineering Faculty of Engineering Universitas Samudra

This study evaluates the flood hazard index in Kuala Langsa Village, Langsa City, Aceh, a region prone to frequent flooding due to its low-lying coastal location in eastern Aceh. It serves as a crucial link between North Sumatra and Aceh provinces. The area's significance underscores the importance of understanding and mitigating flood risks. By utilizing the coupled 1D/2D Hydraulic model, flood hazards are accurately predicted and mapped. The field data collection (as calibration) measures the maximum tidal water level based on information from residents affected by flooding on the land surface under their houses. Hydrological conditions during the December 2017 flood event are simulated using HEC-HMS. Hydraulic conditions are evaluated using HEC-RAS 6.4.1. The research aims to assess the flood hazard index in Kuala Langsa Village under spring tide conditions, considering variations in base flow and flood flow with return periods of 2 and 50 years upstream. The main results show that an FHI >2.62 (high) covers an area of 169.74 ha, 2.62 > FHI >2.23 (moderate) encompasses 172.60 ha, and FHI <2.23 (low) accounts for 222.32 ha. During the Q50 upstream flood, there was an increase in flood extent: low by 61.20 %, medium by 25.32 %, and high by 5.57 %. These findings deepen our understanding of estuary flood mechanisms and emphasize the importance of developing more effective adaptation strategies to reduce flood risks in the future. © 2024 The Authors

关键词： Adaptation calibration Flood hazard index Hydraulic 1D/2D Hydrological Kuala Langsa village Tidal propagation

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Enhancing Fault Diagnosis Accuracy in Electric Motors: A Digital Twin Approach with Transformer Model

Enhancing Fault Diagnosis Accuracy in Electric Motors: A Dig...

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Asian Control Conference

作者： Imron Rosyadi Yul Yunazwin Nazaruddin Parsaulian Ishaya Siregar Doctoral Program of Engineering Physics Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia Electrical Engineering Department Universitas Jenderal Soedirman Purwokerto Indonesia Department of Engineering Physics Instrumentation and Control Research Group Institut Teknologi Bandung Bandung Indonesia National Center for Sustainable Transporation Technology CRCS Building 2nd Floor Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

This paper explores the integration of Digital Twin (DT) technology and machine learning (ML) techniques for predictive maintenance in electric vehicles (EVs), particularly focusing on fault diagnosis in electric motors (EMs). DT technology, which creates a digital replica of a physical entity, combined with real-time sensor data analytics, offers significant potential for improving the reliability and operational efficiency of EMs under variable and intense conditions. By incorporating advanced ML models like Transformers, which are adept at capturing long-range dependencies, into the DT framework, the study addresses critical challenges in fault diagnosis under non-stationary conditions typical in EVs. The research introduces a novel approach that combines Transformers with convolutional layers to enhance the fault diagnosis capabilities by effectively capturing complex patterns in time-series data. Furthermore, incremental learning is proposed within the DT framework to adapt to evolving environments and maintain high diagnostic performance, aiming for domain invariance in fault diagnosis models. The paper evaluates various deep learning models, including Transformers, FCNs, CNNs, Inception, ResNet, and MLP, across metrics such as accuracy, training duration, and inference time, identifying CNNs and FCNs as optimal due to their balance between performance and processing time. This study not only demonstrates the feasibility of advanced ML techniques in enhancing DT-based fault diagnosis but also provides insights into selecting appropriate models for predictive maintenance applications in EVs, highlighting the potential for future research in adapting these technologies to different datasets and operational scenarios.

关键词： Fault diagnosis Deep learning Training Adaptation models Accuracy Transformers Digital twins

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