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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Utamajaya, Joy Nashar Supangat, Suhono Harso Gaol, Ford Lumban Ranti, Benny BINUS University BINUS Graduate Program School of Information System Jakarta Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatic Bandung Indonesia Bina Nusantara University Computer Science Department BINUS Graduate Program Computer Science Jakarta Indonesia University of Indonesia Computer Science Department Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798350320923

In general, public or private organizations or companies have used information-based technology as a support to improve business performance to be more effective and efficient in order to achieve a company's business goals. Given the large contribution of information technology in the application of hotel applications as a supporting information system, it is a vital system that must avoid risks that can hinder and cause harm to the hotel management business processes. Therefore, it is necessary to carry out risk management using the COBIT 5 framework to manage possible risks that may occur based on the APO12 (Manage Risk) domain. From the evaluation results of the data obtained through observation and interviews as well as the calculation of the results of the questionnaire based on 6 APO12 subdomains, the results of the risk management level capability assessment in hotel applications are still at level 3 or have reached the level of established process with the target to be achieved at level 4 resulting in a gap of 1 level. Based on the results obtained, it is necessary to propose recommendations that can be used by hotels in improving the application of information technology risk management so that in the future it can achieve the expected target level so that the APO12 level of capability can increase and become more optimal. © 2023 IEEE.

关键词： Risk management

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Hybrid Deep Learning Model for Accurate Short-Term Electricity Price Forecasting

Hybrid Deep Learning Model for Accurate Short-Term Electrici...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Moradzadeh, Arash Mouhammadpourfard, Mostafa Weng, Yang Pol, Suhas Muyeen, S.M. Qatar University Department of Electrical Engineering Doha Qatar National Wind Institute Texas Tech University LubbockTX United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ United States Texas Tech University Renewable Energy Program LubbockTX United States

ISBN: (纸本)9798331541125

Accurate short-term electricity price forecasting (STEPF) is critical for efficient energy market operations, guiding investment strategies, resource allocation, and consumer behavior. This study introduces a hybrid deep learning approach specifically designed to improve STEPF accuracy by leveraging historical Hourly Ontario Energy Price (HOEP) data from 2017 to 2019. The model integrates advanced techniques, including data preprocessing and denoising through a Stacked Denoising Autoencoder (SDAE), along with enhanced temporal modeling via Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU) networks. By capturing the complex dynamics inherent in electricity pricing data, the proposed hybrid model significantly enhances forecasting accuracy. Trained on data from 2017 and 2018, with 2019 used for testing, the model achieves a strong correlation coefficient (R = 99.86%) and substantially lowers forecasting errors. Comparative evaluations against established forecasting methods highlight the model's superior performance. This work demonstrates the practical value of deep learning techniques in the energy sector, particularly in responding to the volatility of demand and supply in real-time electricity markets. © 2025 IEEE.

关键词： Electricity market forecasting Energy management Hybrid deep learning models Short-term electricity price forecasting Temporal data analysis

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Dynamic Stability of a Sieve Shaker Used in a Pachouli Oil Refining Process 3rd

Dynamic Stability of a Sieve Shaker Used in a Pachouli Oil R...

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3rd International Conference on Experimental and Computational Mechanics in engineering, ICECME 2021

作者： Kurniawan, Rudi Fuadi, Zahrul Abubakar, Fathurrahman Afdhal, Afdhal Department of Mechanical and Industrial Engineering Universitas Syiah Kuala Banda Aceh Indonesia Undergraduate Study Program Department of Mechanical Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (纸本)9789811936289

One of the problems causing low quality of essential oils is the high content of solvent resulting from traditional extraction process employing low purity metallic components. This extracted oil therefore requires further process to improve the patchouli alcohol content of the oil by removing unwanted components. One of the process is by mixing the oil with bentonite and zeolite. In this study, a sieve shaker is designed to be used at an integrated equipment for patchouli oil refining process. In order to have a dynamically stable shaker, it is necessary to investigate the effect of various components for the stability, particularly the spring stiffness. The simulation was conducted using Autodesk Inventor using a one degree of freedom model. It is shown from the result that, for the defined condition, the sieve shaker shown a stable condition for the spring stiffness value at least 10 kN/m. Spring stiffness lower than the value could cause detachment of the roll follower from the cam, resulting in impact between the components. © 2023, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Stiffness

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Revolutionizing Neurological Diagnostics: Integrating 6G Technology with Deep Learning for Enhanced Detection of Multiple Sclerosis and Myelitis

Revolutionizing Neurological Diagnostics: Integrating 6G Tec...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Noureldeen, Rana M. Elshshtawy, Rahma A. Elgohary, Omnia A. Wahba, Fatma Alzahraa A. Attia, Marco K. Abo Elazm, Zeinab B. Khattap, Mohamed G. Galal Eldeen Mohamed Ali Hassan, Hend Mostafa, Mariam Ibrahim, Nayra Ramadan, Reem Refaat, Yomna Khaled, Mennatallah Maher, Ali Abd Elaziz, Mohamed Gamal Abdellatif Ibrahim, Ahmed Faculty of Applied Health Sciences Galala University Suez435611 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Air Defense College Department of Electronics and Electrical Communications Engineering Alexandria Egypt

ISBN: (纸本)9798350351408

This paper proposes an alternative detection frame-work for multiple sclerosis (MS) and idiopathic acute transverse myelitis (ATM) within the 6G-enabled Internet of Medical Things (IoMT) environment. The developed framework relies on the implementation of a deep learning technique known as Dense Convolutional Networks (DenseNets) in the 6G-enabled IoMT to enhance prediction performance. To validate the performance of DenseNets, we compared it with other deep learning techniques, including Convolutional Neural Networks (CNN) and MobileNet, using real-world datasets. The experimental results show the high performance of DenseNets in predicting MS and ATM compared to other methods, achieving an accuracy of nearly 90 %. © 2024 IEEE.

关键词： Convolutional neural networks

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Bubble-pen lithography:Fundamentals and applications

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Aggregate 2022年第4期3卷 85-100页

作者： Pavana Siddhartha Kollipara Ritvik Mahendra Jingang Li Yuebing Zheng Walker Department of Mechanical Engineering The University of Texas at AustinAustinTexasUSA Department of Electrical and Computer Engineering The University of Texas at AustinAustinTexasUSA Material Science and Engineering Program Texas Materials InstituteThe University of Texas at AustinAustinTexasUSA

Developing on-chip functional devices requires reliable fabrication methods with high resolution for miniaturization,desired components for enhanced performance,and high throughput for fast prototyping and mass ***,laser-based bubble-pen lithography(BPL)has been developed to enable sub-micron linewidths,in situ synthesis of custom materials,and on-demand patterning for various functional components and *** exploits Marangoni convection induced by a laser-controlled microbubble to attract,accumulate,and immobilize particles,ions,and molecules onto different *** years have witnessed tremendous progress in theory,engineering,and application of BPL,which motivated us to write this ***,an overview of experimental demonstrations and theoretical understandings of BPL is ***,we discuss the advantages of BPL and its diverse applications in quantum dot displays,biological and chemical sensing,clinical diagnosis,nanoalloy synthesis,and *** conclude this review with our perspective on the challenges and future directions of BPL.

关键词： additive manufacturing capillary flow lithography marangoni convection microbubbles sensing

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Expanding chiral metamaterials for retrieving fingerprints via vibrational circular dichroism

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Light(Science & Applications) 2023年第7期12卷 1401-1414页

作者： Cheng Xu Zhihao Ren Hong Zhou Jingkai Zhou Chong Pei Ho Nan Wang Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Center for Inteligent Sensors and MEMS(CISM) National University of SingaporeSingapore 117608Singapore institute of Microelectronics(IME) Agency for ScienceTechnology and Research(A*STAR)Singapore 138634Singapore NUS Graduate School for Integrative Science and Engineering Program(ISEP) National University of SingaporeSingapore 117456Singapore

Circular dichroism(CD)spectroscopy has been widely demonstrated for detecting chiral ***,the determination of chiral mixtures with various concentrations and enantiomeric ratios can be a challenging *** solve this problem,we report an enhanced vibrational circular dichroism(VCD)sensing platform based on plasmonic chiral metamaterials,which presents a 6-magnitude signal enhancement with a selectivity of chiral *** by coupled-mode theory,we leverage both in-plane and out-of-plane symmetry-breaking structures for chiral metamaterial design enabled by a two-step lithography process,which increases the near-field coupling strengths and varies the ratio between absorption and radiation loss,resulting in improved chiral light-matter interaction and enhanced molecular VCD ***,we demonstrate the thin-film sensing process of BSA andβ-lactoglobulin proteins,which contain secondary structures a-helix andβ-sheet and achieve a limit of detection down to zeptomole ***,we also,for the first time,explore the potential of enhanced VCD spectroscopy by demonstrating a selective sensing process of chiral mixtures,where the mixing ratio can be successfully differentiated with our proposed chiral *** findings improve the sensing signal of molecules and expand the extractable information,paving the way toward label-free,compact,small-volume chiral molecule detection for stereochemical and clinical diagnosisapplications.

关键词： chiral vibrational circular

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Distributed control of mobile robots in an environment with static obstacles

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IET Cyber-Systems and Robotics 2021年第2期3卷 128-139页

作者： Konstantinos Giannousakis Anthony Tzes Electrical&Computer Engineering Department University of PatrasRioGreece Electrical&Computer Engineering Program New York University Abu DhabiAbu DhabiUnited Arab Emirates

This study addresses the problem of deploying a group of mobile robots over a nonconvex region with *** that the robots are equipped with omnidirectional range sensors of common radius,disjoint subsets of the sensed area are assigned to the *** proximity-based subsets are calculated using the visibility notion,where the cell of each robot is treated as an opaque obstacle for the other *** on that,optimal spatially distributed coordination algorithms are derived for the area coverage problem and for the homing problem,where the swarm needs to move to specific *** studies demonstrate the results.

关键词： robot mobile visibility

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Comparative Analysis of Clusterization Methods Applied to a Solid-State LiDAR

Comparative Analysis of Clusterization Methods Applied to a ...

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IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Henrique N. Poleselo Andre G. S. Conceicao Postgraduate Program in Electrical Engineering Federal University of Bahia Salvador Brazil Department of Electrical and Computer Engineering Federal University of Bahia Salvador Brazil

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Object segmentation is one of the main activities for the robot to create a sense of its environment. This task is a precursor to other activities, such as autonomous navigation in a given environment. Through sensors such as LiDAR, it's possible to generate high-resolution three-dimensional maps of the environment in which the robot is located, thus enabling their interpretation so that tasks such as object segmentation can be performed. In this article, the DBSCAN and HDBSCAN unsupervised clustering methods are explored. Results in a simulated environment in Gazebo together with Robot Operating System ROS framework for capturing sensory data from LiDAR Livox Mid-70 coupled to a mobile robot show the performance of such techniques through comparisons.

关键词： Laser radar Operating systems Clustering methods Object segmentation Robot sensing systems Sensors Mobile robots Autonomous robots

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Performance Evaluation of Deep Feature in Cross Spectral Face Recognition

Performance Evaluation of Deep Feature in Cross Spectral Fac...

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International Conference on Information Technology, computer and electrical engineering (ICITACEE)

作者： Ernita Dewi Meutia Fitri Arnia Nasaruddin Rusdha Muharar Maulisa Oktiana Khairun Saddami Department of Electrical and Computer Engineering Doctoral Program School of Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (数字)9798350389302

ISBN: (纸本)9798350389319

Cross-spectral face recognition (CSFR), particularly in the Near Infra-Red (NIR) and visual (VIS) spectrum, presents challenges due to differences in illumination and sensor characteristics. NIR sensors are commonly used in practical applications, such as in nighttime outdoor surveillance systems, due to their ability to capture images in low light conditions and at various standoff distances. This study proposes a simple CSFR method that combines deep learning techniques with photometric normalization using Homomorphic Filtering (HF) to identify subjects in NIR images across different standoff distances. Both NIR and VIS images are filtered to mitigate illumination discrepancies. A deep convolutional neural network (CNN) model is employed to automatically extract discriminative deep features from the normalized images of both spectrums. Four deep CNN models of ResNet-50, DenseNet, EfficientNet, and MobileNetV2 are tested. The performance of the models in feature extraction for CSFR is evaluated on the LDHF (Long Distance Heterogeneous Face) database. The Hamming distance metric is employed to match NIR probe images with VIS gallery images. The experimental findings, expressed in recognition rate RR, demonstrate the effectiveness of the approach in enhancing CSFR accuracy, with MobileNetV2 achieving the highest accuracy of 90% for a short standoff. In addition, HF improves image quality, enhancing the deep learning model's ability to discern image characteristics.

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Coordinated Feeder Reconfiguration, Capacitor Placement and Allocation of Distributed Energy Resources for Loss Reduction Under Variable Loading Conditions 4

Coordinated Feeder Reconfiguration, Capacitor Placement and ...

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4th Annual IEEE Kansas Power and Energy Conference, KPEC 2023

作者： Mahdavi, Meisam Awaafo, Augustine Djokic, Sasa Z. Schmitt, Konrad Chamana, Manohar University of Jaén Department of Electrical Engineering Jaén Linares Spain The University of Edinburgh School of Engineering Edinburgh United Kingdom Texas Tech University Electrical & Computer Engineering Department LubbockTX United States Texas Tech University Renewable Energy Program Lubbock United States

ISBN: (纸本)9798350335408

Reduction of active power losses in distribution networks is an important issue, which is usually addressed through a separate, sequential, or simultaneous feeder reconfiguration (FR), volt-var regulation (e.g., capacitor placement, CP), and control of distributed energy resources (DERs). However, finding optimal solution for a coordinated FR, CP, and DER control requires significant computational efforts, which are additionally increased when variations in loading conditions are considered. Therefore, load variations are usually neglected in related analysis, although changes in power flows influence changes in network losses and, consequently, optimal controls for FR, CP and DERs. This paper presents the analysis of both benefits and disadvantages of including not only the peak demands, but also different loading conditions for a coordinated implementation of FR, CP and DER controls for the reduction of losses. The analysis is illustrated on the several distribution test networks and using representative load patterns from the existing literature. The obtained results suggest that the inclusion of load variations requires significantly longer computational times, but in some cases affects optimal solutions for FR, CP and DER controls (their numbers, locations and settings). © 2023 IEEE.

关键词： Electric power distribution

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