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7th International Conference on engineering, Technology, and Industrial Application, ICETIA 2020

作者： Budi, Suka Handaja Susanto, Heru Hermawan Environmental Science Doctoral Program Diponegoro University Semarang Indonesia Instrumentation Engineering Departement Polytechnic of Energy and Minerals Akamigas Cepu-Blora Indonesia Chemical Engineering Departement Diponegoro University Semarang Indonesia Electrical Engineering Departement Diponegoro University Semarang Indonesia

Fresh water is needed in industrial activities. For these needs sea water is usually used as raw material if the plant is near the sea and far from the river. SWD (sea water desalination) is used to process sea water to fresh water, that is by reducing levels of salinity or reducing levels of total dissolved solid (TDS). There are two technologies in SWD, namely thermal distillation and reverse osmosis (RO) membrane technology. Residual SWD process or also called blowdown water has high salinity levels can be converted into electrical energy through SGP (salinity gradient power). There are two technologies to capture energy from salinity differences, namely PRO (Pressure Retarded Osmosis) and RED (Reverse Electrodialysis). PRO and RED are renewable energy technology, there are no emision, no moving part, abundant availability in nature and can be recycled. In this article, an analysis for SWD of oil refinery is carried out. From the analysis results it is known that the plant uses thermal distillation technology, requires power as 64.25 MW and 3,403 m3/h seawater to produce 242 m3/h freshwater. With SGP, the potential recovery of power gained is 0.786 MW. This recovery potential is still too small when compared to energy requirements of SWD with thermal distillation technology, but if it use RO technology that only requires 3.6-5.7 kWh/m3 or 0.87 - 1.40 MW to produce 242 m3/h, a significant impack to saving energy will be obtained. © Published under licence by IOP Publishing Ltd.

关键词： Recovery

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A Comparative Study of Machine Learning Classifiers for Enhancing Knee Osteoarthritis Diagnosis

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Information (Switzerland) 2024年第4期15卷 183页

作者： Raza, Aquib Phan, Thien-Luan Li, Hung-Chung Hieu, Nguyen Van Nghia, Tran Trung Ching, Congo Tak Shing Graduate Institute of Biomedical Engineering National Chung Hsing University Taichung402 Taiwan Department of Physics and Electronic Engineering University of Science Vietnam National University of Ho Chi Minh City Ho Chi Minh City70000 Viet Nam Undergraduate Program of Intellectual Creativity Engineering National Chung Hsing University Taichung402 Taiwan VNU-HCM Ho Chi Minh City72409 Viet Nam Department of Electrical Engineering National Chi Nan University Puli Township545 Taiwan International Doctoral Program in Agriculture National Chung Hsing University Taichung402 Taiwan

Knee osteoarthritis (KOA) is a leading cause of disability, particularly affecting older adults due to the deterioration of articular cartilage within the knee joint. This condition is characterized by pain, stiffness, and impaired movement, posing a significant challenge in medical diagnostics and treatment planning, especially due to the current inability for early and accurate detection or monitoring of disease progression. This research introduces a multifaceted approach employing feature extraction and machine learning (ML) to improve the accuracy of diagnosing and classifying KOA stages from radiographic images. Utilizing a dataset of 3154 knee X-ray images, this study implemented feature extraction methods such as Histogram of Oriented Gradients (HOG) with Linear Discriminant Analysis (LDA) and Min–Max scaling to prepare the data for classification. The study evaluates six ML classifiers—K Nearest Neighbors classifier, Support Vector Machine (SVM), Gaussian Naive Bayes, Decision Tree, Random Forest, and XGBoost—optimized via GridSearchCV for hyperparameter tuning within a 10-fold Stratified K-Fold cross-validation framework. An ensemble model has also been made for the already high-accuracy models to explore the possibility of enhancing the accuracy and reducing the risk of overfitting. The XGBoost classifier and the ensemble model emerged as the most efficient for multiclass classification, with an accuracy of 98.90%, distinguishing between healthy and unhealthy knees. These results underscore the potential of integrating advanced ML methodologies for the nuanced and accurate diagnosis and classification of KOA, offering new avenues for clinical application and future research in medical imaging diagnostics. © 2024 by the authors.

关键词： Extraction

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Evaluation of Enrichment in Ontology-based Knowledge Management System

Evaluation of Enrichment in Ontology-based Knowledge Managem...

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International Conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS)

作者： Ni Kadek Dessy Hariyanti Linawati Linawati I Made Oka Widyantara Nyoman Putra Sastra Anang Kukuh Adisusilo I Wayan Budi Sentana I Dewa Made Bayu Atmaja Darmawan Study Program of Doctoral Engineering Science Universitas Udayana Indonesia Electrical Engineering Department Universitas Udayana Indonesia Engineering Department Universitas Wijaya Kusuma Surabaya Surabaya Indonesia School Of Computing Macquarie University Sydney New South Wales

Ontology is an organization of knowledge that can represent knowledge in a structured manner. An ontology-based Knowledge Management System is a system that combines elements of knowledge management with the application of ontology as a knowledge base. Meanwhile, knowledge is dynamic and continues to develop all the time, giving rise to new knowledge. Therefore, the ontology must be updated regularly through ontology enrichment to meet adequate knowledge requirements. Ontology enrichment is carried out to ensure that the ontology remains relevant and responsive to developments in its knowledge domain. However, there are no standard stages in implementing enrichment in ontology-based Knowledge Management systems. Therefore, this research conducted a literature review to determine when and how to apply enrichment in ontology development. Data was obtained from related journals in 2018 - 2023. The method used in this research is the Systematic Literature Review method to systematically identify, review, evaluate, and interpret all available research in the area of interest with specific relevant research questions. The research results show that the ontology enrichment procedure generally includes preprocessing stages, relation extraction, and enrichment processes. Ontology enrichment is generally applied during the development of a new ontology as part of the ontology development cycle and on existing ontologies, which are a separate stage from the initial ontology development cycle. Based on these findings, the researcher provides recommendations for researchers who want to use or develop new ontology enrichment methods.

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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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Editorial engineering and Technology Management: A Growing Field

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IEEE Transactions on engineering Management 2020年第3期67卷 514-515页

作者： Daim, Tugrul U Technology Management Doctoral Program Department of Engineering and Technology Management Maseeh College of Engineering and Computer Science Portland State University PortlandOR United States

As the world has struggled against a virus, technology enabled our survival in many dimensions. In many cases adoptions of technologies which would have lasted years happened in weeks if not days. For example, remote health care adoption was accelerated immensely to deal with the challenged health system. We are about to see similar technological innovations ramp up the hard hit economies through many different sectors. As always said, challenges create opportunities. Our field of engineering and Technology Management is growing. IEEE Technology and engineering Management Society (TEMS) just finished the first virtual conference: TEMSCON 2020. As a part of it we held an editors’ panel. Holding the conference on line enabled many editors of the leading journals in the field attend the event.

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Adversarial Search Algorithms Performance in the Yote Game

Adversarial Search Algorithms Performance in the Yote Game

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IEEE International Conference on Automatica (ICA-ACCA)

作者： Luis I. Torres Nicolas A. Barriga Doctoral Program in Engineering Systems Faculty of Engineeering Campus Curicó University of Talca Chile School of Videogames Development and VR Engineering Faculty of Engineering Campus Talca University of Talca Chile

ISBN: (纸本)9781665494090

The performance of several adversarial search algorithms is very dependent on the move generation and ordering procedure, as well as on the transposition table characteristics. In this work we compare Alpha-beta, MTD-f and NegaScout performance in the presence and absence of the following features: transposition table, Hash Move heuristic and move ordering using the evaluation function. We found that NegaScout did not yield major improvements over Alpha-Beta, which is likely due to the move ordering performed using a weak evaluation function. In most cases, MTD-f examines fewer nodes, and completes fixed depth searches in less time than Alpha-beta and NegaScout. This is consistent with the literature when using coarse grained evaluation functions. Finally, we found that enabling move ordering leads to fewer nodes examined than enabling the Hash Move heuristic, but, as sorting is time consuming, search is slower. Best results overall are found using MTD-f with all enhancements enabled. These results, in a previously unexplored game, are consistent with existing literature. This enables us to make predictions of algorithm and heuristics’ performance in novel games, based on an analysis of the games’ features, without having to implement all techniques.

关键词： Heuristic algorithms Games Prediction algorithms Standards Sorting

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Place attachment in supporting the preservation of religious historical built environment 5

Place attachment in supporting the preservation of religious...

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5th International Seminar on Sustainable Urban Development, ISoSUD 2020

作者： Wahyudie, P. Antariksa, A. Wulandari, L.D. Santosa, H. Doctoral Program of Civil Engineering Department Brawijaya University Malang Indonesia Department of Interior Design Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Architecture Brawijaya University Malang Indonesia

The place attachment theory is a scientific part of the relationship between human behavior and built environment study. The built environment study explains the existence of a building and its landscape. The place attachment theory has been used in studying the relationship between human behavior with a religious and historic environment during its development. However, nowadays, this research rarely conducted and often carried out partially. As a consequence, the result analysis is only partially. This study proposed a holistically place attachment to provide a broader discourse in examining the relationship between human behavior and the built environment holistically. Moreover, place attachment theory will be used to preserve the historical religious built environment. In addition, the literature review from the previous studies and discussion methods are essential to support the research. The result shows that there is an opportunity to conduct a study of place attachment theory in supporting the preservation of the historical religious built environment. Thus, the holistic result can be obtained using the place attachment theory, which is the combination of place identity, place dependence, place affect, and place social bonding. © Published under licence by IOP Publishing Ltd.

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Comparison Analysis of Intrusion Detection using Deep Learning in IoT Networks

Comparison Analysis of Intrusion Detection using Deep Learni...

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International Conference of Information and Communication Technology (ICoICT)

作者： Syariful Ikhwan Purwanto Purwanto Adian Fatchur Rochim Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia D3 Telecommunication Engineering Institut Teknologi Telkom Purwokerto Banyumas Indonesia Department of Chemical Engineering Faculty of Engineering Diponegoro University Semarang Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

This study focuses on network security for Internet of Things (IoT) devices, which are particularly vulnerable to attack due to limited security measures. To protect IoT networks from attackers, the research implemented intrusion detection on the networks. The study compared several Deep Learning algorithms, including DNN, CNN, LSTM, and AE, to identify the most effective algorithm for solving network security problems using intrusion detection. The research used the UNSW-NB15 dataset for testing and employed binary classification for evaluation. The results showed that the DNN algorithm achieved an accuracy value of 99.76% and a loss value of 0.006%, outperforming the other algorithms. This study highlights the importance of implementing intrusion detection in protecting IoT devices and networks and demonstrates the efficacy of the DNN algorithm in detecting and preventing network security breaches.

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Guest editorial: Latin American perspectives on sustainability in higher education

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International Journal of Sustainability in Higher Education 2023年第2期24卷 233-234页

作者： Izabela Simon Rampasso Rosley Anholon Osvaldo Luiz Gonçalves Quelhas Luis Velazquez Claudia Mac-Lean Departamento de Ingeniería Industrial Universidad Católica del Norte Antofagasta Chile School of Mechanical Engineering State University of Campinas Campinas Brazil Master Program in Management Systems and Doctoral Program in Sustainable Management Systems Federal Fluminense University Niterói Brazil Department of Industrial Engineering Division of Engineering University of Sonora Hermosillo Mexico GAIA Antarctic Research Center Universidad de Magallanes Punta Arenas Chile

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Design of a Multilingual Translation System with Personalized Voice Characteristics

Design of a Multilingual Translation System with Personalize...

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International SoC Design Conference, ISOCC

作者： Chen-Hua Chen Guan-Yu Lai Chuan Lee Chang-Yu Wu Kuan-Lin Liu Shin-Chi Lai Doctoral Degree Program in Smart Industry Technology Research and Development Huwei Taiwan Dept. of Automation Engineering National Formosa University Huwei Taiwan Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377095

This study combines AI voice synthesis with IoT to create a real-time multilingual translation system. Front-end devices capture the speaker's voice, which is then played back in both original and translated forms, enabling consecutive interpreting. This system is ideal for multilingual scenarios. After digitization, audio signals undergo speech-to-text conversion, multilingual translation, and text-to-speech processing. Speech synthesis matches the speaker's voice characteristics, allowing listeners to hear the translation in the speaker's voice. Both voice-to-text conversion and translated audio playback use Wi-Fi for data transmission, facilitating future applications. In validation, the average translation and transmission time for each speech segment were 410 ms and 3.38 s, respectively. The synthesized voice similarity analysis achieved an accuracy of 88%, showcasing the method's potential for system integration and development.

关键词： Accuracy System integration Real-time systems Text to speech Data communication Artificial intelligence Wireless fidelity Speech to text

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