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7th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2023

作者： Chieh, Nelson Jin Wei Afrouzi, Hadi Nabipour Tavalaei, Jalal Swinburne University of Technology Sarawak Faculty of Engineering Computing and Science Kuching Malaysia

ISBN: (纸本)9798350305517

Sunlight power is a type of green energy that can be harvested by using photovoltaic (PV) panels to convert solar radiation to electrical power for fulfilling the power demand. However, there are limitations on renewable energy as it relies on weather conditions in which a confined portion of power will be generated during the night or cloudy periods. This is where hydrogen comes in and act as an energy career that stores the electrical power generated from the photovoltaic panel and can be converted back to electric power through electrochemical processes. The aim of this article is to analyse the feasibility of implementing PV panels and electrolyser for hydrogen generation. Simulation of solar system for power generation and detailed electrolyser with PEM cell stack is modelled for hydrogen production. Performance evaluation is then fulfilled to the developed simulation model of hydrogen production system for identifying the efficiencies of the developed PV system and PEM electrolyser as well as the solar to hydrogen's turnover of the developed hydrogen production system to find out the feasibility of system. © 2023 IEEE.

关键词： Solar panels

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UAV-Based Lane-Changing Reduction Using Traffic Video Analyser

UAV-Based Lane-Changing Reduction Using Traffic Video Analys...

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ASEAN Australian engineering Congress, AAEC 2022

作者： Bong, Jit Boon Chong, Elizabeth Eu Mee Choo, Chung Siung Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Kuching Malaysia

ISBN: (纸本)9789819955466

Multi-lane roundabouts allow drivers to change lanes by merging and diverging among circulating lanes, yielding weaving in the roundabout. Weaving yields traffic conflicts as crossing traffic slows down the circulating flow. Speed reduction creates significant gaps that reduce capacity. Thus, weaving is crucial due to its significant effect on roundabout capacity. However, little information is available on weaving in multi-lane roundabouts with inscribed circle diameters (ICD) of more than 100 m. A possible reason for the lack of weaving data is the difficulty of collecting lane change data due to the traffic flow complexity, which involves several lane changes and the required resources. Typical data collection methods include using ground-level recorders and surveyors. Ground-level recorders could only provide limited data points due to view coverage constraints. Increasing the workforce and equipment can overcome this limitation, but it may prolong the post-recording analysis due to more views for analysis. Conversely, Unmanned Aerial Vehicles (UAVs) can capture complex traffic interaction data in a single session. Extraction of UAV-based recording by traffic video analyser could provide a wide range of outputs in a shorter time than manual analysis. This paper proposes a novel methodology to extract UAV-based lane change data using a traffic video analyser. The fieldwork involves flying a UAV at a multi-lane roundabout with an ICD of 151 m in Kuching, Malaysia. Turning movement and lane change were obtained using a traffic video analyser. Virtual gates are specified at 10 m intervals to get lane change location. A lane change can be detected when a vehicle crosses two consecutive virtual gates over two adjacent circulating lanes. Weaving occurs when two lane change swap paths along circulating lanes. The findings show that 86% of lane change happens in the weaving section. This paper demonstrated that using UAV and traffic video analyser is feasible to investig

关键词： Antennas

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Advance IoT Intelligent Healthcare System for Lung Disease Classification Using Ensemble Techniques

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Computer Systems Science & engineering 2023年第8期46卷 2141-2157页

作者： J.Prabakaran P.Selvaraj Department of Networking communications Faculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia Department of Computing Technologies Faculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia

In healthcare systems,the Internet of Things(IoT)innovation and development approached new ways to evaluate patient data.A cloud-based platform tends to process data generated by IoT medical devices instead of high storage,and computational *** this paper,an intelligent healthcare system has been proposed for the prediction and severity analysis of lung disease from chest computer tomography(CT)images of patients with pneumonia,Covid-19,tuberculosis(TB),and ***,the CT images are captured and transmitted to the fog node through IoT *** the fog node,the image gets modified into a convenient and efficient format for further *** encryption Standard(AES)algorithm serves a substantial role in IoT and fog nodes for preventing data from being accessed by other operating ***,the preprocessed image can be classified automatically in the cloud by using various transfer and ensemble learning *** different pre-trained deep learning architectures(Inception-ResNet-v2,VGG-19,ResNet-50)used transfer learning is adopted for feature *** softmax of heterogeneous base classifiers assists to make individual *** a meta-classifier,the ensemble approach is employed to obtain final optimal *** predicted image is consigned to the recurrent neural network with long short-term memory(RNN-LSTM)for severity analysis,and the patient is directed to seek therapy based on the *** proposed method achieved 98.6%accuracy,0.978 precision,0.982 recalls,and 0.974 F1-score on five class *** experimental findings reveal that the proposed framework assists medical experts with lung disease screening and provides a valuable second perspective.

关键词： Intelligent health care cloud computing fog computing ensemble learning RNN-LSTM

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To Jack or Not to Jack: Inside the Minds of Pipe Jacking Operators

To Jack or Not to Jack: Inside the Minds of Pipe Jacking Ope...

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ASEAN Australian engineering Congress, AAEC 2022

作者： Yeo, Lit Yen Choo, Chung Siung Lee, Sue Han Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Kuching Malaysia

ISBN: (纸本)9789819955466

Limited available land is leading to the emerging demand for trenchless methods for the installation of buried pipelines. Pipe jacking is one such method. The prediction of jacking forces is crucial in assessing the performance of the pipe jacking process, thus many efforts have been made to predict the jacking forces. Current approaches are based on the negotiated geological conditions, either through empirical or mechanistic approaches. However, such methods are only useful during the planning and design stages. During the construction stage, the microtunnel boring machine (mTBM) operators base their decisions on the operation parameters. This is further complicated when pipe jacking is performed through highly weathered lithology, i.e. ‘soft rocks’. There continues to be a gap in this domain of modelling jacking forces for soft rocks based on operation parameters. In the current study, deep learning techniques were applied to pipe jacking field-measured operation parameters obtained from the Kuching Wastewater Management System. The deep learning models helped to relate the time series input operation parameters to the jacking forces. Subsequently, a study on different moving average periods towards the input features was applied to understand the changes in the prediction performance of the jacking forces using the deep learning model. From the study, the results showed that the cumulative parameters which are the drive length, the cumulative days elapsed, and the cumulative lubricant used have the highest moving average periods. This suggested that along the pipe jacking process, these features would have a significant impact on the jacking forces. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Lithology

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Few-shot learning and modeling of 3D reservoir properties for predicting oil reservoir production

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Neural computing and Applications 2024年第23期36卷 14527-14541页

作者： Cirac, Gabriel Avansi, Guilherme Daniel Farfan, Jeanfranco Schiozer, Denis José Rocha, Anderson Institute of Computing Universidade de Campinas SP Campinas Brazil Department of Energy Faculty of Mechanical Engineering Center for Petroleum Studies Universidade de Campinas SP Campinas Brazil

The oil and gas industry employs numerical simulation tools extensively in reservoir analysis and strategic planning. This study presents a machine-learning proxy model, employing a Few-shot Learning approach with a Deep Convolutional Generative Adversarial Network (DC-GAN) to reduce computational costs using fewer training samples. The DC-GAN generates new training samples by synthesizing spatial attributes, reservoir parameters, and time series data, thus enhancing the sample variability and diversity needed for accurate production prediction. Also, the study proposes a straightforward and efficient method for data augmentation that primarily involves replicating the initial training dataset. The accumulated production forecast generated from geostatistical realizations enables intelligent reservoir management through risk analysis. The technique can reduce the processing footprint by 70%. Unlike most reservoir studies that employ synthetic datasets, this investigation adopts a real, high-dimensional, and complex reservoir model. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Risk analysis

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Gray-Hole Attack Minimization in IoMT with 5G Based D2D Networks

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Computer Systems Science & engineering 2022年第9期42卷 1289-1303页

作者： V.Balaji P.Selvaraj Department of Computing Technologies College of Engineering and TechnologyFaculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia

Reliable transmission is vital to the success of the next generation ofcommunications technologies and Fifth Generation (5G) networks. Many sensitive applications, such as eHealth and medical services, can benefit from a 5G network. The Internet of Medical Things (IoMT) is a new field that fosters themaintenance of trust among various IoMT Device to Device (D2D) modern technologies. In IoMT the medical devices have to be connected through a wirelessnetwork and constantly needs to be self-configured to provide consistent and effi-cient data transmission. The medical devices need to be connected with sophisticated protocols and architecture to handle the synergy of the monitoring ***, one of the commonly used algorithms in D2D communication is the Optimized Link State Routing protocol (OLSR). The OLSR is considerably good ateffectively utilizing the bandwidth and reserving the paths. One of the majorattack against the OLSR is the Node isolation attack, also known as the Gray holedenial of service attack. The Gray hole attack exploits the vulnerabilities presentwith sharing the topological information of the network. The attackers may usethis topological information to maliciously disconnect the target nodes from theexisting network and stops rendering the communication services to the victimnode. Hence, considering the sensitivity and security concerns of the data usedin e-Health applications, these types of attacks must be detected and disabledproactively. In this work, a novel Node Authentication (NA) with OLSR is proposed. The simulation experiments illustrated that the proposed protocol has anexcellent Packet Delivery Ratio, minimal End-End delay, and minimal Packet losswhen compared to the Ad-hoc On-Demand Distance Victor (AODV) protocol andthe proposed authentication scheme was able to protect the OLSR protocol from anode isolation attack.

关键词： 5G AODV D2D IoMT OLSR security issues

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Development of Smart HVAC Thermostat with AI Using Passive Infrared Sensors for Energy Saving

Development of Smart HVAC Thermostat with AI Using Passive I...

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ASEAN Australian engineering Congress, AAEC 2022

作者： Wong, Basil T. Sim, Rui Li Faculty of Engineering Computing and Science Swinburne University of Technology Kuching Sarawak Malaysia

ISBN: (纸本)9789819955466

Global warming is causing climate change, disrupting the national economy, and affecting people's living habits. Numerous papers point out that human activity shows that large amounts of waste heat or energy are being continuously released into the climate system. This heat is thought to be responsible for the current global warming. In particular, there is strong evidence that HVAC systems consume more than 50% of total electricity consumption. Therefore, today's social products are environmentally friendly and energy efficient to reduce heat and energy waste. In addition, homeowners who use programmable thermostats correctly can save a lot of electricity. The reduction in energy consumption is due to the automatic use of energy-saving settings, i.e., shutting down the HVAC system when not needed. However, many programmable thermostats have user interfaces that are difficult to navigate and program. To overcome the shortcomings of programmed thermostats, smart thermostats can recognize occupancy and create schedules automatically without human involvement. The project aims to develop a system for sensing room occupants through passive infrared sensors. It then adjusts the thermostat temperature and fan speed accordingly to ensure thermal comfort. To some extent, energy savings can also be achieved through intelligent automatic control settings. Results show that using flow simulations, fluid motion in a room can be assessed, a database developed, and a smart thermostat can be used to automatically configure HVAC systems to reduce energy consumption while maintaining acceptable comfort levels for most indoor occupants. The results showed that the average energy consumption of an HVAC system controlled by a smart thermostat was 22.36% lower than that of a manually programmed thermostat. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy utilization

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Artificial Intelligence Fuzzy Logic Expert System for Prediction of Bevel Angle Quality Response in Plasma Jet Manufacturing Process 1

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1st International Conference on Digital Transformation in Education and Artificial Intelligence Applications, MoStart 2023

作者： Peko, Ivan Crnokić, Boris Planinić, Igor Faculty of Science University of Split Split Croatia Faculty of Mechanical Engineering Computing and Electrical Engineering University of Mostar Mostar Bosnia and Herzegovina

ISBN: (数字)9783031368332

ISBN: (纸本)9783031368325

This paper presents application of artificial intelligence (AI) fuzzy logic technique for creating expert system that will be able to predict bevel angle as quality response in plasma jet cutting process. Bevel angle is significant cut quality response in plasma nonconventional manufacturing process that appears due to instability and deflection of the plasma jet during cutting and penetration in workpiece. Initial point for development of AI fuzzy logic expert system is experimental work. In this paper experimentations were conducted on aluminum sheet 5083 thickness 8 mm by varying four process parameters gas pressure, cutting speed, arc current and cutting height. Due to complexity of manufacturing process a few constraints regarding parameters values combinations in experimental plan were identified. In order to define relations between input parameters and bevel angle response fuzzy logic AI technique was applied. Prediction accuracy of developed fuzzy logic model was checked by using mean absolute percentage error (MAPE) and coefficient of determination (R2) between experimental and predicted data. Also, response surface plots were created to visualize input parameters effects on the analyzed cut quality response. Presented fuzzy logic expert system enables better understanding and control of manufacturing process as well as it serves as basis for further and more detailed experimentations in this area. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fuzzy logic

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Blockchain Interoperability in Secure E-Governance: A Systematic Literature Review

Blockchain Interoperability in Secure E-Governance: A System...

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2024 International Conference on Information Technology Systems and Innovation, ICITSI 2024

作者： Fathiyana, Rana Zaini Ramli, Kalamullah Yutia, Syifa Nurgaida Universitas Indonesia Faculty of Engineering Electrical Engineering Departement Depok Indonesia Telkom Uviversity Information Technology School of Computing Jakarta Indonesia

ISBN: (纸本)9798331511470

Ensuring data security, integrity, and privacy despite increased cyber threats and hacking attempts is one of the primary implementation difficulties of secure e-governance. Interoperability and seamless exchange of data are key components of e-governance, enabling the secure sharing of data, information, and resources among diverse government institutions and agencies, irrespective of the platforms or technologies utilized. Blockchain technology has surfaced as a viable answer, offering enhanced trust, transparency, and collaborative capabilities within government intra-system networks. Its inherent characteristics position blockchain as a transformative technology for the development of secure, reliable, and collaborative digital governance frameworks. This article provides a comprehensive literature review that examines the application of blockchain technology in enhancing interoperability and secure data exchange across various sectors, particularly in the realm of government governance. This review also identifies the types of interoperability that blockchain supports. This article concludes by outlining the methodology and security requirements essential for building a secure e-governance system. © 2024 IEEE.

关键词： Data integrity

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Navigating the Future: A Review of Contemporary AI Methodologies in Autonomous Vehicle Development for 6G Networks 5

Navigating the Future: A Review of Contemporary AI Methodolo...

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5th IEEE International Conference on Emerging Trends in Electrical, Electronic and Communications engineering, ELECOM 2024

作者： Lama, Sirine Akinsolu, Mobayode O. Wrexham University Faculty of Arts Computing and Engineering Wrexham United Kingdom School of Electrical and Mechanical Engineering ECAM LaSalle Lyon France

ISBN: (纸本)9798331532475

The development of autonomous vehicles (AVs) has advanced dramatically due to the rapid evolution of artificial intelligence (AI) technologies, which hold the potential to revolutionize the transportation industry. This paper investigates the integration of sensor fusion, deep learning (DL), and machine learning (ML) techniques with 6G network infrastructure to enhance A V capabilities. 6G networks' improved connectivity and reduced latency enable real-Time vehicle-To-everything (V2X) communication and data processing, which are essential for the effective and safe operation of A Vs. Through a comprehensive review of recent literature and case studies, this paper assesses the effectiveness of AI technologies in real-world scenarios, highlighting their impact on the automotive industry. It covers ethical and technological issues, including data privacy, decision-making, cybersecurity risks, data processing, and sensor reliability. The paper also examines the regulatory environment, underscoring the necessity of well-coordinated international frameworks. The findings emphasize a multidisciplinary approach to developing A V technology and creating laws that promote public safety and confidence. Significant research gaps are identified, and future research directions are suggested, including developing reliable sensors, maintaining cybersecurity, and creating efficient algorithms and regulatory frameworks. These initiatives will help AVs operate seamlessly in an intelligent transportation ecosystem supported by 6G networks. © 2024 IEEE.

关键词： Cybersecurity

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