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5th International Conference on Photonics, ICP 2014

作者： Mohamed Halip, N. Hafizah Mokhtar, M. Buhari, A. Dept. of Computer and Communication System Engineering Faculty of Engineering Universiti Putra Malaysia Serdang Malaysia Faculty of Computer Science and Information Universiti Putra Malaysia Serdang Malaysia

ISBN: (纸本)9781479948833

A simulation of BB84 protocol of QKD using photonic simulator Optisystem is demonstrated. The simulation is divided into two categories which are without security attacks scenario and with several attacks scenario that consists of Intercept and Resend and sent new qubits. The simulation results meet the theory of BB84. This project is able to provide better understanding about the quantum key distribution process especially in BB84 protocol. © 2014 IEEE.

关键词： Quantum cryptography

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Instance-based Deep Transfer Learning on Cross-domain Image Captioning 21

Instance-based Deep Transfer Learning on Cross-domain Image ...

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21st International Electronics Symposium, IES 2019

作者： Perdana, Rizal Setya Ishida, Yoshiteru Toyohashi University of Technology Department of Computer Science and Engineering Toyohashi Japan Department of Information System Faculty of Computer Science Brawijaya University Malang Indonesia

ISBN: (纸本)9781728144498

Image captioning task achieves imposing result in generating text description of image by training on the large image and sentence pairs dataset (e.g., MSCOCO). Applied the large publicly available datasets to the specific new task will lead to the problem known as domain shifting due to the different probability distribution. In this research, we propose a Multimodal Instance-based Deep Transfer Learning (MIBTL) for cross-domain image captioning. Generally, transferring knowledge from the source domain into the target domain is pertinent due to the limited number of the dataset from the target domain. The limited number of the dataset also lead to a problem called overfitting. The instance-based strategy intuitively takes into account the influence of data by selecting the most representative data from source domain as the supplement for training on the target domain. We employ deep hash binary code representation of image and text pair to determine the distance between two data points. The experiment conducted by two types of transfer condition includes a slight shift (from MSCOCO to Flickr30k) and significant shift (from MSCOCO to CUB-200 and Oxford-102). The result shows that MIBTL can outperform other baselines to achieve state-of-the-art performance in cross-domain image captioning transfer learning. © 2019 IEEE.

关键词： Probability distributions

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A comparative analysis of MQTT and IoT application protocols 2

A comparative analysis of MQTT and IoT application protocols

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2nd IEEE International Conference on Electronic engineering, ICEEM 2021

作者： Al Enany, Marwa O Harb, Hany M. Attiya, Gamal Ministry of Higher Education Higher Institute of Computer Science and Information System Computer Science Department Egypt Al-Azhar University Faculty of Engineering Systems and Computers Department Cairo Egypt Menoufia University Faculty of Electronic Engineering Computer Science and Engineering Department Egypt

ISBN: (纸本)9781665418423

Choosing the appropriate IoT protocol for a certain application was and still a critical issue for IoT applications, as protocols vary in behavior in different applications and under different network status. IoT application protocols have a recent focus of attention especially those protocols for the low-powered IoT applications and constrained devices. A brief survey of IoT layers and applied protocols for each layer and their applications were presented, besides, a detailed survey of IoT protocols with a presentation of the main researches that have been done to evaluate their performance. As a use case for this comparative study, the MQTT protocol was chosen because of its simplicity and its publish /subscribe model to be evaluated and compared against other application protocols. © 2021 IEEE.

关键词： Surveys

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An Efficient Schema Transformation Technique for Data Migration from Relational to Column-Oriented Databases

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computer systems science & engineering 2022年第12期43卷 1175-1188页

作者： Norwini Zaidi Iskandar Ishak Fatimah Sidi Lilly Suriani Affendey System Development and Engineering Center Universiti Sains Islam Malaysia71800Negeri SembilanMalaysia Faculty of Computer Science and Information Technology Universiti Putra MalaysiaUPM Serdang43400SelangorMalaysia

Data transformation is the core process in migrating database from relational database to NoSQL database such as column-oriented database. However,there is no standard guideline for data transformation from relational database toNoSQL database. A number of schema transformation techniques have been proposed to improve data transformation process and resulted better query processingtime when compared to the relational database query processing time. However,these approaches produced redundant tables in the resulted schema that in turnconsume large unnecessary storage size and produce high query processing timedue to the generated schema with redundant column families in the transformedcolumn-oriented database. In this paper, an efficient data transformation techniquefrom relational database to column-oriented database is proposed. The proposedschema transformation technique is based on the combination of denormalizationapproach, data access pattern and multiple-nested schema. In order to validate theproposed work, the proposed technique is implemented by transforming data fromMySQL database to MongoDB database. A benchmark transformation techniqueis also performed in which the query processing time and the storage size arecompared. Based on the experimental results, the proposed transformation technique showed significant improvement in terms query processing time and storagespace usage due to the reduced number of column families in the column-orienteddatabase.

关键词： Data migration data transformation column-oriented database relational database big data

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Unified resilience model using deep learning for assessing power system performance

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Heliyon 2025年第4期11卷 e42802页

作者： Artemchuk, Volodymyr Garbuz, Iurii Jamil Alsayaydeh, Jamil Abedalrahim Shkarupylo, Vadym Oliinyk, Andrii Bin Yusof, Mohd Faizal Herawan, Safarudin Gazali Department of Mathematical and Econometric Modelling G.E. Pukhov Institute for Modelling in Energy Engineering of the NAS of Ukraine Kyiv Ukraine Department of Environmental Protection Technologies and Radiation Safety Center for Information-analytical and Technical Support of Nuclear Power Facilities Monitoring of the NAS of Ukraine Kyiv Ukraine Department of Іnformation Systems in Economics Kyiv National Economic University Named After Vadym Hetman Kyiv Ukraine Department of Computerized Control System National Aviation University Kyiv Ukraine Department of Engineering Technology Fakulti Teknologi Dan Kejuruteraan Elektronik Dan Komputer (FTKEK) Universiti Teknikal Malaysia Melaka (UTeM) Melaka 76100 Malaysia Department of Computer Systems Networks and Cybersecurity of the National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Software Tools Faculty of Computer Science and Technology Zaporizhzhia Polytechnic National University Zaporizhzhia Ukraine Research Section Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta 11480 Indonesia

Energy resilience in renewable energy sources dissemination components such as batteries and inverters is crucial for achieving high operational fidelity. Resilience factors play a vital role in determining the performance of power systems, regardless of their operating environment and interruptions. This article introduces a Unified Resilience Model (URM) using Deep Learning (DL) to enhance power system performance. The proposed model analyzes environmental factors impacting the resilience of batteries and energy storage devices. This deep learning approach trains performance-impacting factors using previously known low resilience drain data. The learning output is utilized to augment various strengthening factors, thereby improving resilience. Drain mitigation and performance improvements are combined for direct impact verification. This process validates the model's fidelity in enhancing power system performance, with a specific focus on the impact of weather factors. © 2025 The Authors

关键词： Deep learning Energy resilience Fidelity Weather impact

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Battery Monitoring system on IoT-Based Smart Micro Grid 4

Battery Monitoring System on IoT-Based Smart Micro Grid

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4th International Conference on Electronic and Electrical engineering and Intelligent system, ICE3IS 2024

作者： Iksan, Nur Widodo, Djoko Adi Djuniadi, Djuniadi Putri, Riana Defi Mahadji Suryanto, Agus Udayanti, Erika Devi Universitas Negeri Semarang Faculty of Engineering Department of Electrical Engineering Indonesia Universitas Dian Nuswantoro Faculty of Computer Science Department of Information System Indonesia

ISBN: (纸本)9798350378368

The use of batteries as an energy storage medium has a very important role in the installation of renewable energy power plants, such as photovoltaics to overcome intermittency in photovoltaics and to maintain stable power quality. In this study, the installation of solar plants that was used as the research object was a smart micro grid that integrates photovoltaic electrical energy sources with the Utility electricity network for residential scale. The battery monitoring system is used to manage the performance of existing batteries on the electricity grid through analysis of the battery's electrical values such as current, voltage, power and energy, so that it can function optimally in storing electrical energy and supplying electricity to users. Internet of things (IoT) technology is used to provide easy transfer of data obtained through the process of data acquisition to data visualization provided to be accessed by users in real time. This research aims to develop an IoT-based battery monitoring system. The development carried out focuses on the data acquisition process, the data transfer process using IoT technology, the process of analyzing and visualizing data on the IoT cloud and remote access to users. © 2024 IEEE.

关键词： Battery IoT Monitoring Smart Micro Grid

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Obsolescence optimization of electronic and mechatronic components by considering dependability and energy consumption

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Journal of Central South University 2013年第5期20卷 1221-1225页

作者： M.A.Mellal S.Adjerid D.Benazzouz S.Berrazouane E.J.Williams Solid Mechanics and System Laboratory Faculty of Engineering Sciences (FSI)M'Hamed Bougara University Faculty of Engineering Sciences (FSI) M'Hamed Bougara University Decision Sciences College of BusinessUniversity of Michigan Industrial and Manufacturing Systems Engineering Department College of Engineering and Computer ScienceUniversity of Michigan

Nowadays, rapid technological progress influences the dependability of equipments and also causes rapid obsolescence. The mechatronic and electronic equipment components are mostly affected by obsolescence. A new challenger unit possesses identical functionalities, but with higher performances. This work aims to find the optimal number of components which should be replaced by new-type units, under budgetary constraints. In this work, the new challenger unit is characterized by lower energy consumption and the optimization steps are based on genetic algorithm (GA). The result shows the importance of this type of replacement in order to economize energy consumption and to deal with obsolescence.

关键词： obsolescence lower energy consumption mechatronic and electronic components genetic algorithm

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Multispectral sensors calibration for lightweight UAV 9

Multispectral sensors calibration for lightweight UAV

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9th IGRSM International Conference and Exhibition on Geospatial and Remote Sensing: Geospatial Enablement, IGRSM 2018

作者： Mansor, Yaakob Mansor, Shattri Zulhaidi, Helmi Ramli, Abdul Rahman Isola, Ajibola I Centre of Foundation Studies for Agricultural Science Malaysia Department of Civil Faculty of Engineering Malaysia Department of Computer and Communication System Faculty of Engineering Malaysia Malaysia

Using a suitable gimbal sensor, a lightweight multispectral sensor weight less than 500 g, such as visible imagery in the 400-700 nm (4000x3000 pixels), NIR imagery in the 520-920 nm region (2048 x 1536 pixels) and thermal 8-14μm (160x120 pixels) were placed on low-cost Hexacopter UAVs to perform aerial acquisitions of oil palm plant at very low altitude. Reliability data analysis is generated from the correction of undesirable and defect of sensor characteristics. Here, the sensor correction and calibration such as geometric, radiometric, vignetting, resolution and directional effects. Radiometric calibration using an upwelling water methodology where with thermosensor, the coefficient of determination, R2 = 1 and without thermosensor, R2 = 0.9455. For multi-temporal or multi-sensor image analysis, relative DN-to-radiance radiometric calibration was performed by using the white material as calibration, then convert the other colored area without the material based on the ratio of the white panel area and the color reflected in that area and compatible with in-situ spectroscopy device. © Published under licence by IOP Publishing Ltd.

关键词： Pixels

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Shortest Path Priority-based RPL (SPPB-RPL): The Case of a Smart Campus

Shortest Path Priority-based RPL (SPPB-RPL): The Case of a S...

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2020 IEEE Conference on Application, Information and Network Security, AINS 2020

作者： Onwuegbuzie, Innocent Uzougbo Razak, Shukor Abd Isnin, Ismail Fauzi Anuar, Nor Badrul School of Computing Faculty of Engineering Johor Malaysia Faculty of Computer Science and Information Technology University of Malaya Department of Computer System and Technology Kuala Lumpur Malaysia

ISBN: (纸本)9781728192406

Wireless Sensor Networks (WSN) is the network of embedded and resource-constrained nodes, that forms the foundation of the Internet of Things (IoT). These networks are designed to sense, aggregate and transport sensed data to locations that may be local or remote to the sensory field, for immediate or stored for later use. In most cases, WSN generates heterogeneous data that conveys information of varying priorities which may be real-time (RT) or non-real-time (NRT). However, the de facto routing protocol;Routing Protocol for Low-power and Lossy Networks (RPL) is not optimized for heterogeneous data routing. Likewise, routing schemes proposed in the literature have not proffered a lasting solution. In this paper, we proposed the Shortest Path Priority-based RPL (SPPB-RPL). The SPPB-RPL scheme not only routes data with varying priorities but also does so with optimal network performances. Its application in a smart campus is used as a case study. The scheme uses the mechanism of class of service (CS) to classify and prioritized data into various priority classes. Subsequently, the standard RPL DIO base object was improved upon to accommodate the prioritized data classes, and finally, the Dijkstra Algorithm (DA) was adopted to generate the optimal shortest routing path leading to the destination node which is guided by the priority of the data. Benchmarked with related schemes, SPPB-RPL performed optimally with a 99.98% packet delivery ratio with the least latency. This enhanced the network performance for a prolonged operational lifetime. © 2020 IEEE.

关键词： Routing protocols

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Weather Data Analysis Using Hadoop: Applications and Challenges

Weather Data Analysis Using Hadoop: Applications and Challen...

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International Conference on Green engineering Technology and Applied Computing 2019, IConGETech2 019 and International Conference on Applied Computing 2019, ICAC 2019

作者： Adam Ibrahim Fakherldin, Mohammed Adam, Khalid Akma Abu Bakar, Noor Abdul Majid, Mazlina Faculty of Computer System and Software Engineering Universiti Malaysia Pahang Malaysia Faculty of Electric and Electronic Engineering Universiti Malaysia Pahang Malaysia Faculty of Computer Science and Information Systems Jazan University Malaysia

Weather data is very crucial in every aspect of human daily life. It plays an important role in many sectors such as agriculture, tourism, government planning, industry and so on. Weather has a variety of parameters like temperature, pressure, humidity and wind speed. The meteorological department deployed sensors for each weather parameter at different geographical locations to collect data. This data is stored mostly in the unstructured format. Thus, a big amount of data has been collected and archived. Therefore, storage and processing of this big data for accurate weather prediction is a huge challenge. Hadoop an apache product it used to support big data sets in a distributed environment. Hadoop has greatest advantages over scalable and fault-tolerant distributed processing technologies. This paper explains a system that uses the historical weather data of a region and apply the MapReduce and Hadoop techniques to analysis these historical data. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Digital storage

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