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Recent Advances and Innovations in Engineering (ICRAIE)

作者： Tajul Rosli Razak Hasila Jarimi Emy Zairah Ahmad School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Solar Energy Research Institute University Kebangsaan Malaysia Bangi Selangor Fakulti Teknologi dan Kejuruteraan Elektrik Universiti Teknikal Malaysia Melaka Durian Tunggal

The configuration of Artificial Neural Networks (ANNs) in the context of predictive modelling can provide considerable difficulty owing to the complex nature of their arrangements and the need for meticulous hyperparameter adjustment. The present study addresses the issue by proposing a more straightforward methodology for configuring Artificial Neural Networks (ANNs) through the R programming language. The methodology presented in this study offers a systematic and comprehensive framework, ensuring accessibility and simplicity of implementation. This approach aims to enhance the usability of Artificial Neural Networks (ANNs) for practitioners who need advanced machine learning knowledge. In order to demonstrate the applicability of the proposed methodology, a series of experiments were conducted on a case study in sustainable energy research. This study makes a valuable contribution to academic discipline by establishing a connection between artificial neural network (ANN) theory and its practical application. This study aims to enhance the accessibility of artificial neural network (ANN) setup and provide significant insights to further progress predictive modelling, specifically focusing on sustainable energy research.

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Modeling of Interconnected Systems with Batteries and Mathematical Formulation of Charge and Discharge Planning for Peak Demand Reduction

Modeling of Interconnected Systems with Batteries and Mathem...

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The Society of Instrument and Control Engineers Annual Conference 2015

作者： Tomotake Sasaki Hitoshi Yanami Junji Kaneko Shinji Hara Yafei Wang Hidenao Iwane HPC Application Solutions Division Fujitsu Limited Department of Information Physics and Computing The University of Tokyo Department of Advanced Energy The University of Tokyo Research Center for Community Knowledge National Institute of Informatics

This paper proposes a model of a certain class of interconnected systems including multiple batteries such as a building or a group of buildings, which describes 1) interconnection and energy flows among components, and 2) flows,storage and release of energy within the components. The charge and discharge planning problems for peak demand reduction are formulated as optimization problems based on the model.

关键词： Modeling,Interconnected Systems,energy Management,Battery,energy Storage,Optimization Problem

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A Review on Building energy Savings Strategies and Systems (BE3S)

A Review on Building Energy Savings Strategies and Systems (...

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IEEE International Conference on Power and energy Applications (ICPEA)

作者： Abbas M. Al-Ghaili Hairoladenan Kasim Marini Othman Bo Nørregaard Jørgensen Zainuddin Hassan Institute of Informatics and Computing in Energy (IICE) Universiti Tenaga Nasional (UNITEN) Selangor Malaysia College of Computer Science and Information Technology (CSIT) Universiti Tenaga Nasional (UNITEN) Selangor Malaysia Center for Energy Informatics University of Southern Denmark Odense Denmark CSIT Universiti Tenaga Nasional (UNITEN) Selangor Malaysia

energy savings in buildings as a goal in recently established buildings either residential or commercial has been nowadays paid much attention by many researchers, designers, and decision makers due to the need of reducing energy demand and global warming issues. This challenging goal is affected by several factors and roles which play important roles by which this goal is affected. Those factors in most cases are difficult to be treated by one method or design. Many proposed systems have been introduced for many years; their performance(s) have varied percentage(s) of achievement in terms of energy savings. In this setting, such a review paper could help much to draw a clear plan for researchers and buildings' designers to follow and get them directed to best and easiest systems. However, many review papers have been published which included many limitations, e.g., some review papers focused only on residential buildings. In general, there is a need to give an overview of reviewed papers with taking into account many factors and policies together with strategies applied. In this setting, it is understood that different designs have produced different levels of performance being achieved. In this review paper, Building energy Savings Strategies and Systems (BE3S) are reviewed. Different points of views have been considered. For example, lighting control systems, buildings' retrofitting systems, and occupancy profiles related systems have been reviewed. This paper has also included different types of buildings on which strategies and systems have been applied. Reviewed papers are collected from top-tier journals published by very well-known publishers which are IEEE, Elsevier, Emerald, MDPI, and ACM. Indexing services adopted in this BE3S paper are Scopus and WOS. BE3S paper has only considered last 20 years providing a summary of recently designed strategies achieving semi-optimal performance(s) in terms of energy savings in buildings.

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Various Deep Learning Methods for Hyperspectral Images

Various Deep Learning Methods for Hyperspectral Images

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2020 International Conference on computing and Information Technology, ICCIT 2020

作者： Sivaram, M. Shobana, S. Jeya Khan, Mujeebudheen Ramakrishnan, Jayabrabu Goel, Pallavi Murghai Maseleno, Andino Lebanese French University Department of Computer Networking Erbil Kurdistan Region Iraq College of Science Knowledge University Department of Computer Science Erbil Kurdistan Region Iraq Ibra College of Technology Department of Electrical and Electronics Oman Jazan University College of Computer Science and Information Technology Department of Information Technology and Security Saudi Arabia Galgotias University School of Computing Science and Engineering Greater Noida India Universiti Tenaga Nasional Institute of Informatics and Computing Energy Department of Business Administration and Information System Malaysia

ISBN: (纸本)9781728126807

Hyperspectral imagery is widely used in remote sensing applications that take into account thousands of spectral channel compositions over a single scene. Hyperspectral imagery requires accurate models of learning to extract the hyperspectral features in an image. Due to the presence of its spatial and spectral resolution, the image learning model presents a core challenge due to its complicated nature of image frames. In order to assist it during the learning process, several attempts have been made to address its complicated nature. However, these methods failed to provide the hyperspectral imagery with a deeper understanding. Because of the presence of mixed pixels, limited training samples and redundant data, the utilization of deep learning techniques addresses the problems. The deep learning process addresses the complex image data relationship. In this paper, various deep learning methods are studied which are used for the learning of hyperspectral imagery. Initially, we present an overview on various deep learning methods for various image processing techniques. A system review is then carried out on various hyperspectral image learning models based on deep learning. © 2020 IEEE.

关键词： Remote sensing

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Recognizing Malaysia Traffic Signs with Pre-Trained Deep Convolutional Neural Networks

Recognizing Malaysia Traffic Signs with Pre-Trained Deep Con...

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International Conference on Cybernetics, Robotics and Control (CRC)

作者： Dickson Neoh Tze How Khairul Salleh Mohamed Sahari Yew Cheong Hou Omar Gumaan Saleh Basubeit Universiti Tenaga Nasional Kajang Malaysia Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Kajang Selangor Malaysia Department of Electrical & Electronics Engineering Universiti Tenaga Nasional Kajang Selangor Malaysia

ISBN: (数字)9781728146201

ISBN: (纸本)9781728146218

An essential component in the race towards the self-driving car is automatic traffic sign recognition. The capability to automatically recognize road signs allow self-driving cars to make prompt decisions such as adhering to speed limits, stopping at traffic junctions and so forth. Traditionally, feature-based computer vision techniques were employed to recognize traffic signs. However, recent advancements in deep learning techniques have shown to outperform traditional color and shape based detection methods. Deep convolutional neural network (DCNN) is a class of deep learning method that is most commonly applied to vision-related tasks such as traffic sign recognition. For DCNN to work well, it is imperative that the algorithm is given a vast amount of training data. However, due to the scarcity of a curated dataset of the Malaysian traffic signs, training DCNN to perform well can be very challenging. In this demonstrate that DCNN can be trained with little training data with excellent accuracy by using transfer learning. We retrain various pre-trained DCNN from other image recognition tasks by fine-tuning only the top layers on our dataset. Experiment results confirm that by using as little as 100 image samples for 5 different classes, we are able to classify hitherto traffic signs with above 90% accuracy for most pre-trained models and 98.33% for the DenseNet169 pre-trained model.

关键词： Computer vision Deep learning Convolutional neural networks Object recognition Image classification

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Conjoint dynamic aggregation and scheduling methods for dynamic virtual power plants

Conjoint dynamic aggregation and scheduling methods for dyna...

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Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Astrid Nieße Sebastian Beer Jörg Bremer Christian Hinrichs Ontje Lünsdorf Michael Sonnenschein R&D Division Energy OFFIS - Institute for Information Technology Escherweg 2 D-26121 Oldenburg Germany R&D Division Energy OFFIS – Institute for Information Technology Oldenburg Germany Environmental Informatics Department of Computing Science University of Oldenburg Oldenburg Germany

ISBN: (纸本)9781479928538

The increasing pervasion of information and communication technology (ICT) in energy systems allows for the development of new control concepts on all voltage levels. In the distribution grid, this development is accompanied by a still increasing penetration with distributed energy resources like photovoltaic (PV) plants, wind turbines or small scale combined heat and power (CHP) plants. Combined with shiftable loads and electrical storage, these energy units set up a new flexibility potential in the distribution grid that can be tapped with ICT-based control following the long-term goal of substituting conventional power generation. In this contribution, we propose an architectural model and algorithms for the self-organization of these distributed energy units within dynamic virtual power plants (DVPP) along with first results from a feasibility study of the integrated process chain from market-driven DVPP formation to product delivery.

关键词： Schedules Optimization Load flow analysis Heat engines Topology

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Local meshless methods for elliptic PDEs with multipoint boundary conditions: investigating efficiency and accuracy of various RBFs

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The European Physical Journal Special Topics 2024年 1-18页

作者： Ahmad, Masood Khan, Muhammad Nawaz Ahmad, Imtiaz Department of Basic Sciences University of Engineering and Technology Peshawar Peshawar Pakistan Mathematics in Applied Sciences and Engineering Research Group Scientific Research Center Al-Ayen University Nasiriyah Iraq Institute of Informatics and Computing in Energy (IICE) Universiti Tenaga Nasional Kajang Malaysia

The present study addresses the numerical solution of two-dimensional steady-state heat conduction problems with nonlocal multi-point boundary conditions across three distinct domains: a unit rectangle with a quarter-circle cutout of radius 0.5, an irregular domain, and a Cassini curve. Dirichlet boundary conditions are imposed on specific segments, while nonlocal boundary conditions are applied to the remaining portions. The Kansa method is employed to solve the steady-state heat conduction equation, utilizing three types of radial basis functions (RBFs) to explore the influence of the shape parameter on accuracy and matrix conditioning. These include the inverse multiquadric RBF, a modified inverse multiquadric RBF proposed here for the first time, and a hybrid RBF [1]. As a meshless method, the Kansa approach eliminates the need for mesh generation or node connectivity within local subdomains. To evaluate accuracy and performance, the $$L_{\infty }$$ error norm is employed. The results demonstrate the effectiveness of the proposed techniques in solving the 2D steady-state heat conduction problem. A comparative analysis is conducted to assess the accuracy and computational efficiency of the methods.

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Category Classification of Deformable Object using Hybrid Dynamic Model for Robotic Grasping

Category Classification of Deformable Object using Hybrid Dy...

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International Conference on Cybernetics, Robotics and Control (CRC)

作者： Yew Cheong Hou Khairul Salleh Mohamed Sahari Dickson Neoh Tze How Leong Yeng Weng Universiti Tenaga Nasional Kajang Malaysia Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Kajang Selangor Malaysia Department of Electrical and Electronics College of Engineering Universiti Tenaga Nasional Kajang Selangor Malaysia

ISBN: (数字)9781728146201

ISBN: (纸本)9781728146218

This work studies the problem of classification of a hung garment in the unfolding procedure by a home service robot. The sheer number of unpredictable configurations that the deformable object can end up in makes the visual identification of the object shape and size difficult. In this paper, we propose a hybrid dynamic model to recognize the pose of hung garment using a single manipulator. A dataset of hung garment is generated by capturing the depth images of real garments at the robotic platform (real images) and also the images of garment mesh model from offline simulation (synthetic images) respectively. Deep convolutional neural network is implemented to classify the category and estimate the pose of garment. Experiment results show that the proposed method performs well and is applicable to different garments in robotic manipulation.

关键词： Clothing Robot sensing systems Grasping Service robots Manipulators Deformable models

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Modeling epidemic risk perception in networks with community structure 7th

Modeling epidemic risk perception in networks with community...

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7th International ICST Conference on Bio-Inspired Models of Network, Information, and computing Systems, BIONETICS 2012

作者： Bagnoli, Franco Borkmann, Daniel Guazzini, Andrea Massaro, Emanuele Rudolph, Stefan Department of Energy University of Florence Florence Italy Center for the Study of Complex Systems University of Florence Florence Italy National Institute for Nuclear Physics Florence Section Florence Italy Communication Systems Group ETH Zurich Zurich Switzerland National Research Council Institute for Informatics and Telematics Pisa Italy Department of Psychology University of Florence Florence Italy Department of Computer Science and Systems University of Florence Florence Italy Organic Computing Group University of Augsburg Augsburg Germany

ISBN: (纸本)9783319069432

We study the influence of global, local and community-level risk perception on the extinction probability of a disease in several models of social networks. In particular, we study the infection progression as a susceptible-infected-susceptible (SIS) model on several modular networks, formed by a certain number of random and scale-free communities. We find that in the scale-free networks the progression is faster than in random ones with the same average connectivity degree. For what concerns the role of perception, we find that the knowledge of the infection level in one’s own neighborhood is the most effective property in stopping the spreading of a disease, but at the same time the more expensive one in terms of the quantity of required information, thus the cost/effectiveness optimum is a tradeoff between several parameters. © institute for Computer Sciences, Social informatics and Telecommunications Engineering 2014.

关键词： Risk perception

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Automatic document clustering and indexing of multiple documents using KNMF for feature extraction through Hadoop and lucene on big data

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Test Engineering and Management 2019年第11-12期81卷 1107-1130页

作者： Laxmi Lydia, E. Sharmili, N. Nguyen, Phong Thanh Hashim, Wahidah Maseleno, Andino Computer Science and Engineering Vignan's Institute of Information Technology India CSE Department GayatriVidyaParishad College of Engineering for Women Viskahpatnam Andhra Pradesh India Department of Project Management Ho Chi Minh City Open University Viet Nam Institute of Informatics and Computing Energy UniversitiTenagaNasional Malaysia

The existence of unlabeledtext data in documents has become larger and excavating such datasets is a provocative task. The objective of Big Data is to store, retrieve and analyse multipletext documents. Problem Statement:The retrieval of the identical data over large databases is of major concern. Existing Solution:Existing problem is solved by Full-Text Search (FTS) which means pattern matching technique that allows searching of multiple keywords at specific *** Solution: In this paper, we consider multiple text documents as input and processed using text mining pre-processing algorithms like Key Phrase extraction, Porters stemming for tokenizing and TF_IDF toobtain all non-negative values. These values further processed to get matrix data throughNonnegative matrix factorization (NMF). On performing NMF, K-means algorithmis upgraded with NMF to obtain quality clusters of data *** of the algorithms are tested using Newsgroup20 data in Open Source Hadoop software environment which also analyses the performance of the MapReduce framework. The final outcome is to generate clusters and index them for the Newsgroup20dataset. Later on, Apache Lucene is presented for automatic document clustering with aGUI interface developed for indexing. Thus, this proposed algorithm resultsby improving the performance of document clustering through Map Reduce framework in Hadoop. © 2019 Mattingley Publishing. All rights reserved.

关键词： Big data

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