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15th International Conference on Evaluation of Novel Approaches to software Engineering, ENASE 2020

作者： Schaarschmidt, Marco Uelschen, Michael Pulvermuller, Elke Westerkamp, Clemens Faculty of Engineering and Computer Science University of Applied Sciences Osnabrück Germany Software Engineering Research Group University of Osnabrück Germany

ISBN: (纸本)9789897584213

With the increasing size and complexity of embedded systems, the impact of software on energy consumption is becoming more important. Previous research focused mainly on energy optimization at the hardware level. However, little research has been carried out regarding energy optimization at the software design level. This paper focuses on the software design level and addresses the gap between software and hardware design for embedded systems. This is achieved by proposing a framework for software design patterns, which takes aspects of power consumption and time behavior of the hardware level into account. We evaluate the expressiveness of the framework by applying it to well-known and novel design patterns. Furthermore, we introduce a dimensionless numerical efficiency factor to make possible energy savings quantifiable. © Copyright 2020 by SCITEPRESS - science and Technology Publications, Lda. All rights reserved.

关键词： Embedded systems

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Studying the effects of feature selection approaches on machine learning techniques for Mushroom classification problem

Studying the effects of feature selection approaches on mach...

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IT and Industrial Technologies (ICIT), International Conference on

作者： Abu Bakar Siddique Muhammad Abu Bakar Raja Hashim Ali Usama Arshad Nisar Ali Zain Ul Abideen Talha Ali Khan Ali Zeeshan Ijaz Muhammad Imad Faculty of Computer Science & Engg GIK Institute of Engg. Sciences & Tech Khyber Pakhtunkhwa Pakistan Faculty of Computer Science & Engg AI Research Group GIK Institute of Engg. Sciences & Tech Khyber Pakhtunkhwa Pakistan Faculty of Electronic Systems Engg University of Regina Regina Canada BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Tech & Software Engg Univ. of Europe of Applied Sciences Berlin Germany

Feature selection is a critical factor affecting the performance of optimization algorithms. Without proper feature selection, optimization algorithms may suffer from slow convergence, overfitting, increased computational requirements, and longer execution times. On the other hand, omitting important features can lead to loss of relevant information, decreased accuracy, bias, and increased vulnerability to noise and outliers. This study investigates the use of genetic algorithms as a feature selection technique for a classification problem, specifically the mushrooms classification problem. Random forest is employed as the machine learning classifier, and genetic algorithms are compared with correlation as the feature selection method. The results show that genetic algorithms achieve higher accuracy, precision, recall, and F1-score compared to correlation-based feature selection. However, genetic algorithms have limitations in their applicability to specific optimization problems, the need for proper parameter setup, and longer convergence times. Despite these drawbacks, genetic algorithms prove to be superior to other feature selection techniques, particularly correlation-based approaches. This study highlights the importance of selecting appropriate feature selection techniques for optimization algorithms to improve their performance and achieve better results. In addition, this study explored the performance of various machine learning approaches on the complete mushroom dataset with 22 features and shows that genetic algorithms with feature selection as the most accurate method.

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A New Deep Learning-Based Mobile Application for Komering Character Recognition

A New Deep Learning-Based Mobile Application for Komering Ch...

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International Seminar on research of Information Technology and Intelligent Systems (ISRITI)

作者： Yesi Novaria Kunang Ilman Zuhri Yadi Mahmud Mansyur Husin Intelligent System Research Group Faculty of Computer Science Universitas Bina Darma Palembang Indonesia Software Developer OXY creative. inc Jakarta Indonesia Provincial Archives Service Palembang Sumatera Selatan Indonesia

The Komering script, also known as the Ulu script, is one of the scripts that must be conserved in the South Sumatra region. Some historical manuscripts are composed in the Komering script, which requires transliteration. Using technology, this study intends to preserve and revitalize the Komering script. The application technology developed can assist in reading documents written in the Komering script. The produced mobile application technology product recognizes images using deep learning technology. The Convolutional Neural Network classification algorithm is used to categorize Komering characters. The trained deep learning model can classify 336 Komering characters with a 99% accuracy rate. The learned deep learning model is subsequently incorporated into the Android application to facilitate the program's portability.

关键词： Deep learning Training Handwriting recognition Image recognition Computational modeling Training data Mobile applications

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A Comprehensive Review of Active EV Battery Cell Voltage Balancing Systems: Current Issues and Prospective Solutions

A Comprehensive Review of Active EV Battery Cell Voltage Bal...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Chandan Chetri Alvin Huynh Sheldon S. Williamson Department of Electrical Computer and Software Engineering Smart Transportation Electrification and Energy Research (STEER) Group Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Faculty of Engineering and Applied Science Ontario Tech University Oshawa Canada

The performance of a battery pack is greatly affected by an imbalance between the cells. Cell balancing is a very important criterion for Battery Management System (BMS) to operate properly. This paper presents the various cell balancing topologies which have been presented by various researchers in the past years. A novel topology has also been discussed, which improves the overall efficiency of the components being used. Various comparison parameters such as Control, Speed, Efficiency, Power Effects, Manufacturability, Size, and Cost have been considered.

关键词： Industrial electronics Electric potential Costs Equalizers Battery management systems Transformers Power electronics

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THE DOUBLE DIHEDRAL DUNKL TOTAL ANGULAR MOMENTUM ALGEBRA

arXiv

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arXiv 2023年

作者： de Martino, Marcelo Langlois-Rémillard, Alexis Oste, Roy Clifford Research Group Department of Electronics and Information Systems Faculty of Engineering and Architecture Ghent University Krijgslaan 281–S8 Gent9000 Belgium Department of Applied Mathematics Computer Science and Statistics Faculty of Sciences Ghent University Krijgslaan 281–S9 Gent9000 Belgium ScaDS.AI Leipzig University Leipzig04105 Germany

The Dunkl deformation of the Dirac operator is part of a realisation of an orthosymplectic Lie superalgebra inside the tensor product of a rational Cherednik algebra and a Clifford algebra. The Dunkl total angular momentum algebra (TAMA) occurs as the supercentraliser, or dual partner, of this Lie superalgebra. In this paper, we consider the case when the reflection group associated with the Dunkl operators is a product of two dihedral groups acting on a four-dimensional Euclidean space. We introduce a subalgebra of the total angular momentum algebra that admits a triangular decomposition and, in analogy to the celebrated theory of semisimple Lie algebras, we use this triangular subalgebra to give precise necessary conditions that a finite-dimensional irreducible representation must obey, in terms of weights. Furthermore, we construct a basis for representations of the TAMA with explicit actions. Examples of these modules occur in the kernel of the Dunkl–Dirac operator in the context of deformations of Howe dual pairs. © 2023, CC BY.

关键词： Angular momentum

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CSI-Based Human Activity Recognition using Convolutional Neural Networks 11

CSI-Based Human Activity Recognition using Convolutional Neu...

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11th International Conference on computer Engineering and Knowledge, ICCKE 2021

作者： Moshiri, Parisa Fard Nabati, Mohammad Shahbazian, Reza Ghorashi, Seyed Ali Shahid Beheshti University G. C. Cognitive Telecommunication Research Group Department of Electrical Engineering Tehran Iran Standard Research Institute Faculty of Technology and Engineering Department of Electrical Engineering Alborz31745-139 Iran University of East London School of Architecture Computing and Engineering Department of Computer Science and Digital Technologies London United Kingdom

ISBN: (纸本)9781665402088

Human activity recognition (HAR) as an emerging technology can have undeniable impacts on several applications such as health monitoring, context-aware systems, transportation, robotics, and smart cities. Among the main research methods in HAR (sensor, image, and WiFi-based), the WiFi-based method has attracted considerable attention due to the ubiquity of WiFi devices. WiFi devices can be utilized to distinguish daily activities such as "walk", "run", and "sleep". These activities affect WiFi signal propagation and can be further used to recognize activities. This paper proposes a Deep Learning method for HAR tasks using channel state information (CSI). A new model is developed in which CSI data are converted to grayscale images. These images are then fed into a 2D-Convolutional Neural Network (CNN) for activity classification. We take advantage of CNN's high accuracy on image classification along with WiFi-based ubiquity. The experimental results demonstrate that our proposed approach achieves acceptable performance in HAR tasks. © 2021 IEEE.

关键词： Channel state information

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Importance of Synteny in Homology Inference

Importance of Synteny in Homology Inference

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International Conference on Emerging Technologies, ICET

作者： Ali Zeeshan Ijaz Raja Hashim Ali Asima Sarwar Talha Ali Khan Muhammad Muneeb Baig Faculty of Computer Science & Engg. AI Research Group GIK Institute of Engg. Sciences & Tech. Topi Khyber Pakhtunkhwa Pakistan Dept. of Tech & Software Engg. Univ. of Europe of Applied Sciences Berlin Germany

ISBN: (纸本)9781665459938

Homology inference is central to many Bioinformatics analyses, from phylogeny to evolutionary rates, where the goal is to identify the genes that have evolved from the same last common ancestor. An error at the stage of inferring homology may be carried down to further analysis and may cause severe errors at the later stages. For example, an incorrect pair of genes classified as homologous may lead to incorrectly changing the topology of phylogeny inferred from using the non-homologous pair or incorrect evolutionary rate inference. At the moment, most homology inference algorithms are based purely on similarity in gene contents, which is known to be inversely proportional to the rate of evolution and time since the divergence of both sequences. However, another important characteristic for homology inference is synteny - the order of genes on the homologous chromosomes, which has been rarely used in homology inference. The major reason for not using synteny for homology inference is equal or sometimes drastic affected by the rate of evolution and time since divergence of both sequences. However, using synteny in addition to gene similarity has shown promising results. In the current study, we have tried to explore the usefulness of synteny and present cases, where synteny can aid in differentiating for borderline homologs.

关键词： Correlation Inference algorithms Phylogeny Topology Classification algorithms Bioinformatics Biological cells

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A cooperative heterogeneous vehicular clustering framework for

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Frontiers of Information Technology & Electronic Engineering 2021年第9期22卷 1247-1259页

作者： Iftikhar AHMAD Rafidah Md NOOR Zaheed AHMED Umm-e-HABIBA Naveed AKRAM Fausto Pedro GARCIA MARQUEZ Department of Computer Science and Information Technology MirpurUniversity of Science and TechnologyMirpur-10250(AJK)Pakistan Facultyof Computer Science and Information Technology University of MalayaKualaLumpur 50603Malaysia Departmentof Software Engineering University of KotliAJK 11100Pakistan Department of Mechanical Engineering Mirpur University of Science andTechnologyMirpur-10250(AJK)Pakistan Department of MechanicalEngineering Faculty of Mechanical EngineeringUniversity of MalayaKualaLumpur 50603Malaysia Ingenium Research Group University of Castilla-LaManchaSpain

Heterogeneous vehicular clustering integrates multiple types of communication networks to work efficiently for various vehicular *** popular form of heterogeneous network is the integration of long-term evolution(LTE)and dedicated short-range *** heterogeneity of such a network infrastructure and the non-cooperation involved in sharing cost/data are potential problems to solve.A vehicular clustering framework is one solution to these problems,but the framework should be formally verified and validated before being deployed in the real *** solve these issues,first,we present a het-erogeneous framework,named destination and interest-aware clustering,for vehicular clustering that integrates vehicular ad hoc networks with the LTE network for improving road traffic ***,we specify a model system of the proposed *** model is formally verified to evaluate its performance at the functional level using a model checking *** evaluate the performance of the proposed framework at the micro-level,a heterogeneous simulation environment is created by integrating state-of-the-art *** comparison of the simulation results with those of other known approaches shows that our proposed framework performs better.

关键词： Vehicular cluster Heterogeneity Cooperation Formal verification System model

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A fast and effective maximum power point tracking method under various weather conditions for grid-connected photovoltaic systems

A fast and effective maximum power point tracking method und...

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IEEE Conference on Power Electronics and Renewable Energy (CPERE)

作者： Karam Khairullah Mohammed Saad Mekhilef Muhyaddin Rawa Control and Mechatronics Engineering Department School of Electrical Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia School of Software and Electrical Engineering Faculty of Science Engineering and Technology Swinburne University of Technology Hawthorn VIC Australia Smart Grids Research Group Center of Research Excellence in Renewable Energy and power Systems King Abdulaziz University Jeddah Saudi Arabia Department Of Electrical and computer Engineering Faculty of engineering K. A Care Energy Research and Innovation Center King Abdulaziz University Jeddah Saudi Arabia

Photovoltaic systems (PV) are exposed to various weather conditions during the day and getting the maximum power of a PV system is one of its major challenges. While the conventional methods take time to track the maximum power accompanying the fluctuation around the maximum power. Consequently, several metaheuristic algorithms have been presented to overcome the fluctuating around the maximum power. On the other hand optimization methods will search into entire the PV curve to extract the maximum power. This leads to unnecessary search points during the uniform shading conditions which decrease the convergence speed towards the maximum power. In overcoming the tracking time issues as well as the capacity to distinguish between uniform and partial shading conditions, a quick and efficient tracking technique connected to the grid system was presented in this paper. The proposed system, has been tested using MATLAB/SIMULINK, which includes a boost converter to track the maximum power with a sampling time of 0.05 seconds, While a voltage source converter (VSC) is used to transfer the power generated by solar panels to the grid. The simulation results demonstrated that the proposed method was successfully implemented, with an average tracking time of 0.412 s for different weather conditions. Moreover, the comparison with the conventional method of perturb and observe (P& O) has been presented to evaluate the proposed method’s efficiency.

关键词： Maximum power point trackers Renewable energy sources Fluctuations Simulation Optimization methods Power electronics Solar panels

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DFGC-VRA: DeepFake Game Competition on Visual Realism Assessment

DFGC-VRA: DeepFake Game Competition on Visual Realism Assess...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Bo Peng Xianyun Sun Caiyong Wang Wei Wang Jing Dong Zhenan Sun Rongyu Zhang Heng Cong Lingzhi Fu Hao Wang Yusheng Zhang Hanyuan Zhang Xin Zhang Boyuan Liu Hefei Ling Luka Dragar Borut Batagelj Peter Peer Vitomir Štruc Xinghui Zhou Kunlin Liu Weitao Feng Weiming Zhang Haitao Wang Wenxiu Diao Center for Research on Intelligent Perception and Computing (CRIPAC) Institute of Automation Chinese Academy of Sciences (CASIA) Beijing China Beijing University of Civil Engineering and Architecture (BUCEA) China Interactive Entertainment Group of Netease Inc Guangzhou China Huazhong University of Science and Technology (HUST) China Faculty of Computer and Information Science University of Ljubljana (UNILJ) Slovenia Faculty of Electrical Engineering University of Ljubljana (UNILJ) Slovenia University of Science and Technology of China (USTC) China Institute of Network Technology (INT) Yantai China Nanjing University of Science and Technology (NUST) China

This paper presents the summary report on the DeepFake Game Competition on Visual Realism Assessment (DFGC-VRA). Deep-learning based face-swap videos, also known as deepfakes, are becoming more and more realistic and deceiving. The malicious usage of these face-swap videos has caused wide concerns. There is a ongoing deepfake game between its creators and detectors, with the human in the loop. The research community has been focusing on the automatic detection of these fake videos, but the assessment of their visual realism, as perceived by human eyes, is still an unexplored dimension. Visual realism assessment, or VRA, is essential for assessing the potential impact that may be brought by a specific face-swap video, and it is also useful as a quality metric to compare different face-swap methods. This is the third edition of DFGC competitions, which focuses on the new visual realism assessment topic, different from previous ones that compete creators versus detectors. With this competition, we conduct a comprehensive study of the SOTA performance on the new task. We also release our MindSpore codes to further facilitate research in this field (https://***/bomb2peng/DFGC-VRA-benckmark).

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