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Smart Grids and Energy Systems (SGES), International Conference on

作者： Lu Lu Zhengdong Shen Shuting Li Weili Wang Faculty of Science and Technology BEIJING NORMAL UNIVERSITY HONG KONG BAPTIST UNIVERSITY UNITED INTERNATIONAL COLLEGE Zhuhai China Department of Electronic and Information Engineering NANJING UNIVERSITY OF INFORMATION SCIENCE AND TECHNOLOGY Nanjing China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology Macao China School of Computer Science South China Normal University Guangzhou China

ISBN: (数字)9798331539634

ISBN: (纸本)9798331539641

We present an advanced energy prediction model that addresses energy imbalance by integrating Long Short-Term Memory (LSTM) with Attention Mechanism for time-series analysis and categorical boosting (CatBoost) for categorical feature optimization. This model accurately predicts the complex energy behaviors of prosumers who both generate and consume energy. Simulation results show that our model outperforms traditional methods with the lowest Mean Absolute Error (MAE) of 48.514. By achieving such a high level of accuracy, this model improves grid operation efficiency, offers a practical solution for smart grid management.

关键词： Analytical models Accuracy Simulation Time series analysis Weather forecasting Predictive models Data models Smart grids Forecasting Long short term memory

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CV-MOS: A Cross-View Model for Motion Segmentation

arXiv

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arXiv 2024年

作者： Tang, Xiaoyu Chen, Zeyu Cheng, Jintao Chen, Xieyuanli Wu, Jin Xue, Bohuan The School of Electronic and Information Engineering Faculty of Engineering South China Normal University Guangdong Foshan528225 China Xingzhi College South China Normal University Guangdong Guangzhou510000 China The College of Intelligence Science and Technology National University of Defense Technology Changsha China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

In autonomous driving, accurately distinguishing between static and moving objects is crucial for the autonomous driving system. When performing the motion object segmentation (MOS) task, effectively leveraging motion information from objects becomes a primary challenge in improving the recognition of moving objects. Previous methods either utilized range view (RV) or bird’s eye view (BEV) residual maps to capture motion information. Unlike traditional approaches, we propose combining RV and BEV residual maps to exploit a greater potential of motion information jointly. Thus, we introduce CV-MOS, a cross-view model for moving object segmentation. Novelty, we decouple spatial-temporal information by capturing the motion from BEV and RV residual maps and generating semantic features from range images, which are used as moving object guidance for the motion branch. Our direct and unique solution maximizes the use of range images and RV and BEV residual maps, significantly enhancing the performance of LiDAR-based MOS task. Our method achieved leading IoU(%) scores of 77.5% and 79.2% on the validation and test sets of the SemanticKitti dataset. In particular, CV-MOS demonstrates SOTA performance to date on various datasets. The CV-MOS implementation is available at https://***/SCNU-RISLAB/CV-MOS Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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SugarcaneNet: An Optimized Ensemble of LASSO-Regularized Pre-trained Models for Accurate Disease Classification

arXiv

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arXiv 2024年

作者： Talukder, Md Simul Hasan Akter, Sharmin Nur, Abdullah Hafez Aljaidi, Mohammad Sulaiman, Rejwan Bin Alkoradees, Ali Fayez Department of Electrical and Electronic Engineering Dhaka University of Engineering & Technology Gazipur Bangladesh Department of Biomedical Engineering Jashore University of Science and Technology Jashore Bangladesh Department of Electronic and Telecommunication Engineering International Islamic University Chittagong Chittagong Bangladesh Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan School of Computer science and Technology Northumbria University United Kingdom Unit of Scientific Research Applied College Qassim University Saudi Arabia

Sugarcane, a key crop for the world's sugar industry, is prone to several diseases that have a substantial negative influence on both its yield and quality. To effectively manage and implement preventative initiatives, diseases must be detected promptly and accurately. In this study, we present a unique model called SugarcaneNet that outperforms previous methods for automatically and quickly detecting sugarcane disease through leaf image processing. Our proposed model consolidates an optimized weighted average ensemble of seven customized and LASSO-regularized pre-trained models, particularly InceptionV3, InceptionResNetV2, DenseNet201, DenseNet169, Xception, and ResNet152V2. Initially, we added three more dense layers with 0.0001 LASSO regularization, three 30% dropout layers, and three batch normalizations with "renorm" enabled at the bottom of these pre-trained models to improve the performance. The accuracy of sugarcane leaf disease classification was greatly increased by this addition. Following this, several comparative studies between the average ensemble and individual models were carried out, indicating that the ensemble technique performed better. The average ensemble of all modified pre-trained models produced outstanding outcomes: 100%, 99%, and 99.45% for f1 score, precision, recall, and accuracy, respectively. Performance was further enhanced by the implementation of an optimized weighted average ensemble technique incorporated with grid search. This optimized "sugarcaneNet" model performed the best for detecting sugarcane diseases, having achieved accuracy, precision, recall, and F1 score of 99.67%, 100%, 100%, and 100%, respectively. © 2024, CC BY.

关键词： Sugar industry

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Tai le character recognition using deep convolution neural networks

Tai le character recognition using deep convolution neural n...

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2021 International Conference on computer Communication and Artificial Intelligence, CCAI 2021

作者： Zhao, Jingying Guo, Hai Liu, Yifan Dong, Na Wang, Xinyue Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China College of Computer Science and Engineering Dalian Minzu University Dalian China

ISBN: (纸本)9781728194011

Tai Le characters are used widely in Southwest China and Southeast Asia, and it is necessary to recognize Tai Le characters. In this paper, a Tai Le characters recognition model base on the deep convolution neural network (DCNN) has been established. In addition, to recognize Tai Le characters, the printed Tai Le database YDH2019.1 has been constructed, and image digital processing technology has been used to preprocess the sample images of YDH2019.1. In the modeling of DCNN, 50% data of YDH2019.1 has been used as train data, and the performance of the recognition, model has been evaluated on the remaining 50%. The results show that the recognition accuracy of Tai Le characters reaches 99.98%, which proved the effectiveness of DCNN as a printed Tai Le character recognition model. © 2021 IEEE.

关键词： Character recognition

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Improving Brain MRI Image Segmentation Quality: A Hybrid Technique for Intensity Inhomogeneity Correction 34

Improving Brain MRI Image Segmentation Quality: A Hybrid Tec...

引用

34th Conference of Open Innovations Association FRUCT, FRUCT 2023

作者： Aziz, Samah Abdel Hawbani, Ammar Wang, Xingfu Abdelhamid, Talaat Maolood, Ismail Y. Alsamhi, Saeed Ismail, A.S. School of Computer Science and Technology University of Science and Technology of China Hefei23002 China Zagazig University Zagazig44519 Egypt Menoufia University Faculty of Electronic Engineering Menoufia Egypt -System Information Ministry of Higher Education and Scientific Research Kurdistan Region Erbil Iraq National University of Ireland Insight Centre for Data Analytics Galway Ireland

ISBN: (纸本)9789526524603

Intensity inhomogeneity is a significant issue in magnetic resonance imaging (MRI), where the presence of bias field causes distortions in pixel values, resulting in inconsistent and erroneous intensities across the image. This artifact not only hampers accurate diagnosis by radiologists but also negatively impacts the performance of computer-aided diagnosis algorithms, particularly in tasks like segmentation. In our proposed approach, we use a hybrid technique called KIFCM, which integrates K-means and Fuzzy C-means to enhance brain tumor segmentation. K-means provides computational efficiency, while Fuzzy C-means improves accuracy by detecting missed tumor cells. We employ a bias correction method based on the level set framework, removing noise with a median filter and applying the hybrid KIFCM technique for optimal segmentation. Our method effectively addresses intensity variation challenges, ensuring precise brain tumor region segmentation. We compare our results with DFCM and MFFLs, and the comparison shows the efficiency of our proposed method by highlighting the superior quality and accuracy of 81% achieved, requiring less computational time. Consequently, our results demonstrate KIFCM's potential to boost both accuracy and speed in MRI-based brain tumor detection through computer-aided diagnosis. © 2023 FRUCT Oy.

关键词： Magnetic resonance imaging

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Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model

引用

computers, Materials & Continua 2022年第1期70卷 1297-1313页

作者： C.S.S.Anupama L.Natrayan E.Laxmi Lydia Abdul Rahaman Wahab Sait Jose Escorcia-Gutierrez Margarita Gamarra Romany F.Mansour Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Archives and Communication King Faisal University31982Kingdom of Saudi Arabia Electronic and Telecommunicacions Program Universidad Autonoma del CaribeBarranquilla08001Colombia Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla08001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Nowadays,quality improvement and increased accessibility to patient data,at a reasonable cost,are highly challenging tasks in healthcare *** of Things(IoT)and Cloud Computing(CC)architectures are utilized in the development of smart healthcare *** entities can support real-time applications by exploiting massive volumes of data,produced by wearable sensor *** advent of evolutionary computation algorithms andDeep Learning(DL)models has gained significant attention in healthcare diagnosis,especially in decision making *** cancer is the deadliest disease which affects people across the *** skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin *** current research article presents a new skin lesion diagnosis model i.e.,Deep Learning with Evolutionary Algorithm based Image Segmentation(DL-EAIS)for IoT and cloud-based smart healthcare ***,the dermoscopic images are captured using IoT devices,which are then transmitted to cloud servers for further ***,Backtracking Search optimization Algorithm(BSA)with Entropy-Based Thresholding(EBT)i.e.,BSA-EBT technique is applied in image *** by,Shallow Convolutional Neural Network(SCNN)model is utilized as a feature *** addition,Deep-Kernel Extreme LearningMachine(D-KELM)model is employed as a classification model to determine the class labels of dermoscopic *** extensive set of simulations was conducted to validate the performance of the presented method using benchmark *** experimental outcome infers that the proposed model demonstrated optimal performance over the compared techniques under diverse measures.

关键词： Intelligent models skin lesion dermoscopic images smart healthcare internet of things

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Power Factor Improvement by Shunt Capacitor Bank at 33 Kv Busbar in a Distribution Substation 3

Power Factor Improvement by Shunt Capacitor Bank at 33 Kv Bu...

引用

3rd Information technology to Enhance e-Learning and Other Application, IT-ELA 2022

作者： Sahib, Abdullah Al-Baidhani, Humam AL-Furat AL-Awsat Technical University Department of Electronic and Communication Technologies Najaf Iraq Imam Ja'afar Al-Sadiq University Faculty of Information Technology Department of Computer Techniques Engineering Baghdad Iraq

ISBN: (纸本)9798350334968

Power factor (PF) is one of the important aspects affecting the performance of the electrical network. This phenomenon results from an increase in inductive loads, which leads to lower voltage, increase losses, and lower efficiency in the electrical network. Different types of shunt capacitor bank (SCB) configurations are installed in the distribution substation (DS), which can be grounded or ungrounded, fused or without fuses. These SCBs provide voltage support, power factor correction, power transport increment, loss reduction, and enhancement of system quality. This paper presents the evaluation of two fixed SCBs isolated by isolator at 33 Kv busbar in DS for PFC. The work is simulated and analyzed using MATLAB. © 2022 IEEE.

关键词： Busbars

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Text Line Detection and Correction for Challenging Datasets: A Case Study with Newspapers Dataset

SSRN

引用

SSRN 2023年

作者： Fateh, Amirreza Fateh, Mansoor Abolghasemi, Vahid Iran Faculty of Computer Engineering Shahrood University of Technology Shahrood Iran School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom

Official Iranian Newspapers (OIN) are those in which companies’ registration information is published. To digitally store the data of these newspapers, we need to convert images of newspaper pages into editable text through OCR algorithms. Text line detection is one of the most critical steps in converting document images into text. However, challenges such as non-uniform text distribution, variations in line spacing, and diverse fonts pose difficulties for TLD algorithms. This paper presents a novel TLD algorithm for Official Iranian Newspapers (OIN), a dataset with unique challenges such as overlapping text lines and diverse text orientations. The proposed approach applies pre-processings such as denoising and binarization to raw images. Then, the diameter of each extracted connected component in the image is measured to detect and suggest the optimum font size. Afterwards, all horizontal connected components (CCs) are adjusted in the same direction and are then connected.-This-way,-a-baseline-is-formed.-Finally,-a-post-processing-stage-is-performed-to-retrieve-the-words-that-have-been-removed-due-to-line-curvature.-To-evaluate-the-performance-of-the-proposed-method,-we-collected-images-from-OIN.-Two-other-datasets-***-to-the-obtained-results,-the-proposed-method-achieved-99.49\%-accuracy-in-line-detection-in-the-OIN-dataset.-Also,-to-prove-that-line-detection-increases-OCR-accuracy,-we-used-the-Tesseract-OCR.-we-demonstrated-that-the-proposed-method-improved-the-accuracy-of-the-Tesseract-OCR-by-2.8\%-in-the-OIN-dataset.-Overall,-our-method-demonstrates-promising-results-and-can-serve-as-a-basis-for-developing-TLD-algorithms-for-other-challenging-datasets. © 2023, The Authors. All rights reserved.

关键词： Newsprint

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Cancelable speaker identification based on cepstral coefficients and comb filters

引用

International Journal of Speech technology 2022年第2期25卷 471-492页

作者： Monir, Mohamed Kareem, Mona El-Dolil, Sami M. Saleeb, Adel El-Fishawy, Adel S. Nassar, Mohamed Abd-Elsalam Zein Eldin, Mohamed A. Abd El-Samie, Fathi E. Sharm El-Shaikh Airport Sharm El Sheikh Egypt Communications and Electronics Department Faculty of Electronic Engineering Manoufia University Menouf Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

This paper presents a proposed approach for cancelable biometric recognition related to speaker identification. Both comb and inverse comb filters are used to distort speech signals intentionally prior to and after feature extraction. The objective of this approach is to generate protected templates of speech signals representing speakers without subjecting the original speech signals or features to violations of attackers. Both comb and inverse comb filters are used for this purpose. In addition, a satisfactory performance represented in the speaker identification rate is retained. In case the database of speaker features is compromised, it is possible to change the comb or inverse comb filter orders to generate new features for the same speakers. Simulation results reveal the possibility to identify speakers from their deteriorated speech signals, which proves the robustness of the proposed cancelable speaker identification system. The reason of choosing the comb filter is that it is a multi-band filter with multiple nulls in its frequency response. Hence, its inversion is difficult due to the nulling effect. This characteristic can induce non-invertible distortion in speech signals. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.

关键词： Neural networks

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Securing Data in an Internet of Things Network Using Blockchain technology: Smart Home Case

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SN computer Science 2022年第2期3卷 1-10页

作者： Tchagna Kouanou, Aurelle Tchito Tchapga, Christian Sone Ekonde, Michael Monthe, Valery Mezatio, Brice Anicet Manga, Josépha Simo, Gael R. Muhozam, Yves Department of Computer Engineering College of Technology University of Buea Buea Cameroon Department of Training Research Development and Innovation InchTech’s Solutions Yaoundé Cameroon Department of Electrical and Electronic Engineering College of Technology University of Buea Buea Cameroon Department of Computer Science Faculty of Sciences University of Yaoundé 1 Yaoundé Cameroon Research Unit Condensed Matter Electronics and Signal Processing Department of Physics Faculty of Science University of Dschang PO Box 67 Dschang Cameroon

Every day, innovative technologies emerge (5G networks, quantum computing) or change to existing technologies (big data, deep learning, blockchain, etc.). These technologies are expected to play an essential role on the Internet of Things (IoT) systems and applications. The IoT architecture generates substantial amounts of diverse types of data at extremely high speeds. All these generated data need to be secure. Scalable and secure data sharing is essential for all technological domains. Taking into consideration of blockchain technology today in data security, we can also think about a method to secure IoT data based on blockchain. In this paper, we proposed a blockchain method to secure data in an IoT architecture. We used EOS Blockchain for its scalability, flexibility and native enhanced throughput. Architecture in a concept of a smart home using blockchain is proposed. We combine Arduino, Raspberry Pi and sensors to create an IoT ecosystem for a smart home. Data are collected and stored in EOS blockchain for better security and integrity. We use Raspberry Pi as a client to get data in our developed blockchain. The real implementation is performed and the obtained results show us that we can send more than 500 data per second in our EOS blockchain. We discuss our results and saw that our blockchain method outperformed those in related works in terms of the number of data stored per millisecond in blockchain and the scalability. The proposed EOS blockchain pipeline for securing Smart Home data is provided at the end of this paper. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022.

关键词： Architecture Blockchain Data security IoT Smart home

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