{"title":"Retracted: A Multimodal Information Fusion Model for Robot Action Recognition with Time Series","authors":"Journal of Electrical and Computer Engineering","doi":"10.1155/2023/9780325","DOIUrl":"https://doi.org/10.1155/2023/9780325","url":null,"abstract":"<jats:p />","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138958557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Retracted: Improved Blockchain Technology for Performance Optimization Model Design of Sports Clubs","authors":"Journal of Electrical and Computer Engineering","doi":"10.1155/2023/9862625","DOIUrl":"https://doi.org/10.1155/2023/9862625","url":null,"abstract":"<jats:p />","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139170193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cognitive radio (CR) is the best way to improve the efficiency of spectrum consumption for wireless multimedia communications. Spectrum sensing, which allows legitimate secondary users (SU) to find vacant bands in the spectrum, plays a vital role in CR networks. When cooperative sensing is used in CR networks, spectrum availability must be taken into account. In many ways, the shared cooperative spectrum sensing (CSS) data among SU. The presence of a malicious user (MU) in the system and sending false sensing data can degrade the performance of cooperative CR. The sharp rise in mobile data traffic causes congestion in the licensed band for the transmission of signals. Handling this security issue in real time, on top of spectrum sharing, is a challenge in such networks. In order to manage the spectrum and identify MU, blockchain-based CSS is developed in this article. To gauge the efficiency of the proposed topology, performance metrics like sensitivity, node selection, throughput measurement, and energy efficiency are used. This work suggests a unique, easier-to-use CSS method with MU suppression that outperforms the current one. According to simulation studies, the suggested topology can increase the likelihood of MU detection by roughly 15% when 40% of system users are malicious.
{"title":"Enhance the Probability of Detection of Cooperative Spectrum Sensing in Cognitive Radio Networks Using Blockchain Technology","authors":"D. Balakumar, Nandakumar Sendrayan","doi":"10.1155/2023/8920243","DOIUrl":"https://doi.org/10.1155/2023/8920243","url":null,"abstract":"Cognitive radio (CR) is the best way to improve the efficiency of spectrum consumption for wireless multimedia communications. Spectrum sensing, which allows legitimate secondary users (SU) to find vacant bands in the spectrum, plays a vital role in CR networks. When cooperative sensing is used in CR networks, spectrum availability must be taken into account. In many ways, the shared cooperative spectrum sensing (CSS) data among SU. The presence of a malicious user (MU) in the system and sending false sensing data can degrade the performance of cooperative CR. The sharp rise in mobile data traffic causes congestion in the licensed band for the transmission of signals. Handling this security issue in real time, on top of spectrum sharing, is a challenge in such networks. In order to manage the spectrum and identify MU, blockchain-based CSS is developed in this article. To gauge the efficiency of the proposed topology, performance metrics like sensitivity, node selection, throughput measurement, and energy efficiency are used. This work suggests a unique, easier-to-use CSS method with MU suppression that outperforms the current one. According to simulation studies, the suggested topology can increase the likelihood of MU detection by roughly 15% when 40% of system users are malicious.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138965186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Harish Kumar Khyani, Jayashri Vajpai, R. Karwa, Mahendra Bhadu
The rise in the temperature severely affects photovoltaic cell efficiency and hence its power output. Moreover, it also causes the development of thermal stresses that may reduce their life span. Thus, there is a need for an accurate estimation of the cell’s working temperature. In this paper, a detailed thermal model based on various heat transfer modes involved and their governing equations has been presented to estimate the cell temperature of a PV module using MATLAB software under different climatic and solar insolation conditions. In order to validate the presented model, an experimental setup has been built and operated under actual outdoor conditions of Jodhpur, a city in the Thar Desert of Rajasthan. For the peak summer month of June, the predicted glass cover outer surface temperature has been found to be within 0.2–4.5°C of experimentally measured values and the back sheet temperature is found to be within 0.5–5.5°C. The predicted and measured power outputs have been found to be within 0.85–1.2 W while the efficiency values are within 0.17–0.38%. For the early summer month of April, the variations are 0.13–4.1°C, 0.2–4.1°C, 0.44–1.65 W, and 0.1–0.5% for glass cover temperature, back sheet temperature, power output, and efficiency, respectively. Thus, the predictions of the developed thermal model have exhibited a good agreement with the experimental results. The maximum glass cover temperatures recorded were 60°C and 65.5°C when the ambient temperatures were 35°C and 42°C near the noon for the early summer and peak summer day experiments, respectively. The presented model can be used to generate a year-round cell temperature data for the known environmental data of a location, which can help in the selection or development of appropriate cooling technology at the planning stage of the installation of a solar PV plant.
{"title":"Thermal Modeling of Photovoltaic Panel for Cell Temperature and Power Output Predictions under Outdoor Climatic Conditions of Jodhpur","authors":"Harish Kumar Khyani, Jayashri Vajpai, R. Karwa, Mahendra Bhadu","doi":"10.1155/2023/5973076","DOIUrl":"https://doi.org/10.1155/2023/5973076","url":null,"abstract":"The rise in the temperature severely affects photovoltaic cell efficiency and hence its power output. Moreover, it also causes the development of thermal stresses that may reduce their life span. Thus, there is a need for an accurate estimation of the cell’s working temperature. In this paper, a detailed thermal model based on various heat transfer modes involved and their governing equations has been presented to estimate the cell temperature of a PV module using MATLAB software under different climatic and solar insolation conditions. In order to validate the presented model, an experimental setup has been built and operated under actual outdoor conditions of Jodhpur, a city in the Thar Desert of Rajasthan. For the peak summer month of June, the predicted glass cover outer surface temperature has been found to be within 0.2–4.5°C of experimentally measured values and the back sheet temperature is found to be within 0.5–5.5°C. The predicted and measured power outputs have been found to be within 0.85–1.2 W while the efficiency values are within 0.17–0.38%. For the early summer month of April, the variations are 0.13–4.1°C, 0.2–4.1°C, 0.44–1.65 W, and 0.1–0.5% for glass cover temperature, back sheet temperature, power output, and efficiency, respectively. Thus, the predictions of the developed thermal model have exhibited a good agreement with the experimental results. The maximum glass cover temperatures recorded were 60°C and 65.5°C when the ambient temperatures were 35°C and 42°C near the noon for the early summer and peak summer day experiments, respectively. The presented model can be used to generate a year-round cell temperature data for the known environmental data of a location, which can help in the selection or development of appropriate cooling technology at the planning stage of the installation of a solar PV plant.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138966980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Grant-free random access (RA) utilizing massive multiple-input multiple-output (MIMO) technology has attracted considerable attention in recent years due to its potential to enhance spectral efficiency. This paper introduces an innovative and advanced approach for the joint detection of users and estimation of channels in grant-free RA. The approach incorporates two distinct preamble structures: the single orthogonal preamble (SOP) and the concatenated orthogonal preamble (COP). The proposed algorithms make full use of the inherent quasiorthogonal characteristic of massive MIMO, thereby enabling the accurate estimation of user channels while effectively avoiding collisions in the preambles. As a result, these algorithms generate highly precise estimations of user channels. To substantiate the effectiveness of the proposed algorithms, this paper provides an extensive theoretical analysis and presents a comprehensive set of experimental results. These findings offer robust evidence for the efficacy of the algorithms in substantially bolstering the performance of grant-free RA. Additionally, we have conducted further research and analysis, which has led to additional insights and refinements in our proposed approach. Moreover, the experimental results validate the statistical significance and reliability of the performance enhancements achieved by these algorithms. Moreover, the proposed approach exhibits robustness in scenarios with different levels of user density and varying channel conditions. Through a thorough analysis of these scenarios, we showcase the versatility and applicability of our algorithms in real-world environments.
近年来，利用大规模多输入多输出（MIMO）技术的免授权随机接入（RA）因其提高频谱效率的潜力而备受关注。本文介绍了一种创新的先进方法，用于在无补助随机接入中联合检测用户和估计信道。该方法包含两种不同的前导码结构：单正交前导码（SOP）和并集正交前导码（COP）。所提出的算法充分利用了大规模多输入多输出（MIMO）固有的准正交特性，从而实现了对用户信道的精确估计，同时有效避免了前置信号中的碰撞。因此，这些算法能产生高度精确的用户信道估计。为了证实所提算法的有效性，本文进行了广泛的理论分析，并给出了一组全面的实验结果。这些研究结果提供了有力的证据，证明了这些算法在大幅提高免授权 RA 性能方面的功效。此外，我们还进行了进一步的研究和分析，从而对我们提出的方法有了更多的了解和改进。此外，实验结果也验证了这些算法在提高性能方面的统计意义和可靠性。此外，所提出的方法在不同用户密度和不同信道条件的情况下都表现出稳健性。通过对这些场景的深入分析，我们展示了算法在现实环境中的多功能性和适用性。
{"title":"Joint User Detection and Channel Estimation in Grant-Free Random Access for Massive MIMO Systems","authors":"Yang Yang, Guang Song, Hui Liu","doi":"10.1155/2023/1672421","DOIUrl":"https://doi.org/10.1155/2023/1672421","url":null,"abstract":"Grant-free random access (RA) utilizing massive multiple-input multiple-output (MIMO) technology has attracted considerable attention in recent years due to its potential to enhance spectral efficiency. This paper introduces an innovative and advanced approach for the joint detection of users and estimation of channels in grant-free RA. The approach incorporates two distinct preamble structures: the single orthogonal preamble (SOP) and the concatenated orthogonal preamble (COP). The proposed algorithms make full use of the inherent quasiorthogonal characteristic of massive MIMO, thereby enabling the accurate estimation of user channels while effectively avoiding collisions in the preambles. As a result, these algorithms generate highly precise estimations of user channels. To substantiate the effectiveness of the proposed algorithms, this paper provides an extensive theoretical analysis and presents a comprehensive set of experimental results. These findings offer robust evidence for the efficacy of the algorithms in substantially bolstering the performance of grant-free RA. Additionally, we have conducted further research and analysis, which has led to additional insights and refinements in our proposed approach. Moreover, the experimental results validate the statistical significance and reliability of the performance enhancements achieved by these algorithms. Moreover, the proposed approach exhibits robustness in scenarios with different levels of user density and varying channel conditions. Through a thorough analysis of these scenarios, we showcase the versatility and applicability of our algorithms in real-world environments.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139004476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Achmad Rizal, S. Hadiyoso, S. Aulia, I. Wijayanto, Triwiyanto, Ziani Said
The electroencephalogram (EEG) examination provides information on the brain’s electricity, especially in cases of epilepsy. Since the characteristics of EEG signals are nonlinear and nonstationary, visual inspection becomes very difficult. To overcome this problem, digital EEG signal processing was developed. Automatic epileptic EEG recognition is an area of interest on which much research focuses. The complexity approach to EEG signal analysis is interesting to be used as feature extraction, referring to the nonlinear characteristics of the signal. This study proposed an automatic epileptic EEG classification method based on the multiscale Hjorth descriptor measurement. EEG signals consisting of normal, interictal, and seizure (ictal) were simulated. The signal is scaled into new signals using the coarse-grained procedure on a scale of 1–20. Then, the Hjorth parameter which consists of activity, mobility, and complexity is calculated on the new signal. This process produces a feature vector that is used in the classification stage. Support vector machine (SVM) is used to evaluate the proposed feature extraction method. Simulation results showed that the Hjorth parameter on a scale of 1–15 yields 99.5% accuracy. The proposed method is expected to be applied to digital EEG for seizure detection and prediction.
{"title":"Multiscale Hjorth Descriptor on Epileptic EEG Classification","authors":"Achmad Rizal, S. Hadiyoso, S. Aulia, I. Wijayanto, Triwiyanto, Ziani Said","doi":"10.1155/2023/4961637","DOIUrl":"https://doi.org/10.1155/2023/4961637","url":null,"abstract":"The electroencephalogram (EEG) examination provides information on the brain’s electricity, especially in cases of epilepsy. Since the characteristics of EEG signals are nonlinear and nonstationary, visual inspection becomes very difficult. To overcome this problem, digital EEG signal processing was developed. Automatic epileptic EEG recognition is an area of interest on which much research focuses. The complexity approach to EEG signal analysis is interesting to be used as feature extraction, referring to the nonlinear characteristics of the signal. This study proposed an automatic epileptic EEG classification method based on the multiscale Hjorth descriptor measurement. EEG signals consisting of normal, interictal, and seizure (ictal) were simulated. The signal is scaled into new signals using the coarse-grained procedure on a scale of 1–20. Then, the Hjorth parameter which consists of activity, mobility, and complexity is calculated on the new signal. This process produces a feature vector that is used in the classification stage. Support vector machine (SVM) is used to evaluate the proposed feature extraction method. Simulation results showed that the Hjorth parameter on a scale of 1–15 yields 99.5% accuracy. The proposed method is expected to be applied to digital EEG for seizure detection and prediction.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139008094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Di Hu, Zhong Chen, Wei Yang, Taiyun Zhu, Y. Ke, Kaiyang Yin
Different types of partial discharge (PD) cause different damages to gas-insulated substation (GIS), so it is very important to correctly identify the type of PD for evaluating the GIS insulation condition. The traditional PD pattern recognition algorithm has the limitations of low recognition accuracy and slow recognition speed in engineering applications. To effectively diagnose the GIS PD type and safeguard the safe and reliable operation of the distribution network, a GIS PD method based on improved CBAM-ResNet was proposed in this paper. And the improved CBAM-ResNet takes advantage of the residual neural network and attention mechanism. In particular, the channel attention module and the spatial attention module are connected in parallel in the improved CBAM. The experimental results showed that the GIS PD pattern recognition method proposed herein has a recognition rate of 93.58%, 95.00%, 93.55%, and 93.88% against the four PD types. Compared with the traditional PD pattern recognition algorithm, the algorithm has the advantages of a lightweight model and more accurate recognition results, which carry better engineering application values.
{"title":"A GIS Partial Discharge Pattern Recognition Method Based on Improved CBAM-ResNet","authors":"Di Hu, Zhong Chen, Wei Yang, Taiyun Zhu, Y. Ke, Kaiyang Yin","doi":"10.1155/2023/9948438","DOIUrl":"https://doi.org/10.1155/2023/9948438","url":null,"abstract":"Different types of partial discharge (PD) cause different damages to gas-insulated substation (GIS), so it is very important to correctly identify the type of PD for evaluating the GIS insulation condition. The traditional PD pattern recognition algorithm has the limitations of low recognition accuracy and slow recognition speed in engineering applications. To effectively diagnose the GIS PD type and safeguard the safe and reliable operation of the distribution network, a GIS PD method based on improved CBAM-ResNet was proposed in this paper. And the improved CBAM-ResNet takes advantage of the residual neural network and attention mechanism. In particular, the channel attention module and the spatial attention module are connected in parallel in the improved CBAM. The experimental results showed that the GIS PD pattern recognition method proposed herein has a recognition rate of 93.58%, 95.00%, 93.55%, and 93.88% against the four PD types. Compared with the traditional PD pattern recognition algorithm, the algorithm has the advantages of a lightweight model and more accurate recognition results, which carry better engineering application values.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139009491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aqib Raqeeb, Fahim Shah, Zaheer Alam, Subhashree Choudhury, Bilal Khan, R. Palanisamy
Bearings are critical components in modern manufacturing, yet they are prone to failures in induction machines. Detecting these faults early can reduce repair costs. To achieve efficient and accurate fault detection, we explore vibration-based analysis. Traditional methods rely on manual feature extraction, which is time-consuming. In contrast, our work leverages deep learning, particularly convolutional neural networks, to automatically extract fault features from raw data. We investigate various image sizes (16 × 16, 32 × 32, 64 × 64, 128 × 128, 256 × 256) and their performance in bearing fault diagnosis. Our convolutional neural networks-based approach is compared to traditional methods such as support vector machine, nearest neighbors, and artificial neural networks. Results demonstrate the superior performance of our data-driven fault diagnosis using convolutional neural networks.
{"title":"Data-Driven Bearing Fault Diagnosis for Induction Motor","authors":"Aqib Raqeeb, Fahim Shah, Zaheer Alam, Subhashree Choudhury, Bilal Khan, R. Palanisamy","doi":"10.1155/2023/7173989","DOIUrl":"https://doi.org/10.1155/2023/7173989","url":null,"abstract":"Bearings are critical components in modern manufacturing, yet they are prone to failures in induction machines. Detecting these faults early can reduce repair costs. To achieve efficient and accurate fault detection, we explore vibration-based analysis. Traditional methods rely on manual feature extraction, which is time-consuming. In contrast, our work leverages deep learning, particularly convolutional neural networks, to automatically extract fault features from raw data. We investigate various image sizes (16 × 16, 32 × 32, 64 × 64, 128 × 128, 256 × 256) and their performance in bearing fault diagnosis. Our convolutional neural networks-based approach is compared to traditional methods such as support vector machine, nearest neighbors, and artificial neural networks. Results demonstrate the superior performance of our data-driven fault diagnosis using convolutional neural networks.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139237243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Al-Nima, Marwa Mawfaq Mohamedsheet Al-Hatab, Maysaloon Abed Qasim
Deoxyribonucleic acid (DNA) can be considered as one of the most useful biometrics. It has effectively been used for recognizing persons. However, it seems that there is still a need to propose a new approach for verifying humans, especially after the recent big wars, where too many people lost and die. This approach should have the capability to provide high personal verification performance. In this paper, a personal recognition approach based on artificial intelligence is proposed. This approach is called the artificial DNA algorithm for recognition (ADAR). It utilizes a unique identity for each person acquired from DNA nucleotides, and it can verify individuals efficiently with high performance. The ADAR has been designed and applied to multiple datasets, namely, the DNA classification (DC), sample DNA sequence (SDS), human DNA sequences (HDS), and DNA sequences (DS). For all datasets, a low value of 0% is achieved for each of the false acceptance rate (FAR) and false rejection rate (FRR).
脱氧核糖核酸（DNA）可以说是最有用的生物识别技术之一。它已被有效地用于识别人的身份。然而，似乎仍有必要提出一种新的方法来验证人类，特别是在最近的大战之后，有太多的人失去了生命。这种方法应该能够提供较高的个人验证性能。本文提出了一种基于人工智能的个人识别方法。这种方法被称为人工 DNA 识别算法（ADAR）。它利用从 DNA 核苷酸中获取的每个人的唯一身份，可以高效、高性能地验证个人。ADAR 已被设计并应用于多个数据集，即 DNA 分类（DC）、样本 DNA 序列（SDS）、人类 DNA 序列（HDS）和 DNA 序列（DS）。在所有数据集中，错误接受率（FAR）和错误拒绝率（FRR）均达到了 0% 的低值。
{"title":"An Artificial Intelligence Approach for Verifying Persons by Employing the Deoxyribonucleic Acid (DNA) Nucleotides","authors":"R. Al-Nima, Marwa Mawfaq Mohamedsheet Al-Hatab, Maysaloon Abed Qasim","doi":"10.1155/2023/6678837","DOIUrl":"https://doi.org/10.1155/2023/6678837","url":null,"abstract":"Deoxyribonucleic acid (DNA) can be considered as one of the most useful biometrics. It has effectively been used for recognizing persons. However, it seems that there is still a need to propose a new approach for verifying humans, especially after the recent big wars, where too many people lost and die. This approach should have the capability to provide high personal verification performance. In this paper, a personal recognition approach based on artificial intelligence is proposed. This approach is called the artificial DNA algorithm for recognition (ADAR). It utilizes a unique identity for each person acquired from DNA nucleotides, and it can verify individuals efficiently with high performance. The ADAR has been designed and applied to multiple datasets, namely, the DNA classification (DC), sample DNA sequence (SDS), human DNA sequences (HDS), and DNA sequences (DS). For all datasets, a low value of 0% is achieved for each of the false acceptance rate (FAR) and false rejection rate (FRR).","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139263429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The fundamental challenge in video generation is not only generating high-quality image sequences but also generating consistent frames with no abrupt shifts. With the development of generative adversarial networks (GANs), great progress has been made in image generation tasks which can be used for facial expression synthesis. Most previous works focused on synthesizing frontal and near frontal faces and manual annotation. However, considering only the frontal and near frontal area is not sufficient for many real-world applications, and manual annotation fails when the video is incomplete. AffineGAN, a recent study, uses affine transformation in latent space to automatically infer the expression intensity value; however, this work requires extraction of the feature of the target ground truth image, and the generated sequence of images is also not sufficient. To address these issues, this study is proposed to infer the expression of intensity value automatically without the need to extract the feature of the ground truth images. The local dataset is prepared with frontal and with two different face positions (the left and right sides). Average content distance metrics of the proposed solution along with different experiments have been measured, and the proposed solution has shown improvements. The proposed method has improved the ACD-I of affine GAN from 1.606 ± 0.018 to 1.584 ± 0.00, ACD-C of affine GAN from 1.452 ± 0.008 to 1.430 ± 0.009, and ACD-G of affine GAN from 1.769 ± 0.007 to 1.744 ± 0.01, which is far better than AffineGAN. This work concludes that integrating self-attention into the generator network improves a quality of the generated images sequences. In addition, evenly distributing values based on frame size to assign expression intensity value improves the consistency of image sequences being generated. It also enables the generator to generate different frame size videos while remaining within the range [0, 1].
{"title":"Attention-Based Image-to-Video Translation for Synthesizing Facial Expression Using GAN","authors":"Kidist Alemayehu, Worku Jifara, Demissie Jobir","doi":"10.1155/2023/6645356","DOIUrl":"https://doi.org/10.1155/2023/6645356","url":null,"abstract":"The fundamental challenge in video generation is not only generating high-quality image sequences but also generating consistent frames with no abrupt shifts. With the development of generative adversarial networks (GANs), great progress has been made in image generation tasks which can be used for facial expression synthesis. Most previous works focused on synthesizing frontal and near frontal faces and manual annotation. However, considering only the frontal and near frontal area is not sufficient for many real-world applications, and manual annotation fails when the video is incomplete. AffineGAN, a recent study, uses affine transformation in latent space to automatically infer the expression intensity value; however, this work requires extraction of the feature of the target ground truth image, and the generated sequence of images is also not sufficient. To address these issues, this study is proposed to infer the expression of intensity value automatically without the need to extract the feature of the ground truth images. The local dataset is prepared with frontal and with two different face positions (the left and right sides). Average content distance metrics of the proposed solution along with different experiments have been measured, and the proposed solution has shown improvements. The proposed method has improved the ACD-I of affine GAN from 1.606 ± 0.018 to 1.584 ± 0.00, ACD-C of affine GAN from 1.452 ± 0.008 to 1.430 ± 0.009, and ACD-G of affine GAN from 1.769 ± 0.007 to 1.744 ± 0.01, which is far better than AffineGAN. This work concludes that integrating self-attention into the generator network improves a quality of the generated images sequences. In addition, evenly distributing values based on frame size to assign expression intensity value improves the consistency of image sequences being generated. It also enables the generator to generate different frame size videos while remaining within the range [0, 1].","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134957588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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