Advanced video codecs such as High Efficiency Video Coding/H.265 (HEVC) and Versatile Video Coding/H.266 (VVC) are vital for streaming high-quality online video content, as they compress and transmit data efficiently. However, these codecs can occasionally degrade video quality by adding undesirable artifacts such as blockiness, blurriness, and ringing, which can detract from the viewer’s experience. To ensure a seamless and engaging video experience, it is essential to remove these artifacts, which improves viewer comfort and engagement. In this paper, we propose a deep feature fusion based convolutional neural network (CNN) architecture (VVC-PPFF) for post-processing approach to further enhance the performance of VVC. The proposed network, VVC-PPFF, harnesses the power of CNNs to enhance decoded frames, significantly improving the coding efficiency of the state-of-the-art VVC video coding standard. By combining deep features from early and later convolution layers, the network learns to extract both low-level and high-level features, resulting in more generalized outputs that adapt to different quantization parameter (QP) values. The proposed VVC-PPFF network achieves outstanding performance, with Bjøntegaard Delta Rate (BD-Rate) improvements of 5.81% and 6.98% for luma components in random access (RA) and low-delay (LD) configurations, respectively, while also boosting peak signal-to-noise ratio (PSNR).
{"title":"Versatile Video Coding-Post Processing Feature Fusion: A Post-Processing Convolutional Neural Network with Progressive Feature Fusion for Efficient Video Enhancement","authors":"Tanni Das, Xilong Liang, Kiho Choi","doi":"10.3390/app14188276","DOIUrl":"https://doi.org/10.3390/app14188276","url":null,"abstract":"Advanced video codecs such as High Efficiency Video Coding/H.265 (HEVC) and Versatile Video Coding/H.266 (VVC) are vital for streaming high-quality online video content, as they compress and transmit data efficiently. However, these codecs can occasionally degrade video quality by adding undesirable artifacts such as blockiness, blurriness, and ringing, which can detract from the viewer’s experience. To ensure a seamless and engaging video experience, it is essential to remove these artifacts, which improves viewer comfort and engagement. In this paper, we propose a deep feature fusion based convolutional neural network (CNN) architecture (VVC-PPFF) for post-processing approach to further enhance the performance of VVC. The proposed network, VVC-PPFF, harnesses the power of CNNs to enhance decoded frames, significantly improving the coding efficiency of the state-of-the-art VVC video coding standard. By combining deep features from early and later convolution layers, the network learns to extract both low-level and high-level features, resulting in more generalized outputs that adapt to different quantization parameter (QP) values. The proposed VVC-PPFF network achieves outstanding performance, with Bjøntegaard Delta Rate (BD-Rate) improvements of 5.81% and 6.98% for luma components in random access (RA) and low-delay (LD) configurations, respectively, while also boosting peak signal-to-noise ratio (PSNR).","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181127","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}
Human Immunodeficiency Virus (HIV) is a diploid, C-type enveloped retrovirus belonging to the Lentivirus genus, characterized by two positive-sense single-stranded RNA genomes, that transitioned from non-human primates to humans and has become globally widespread. In its advanced stages, HIV leads to Acquired Immune Deficiency Syndrome (AIDS), which severely weakens the immune system by depleting CD4+ helper T cells. Without treatment, HIV progressively impairs immune function, making the body susceptible to various opportunistic infections and complications, including cardiovascular, respiratory, and neurological issues, as well as secondary cancers. The envelope glycoprotein complex (Env), composed of gp120 and gp41 subunits derived from the precursor gp160, plays a central role in cycle entry. gp160, synthesized in the rough endoplasmic reticulum, undergoes glycosylation and proteolytic cleavage, forming a trimeric spike on the virion surface. These structural features, including the transmembrane domain (TMD), membrane-proximal external region (MPER), and cytoplasmic tail (CT), are critical for viral infectivity and immune evasion. Glycosylation and proteolytic processing, especially by furin, are essential for Env’s fusogenic activity and capacity to evade immune detection. The virus’s outer envelope glycoprotein, gp120, interacts with host cell CD4 receptors. This interaction, along with the involvement of coreceptors CXCR4 and CCR5, prompts the exposure of the gp41 fusogenic components, enabling the fusion of viral and host cell membranes. While this is the predominant pathway for viral entry, alternative mechanisms involving receptors such as C-type lectin and mannose receptors have been found. This review aims to provide an in-depth analysis of the structural features and functional roles of HIV entry proteins, particularly gp120 and gp41, in the viral entry process. By examining these proteins’ architecture, the review elucidates how their structural properties facilitate HIV invasion of host cells. It also explores the synthesis, trafficking, and structural characteristics of Env/gp160 proteins, highlighting the interactions between gp120, gp41, and the viral matrix. These contributions advance drug resistance management and vaccine development efforts.
人类免疫缺陷病毒（HIV）是一种二倍体 C 型包膜逆转录病毒，属于慢病毒属，具有两个正义单链 RNA 基因组。艾滋病病毒晚期会导致获得性免疫缺陷综合症（AIDS），CD4+辅助性 T 细胞耗竭，严重削弱免疫系统。如果不进行治疗，艾滋病毒会逐渐损害免疫功能，使人体容易受到各种机会性感染和并发症的影响，包括心血管、呼吸和神经系统问题，以及继发性癌症。包膜糖蛋白复合物（Env）由前体 gp160 衍生出的 gp120 和 gp41 亚基组成，在病毒进入循环过程中起着核心作用。gp160 在粗面内质网中合成，经过糖基化和蛋白水解，在病毒表面形成三聚体尖峰。这些结构特征包括跨膜结构域（TMD）、膜近端外部区域（MPER）和胞质尾（CT），对于病毒的感染性和免疫逃避至关重要。糖基化和蛋白水解加工，尤其是呋喃蛋白的加工，对 Env 的致熔活性和逃避免疫检测的能力至关重要。病毒外包膜糖蛋白 gp120 与宿主细胞 CD4 受体相互作用。这种相互作用以及核心受体 CXCR4 和 CCR5 的参与，促使 gp41 致熔成分暴露，从而使病毒和宿主细胞膜融合。虽然这是病毒进入的主要途径，但也发现了涉及 C 型凝集素和甘露糖受体等受体的替代机制。本综述旨在深入分析 HIV 进入蛋白（尤其是 gp120 和 gp41）在病毒进入过程中的结构特征和功能作用。通过研究这些蛋白的结构，综述阐明了它们的结构特性是如何促进 HIV 入侵宿主细胞的。综述还探讨了Env/gp160蛋白的合成、贩运和结构特征，重点介绍了gp120、gp41和病毒基质之间的相互作用。这些贡献推动了耐药性管理和疫苗开发工作。
{"title":"Comprehensive Insights into the Molecular Basis of HIV Glycoproteins","authors":"Amir Elalouf, Hanan Maoz, Amit Yaniv Rosenfeld","doi":"10.3390/app14188271","DOIUrl":"https://doi.org/10.3390/app14188271","url":null,"abstract":"Human Immunodeficiency Virus (HIV) is a diploid, C-type enveloped retrovirus belonging to the Lentivirus genus, characterized by two positive-sense single-stranded RNA genomes, that transitioned from non-human primates to humans and has become globally widespread. In its advanced stages, HIV leads to Acquired Immune Deficiency Syndrome (AIDS), which severely weakens the immune system by depleting CD4+ helper T cells. Without treatment, HIV progressively impairs immune function, making the body susceptible to various opportunistic infections and complications, including cardiovascular, respiratory, and neurological issues, as well as secondary cancers. The envelope glycoprotein complex (Env), composed of gp120 and gp41 subunits derived from the precursor gp160, plays a central role in cycle entry. gp160, synthesized in the rough endoplasmic reticulum, undergoes glycosylation and proteolytic cleavage, forming a trimeric spike on the virion surface. These structural features, including the transmembrane domain (TMD), membrane-proximal external region (MPER), and cytoplasmic tail (CT), are critical for viral infectivity and immune evasion. Glycosylation and proteolytic processing, especially by furin, are essential for Env’s fusogenic activity and capacity to evade immune detection. The virus’s outer envelope glycoprotein, gp120, interacts with host cell CD4 receptors. This interaction, along with the involvement of coreceptors CXCR4 and CCR5, prompts the exposure of the gp41 fusogenic components, enabling the fusion of viral and host cell membranes. While this is the predominant pathway for viral entry, alternative mechanisms involving receptors such as C-type lectin and mannose receptors have been found. This review aims to provide an in-depth analysis of the structural features and functional roles of HIV entry proteins, particularly gp120 and gp41, in the viral entry process. By examining these proteins’ architecture, the review elucidates how their structural properties facilitate HIV invasion of host cells. It also explores the synthesis, trafficking, and structural characteristics of Env/gp160 proteins, highlighting the interactions between gp120, gp41, and the viral matrix. These contributions advance drug resistance management and vaccine development efforts.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":"104 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181129","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}
This paper presents a novel hybrid optimization algorithm that combines JADE Adaptive Differential Evolution with Artificial Protozoa Optimizer (APO) to solve complex optimization problems and detect attacks. The proposed Hybrid APO-JADE Algorithm leverages JADE’s adaptive exploration capabilities and APO’s intensive exploitation strategies, ensuring a robust search process that balances global and local optimization. Initially, the algorithm employs JADE’s mutation and crossover operations, guided by adaptive control parameters, to explore the search space and prevent premature convergence. As the optimization progresses, a dynamic transition to the APO mechanism is implemented, where Levy flights and adaptive change factors are utilized to refine the best solutions identified during the exploration phase. This integration of exploration and exploitation phases enhances the algorithm’s ability to converge to high-quality solutions efficiently. The performance of the APO-JADE was verified via experimental simulations and compared with state-of-the-art algorithms using the 2022 IEEE Congress on Evolutionary Computation benchmark (CEC) 2022 and 2021. Results indicate that APO-JADE achieved outperforming results compared with the other algorithms. Considering practicality, the proposed APO-JADE was used to solve a real-world application in attack detection and tested on DS2OS, UNSW-NB15, and ToNIoT datasets, demonstrating its robust performance.
{"title":"Hybrid Artificial Protozoa-Based JADE for Attack Detection","authors":"Ahmad k. Al Hwaitat, Hussam N. Fakhouri","doi":"10.3390/app14188280","DOIUrl":"https://doi.org/10.3390/app14188280","url":null,"abstract":"This paper presents a novel hybrid optimization algorithm that combines JADE Adaptive Differential Evolution with Artificial Protozoa Optimizer (APO) to solve complex optimization problems and detect attacks. The proposed Hybrid APO-JADE Algorithm leverages JADE’s adaptive exploration capabilities and APO’s intensive exploitation strategies, ensuring a robust search process that balances global and local optimization. Initially, the algorithm employs JADE’s mutation and crossover operations, guided by adaptive control parameters, to explore the search space and prevent premature convergence. As the optimization progresses, a dynamic transition to the APO mechanism is implemented, where Levy flights and adaptive change factors are utilized to refine the best solutions identified during the exploration phase. This integration of exploration and exploitation phases enhances the algorithm’s ability to converge to high-quality solutions efficiently. The performance of the APO-JADE was verified via experimental simulations and compared with state-of-the-art algorithms using the 2022 IEEE Congress on Evolutionary Computation benchmark (CEC) 2022 and 2021. Results indicate that APO-JADE achieved outperforming results compared with the other algorithms. Considering practicality, the proposed APO-JADE was used to solve a real-world application in attack detection and tested on DS2OS, UNSW-NB15, and ToNIoT datasets, demonstrating its robust performance.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181132","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 purpose of the present study was to investigate the relationship between re-injury worry, confidence, and attention and athletes’ functional status upon returning to sport after an injury. The sample consisted of 28 amateur-level male football players, aged 18 to 35 years, with a previous lower-limb injury. The athletes followed a physiotherapy rehabilitation program and completed three valid questionnaires examining re-injury worry, sport confidence, and attention returning to sport. The sample also performed three functional tests: (a) single-leg hop for distance, (b) side hop, and (c) the vertical jump. The results showed high correlations between the psychological factors between the functional tests. Physiotherapy duration was positively highly correlated with the time of absence from sport and severity of injury. The severity of the injury was also positively highly correlated with the time of absence from sport. The factors “Functional Attention” and “Distraction Attention” showed a positive and negative correlation with the single-leg hop for distance and the vertical jump, respectively. Athletes with a grade II severity injury showed greater attention compared to grade III severity injury. The increased level of re-injury worry was positively related to “Distraction Attention” and negatively related to the functional ability of the injured limb. The psychological readiness was partially related to the athletes’ functional status. The present study reports the importance of psychological readiness and its relationship with athletes’ functional status of returning to sport following a musculoskeletal sport injury.
{"title":"Examining the Relationship between Psychological and Functional Status after a Sports Musculoskeletal Injury","authors":"Konstantinos Parlakidis, Dimitrios Krokos, Maria-Louiza Sagredaki, Lazaros Alexandros Kontopoulos, Anna Christakou","doi":"10.3390/app14188258","DOIUrl":"https://doi.org/10.3390/app14188258","url":null,"abstract":"The purpose of the present study was to investigate the relationship between re-injury worry, confidence, and attention and athletes’ functional status upon returning to sport after an injury. The sample consisted of 28 amateur-level male football players, aged 18 to 35 years, with a previous lower-limb injury. The athletes followed a physiotherapy rehabilitation program and completed three valid questionnaires examining re-injury worry, sport confidence, and attention returning to sport. The sample also performed three functional tests: (a) single-leg hop for distance, (b) side hop, and (c) the vertical jump. The results showed high correlations between the psychological factors between the functional tests. Physiotherapy duration was positively highly correlated with the time of absence from sport and severity of injury. The severity of the injury was also positively highly correlated with the time of absence from sport. The factors “Functional Attention” and “Distraction Attention” showed a positive and negative correlation with the single-leg hop for distance and the vertical jump, respectively. Athletes with a grade II severity injury showed greater attention compared to grade III severity injury. The increased level of re-injury worry was positively related to “Distraction Attention” and negatively related to the functional ability of the injured limb. The psychological readiness was partially related to the athletes’ functional status. The present study reports the importance of psychological readiness and its relationship with athletes’ functional status of returning to sport following a musculoskeletal sport injury.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181097","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}
Honglin Yan, Guanghui Du, Wentao Gao, Yanhong Chen, Cunlong Cui, Kai Xu
In the optimization of high-speed permanent magnet motors (HSPMMs), electromagnetic characteristics, rotor stress, rotor dynamics, and temperature characteristics must all be considered simultaneously, and there are numerous optimization parameters for both the stator and rotor. These factors pose significant challenges to the multiphysics optimization of HSPMMs. Therefore, this paper presents a multiphysics optimization process for the HSPMM of 60 kW 30,000 rpm by combining subspace strategy and sequential strategy to mitigate the issues of high training volume and mutual coupling. Ten optimization parameters of stator and rotor are determined firstly. Then, using finite element analysis of rotor stress and rotor dynamics, the range of values for critical parameters of the rotor is established. Next, in the electromagnetic optimization, the process is divided into rotor parameter subspace and stator parameter subspace according to the subspace optimization strategy. The temperature field is also checked based on the optimization results. Finally, a prototype is manufactured and the comprehensive performance is tested to validate the multiphysics optimization process.
{"title":"Multiphysics Optimization of a High-Speed Permanent Magnet Motor Based on Subspace and Sequential Strategy","authors":"Honglin Yan, Guanghui Du, Wentao Gao, Yanhong Chen, Cunlong Cui, Kai Xu","doi":"10.3390/app14188267","DOIUrl":"https://doi.org/10.3390/app14188267","url":null,"abstract":"In the optimization of high-speed permanent magnet motors (HSPMMs), electromagnetic characteristics, rotor stress, rotor dynamics, and temperature characteristics must all be considered simultaneously, and there are numerous optimization parameters for both the stator and rotor. These factors pose significant challenges to the multiphysics optimization of HSPMMs. Therefore, this paper presents a multiphysics optimization process for the HSPMM of 60 kW 30,000 rpm by combining subspace strategy and sequential strategy to mitigate the issues of high training volume and mutual coupling. Ten optimization parameters of stator and rotor are determined firstly. Then, using finite element analysis of rotor stress and rotor dynamics, the range of values for critical parameters of the rotor is established. Next, in the electromagnetic optimization, the process is divided into rotor parameter subspace and stator parameter subspace according to the subspace optimization strategy. The temperature field is also checked based on the optimization results. Finally, a prototype is manufactured and the comprehensive performance is tested to validate the multiphysics optimization process.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181121","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}
Analyzing the carbon emission behavior of a regional integrated energy system (RIES) is crucial for aligning with carbon-peaking development strategies and ensuring compliance with carbon-peaking implementation pathways. This study focuses on a building cluster area in Shanghai, China, aiming to provide a comprehensive analysis from both macro and micro perspectives. From a macro viewpoint, an extended STIRPAT model, incorporating the environmental Kuznets curve, is proposed to predict the carbon-peaking trajectory in Shanghai. This approach yields carbon-peaking implementation pathways for three scenarios: rapid development, stable development, and green development, spanning the period of 2020–2040. At a micro scale, three distinct RIES system configurations—fossil, hybrid, and clean—are formulated based on the renewable energy penetration level. Utilizing a multi-objective optimization model, this study explores the carbon emission behavior of a RIES while adhering to carbon-peaking constraints. Four scenarios of carbon emission reduction policies are implemented, leveraging green certificates and carbon-trading mechanisms. Performance indicators, including carbon emissions, carbon intensity, and marginal emission reduction cost, are employed to scrutinize the carbon emission behavior of the cross-regional integrated energy system within the confines of carbon peaking.
{"title":"Carbon Emission Analysis of Low-Carbon Technology Coupled with a Regional Integrated Energy System Considering Carbon-Peaking Targets","authors":"Yipu Zeng, Yiru Dai, Yiming Shu, Ting Yin","doi":"10.3390/app14188277","DOIUrl":"https://doi.org/10.3390/app14188277","url":null,"abstract":"Analyzing the carbon emission behavior of a regional integrated energy system (RIES) is crucial for aligning with carbon-peaking development strategies and ensuring compliance with carbon-peaking implementation pathways. This study focuses on a building cluster area in Shanghai, China, aiming to provide a comprehensive analysis from both macro and micro perspectives. From a macro viewpoint, an extended STIRPAT model, incorporating the environmental Kuznets curve, is proposed to predict the carbon-peaking trajectory in Shanghai. This approach yields carbon-peaking implementation pathways for three scenarios: rapid development, stable development, and green development, spanning the period of 2020–2040. At a micro scale, three distinct RIES system configurations—fossil, hybrid, and clean—are formulated based on the renewable energy penetration level. Utilizing a multi-objective optimization model, this study explores the carbon emission behavior of a RIES while adhering to carbon-peaking constraints. Four scenarios of carbon emission reduction policies are implemented, leveraging green certificates and carbon-trading mechanisms. Performance indicators, including carbon emissions, carbon intensity, and marginal emission reduction cost, are employed to scrutinize the carbon emission behavior of the cross-regional integrated energy system within the confines of carbon peaking.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181128","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}
When a single-phase grounding fault occurs in resonant ground distribution network, the fault characteristics are weak and it is difficult to detect the fault line. Therefore, a fast fault line selection method based on MCECA-CloFormer is proposed in this paper. Firstly, zero-sequence current signals were converted into images using the moving average filter method and motif difference field to construct fault data set. Then, the ECA module was modified to MCECA (MultiCNN-ECA) so that it can accept data input from multiple measurement points. Secondly, the lightweight model CloFormer was used in the back end of MCECA module to further perceive the feature map and complete the establishment of the line selection model. Finally, the line selection model was trained, and the information such as model weight was saved. The simulation results demonstrated that the pre-trained MCECA-CloFormer achieved a line selection accuracy of over 98% under 10 dB noise, with a remarkably low single fault processing time of approximately 0.04 s. Moreover, it exhibited suitability for arc high-resistance grounding faults, data-missing cases, neutral-point ungrounded systems, and active distribution networks. In addition, the method was still valid when tested with actual field recording data.
当谐振接地配电网发生单相接地故障时，故障特征较弱，很难检测到故障线路。因此，本文提出了一种基于 MCECA-CloFormer 的快速故障选线方法。首先，利用移动平均滤波法和动差场将零序电流信号转换为图像，构建故障数据集。然后，将 ECA 模块修改为 MCECA（MultiCNN-ECA），使其可以接受来自多个测量点的数据输入。其次，在 MCECA 模块后端使用轻量级模型 CloFormer 进一步感知特征图，完成选线模型的建立。最后，对选线模型进行训练，并保存模型权重等信息。仿真结果表明，预训练的 MCECA-CloFormer 在 10 dB 噪声下的选线准确率达到 98% 以上，单次故障处理时间仅为 0.04 s 左右。此外，在使用实际现场记录数据进行测试时，该方法仍然有效。
{"title":"Fast Fault Line Selection Technology of Distribution Network Based on MCECA-CloFormer","authors":"Can Ding, Pengcheng Ma, Changhua Jiang, Fei Wang","doi":"10.3390/app14188270","DOIUrl":"https://doi.org/10.3390/app14188270","url":null,"abstract":"When a single-phase grounding fault occurs in resonant ground distribution network, the fault characteristics are weak and it is difficult to detect the fault line. Therefore, a fast fault line selection method based on MCECA-CloFormer is proposed in this paper. Firstly, zero-sequence current signals were converted into images using the moving average filter method and motif difference field to construct fault data set. Then, the ECA module was modified to MCECA (MultiCNN-ECA) so that it can accept data input from multiple measurement points. Secondly, the lightweight model CloFormer was used in the back end of MCECA module to further perceive the feature map and complete the establishment of the line selection model. Finally, the line selection model was trained, and the information such as model weight was saved. The simulation results demonstrated that the pre-trained MCECA-CloFormer achieved a line selection accuracy of over 98% under 10 dB noise, with a remarkably low single fault processing time of approximately 0.04 s. Moreover, it exhibited suitability for arc high-resistance grounding faults, data-missing cases, neutral-point ungrounded systems, and active distribution networks. In addition, the method was still valid when tested with actual field recording data.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181122","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}
Graheeth Hazare, Mohamed Thariq Hameed Sultan, Dariusz Mika, Farah Syazwani Shahar, Grzegorz Skorulski, Marek Nowakowski, Andriy Holovatyy, Ile Mircheski, Wojciech Giernacki
This study introduces a novel methodology for enhancing the efficiency of solar-powered unmanned aerial vehicles (UAVs) through azimuthal solar synchronization and aerodynamic neuro-optimization, leveraging the principles of slime mold neural networks. The objective is to broaden the operational capabilities of solar UAVs, enabling them to perform over extended ranges and in varied weather conditions. Our approach integrates a computational model of slime mold networks with a simulation environment to optimize both the solar energy collection and the aerodynamic performance of UAVs. Specifically, we focus on improving the UAVs’ aerodynamic efficiency in flight, aligning it with energy optimization strategies to ensure sustained operation. The findings demonstrated significant improvements in the UAVs’ range and weather resilience, thereby enhancing their utility for a variety of missions, including environmental monitoring and search and rescue operations. These advancements underscore the potential of integrating biomimicry and neural-network-based optimization in expanding the functional scope of solar UAVs.
{"title":"Azimuthal Solar Synchronization and Aerodynamic Neuro-Optimization: An Empirical Study on Slime-Mold-Inspired Neural Networks for Solar UAV Range Optimization","authors":"Graheeth Hazare, Mohamed Thariq Hameed Sultan, Dariusz Mika, Farah Syazwani Shahar, Grzegorz Skorulski, Marek Nowakowski, Andriy Holovatyy, Ile Mircheski, Wojciech Giernacki","doi":"10.3390/app14188265","DOIUrl":"https://doi.org/10.3390/app14188265","url":null,"abstract":"This study introduces a novel methodology for enhancing the efficiency of solar-powered unmanned aerial vehicles (UAVs) through azimuthal solar synchronization and aerodynamic neuro-optimization, leveraging the principles of slime mold neural networks. The objective is to broaden the operational capabilities of solar UAVs, enabling them to perform over extended ranges and in varied weather conditions. Our approach integrates a computational model of slime mold networks with a simulation environment to optimize both the solar energy collection and the aerodynamic performance of UAVs. Specifically, we focus on improving the UAVs’ aerodynamic efficiency in flight, aligning it with energy optimization strategies to ensure sustained operation. The findings demonstrated significant improvements in the UAVs’ range and weather resilience, thereby enhancing their utility for a variety of missions, including environmental monitoring and search and rescue operations. These advancements underscore the potential of integrating biomimicry and neural-network-based optimization in expanding the functional scope of solar UAVs.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":"2017 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181108","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 objective of person re-identification (ReID) tasks is to match a specific individual across different times, locations, or camera viewpoints. The prevalent issue of occlusion in real-world scenarios affects image information, rendering the affected features unreliable. The difficulty and core challenge lie in how to effectively discern and extract visual features from human images under various complex conditions, including cluttered backgrounds, diverse postures, and the presence of occlusions. Some works have employed pose estimation or human key point detection to construct graph-structured information to counteract the effects of occlusions. However, this approach introduces new noise due to issues such as the invisibility of key points. Our proposed module, in contrast, does not require the use of additional feature extractors. Our module employs multi-scale graph attention for the reweighting of feature importance. This allows features to concentrate on areas genuinely pertinent to the re-identification task, thereby enhancing the model’s robustness against occlusions. To address these problems, a model that employs multi-scale graph attention to reweight the importance of features is proposed in this study, significantly enhancing the model’s robustness against occlusions. Our experimental results demonstrate that, compared to baseline models, the method proposed herein achieves a notable improvement in mAP on occluded datasets, with increases of 0.5%, 31.5%, and 12.3% in mAP scores.
{"title":"A Multi-Scale Graph Attention-Based Transformer for Occluded Person Re-Identification","authors":"Ming Ma, Jianming Wang, Bohan Zhao","doi":"10.3390/app14188279","DOIUrl":"https://doi.org/10.3390/app14188279","url":null,"abstract":"The objective of person re-identification (ReID) tasks is to match a specific individual across different times, locations, or camera viewpoints. The prevalent issue of occlusion in real-world scenarios affects image information, rendering the affected features unreliable. The difficulty and core challenge lie in how to effectively discern and extract visual features from human images under various complex conditions, including cluttered backgrounds, diverse postures, and the presence of occlusions. Some works have employed pose estimation or human key point detection to construct graph-structured information to counteract the effects of occlusions. However, this approach introduces new noise due to issues such as the invisibility of key points. Our proposed module, in contrast, does not require the use of additional feature extractors. Our module employs multi-scale graph attention for the reweighting of feature importance. This allows features to concentrate on areas genuinely pertinent to the re-identification task, thereby enhancing the model’s robustness against occlusions. To address these problems, a model that employs multi-scale graph attention to reweight the importance of features is proposed in this study, significantly enhancing the model’s robustness against occlusions. Our experimental results demonstrate that, compared to baseline models, the method proposed herein achieves a notable improvement in mAP on occluded datasets, with increases of 0.5%, 31.5%, and 12.3% in mAP scores.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181134","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}
Francisco Carvalho, João Manuel R. S. Tavares, Marta Campos Ferreira
This study explores the prediction and mitigation of pallet collapse during transportation within the glass packaging industry, employing a machine learning approach to reduce cargo loss and enhance logistics efficiency. Using the CRoss-Industry Standard Process for Data Mining (CRISP-DM) framework, data were systematically collected from a leading glass manufacturer and analysed. A comparative analysis between the Decision Tree and Random Forest machine learning algorithms, evaluated using performance metrics such as F1-score, revealed that the latter is more effective at predicting pallet collapse. This study is pioneering in identifying new critical predictive variables, particularly geometry-related and temperature-related features, which significantly influence the stability of pallets. Based on these findings, several strategies to prevent pallet collapse are proposed, including optimizing pallet stacking patterns, enhancing packaging materials, implementing temperature control measures, and developing more robust handling protocols. These insights demonstrate the utility of machine learning in generating actionable recommendations to optimize supply chain operations and offer a foundation for further academic and practical advancements in cargo handling within the glass industry.
本研究探讨了玻璃包装行业在运输过程中托盘倒塌的预测和缓解方法，采用机器学习方法来减少货物损失并提高物流效率。利用数据挖掘行业标准流程（CRISP-DM）框架，从一家领先的玻璃制造商处系统地收集并分析了数据。通过使用 F1 分数等性能指标对决策树和随机森林机器学习算法进行比较分析，发现后者在预测托盘坍塌方面更为有效。这项研究开创性地确定了新的关键预测变量，特别是与几何形状和温度相关的特征，它们对托盘的稳定性有重大影响。基于这些发现，我们提出了几种防止托盘坍塌的策略，包括优化托盘堆叠模式、改进包装材料、实施温度控制措施以及制定更稳健的处理规程。这些见解证明了机器学习在生成可操作建议以优化供应链运营方面的实用性，并为玻璃行业货物装卸方面的进一步学术和实践进步奠定了基础。
{"title":"A Machine Learning Approach for Predicting and Mitigating Pallet Collapse during Transport: The Case of the Glass Industry","authors":"Francisco Carvalho, João Manuel R. S. Tavares, Marta Campos Ferreira","doi":"10.3390/app14188256","DOIUrl":"https://doi.org/10.3390/app14188256","url":null,"abstract":"This study explores the prediction and mitigation of pallet collapse during transportation within the glass packaging industry, employing a machine learning approach to reduce cargo loss and enhance logistics efficiency. Using the CRoss-Industry Standard Process for Data Mining (CRISP-DM) framework, data were systematically collected from a leading glass manufacturer and analysed. A comparative analysis between the Decision Tree and Random Forest machine learning algorithms, evaluated using performance metrics such as F1-score, revealed that the latter is more effective at predicting pallet collapse. This study is pioneering in identifying new critical predictive variables, particularly geometry-related and temperature-related features, which significantly influence the stability of pallets. Based on these findings, several strategies to prevent pallet collapse are proposed, including optimizing pallet stacking patterns, enhancing packaging materials, implementing temperature control measures, and developing more robust handling protocols. These insights demonstrate the utility of machine learning in generating actionable recommendations to optimize supply chain operations and offer a foundation for further academic and practical advancements in cargo handling within the glass industry.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181126","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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