Marta Maciejewska, Paula Kurzawska-Pietrowicz, Marta Galant-Gołębiewska, Michał Gołębiewski, Remigiusz Jasiński
The paper discusses a case study of obtaining an airline pilot license in integrated training—the so-called “from zero to Airline Transport Pilot License”. The environmental implications of simulator-based training were examined across multiple dimensions. Key areas of research include the reduction of harmful exhaust gases pollution associated with traditional flight training activities. Based on our analysis, it can be stated that increasing the use of Flight Simulation Training Devices in pilot training should be significant consideration. This approach brings many benefits, especially ecological ones. Changing the training program and increasing the use of flight simulators can result in a reduction of CO2 emissions by up to 70%. Based on country specific electricity factors, CO2 emissions during flight training in each EU country were calculated. Using Levelized Cost of Electricity average value to calculate training costs in EU countries depends on the mix of energy sources (wind, photovoltaics, carbon and gas). The findings highlight the significant ecological advantages of simulator-based training methods in mitigating the environmental footprint of aviation operations. By seeking to minimize environmental disruption and increase training efficiency, the adoption of simulators is a sustainable approach to pilot training that is consistent with global efforts to mitigate climate change and protect natural ecosystems.
{"title":"Ecological and Cost Advantage from the Implementation of Flight Simulation Training Devices for Pilot Training","authors":"Marta Maciejewska, Paula Kurzawska-Pietrowicz, Marta Galant-Gołębiewska, Michał Gołębiewski, Remigiusz Jasiński","doi":"10.3390/app14188401","DOIUrl":"https://doi.org/10.3390/app14188401","url":null,"abstract":"The paper discusses a case study of obtaining an airline pilot license in integrated training—the so-called “from zero to Airline Transport Pilot License”. The environmental implications of simulator-based training were examined across multiple dimensions. Key areas of research include the reduction of harmful exhaust gases pollution associated with traditional flight training activities. Based on our analysis, it can be stated that increasing the use of Flight Simulation Training Devices in pilot training should be significant consideration. This approach brings many benefits, especially ecological ones. Changing the training program and increasing the use of flight simulators can result in a reduction of CO2 emissions by up to 70%. Based on country specific electricity factors, CO2 emissions during flight training in each EU country were calculated. Using Levelized Cost of Electricity average value to calculate training costs in EU countries depends on the mix of energy sources (wind, photovoltaics, carbon and gas). The findings highlight the significant ecological advantages of simulator-based training methods in mitigating the environmental footprint of aviation operations. By seeking to minimize environmental disruption and increase training efficiency, the adoption of simulators is a sustainable approach to pilot training that is consistent with global efforts to mitigate climate change and protect natural ecosystems.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249268","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 designs a minimal neural network (NN)-based model-free control structure for the fast, accurate trajectory tracking of robotic arms, crucial for large movements, velocities, and accelerations. Trajectory tracking requires an accurate dynamic model or aggressive feedback. However, such models are hard to obtain due to nonlinearities and uncertainties, especially in low-cost, 3D-printed robotic arms. A recently proposed model-free architecture has used an NN for the dynamic compensation of a proportional derivative controller, but the minimal requirements and optimal conditions remain unclear, leading to overly complex architectures. This study aims to identify these requirements and design a minimal NN-based model-free control structure for trajectory tracking. Two architectures are compared, one NN per joint (INN) and one global NN (GNN), each tested on two serial robotic arms in simulations and real scenarios. The results show that the architecture reduces tracking errors (RMSE < 2°). The INN is accurate for decoupled joint dynamics and requires fewer training data than the GNN. A table summarizes the design process. Future works will apply this control structure to low-cost robotic arms and micro-movements.
本文设计了一种基于最小神经网络（NN）的无模型控制结构，用于快速、准确地跟踪机器人手臂的轨迹，这对于大运动、大速度和大加速度至关重要。轨迹跟踪需要精确的动态模型或积极的反馈。然而，由于非线性和不确定性，这种模型很难获得，尤其是在低成本的 3D 打印机械臂中。最近提出的一种无模型架构使用了 NN 对比例导数控制器进行动态补偿，但其最低要求和最佳条件仍不明确，导致架构过于复杂。本研究旨在确定这些要求，并为轨迹跟踪设计一种基于 NN 的最小无模型控制结构。研究比较了两种架构，一种是每个关节一个 NN（INN），另一种是一个全局 NN（GNN），每种架构都在两个串行机械臂上进行了模拟和实际场景测试。结果表明，该架构可减少跟踪误差（RMSE < 2°）。与 GNN 相比，INNN 对解耦关节动态的处理更加准确，所需的训练数据也更少。表格总结了设计过程。未来的工作将把这种控制结构应用于低成本机械臂和微型运动。
{"title":"Design of Minimal Model-Free Control Structure for Fast Trajectory Tracking of Robotic Arms","authors":"Baptiste Toussaint, Maxime Raison","doi":"10.3390/app14188405","DOIUrl":"https://doi.org/10.3390/app14188405","url":null,"abstract":"This paper designs a minimal neural network (NN)-based model-free control structure for the fast, accurate trajectory tracking of robotic arms, crucial for large movements, velocities, and accelerations. Trajectory tracking requires an accurate dynamic model or aggressive feedback. However, such models are hard to obtain due to nonlinearities and uncertainties, especially in low-cost, 3D-printed robotic arms. A recently proposed model-free architecture has used an NN for the dynamic compensation of a proportional derivative controller, but the minimal requirements and optimal conditions remain unclear, leading to overly complex architectures. This study aims to identify these requirements and design a minimal NN-based model-free control structure for trajectory tracking. Two architectures are compared, one NN per joint (INN) and one global NN (GNN), each tested on two serial robotic arms in simulations and real scenarios. The results show that the architecture reduces tracking errors (RMSE < 2°). The INN is accurate for decoupled joint dynamics and requires fewer training data than the GNN. A table summarizes the design process. Future works will apply this control structure to low-cost robotic arms and micro-movements.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249275","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}
Luca Muscarà, Marco Cisternino, Andrea Ferrero, Andrea Iob, Francesco Larocca
The prediction of separated flows at low Reynolds numbers is crucial for several applications in aerospace and energy fields. Reynolds-averaged Navier–Stokes (RANS) equations are widely used but their accuracy is limited in the presence of transition or separation. In this work, two different strategies for improving RANS simulations by means of field inversion are discussed. Both strategies require solving an optimization problem to identify a correction field by minimizing the error on some measurable data. The obtained correction field is exploited with two alternative strategies. The first strategy aims to the identification of a relation that allows to express the local correction field as a function of some local flow features. However, this regression can be difficult or even impossible because the relation between the assumed input variables and the local correction could not be a function. For this reason, an alternative is proposed: a U-Net model is trained on the original and corrected RANS results. In this way, it is possible to perform a prediction with the original RANS model and then correct it by means of the U-Net. The methodologies are evaluated and compared on the flow around the NACA0021 and the SD7003 airfoils.
{"title":"A Comparison of Local and Global Strategies for Exploiting Field Inversion on Separated Flows at Low Reynolds Number","authors":"Luca Muscarà, Marco Cisternino, Andrea Ferrero, Andrea Iob, Francesco Larocca","doi":"10.3390/app14188382","DOIUrl":"https://doi.org/10.3390/app14188382","url":null,"abstract":"The prediction of separated flows at low Reynolds numbers is crucial for several applications in aerospace and energy fields. Reynolds-averaged Navier–Stokes (RANS) equations are widely used but their accuracy is limited in the presence of transition or separation. In this work, two different strategies for improving RANS simulations by means of field inversion are discussed. Both strategies require solving an optimization problem to identify a correction field by minimizing the error on some measurable data. The obtained correction field is exploited with two alternative strategies. The first strategy aims to the identification of a relation that allows to express the local correction field as a function of some local flow features. However, this regression can be difficult or even impossible because the relation between the assumed input variables and the local correction could not be a function. For this reason, an alternative is proposed: a U-Net model is trained on the original and corrected RANS results. In this way, it is possible to perform a prediction with the original RANS model and then correct it by means of the U-Net. The methodologies are evaluated and compared on the flow around the NACA0021 and the SD7003 airfoils.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249217","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 proposes a new robust model for text classification on the Stanford Sentiment Treebank v2 (SST-2) dataset in terms of model accuracy. We developed a Recurrent Neural Network Bert based (RNN_Bert_based) model designed to improve classification accuracy on the SST-2 dataset. This dataset consists of movie review sentences, each labeled with either positive or negative sentiment, making it a binary classification task. Recurrent Neural Networks (RNNs) are effective for text classification because they capture the sequential nature of language, which is crucial for understanding context and meaning. Bert excels in text classification by providing bidirectional context, generating contextual embeddings, and leveraging pre-training on large corpora. This allows Bert to capture nuanced meanings and relationships within the text effectively. Combining Bert with RNNs can be highly effective for text classification. Bert’s bidirectional context and rich embeddings provide a deep understanding of the text, while RNNs capture sequential patterns and long-range dependencies. Together, they leverage the strengths of both architectures, leading to improved performance on complex classification tasks. Next, we also developed an integration of the Bert model and a K-Nearest Neighbor based (KNN_Bert_based) method as a comparative scheme for our proposed work. Based on the results of experimentation, our proposed model outperforms traditional text classification models as well as existing models in terms of accuracy.
{"title":"Improving the Accuracy and Effectiveness of Text Classification Based on the Integration of the Bert Model and a Recurrent Neural Network (RNN_Bert_Based)","authors":"Chanthol Eang, Seungjae Lee","doi":"10.3390/app14188388","DOIUrl":"https://doi.org/10.3390/app14188388","url":null,"abstract":"This paper proposes a new robust model for text classification on the Stanford Sentiment Treebank v2 (SST-2) dataset in terms of model accuracy. We developed a Recurrent Neural Network Bert based (RNN_Bert_based) model designed to improve classification accuracy on the SST-2 dataset. This dataset consists of movie review sentences, each labeled with either positive or negative sentiment, making it a binary classification task. Recurrent Neural Networks (RNNs) are effective for text classification because they capture the sequential nature of language, which is crucial for understanding context and meaning. Bert excels in text classification by providing bidirectional context, generating contextual embeddings, and leveraging pre-training on large corpora. This allows Bert to capture nuanced meanings and relationships within the text effectively. Combining Bert with RNNs can be highly effective for text classification. Bert’s bidirectional context and rich embeddings provide a deep understanding of the text, while RNNs capture sequential patterns and long-range dependencies. Together, they leverage the strengths of both architectures, leading to improved performance on complex classification tasks. Next, we also developed an integration of the Bert model and a K-Nearest Neighbor based (KNN_Bert_based) method as a comparative scheme for our proposed work. Based on the results of experimentation, our proposed model outperforms traditional text classification models as well as existing models in terms of accuracy.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249223","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}
Malaria is a leading cause of morbidity and mortality in tropical and sub-tropical regions. This research proposed a malaria diagnosis system based on the you only look once algorithm for malaria parasite detection and the convolutional neural network algorithm for malaria parasite life stage classification. Two public datasets are utilized: MBB and MP-IDB. The MBB dataset includes human blood smears infected with Plasmodium vivax (P. vivax). While the MP-IDB dataset comprises 4 species of malaria parasites: P. vivax, P. ovale, P. malariae, and P. falciparum. Four distinct stages of life exist in every species, including ring, trophozoite, schizont, and gametocyte. For the MBB dataset, detection and classification accuracies of 0.92 and 0.93, respectively, were achieved. For the MP-IDB dataset, the proposed algorithms yielded the accuracies for detection and classification as follows: 0.84 and 0.94 for P. vivax; 0.82 and 0.93 for P. ovale; 0.79 and 0.93 for P. malariae; and 0.92 and 0.96 for P. falciparum. The detection results showed the models trained by P. vivax alone provide good detection capabilities also for other species of malaria parasites. The classification performance showed the proposed algorithms yielded good malaria parasite life stage classification performance. The future directions include collecting more data and exploring more sophisticated algorithms.
{"title":"Staining-Independent Malaria Parasite Detection and Life Stage Classification in Blood Smear Images","authors":"Tong Xu, Nipon Theera-Umpon, Sansanee Auephanwiriyakul","doi":"10.3390/app14188402","DOIUrl":"https://doi.org/10.3390/app14188402","url":null,"abstract":"Malaria is a leading cause of morbidity and mortality in tropical and sub-tropical regions. This research proposed a malaria diagnosis system based on the you only look once algorithm for malaria parasite detection and the convolutional neural network algorithm for malaria parasite life stage classification. Two public datasets are utilized: MBB and MP-IDB. The MBB dataset includes human blood smears infected with Plasmodium vivax (P. vivax). While the MP-IDB dataset comprises 4 species of malaria parasites: P. vivax, P. ovale, P. malariae, and P. falciparum. Four distinct stages of life exist in every species, including ring, trophozoite, schizont, and gametocyte. For the MBB dataset, detection and classification accuracies of 0.92 and 0.93, respectively, were achieved. For the MP-IDB dataset, the proposed algorithms yielded the accuracies for detection and classification as follows: 0.84 and 0.94 for P. vivax; 0.82 and 0.93 for P. ovale; 0.79 and 0.93 for P. malariae; and 0.92 and 0.96 for P. falciparum. The detection results showed the models trained by P. vivax alone provide good detection capabilities also for other species of malaria parasites. The classification performance showed the proposed algorithms yielded good malaria parasite life stage classification performance. The future directions include collecting more data and exploring more sophisticated algorithms.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249271","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}
A bi-Gamma distribution model is proposed to determine the probability density function (PDF) of broadband non-Gaussian random stress rainflow ranges during vibration fatigue. A series of stress Power Spectral Densities (PSD) are provided, and the corresponding Gaussian random stress time histories are generated using the inverse Fourier transform and time-domain randomization methods. These Gaussian random stress time histories are then transformed into non-Gaussian random stress time histories. The probability density values of the stress ranges are obtained using the rainflow counting method, and then the bi-Gamma distribution PDF model is fitted to these values to determine the model’s parameters. The PSD parameters and the kurtosis, along with their corresponding model parameters, constitute the neural network input–output dataset. The neural network model established after training can directly provide the parameter values of the bi-Gamma model based on the input PSD parameters and kurtosis, thereby obtaining the PDF of the stress rainflow ranges. The predictive capability of the neural network model is verified and the effects of non-Gaussian random stress with different kurtosis on the structural fatigue life are compared for the same stress PSD. And all life predicted results were within the second scatter band.
{"title":"A bi-Gamma Distribution Model for a Broadband Non-Gaussian Random Stress Rainflow Range Based on a Neural Network","authors":"Jie Wang, Huaihai Chen","doi":"10.3390/app14188376","DOIUrl":"https://doi.org/10.3390/app14188376","url":null,"abstract":"A bi-Gamma distribution model is proposed to determine the probability density function (PDF) of broadband non-Gaussian random stress rainflow ranges during vibration fatigue. A series of stress Power Spectral Densities (PSD) are provided, and the corresponding Gaussian random stress time histories are generated using the inverse Fourier transform and time-domain randomization methods. These Gaussian random stress time histories are then transformed into non-Gaussian random stress time histories. The probability density values of the stress ranges are obtained using the rainflow counting method, and then the bi-Gamma distribution PDF model is fitted to these values to determine the model’s parameters. The PSD parameters and the kurtosis, along with their corresponding model parameters, constitute the neural network input–output dataset. The neural network model established after training can directly provide the parameter values of the bi-Gamma model based on the input PSD parameters and kurtosis, thereby obtaining the PDF of the stress rainflow ranges. The predictive capability of the neural network model is verified and the effects of non-Gaussian random stress with different kurtosis on the structural fatigue life are compared for the same stress PSD. And all life predicted results were within the second scatter band.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":"116 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249177","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}
Mohamed Badawy, Nada H. Sherief, Ayman A. Abdel-Hamid
As security breaches are increasingly widely reported in today’s culture, cybersecurity is gaining attention on a global scale. Threat modeling methods (TMM) are a proactive security practice that is essential for pinpointing risks and limiting their impact. This paper proposes a hybrid threat modeling framework based on system-centric, attacker-centric, and risk-centric approaches to identify threats in Operational Technology (OT) applications. OT is made up of software and hardware used to manage, secure, and control industrial control systems (ICS), and its environments include factories, power plants, oil and gas refineries, and pipelines. To visualize the “big picture” of its infrastructure risk profile and improve understanding of the full attack surface, the proposed framework builds on several threat modeling methodologies: PASTA modeling, STRIDE, and attack tree components. Nevertheless, the continuity and stability of vital infrastructure will continue to depend heavily on legacy equipment. Thus, protecting the availability, security, and safety of industrial environments and vital infrastructure from cyberattacks requires operational technology (OT) cybersecurity. The feasibility of the proposed approach is illustrated with a case study from a real oil and gas production plant control system where numerous significant cyberattacks in recent years have targeted OT networks more frequently as hackers realized the possibility of disruption due to insufficient OT security, particularly for outdated systems. The proposed framework achieved better results in detecting threats and severity in the design of the case study system, helping to increase security and support cybersecurity assessment of legacy control systems.
在当今文化中，安全漏洞的报道越来越多，网络安全在全球范围内日益受到关注。威胁建模方法（TMM）是一种积极主动的安全实践，对于准确定位风险并限制其影响至关重要。本文提出了一种基于以系统为中心、以攻击者为中心和以风险为中心的混合威胁建模框架，用于识别操作技术（OT）应用中的威胁。OT 由用于管理、保护和控制工业控制系统 (ICS) 的软件和硬件组成，其环境包括工厂、发电厂、油气精炼厂和管道。为了使基础设施风险概况的 "全貌 "可视化，并提高对整个攻击面的理解，拟议框架建立在几种威胁建模方法的基础上：PASTA 建模、STRIDE 和攻击树组件。然而，重要基础设施的连续性和稳定性仍将在很大程度上依赖于传统设备。因此，要保护工业环境和重要基础设施的可用性、安全性和安全免受网络攻击，就需要操作技术（OT）网络安全。近年来，由于黑客意识到 OT 安全性不足（尤其是过时的系统）有可能造成破坏，因此针对 OT 网络的重大网络攻击日益频繁。在案例研究系统的设计中，建议的框架在检测威胁和严重性方面取得了更好的效果，有助于提高安全性并支持对传统控制系统进行网络安全评估。
{"title":"Legacy ICS Cybersecurity Assessment Using Hybrid Threat Modeling—An Oil and Gas Sector Case Study","authors":"Mohamed Badawy, Nada H. Sherief, Ayman A. Abdel-Hamid","doi":"10.3390/app14188398","DOIUrl":"https://doi.org/10.3390/app14188398","url":null,"abstract":"As security breaches are increasingly widely reported in today’s culture, cybersecurity is gaining attention on a global scale. Threat modeling methods (TMM) are a proactive security practice that is essential for pinpointing risks and limiting their impact. This paper proposes a hybrid threat modeling framework based on system-centric, attacker-centric, and risk-centric approaches to identify threats in Operational Technology (OT) applications. OT is made up of software and hardware used to manage, secure, and control industrial control systems (ICS), and its environments include factories, power plants, oil and gas refineries, and pipelines. To visualize the “big picture” of its infrastructure risk profile and improve understanding of the full attack surface, the proposed framework builds on several threat modeling methodologies: PASTA modeling, STRIDE, and attack tree components. Nevertheless, the continuity and stability of vital infrastructure will continue to depend heavily on legacy equipment. Thus, protecting the availability, security, and safety of industrial environments and vital infrastructure from cyberattacks requires operational technology (OT) cybersecurity. The feasibility of the proposed approach is illustrated with a case study from a real oil and gas production plant control system where numerous significant cyberattacks in recent years have targeted OT networks more frequently as hackers realized the possibility of disruption due to insufficient OT security, particularly for outdated systems. The proposed framework achieved better results in detecting threats and severity in the design of the case study system, helping to increase security and support cybersecurity assessment of legacy control systems.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249266","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}
Gilberto Calvillo, Marco A. Panduro, Elizvan Juarez, Alberto Reyna, Carlos del Rio
New configurations of 2-D phased arrays are proposed in this paper for reducing the number of phase shifters. This design methodology is based on the use of a novel coherently radiating periodic structures (CORPSs) block for 2-D phased arrays. Two new antenna systems for 2-D phased arrays are studied and analyzed utilizing the CORPSs blocks of four inputs and nine outputs. These CORPSs feeding blocks are applied in a smart way to feed the planar antenna arrays by generating the required phase plane and reducing the number of control ports. Interesting results are provided based on the experimental measurements and full-wave simulations. These results illustrate a great reduction of the active devices (phase shifters), providing a good design compromise in terms of the scanning range and side lobe level performance. Furthermore, the provided results illustrate a maximum reduction capability in the number of phase shifters of 81%, considering a scanning range of ±30° in azimuth and ±30° in elevation. A raised cosine distribution is applied to reach side lobe levels of −19 dB for ±18° and −17 dB for ±30° in elevation. These benefits could be of interest to designers of phased antenna systems.
{"title":"A Design Proposal Using Coherently Radiating Periodic Structures (CORPSs) for 2-D Phased Arrays of Limited Scanning","authors":"Gilberto Calvillo, Marco A. Panduro, Elizvan Juarez, Alberto Reyna, Carlos del Rio","doi":"10.3390/app14188409","DOIUrl":"https://doi.org/10.3390/app14188409","url":null,"abstract":"New configurations of 2-D phased arrays are proposed in this paper for reducing the number of phase shifters. This design methodology is based on the use of a novel coherently radiating periodic structures (CORPSs) block for 2-D phased arrays. Two new antenna systems for 2-D phased arrays are studied and analyzed utilizing the CORPSs blocks of four inputs and nine outputs. These CORPSs feeding blocks are applied in a smart way to feed the planar antenna arrays by generating the required phase plane and reducing the number of control ports. Interesting results are provided based on the experimental measurements and full-wave simulations. These results illustrate a great reduction of the active devices (phase shifters), providing a good design compromise in terms of the scanning range and side lobe level performance. Furthermore, the provided results illustrate a maximum reduction capability in the number of phase shifters of 81%, considering a scanning range of ±30° in azimuth and ±30° in elevation. A raised cosine distribution is applied to reach side lobe levels of −19 dB for ±18° and −17 dB for ±30° in elevation. These benefits could be of interest to designers of phased antenna systems.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249278","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}
During the developmental stages, eggplants are susceptible to diseases, which can impact crop yields and farmers’ economic returns. Therefore, timely and effective detection of eggplant diseases is crucial. Deep learning-based object detection algorithms can automatically extract features from images of eggplants affected by diseases. However, eggplant disease images captured in complex farmland environments present challenges such as varying disease sizes, occlusion, overlap, and small target detection, making it difficult for existing deep-learning models to achieve satisfactory detection performance. To address this challenge, this study proposed an optimized eggplant disease detection algorithm, YOLOv8-E, based on You Only Look Once version 8 nano (YOLOv8n). Firstly, we integrate switchable atrous convolution (SAConv) into the C2f module to design the C2f_SAConv module, replacing some of the C2f modules in the backbone network of YOLOv8n, enabling our proposed algorithm to better extract eggplant disease features. Secondly, to facilitate the deployment of the detection model on mobile devices, we reconstruct the Neck network of YOLOv8n using the SlimNeck module, making the model lighter. Additionally, to tackle the issue of missing small targets, we embed the large separable kernel attention (LSKA) module within SlimNeck, enhancing the model’s attention to fine-grained information. Lastly, we combined intersection over union with auxiliary bounding box (Inner-IoU) and minimum point distance intersection over union (MPDIoU), introducing the Inner-MPDIoU loss to speed up convergence of the model and raise detection precision of overlapped and occluded targets. Ablation studies demonstrated that, compared to YOLOv8n, the mean average precision (mAP) and F1 score of YOLOv8-E reached 79.4% and 75.7%, respectively, which obtained a 5.5% increment and a 4.5% increase, while also reducing the model size and computational complexity. Furthermore, YOLOv8-E achieved higher detection performance than other mainstream algorithms. YOLOv8-E exhibits significant potential for practical application in eggplant disease detection.
在生长发育阶段，茄子很容易受到病害的侵袭，从而影响作物产量和农民的经济收益。因此，及时有效地检测茄子病害至关重要。基于深度学习的物体检测算法可以自动提取茄子病害图像的特征。然而，在复杂的农田环境中捕获的茄子病害图像存在病害大小不一、遮挡、重叠和小目标检测等挑战，使得现有的深度学习模型难以达到令人满意的检测性能。为解决这一难题，本研究基于 You Only Look Once version 8 nano（YOLOv8n）提出了一种优化的茄子病害检测算法 YOLOv8-E。首先，我们在 C2f 模块中集成了可切换无绳卷积（SAConv），设计了 C2f_SAConv 模块，替代了 YOLOv8n 骨干网络中的部分 C2f 模块，使我们提出的算法能够更好地提取茄子病害特征。其次，为了便于在移动设备上部署检测模型，我们使用 SlimNeck 模块重构了 YOLOv8n 的 Neck 网络，使模型更加轻便。此外，为了解决遗漏小目标的问题，我们在 SlimNeck 中嵌入了大型可分离核关注（LSKA）模块，增强了模型对细粒度信息的关注。最后，我们结合了带辅助边界框的联合交集（Inner-IoU）和联合交集最小点距（MPDIoU），引入了 Inner-MPDIoU 损失，以加快模型的收敛速度，提高重叠和遮挡目标的检测精度。消融研究表明，与 YOLOv8n 相比，YOLOv8-E 的平均精度 (mAP) 和 F1 分数分别达到 79.4% 和 75.7%，分别提高了 5.5% 和 4.5%，同时还减少了模型大小和计算复杂度。此外，与其他主流算法相比，YOLOv8-E 实现了更高的检测性能。YOLOv8-E 在茄子病害检测中的实际应用潜力巨大。
{"title":"YOLOv8-E: An Improved YOLOv8 Algorithm for Eggplant Disease Detection","authors":"Yuxi Huang, Hong Zhao, Jie Wang","doi":"10.3390/app14188403","DOIUrl":"https://doi.org/10.3390/app14188403","url":null,"abstract":"During the developmental stages, eggplants are susceptible to diseases, which can impact crop yields and farmers’ economic returns. Therefore, timely and effective detection of eggplant diseases is crucial. Deep learning-based object detection algorithms can automatically extract features from images of eggplants affected by diseases. However, eggplant disease images captured in complex farmland environments present challenges such as varying disease sizes, occlusion, overlap, and small target detection, making it difficult for existing deep-learning models to achieve satisfactory detection performance. To address this challenge, this study proposed an optimized eggplant disease detection algorithm, YOLOv8-E, based on You Only Look Once version 8 nano (YOLOv8n). Firstly, we integrate switchable atrous convolution (SAConv) into the C2f module to design the C2f_SAConv module, replacing some of the C2f modules in the backbone network of YOLOv8n, enabling our proposed algorithm to better extract eggplant disease features. Secondly, to facilitate the deployment of the detection model on mobile devices, we reconstruct the Neck network of YOLOv8n using the SlimNeck module, making the model lighter. Additionally, to tackle the issue of missing small targets, we embed the large separable kernel attention (LSKA) module within SlimNeck, enhancing the model’s attention to fine-grained information. Lastly, we combined intersection over union with auxiliary bounding box (Inner-IoU) and minimum point distance intersection over union (MPDIoU), introducing the Inner-MPDIoU loss to speed up convergence of the model and raise detection precision of overlapped and occluded targets. Ablation studies demonstrated that, compared to YOLOv8n, the mean average precision (mAP) and F1 score of YOLOv8-E reached 79.4% and 75.7%, respectively, which obtained a 5.5% increment and a 4.5% increase, while also reducing the model size and computational complexity. Furthermore, YOLOv8-E achieved higher detection performance than other mainstream algorithms. YOLOv8-E exhibits significant potential for practical application in eggplant disease detection.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":"116 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249270","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}
There have been numerous theoretical and empirical transportation studies contesting the stability of commuting time over time. The constant commuting time hypothesis posits that people adjust trip durations, shift across modes, and sort through locations, so that their average commuting time remains within a constant budget. There is a discrepancy between studies applying aggregate analysis and those using disaggregate analysis, and differences in data collection may have contributed to the varying conclusions reported in the literature. This study conducts both aggregate and disaggregate analyses with two travel surveys of the Portland region. We employ descriptive analysis and t-tests to compare the aggregate commuting times of two years and use regression models to explore factors affecting the disaggregate commuting time at the individual trip level to examine whether the stability of the commuting time remains after substantial changes in the transportation and land use systems. Our study indicates that the average commuting time, along with the average commuting distance, increased slightly, as the mode share shifted away from driving during the examined period. The growth in shares of non-driving modes, which are slower than driving, coupled with an increased travel distance, contributed to the small increase in the average commuting time. Our analysis also indicates that the average travel speed improved for transit riders as well as drivers, contradicting earlier research that claims that public transit investment has worsened the congestion in Portland.
关于通勤时间随时间变化的稳定性，已有许多理论和实证交通研究提出了质疑。通勤时间恒定假说认为，人们会调整出行时长、转换出行方式并对出行地点进行分类，从而使其平均通勤时间保持在一个恒定的预算范围内。采用总量分析的研究与采用分类分析的研究之间存在差异，数据收集方面的差异可能是导致文献中报告的结论各不相同的原因。本研究通过对波特兰地区的两次旅行调查进行了总量和分类分析。我们采用描述性分析和 t 检验来比较两年的总体通勤时间，并使用回归模型来探讨影响单次出行的分类通勤时间的因素，以研究在交通和土地使用系统发生重大变化后，通勤时间是否保持稳定。我们的研究表明，在研究期间，随着非驾车出行方式所占比例的变化，平均通勤时间和平均通勤距离都略有增加。由于非驾驶模式所占比例的增长比驾驶模式慢，再加上出行距离的增加，导致平均通勤时间略有增加。我们的分析还表明，公交乘客和司机的平均出行速度都有所提高，这与早先的研究相矛盾，因为早先的研究称公共交通投资加剧了波特兰的交通拥堵状况。
{"title":"Application of Historical Comprehensive Multimodal Transportation Data for Testing the Commuting Time Paradox: Evidence from the Portland, OR Region","authors":"Huajie Yang, Jiali Lin, Jiahao Shi, Xiaobo Ma","doi":"10.3390/app14188369","DOIUrl":"https://doi.org/10.3390/app14188369","url":null,"abstract":"There have been numerous theoretical and empirical transportation studies contesting the stability of commuting time over time. The constant commuting time hypothesis posits that people adjust trip durations, shift across modes, and sort through locations, so that their average commuting time remains within a constant budget. There is a discrepancy between studies applying aggregate analysis and those using disaggregate analysis, and differences in data collection may have contributed to the varying conclusions reported in the literature. This study conducts both aggregate and disaggregate analyses with two travel surveys of the Portland region. We employ descriptive analysis and t-tests to compare the aggregate commuting times of two years and use regression models to explore factors affecting the disaggregate commuting time at the individual trip level to examine whether the stability of the commuting time remains after substantial changes in the transportation and land use systems. Our study indicates that the average commuting time, along with the average commuting distance, increased slightly, as the mode share shifted away from driving during the examined period. The growth in shares of non-driving modes, which are slower than driving, coupled with an increased travel distance, contributed to the small increase in the average commuting time. Our analysis also indicates that the average travel speed improved for transit riders as well as drivers, contradicting earlier research that claims that public transit investment has worsened the congestion in Portland.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249171","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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