IEEE Access最新文献

{"title":"Corrections to “A Systematic Literature Review of the IoT in Agriculture–Global Adoption, Innovations, Security Privacy Challenges”","authors":"Asma Naseer;Muhammad Shmoon;Tanzeela Shakeel;Shafiq Ur Rehman;Awais Ahmad;Volker Gruhn","doi":"10.1109/ACCESS.2024.3431749","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3431749","url":null,"abstract":"","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"143401-143401"},"PeriodicalIF":3.4,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10716036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Progressive-Assisted Object Detection Method Based on Instance Attention","authors":"Ziwen Sun;Zhizhong Xi;Hao Li;Chong Ling;Dong Chen;Xiaoyan Qin","doi":"10.1109/ACCESS.2024.3459941","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3459941","url":null,"abstract":"Overcoming the high cost of self-attention operation in Transformer-based object detection methods and improving the detection accuracy of small objects is one of the difficulties in the field of object detection research. This paper designs a progressive assisted object detection method PaoDet based on Transformer, which uses common feature extraction backbone such as Resnet and ViT to extract multi-scale features of the input image, and uses RPN(Region Proposal Network) to extract proposals of different scales; Subsequently, a progressive modeling approach was adopted to perform self-attention and cross-attention operations on proposals of different scales from large to small, achieving feature interaction between instances, ensuring high detection efficiency and low computational complexity. During the training process, each layer of the network has certain generalization ability for detecting adjacent scale objects under the supervision of a dynamic scale division method. Compared with state-of-the-art object detection methods on COCO and UAVDT datasets, the effectiveness and superiority of the proposed method were demonstrated.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"147907-147917"},"PeriodicalIF":3.4,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10713239","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensemble Balanced Nested Dichotomy Fuzzy Models for Software Requirement Risk Prediction","authors":"Ganesh Kumar;Abdullahi Abubakar Imam;Shuib Basri;Ahmad Sobri Hashim;Abdul Ghani Haji Naim;Luiz Fernando Capretz;Abdullateef Oluwagbemiga Balogun;Hussaini Mamman","doi":"10.1109/ACCESS.2024.3473942","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3473942","url":null,"abstract":"Modern software systems are becoming more intricate, making identification of risks in the software requirement phase— a fundamental aspect of the software development life cycle (SDLC)—complex. Inadequate risk assessment may result in the malfunction of a software system, either in the development or production phase. Therefore, risk prediction plays a crucial role in software requirements, serving as the first step in any software project. Hence, developing adaptive predictive models that can offer consistent and explainable insights for handling risk prediction is imperative. This study proposes novel ensemble class balanced nested dichotomy (EBND) fuzzy induction models for risk prediction in software requirement. Specifically, the proposed EBND models employ a hierarchical structure consisting of binary trees featuring distinct nested dichotomies that are generated randomly for each tree. Thereafter, we use an ensemble principle to refine rules generated from the resulting binary tree. The predictive efficacy of the suggested EBND models is further extended by introducing a data sampling method into their prediction process. The inclusion of the data sampling method acts to mitigate the underlying disparity in the class labels that may affect its prediction processes. The efficacy of the EBND models is then evaluated and compared to current solutions using the open-source software risk dataset. The observed findings revealed that the EBND models demonstrated superior predictive capabilities when compared to the conventional models and state-of-the-art methodologies. Specifically, the EBND models achieved an average accuracy threshold value of 98%, as well as high values for the f-measure metric.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"146225-146243"},"PeriodicalIF":3.4,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10709888","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Burn Severity Assessment With Deep Learning: A Comparative Analysis and Computational Efficiency Evaluation","authors":"Saeka Rahman;Md Motiur Rahman;Miad Faezipour;Mo Rastgaar;Elika Ridelman;Justin D. Klein;Beth A. Angst;Christina M. Shanti","doi":"10.1109/ACCESS.2024.3476110","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3476110","url":null,"abstract":"Burn injuries present a substantial global public health challenge. The conventional approach, relying on visual inspection to compute total body surface area (TBSA) for assessing burn severity, encounters the inherent limitations of proper estimation. These limitations prompted the development of computer-based applications, particularly machine learning, and deep learning models, to enhance the performance. This paper presents a comprehensive analytical study of eight deep learning techniques designed for assessing burn severity in terms of four characteristics: inflammation, scar, uniformity, and pigmentation, in small datasets of 2-dimensional (2D) images captured using digital (smartphone) camera. The models are Convolutional Neural Network (CNN), attention-based CNN, decision-level fusion (DF) based on CNN models, DF with attention-based CNN models, autoencoder-NN (Neural Network), and hybrid VGG16-Machine Learning (ML) with Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). Each model is validated with eight datasets collected and annotated by our team at the Children’s Hospital of Michigan in two phases to classify the severity of burns in terms of inflammation, scar, uniformity, and pigmentation. The average test accuracy across the eight datasets using CNN, attention-based CNN, DF with CNN models, DF with attention-based CNN models, autoencoder-NN, VGG16-RF, VGG16-SVM, and VGG16-XGBoost are \u0000<inline-formula> <tex-math>$0.87pm 0.04$ </tex-math></inline-formula>\u0000, \u0000<inline-formula> <tex-math>$0.93pm 0.04$ </tex-math></inline-formula>\u0000, \u0000<inline-formula> <tex-math>$0.90pm 0.01$ </tex-math></inline-formula>\u0000, \u0000<inline-formula> <tex-math>$0.95pm 0.02$ </tex-math></inline-formula>\u0000, \u0000<inline-formula> <tex-math>$0.87pm 0.03$ </tex-math></inline-formula>\u0000, \u0000<inline-formula> <tex-math>$0.63pm 0.03$ </tex-math></inline-formula>\u0000, \u0000<inline-formula> <tex-math>$0.79pm 0.02$ </tex-math></inline-formula>\u0000, \u0000<inline-formula> <tex-math>$0.79pm 0.01$ </tex-math></inline-formula>\u0000, correspondingly. The research also computes and compares the computational complexity of each model in terms of FLoating point Operations Per Second (FLOPS) and Multiply-ACcumulate operations (MACs). Compared with the base CNN model, the decision-level fusion with attention mechanism model outperforms with a gain of 9.19% in test accuracy and an increase of 3321.53% in FLOPS. Considering the priority and constraint of the task, the attention-based CNN model can also be preferable as it achieves an accuracy gain of 6.90% and significantly less computational increase expense (8.62%) compared with the base CNN. The code for the best performing decision-level fusion with attention mechanism model is provided on GitHub link at \u0000<uri>https://github.com/Saeka2022/Burn-Assessment</uri>\u0000.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"147249-147268"},"PeriodicalIF":3.4,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10707626","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142450822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Study on the Representativeness Heuristics Problem in Large Language Models","authors":"Jongwon Ryu;Jungeun Kim;Junyeong Kim","doi":"10.1109/ACCESS.2024.3474677","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3474677","url":null,"abstract":"Large language models (LLMs) exhibit remarkable proficiency in text generation. However, their logical reasoning capabilities require enhancement. Major strides have been achieved in reasoning techniques for LLM, such as Few-shot, Zero-shot, and Chain-of-Thought (CoT). Nevertheless, these techniques have shortcomings, particularly in addressing the representativeness heuristic (RH) phenomenon. RH is a cognitive bias that occurs when a person judges the probability of an event or the likelihood that an object belongs to a particular category based on how well it matches the prototype or stereotype of that category. In this study, we investigated the pervasive issue of RH errors in LLMs. This research surpasses the constraints of previous studies by analyzing various RH scenarios that they did not cover and by directly constructing and testing the corresponding datasets. Moreover, a novel prompt called zero-shot-RH is proposed to augment the reasoning ability of LLMs, mitigate RH errors, and thus bolster logical reasoning. This approach seeks to enable LLMs to comprehend the given information better and reduce the biases stemming from RH errors. The prompt zero-shot-RH achieved an average accuracy higher than zero-shot-CoT by 0.145 and 0.277 in the tasks of correct reasoning and correct reasonings by sex, respectively, without relying on RH. The outcomes of this research endeavor are a deeper understanding of RH errors in LLMs and novel strategies to mitigate these biases, thereby advancing the domain of logical reasoning within LLMs.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"147958-147966"},"PeriodicalIF":3.4,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706240","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Non-Intrusive Load Identification Method Based on Novel Data Acquisition Terminals and Model Fusion","authors":"Jian Zhuge;Guangzheng Lin;Hongfeng Fu;Licheng Zheng","doi":"10.1109/ACCESS.2024.3474798","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3474798","url":null,"abstract":"Due to the randomness and uncertainty of household electricity use, efficient grid management faces challenges. Non-intrusive load monitoring (NILM) technology has become a pivotal solution to understanding the behavior of electricity consumers. However, traditional data acquisition terminals often struggle to balance cost and performance. To address this barrier, this study proposes a novel, low-cost, high-performance data acquisition terminal, which abandons the traditional dedicated chip solution and instead uses a microcontroller to complete all control and data processing tasks. At the same time, by using the Fast Fourier Transform (FFT), the current signal is converted into a frequency domain signal containing rich information such as amplitude and harmonics, providing great convenience for subsequent intelligent algorithm analysis and processing. This study transforms the non-intrusive load identification problem at the algorithm level into a change point detection problem. A proposed fusion algorithm comprises two layers: the first is based on decision tree algorithms XGBoost and LightGBM, used for feature extraction and preliminary classification; the second uses logistic regression algorithms for decoding and outputting results, achieving high-precision load identification. Experimental results show that the method proposed in this study can achieve more than 95% accuracy when dealing with complex scenarios of mixed use of high-power and low-power appliances. Compared with other algorithms, this method shows significant advantages in load identification accuracy.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"146598-146609"},"PeriodicalIF":3.4,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706225","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bird and UAVs Recognition Detection and Tracking Based on Improved YOLOv9-DeepSORT","authors":"Jincan Zhu;Chenhao Ma;Jian Rong;Yong Cao","doi":"10.1109/ACCESS.2024.3475629","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3475629","url":null,"abstract":"At present, the protection of birds, especially endangered birds, faces major challenges. In the process of protection, birds are often mixed with various drones, and it is difficult to accurately count the number of endangered birds, especially at night, which brings great difficulties to bird protection work. So tracking and identifying birds and drones is essential to ensure the accuracy and efficiency of bird conservation efforts. To solve these problems, this paper proposes a new multi-target tracking (MOT) model based on the combination of YOLOv9 detection algorithm and DeepSORT tracking algorithm. Firstly, the original RepNSCPELAN4 module is replaced by CAM context feature enhancement module in Backbone to improve the model’s ability to extract small target features. Following this, the AFF channel attention mechanism has been integrated with RepNSCPELAN4 in the Head section to create the RepNSCPELAN4-AFF module, which aims to better address semantic and scale inconsistencies. Finally, a new RepNSCPELAN4-AKConv module has been developed using the AKConv dynamic Convolution module to replace the RepNSCPELAN4 module in the original Head section, enabling the model to more effectively capture detailed and contextual information. In the bird-UAV visible light comprehensive dataset proposed in this study, the mAP0.50 and F1 Score of all categories are 81.3% and 71.9% respectively by the improved YOLOv9-DeepSORT model. The mAP0.50 and F1 scores of individual birds are 89.1% and 82.4%, respectively. Compared to the Basic YOLOv9 model, the former improves by 7.9% and 5.3%, while the latter improves by 23.9% and 17.0%. On infrared datasets, compared to the original model, the mAP0.50 and F1 scores of the improved model improved by 3.2% and 1.4% across all categories compared to the original model. The average accuracy of identifying individual birds and similarly shaped fixed-wing drones also improved by 2.2% and 7.5% respectively. Moreover, on the mixed visible light and infrared data sets, the model get mAP0.50 of 81.8% higher 0.9% than that of the YOLOv9. These experiments demonstrate the improved YOLOv9-DeepSORT method can expand the multiscene application range of bird recognition and tracking models, effectively promoting the extraction of video frame features in multi-target tracking.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"147942-147957"},"PeriodicalIF":3.4,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706910","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Methodology for Translating Piping and Instrumentation Diagrams (P&ID) Into Labeled Petri Nets for Automatic PLC Code Generation","authors":"Daniel Guevara-Lozano;Durvvin Rozo-Ibañez;Carlos Renato Vázquez;Antonio Ramírez-Treviño","doi":"10.1109/ACCESS.2024.3475410","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3475410","url":null,"abstract":"This work proposes a novel methodology for translating industrial control systems, described in the standard ISA 5.1 (Piping and Instrumentation Diagrams P&ID) and the associated process and operation narratives, into Petri net models. In a first stage, the P&ID information and the process and operation narratives are translated into standard tables. Since narratives are given in natural language, they are a potential source of errors, for this reason a semi-automatic error removal mechanism is included. Next, the tables’ information is translated into plant and specification models, described by Labeled Petri nets (LPNs). Based on these LPNs, a LPN controller can be synthesized to drive the plant behavior according to the specification, by applying the regulation control approach introduced in the literature, resulting in PLC code for the automation of the process. The proposed approach saves time in the controller design, its commissioning, and debugging, in comparison with traditional trial-and-error approach.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"147235-147248"},"PeriodicalIF":3.4,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706908","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142450913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inductor-Less Low-Power Low-Voltage Cross-Coupled Regulated-Cascode Transimpedance Amplifier Circuit in CMOS Technology","authors":"Behnam Abdollahi;Baset Mesgari;Saeed Saeedi;Zahra Sohrabi;Bernhard Goll;Horst Zimmermann","doi":"10.1109/ACCESS.2024.3474788","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3474788","url":null,"abstract":"This paper presents an inductor-less low-power low-voltage cross-coupled regulated cascode (LV CC-RGC) transimpedance amplifier (TIA) design topology with a negative resistance in addition to the traditional gm-boosting approach. Combining the above techniques leads to enhanced design flexibility without needing an on-chip inductor and extra power consumption. It is particularly beneficial as well when the capacitance of a large photodiode (PD) is located at the TIA input. The TIA is implemented in \u0000<inline-formula> <tex-math>$0.35~mu $ </tex-math></inline-formula>\u0000m CMOS technology with a maximum ft of 20 GHz. Experimental results demonstrate a transimpedance gain of 60 dB\u0000<inline-formula> <tex-math>$Omega $ </tex-math></inline-formula>\u0000, a 3-dB bandwidth of 1.8 GHz, and an average input-referred noise current spectral density of 9.2 pA/\u0000<inline-formula> <tex-math>$surd $ </tex-math></inline-formula>\u0000Hz. The circuit’s power consumption and chip area are 2.2 mW and 0.004 mm2, respectively.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"147106-147114"},"PeriodicalIF":3.4,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706244","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transient Fluctuation Suppression Strategy for MMC Flexible DC Systems Based on Gain-Varying Extended Disturbance Observer","authors":"Huanruo Qi;Yilong Kang;Xiaojv Lv;Ziwen Liu;Xiangyang Yan;Fang Guo","doi":"10.1109/ACCESS.2024.3474696","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3474696","url":null,"abstract":"Modular Multilevel Converter Based High Voltage Direct Current (MMC-HVDC) system is an important technical means to realize grid connection and transmission of new energy. When the external disturbance occurs in the MMC-HVDC system, the submodule capacitance voltages suffer instant impact due to the mutual coupling in the MMC, which causes transient fluctuation and unstable operation of the system. In order to suppress the impact of external disturbance on the internal submodule capacitance voltage transient fluctuations of MMC-HVDC system, the transient fluctuation suppression strategy based on gain-varying extended disturbance observer is proposed in this paper. Firstly, the transient response characteristic equation of the submodule capacitor voltage fluctuation component for MMC-HVDC system is constructed, which reveals the relation between the external output and internal dynamic response of the system. Next, a gain-varying extended disturbance observer is proposed to effectively observe the time-varying disturbance current components in the system, and the corresponding submodule capacitor voltage transient fluctuation suppression strategy is then proposed to improve the dynamic response performance and anti-interference ability of the MMC-HVDC system under different disturbances. The simulation model of MMC-HVDC system is built on PSCAD/EMTDC platform, and the validity and correctness of the proposed method are verified by several simulation tests.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"146303-146314"},"PeriodicalIF":3.4,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706228","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}