Wei Cui (92129), Xiao Liao (5142143), Yang Yang (45629) et al. · Figshare · 2025
<div> With the rapid development of smart grids, power grid systems are becoming increasingly complex, posing significant challenges to their security. Traditional network intrusion detection systems often rely on manually engineered features, which are not only resource-intensive but also struggle to handle the diverse range of attack types. This paper aims to address these challenges by proposing an automated DDoS attack detection algorithm using the Informer model. We introduce a windowing technique to segment network traffic into manageable samples, which are then input into the Informer for feature extraction and classification. This model captures both the temporal dependencies and global attention information in the traffic data. Experimental results on the CICIDS-2018 dataset demonstrate the effectiveness of our approach, showing significant improvements in detection accuracy and efficiency. Our findings suggest that the proposed method offers a promising solution for real-time intrusion detection in complex power grid environments. </div>
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Manxian Yang (20521600), Yanhong Chen (304926), Yongke Li (20521603) et al. · Figshare · 2025
<div><p>Automated large-scale farmland preparation operations face significant challenges related to path planning efficiency and uniformity in resource allocation. To improve agricultural production efficiency and reduce operational costs, an enhanced method for planning land preparation paths is proposed. In the initial stage, unmanned aerial vehicles (UAVs) are employed to collect data from the field, which is then used to construct accurate farm models. For single-field operations, a path planning approach is developed that minimizes energy consumption. The approach combines the selection of optimal operational angles with the implementation of efficient turning strategies, aiming to achieve full coverage. In addressing the issue of scheduling multiple machines across multiple fields, a two-stage optimization method, referred to as the BNSGA-III algorithm, is introduced. This algorithm integrates the NSGA-III algorithm with Beluga Whale Optimization (BWO), adaptive parameter adjustment, and Adaptive Inversion Crossover (AIC). The proposed method tackles the inherent complexity of agricultural environments, balancing operational efficiency and resource allocation through multi-objective optimization. Experimental results demonstrate that, compared to random operation directions, the proposed method reduces the path length by 1.9% to 3.1%, decreases the turning frequency by 19.5% to 24.0%, and improves coverage by 1.0% to 1.4%. In the context of multi-machine scheduling, the BNSGA-III algorithm outperforms the NSGA-II, NSGA-III, and MOEA/D algorithms, achieving improvements in total travel distance (12.3% to 34.4%), path balance (60.9% to 66.2%), and workload distribution (78.7% to 92.9%). Further evaluation shows that BNSGA-III excels in key metrics such as convergence (IGD), solution quality (HV), and diversity (Spread), thereby confirming its superiority in solution quality, convergence, and diversity. The findings of this study provide strong support for the advancement of intelligent agriculture.</p></div>
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Xinbo Liu (6000665), Buhong Wang (6000668) · Figshare · 2018
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Shaohua Fu (10573209), Wenjing Xing (501922), Juncheng Wu (2484658) et al. · Figshare · 2023
<div><p>In order to improve the hardware configuration and interaction mode of the fish tank system and realize the diversification of client functions, the purpose of real-time remote monitoring and management is achieved. A set of IoT intelligent fish tank system composed of sensor unit, signal processing unit and wireless transmission unit was designed. The system improves the algorithm of the data collected by the sensor, and proposes an improved first-order lag average filtering algorithm. The system uses composite collection information, intelligent processing, chart data analysis and other methods to transmit the processed data to the cloud server through the WIFI communication module. An APP is designed on the remote monitoring and control end, and a visual data interface of the smart fish tank is made, and the user can modify the environmental parameters conducive to the biological survival inside the fish tank through the APP, it brings great convenience to the family fish tank, and the test shows that the system network is stable and fast in response, and the overall purpose of the intelligent fish tank system is achieved.</p></div>
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Ning Ji (325849), Xi Chen (35903), Xue Qin (123365) et al. · Figshare · 2024
<div><p>In order to improve the accuracy of the transformer fault identification using nature-inspired algorithms, an identification method based on the GWO (Grey Wolf Optimizer)-optimized Dual-channel MLP (Multilayer Perceptron)-Attention is proposed. First, a Dual-channel model is constructed by combining the AM (Attention Mechanism) and MLP. Subsequently, the GWO algorithm is used to optimize the number and the nodes of the hidden layer in the Dual-channel MLP-Attention model. Typical transformer faults are simulated using DDRTS (Digital Dynamic Real-Time Simulator) system. Experiments showed that the GWO- optimized method has an accuracy rate of 95.3%-96.7% in identifying the transformer faults. Compared with BP, SVM, MLP, and single-channel M-A models, the proposed method improved the accuracy by14.1%, 9.6%, 9.3%, and 3.3% respectively. This result indicates the rationality and effectiveness of the proposed method in transformer fault identification.</p></div>
Cited by 0Open access
Wei Cui (92129), Xiao Liao (5142143), Yang Yang (45629) et al. · Figshare · 2025
<div> With the rapid development of smart grids, power grid systems are becoming increasingly complex, posing significant challenges to their security. Traditional network intrusion detection systems often rely on manually engineered features, which are not only resource-intensive but also struggle to handle the diverse range of attack types. This paper aims to address these challenges by proposing an automated DDoS attack detection algorithm using the Informer model. We introduce a windowing technique to segment network traffic into manageable samples, which are then input into the Informer for feature extraction and classification. This model captures both the temporal dependencies and global attention information in the traffic data. Experimental results on the CICIDS-2018 dataset demonstrate the effectiveness of our approach, showing significant improvements in detection accuracy and efficiency. Our findings suggest that the proposed method offers a promising solution for real-time intrusion detection in complex power grid environments. </div>
Cited by 0Open access
Jochen von Spiczak (801404), Gerard Crelier (801405), Daniel Giese (801406) et al. · Figshare · 2015
<p>Vector fields resulting from three different algorithms for vorticity approximation: (A) first order central difference, (B) 8-point-circulation method, and (C) second order central difference. In the bottom left corner, the calculation scheme of each algorithm is given, illustrating adjacent data points that are taken into account for derivative calculation (schemes similar to [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0139025#pone.0139025.ref040" target="_blank">40</a>]). While all three algorithms were adopted to a three-dimensional pattern for this work, 2D patterns are shown for simplicity.</p>
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Kai Kang (195645), Wei Pu (6018803), Yanfang Ma (665605) et al. · Figshare · 2018
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Stephanie J. Grainger (348633), Andrew J. Putnam (220221) · Figshare · 2015
<p>In Panel A, the algorithm makes a 3D overlay of the images of each channel. The user defines the location of a background box. In Panel B, each green image from the 3D z-stack is depicted in gray scale. In Panel C, the maximum projection of the green z-stack is converted to a binary image after the background (defined in Panel A) is eliminated. In Panel D, the portion of the red 3D z-stack that corresponds to the same area depicted in the green image is shown in gray scale as a maximum projection. In Panel E, only those pixels from the red image in Panel D that overlap with the green pixels in Panel C are shown. The pixel intensities from the image in Panel E are tallied and output as a ratio of red:green.</p>
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Xiao Zhou (122661), Ledan Qian (18405498), Haider Aziz (18405501) et al. · Figshare · 2024
<div><p>The Internet of Things (IoT) is gradually changing the way teaching and learning take place in on-campus programs. In particular, face capture services improve student concentration to create an efficient classroom atmosphere by using face recognition algorithms that support end devices. However, reducing response latency and executing face analysis services effectively in real-time is still challenging. For this reason, this paper proposed a pedagogical model of face recognition for IoT devices based on edge computing (TFREC). Specifically, this research first proposed an IoT service-based face capture algorithm to optimize the accuracy of face recognition. In addition, the service deployment method based on edge computing is proposed in this paper to obtain the best deployment strategy and reduce the latency of the algorithm. Finally, the comparative experimental results demonstrate that TFREC has 98.3% accuracy in face recognition and 72 milliseconds in terms of service response time. This research is significant for advancing the optimization of teaching methods in school-based courses, meanwhile, providing beneficial insights for the application of face recognition and edge computing in the field of education.</p></div>
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Zhengyu Hu (290424), Wenrui Liu (443265), Shengchen Ling (11237925) et al. · Figshare · 2021
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Zhengyu Hu (290424), Wenrui Liu (443265), Shengchen Ling (11237925) et al. · Figshare · 2021
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Weina Li (484433), Jiadong Ren (3826267) · Figshare · 2018
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Haiyan Wang (25821), Yanping Zhang (153797), Yangyang Zhang (608493) et al. · Figshare · 2025
(a). Pearson correlation coefficients (PCC) between predicted and true expression patterns for three representative genes (RPS25, TPT1, and MS4A1) across methods. Genes were selected from the top 3 genes with highest median PCC shared by all methods. (b). Violin plots showing gene-wise PCC distributions for all predicted genes in melanoma (left) and mouse brain (right) datasets.
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Lei Liu (5074), Mingwei Cao (3987278), Yeguo Sun (11840775) · Figshare · 2021
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Mingkang Sun (6498791), Xiang Li (114679) · Figshare · 2024
<div><p>The rapid growth of traffic trajectory data and the development of positioning technology have driven the demand for its analysis. However, in the current application scenarios, there are some problems such as the deviation between positioning data and real roads and low accuracy of existing trajectory data traffic prediction models. Therefore, a map matching algorithm based on hidden Markov models is proposed in this study. The algorithm starts from the global route, selects K nearest candidate paths, and identifies candidate points through the candidate paths. It uses changes in speed, angle, and other information to generate a state transition matrix that match trajectory points to the actual route. When processing trajectory data in the experiment, K = 5 is selected as the optimal value, the algorithm takes 51 ms and the accuracy is 95.3%. The algorithm performed well in a variety of road conditions, especially in parallel and mixed road sections, with an accuracy rate of more than 96%. Although the time loss of this algorithm is slightly increased compared with the traditional algorithm, its accuracy is stable. Under different road conditions, the accuracy of the algorithm is 98.3%, 97.5%, 94.8% and 96%, respectively. The accuracy of the algorithm based on traditional hidden Markov models is 95.9%, 95.7%, 95.4% and 94.6%, respectively. It can be seen that the accuracy of the algorithm designed has higher precision. The experiment proves that the map matching algorithms based on hidden Markov models is superior to other algorithms in terms of matching accuracy. This study makes the processing of traffic trajectory data more accurate.</p></div>
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Wang Linfeng (17102068), Liu Yong (1993309), Liu Jiayao (17102071) et al. · Figshare · 2023
<div><p>It is of great significance to identify the pest species accurately and control it effectively to reduce the loss of agricultural products. The research results of this project will provide theoretical basis for preventing and controlling the spread of pests and reducing the loss of agricultural products, and have important practical significance for improving the quality of agricultural products and increasing the output of agricultural products. At the same time, it provides a kind of effective prevention and control measures for farmers, so as to ensure the safety and health of crops. Because of the slow speed and high cost of manual identification, it is necessary to establish a set of automatic pest identification system. The traditional image-based insect classifier is mainly realized by machine vision technology, but because of its high complexity, the classification efficiency is low and it is difficult to meet the needs of applications. Therefore, it is necessary to develop a new automatic insect recognition system to improve the accuracy of insect classification. There are many species and forms of insects, and the field living environment is complex. The morphological similarity between species is high, which brings difficulties to the classification of insects. In recent years, with the rapid development of deep learning technology, using artificial neural network to classify pests is an important method to establish a fast and accurate classification model. In this work, we propose a novel convolutional neural network-based model (MSSN), which includes attention mechanism, feature pyramid, and fine-grained model. The model has good scalability, can better capture the semantic information in the image, and achieve more accurate classification. We evaluated our approach on a common data set: large-scale pest data set, PlantVillage benchmark data set, and evaluated model performance using a variety of evaluation indicators, namely, macro mean accuracy (MPre), macro mean recall rate (MRec), macro mean F1-score (MF1), Accuracy (Acc) and geometric mean (GM). Experimental results show that the proposed algorithm has better performance and universality ability than the existing algorithm. For example, on the data set, the maximum accuracy we obtained was 86.35%, which exceeded the corresponding technical level. The ablation experiment was conducted on the experiment itself, and the comprehensive evaluation of the complete MSSN(scale 1+2+3) was the best in various performance indexes, demonstrating the feasibility of the innovative method in this paper.</p></div>
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Zhengyu Hu (290424), Wenrui Liu (443265), Shengchen Ling (11237925) et al. · Figshare · 2021
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Jie Zhang (64655), Shengping Liu (7377233), Wei Wu (27708) et al. · Figshare · 2022
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Junlong Peng (10918442), Mengyao Wang (829984), Chao Peng (686643) et al. · Figshare · 2022
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