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学者姓名：林寿英
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传统的模型预测控制高度依赖于准确的数学模型,且在一个控制周期内只使用一个电压矢量。针对该问题,本文提出了一种LCL型并网逆变器多矢量鲁棒预测控制(MVRPC)策略。首先,为提高跟踪精度,在一个控制周期内作用3个电压矢量,这使得开关频谱集中,也便于LCL滤波器的设计;其次,为提高控制方法的参数鲁棒性,提出了一种适用于LCL型并网逆变器的鲁棒预测控制策略,该策略仅需采样信息,就可实现对系统参数的在线估计;最后,为解决随机采样噪声对参数辨识的影响,提出了一种可变的计算周期,参数辨识只发生在该计算周期中,计算周期中使系统控制误差最优的参数将被用于进行后续的控制,避免了随机误差对于系统控制性能的影响。通过实验验证了所提方法的有效性。
Keyword ：
多矢量 多矢量 并网逆变器 并网逆变器 鲁棒预测控制 鲁棒预测控制
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| GB/T 7714 | 刘文治 , 林寿英 . LCL型并网逆变器多矢量鲁棒预测控制 [J]. | 电力电子技术 , 2025 , 59 (01) : 77-81 . |
| MLA | 刘文治 等. "LCL型并网逆变器多矢量鲁棒预测控制" . | 电力电子技术 59 . 01 (2025) : 77-81 . |
| APA | 刘文治 , 林寿英 . LCL型并网逆变器多矢量鲁棒预测控制 . | 电力电子技术 , 2025 , 59 (01) , 77-81 . |
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BackgroundTea pests pose a significant threat to tea leaf yield and quality, necessitating fast and accurate detection methods to improve pest control efficiency and reduce economic losses for tea farmers. However, in real tea gardens, some tea pests are small in size and easily camouflaged by complex backgrounds, making it challenging for farmers to promptly and accurately identify them.ResultsTo address this issue, we propose a real-time detection method based on TP-YOLOX for monitoring tea pests in complex backgrounds. Our approach incorporates the CSBLayer module, which combines convolution and multi-head self-attention mechanisms, to capture global contextual information from images and expand the network's perception field. Additionally, we integrate an efficient multi-scale attention module to enhance the model's ability to perceive fine details in small targets. To expedite model convergence and improve the precision of target localization, we employ the SIOU loss function as the bounding box regression function. Experimental results demonstrate that TP-YOLOX achieves a significant performance improvement with a relatively small additional computational cost (0.98 floating-point operations), resulting in a 4.50% increase in mean average precision (mAP) compared to the original YOLOX-s. When compared with existing object detection algorithms, TP-YOLOX outperforms them in terms of mAP performance. Moreover, the proposed method achieves a frame rate of 82.66 frames per second, meeting real-time requirements.ConclusionTP-YOLOX emerges as a proficient solution, capable of accurately and swiftly identifying tea pests amidst the complex backgrounds of tea gardens. This contribution not only offers valuable insights for tea pest monitoring but also serves as a reference for achieving precise pest control. (c) 2023 Society of Chemical Industry.
Keyword ：
attention mechanism attention mechanism small targets small targets tea tree pests tea tree pests YOLOX YOLOX
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| GB/T 7714 | Hu, Xianming , Li, Xinliang , Huang, Ziyan et al. Detecting tea tree pests in complex backgrounds using a hybrid architecture guided by transformers and multi-scale attention mechanism [J]. | JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE , 2024 , 104 (6) : 3570-3584 . |
| MLA | Hu, Xianming et al. "Detecting tea tree pests in complex backgrounds using a hybrid architecture guided by transformers and multi-scale attention mechanism" . | JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 104 . 6 (2024) : 3570-3584 . |
| APA | Hu, Xianming , Li, Xinliang , Huang, Ziyan , Chen, Qibin , Lin, Shouying . Detecting tea tree pests in complex backgrounds using a hybrid architecture guided by transformers and multi-scale attention mechanism . | JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE , 2024 , 104 (6) , 3570-3584 . |
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Enhancing the accuracy of dense classification with limited labeled data and abundant unlabeled data, known as semi-supervised semantic segmentation, is an essential task in vision comprehension. Due to the lack of annotation in unlabeled data, additional pseudo-supervised signals, typically pseudo-labeling, are required to improve the performance. Although effective, these methods fail to consider the internal representation of neural networks and the inherent class-imbalance in dense samples. In this work, we propose an information transfer theory, which establishes a theoretical relationship between shallow and deep representations. We further apply this theory at both the semantic and pixel levels, referred to as IIT-SP, to align different types of information. The proposed IIT-SP optimizes shallow representations to match the target representation required for segmentation. This limits the upper bound of deep representations to enhance segmentation performance. We also propose a momentum-based Cluster-State bar that updates class status online, along with a HardClassMix augmentation and a loss weighting technique to address class imbalance issues based on it. The effectiveness of the proposed method is demonstrated through comparative experiments on PASCAL VOC and Cityscapes benchmarks, where the proposed IIT-SP achieves state-of-the-art performance, reaching mIoU of 68.34% with only 2% labeled data on PASCAL VOC and mIoU of 64.20% with only 12.5% labeled data on Cityscapes.
Keyword ：
Bars Bars Entropy Entropy information transfer information transfer Semantics Semantics semantic segmentation semantic segmentation Semantic segmentation Semantic segmentation Semi-supervised learning Semi-supervised learning Semisupervised learning Semisupervised learning semi-supervised semantic segmentation semi-supervised semantic segmentation Task analysis Task analysis Training Training
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| GB/T 7714 | Wu, Jiawei , Fan, Haoyi , Li, Zuoyong et al. Information Transfer in Semi-Supervised Semantic Segmentation [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2024 , 34 (2) : 1174-1185 . |
| MLA | Wu, Jiawei et al. "Information Transfer in Semi-Supervised Semantic Segmentation" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 34 . 2 (2024) : 1174-1185 . |
| APA | Wu, Jiawei , Fan, Haoyi , Li, Zuoyong , Liu, Guang-Hai , Lin, Shouying . Information Transfer in Semi-Supervised Semantic Segmentation . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2024 , 34 (2) , 1174-1185 . |
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Steel surface defect detection is crucial in manufacturing, but achieving high accuracy and real-time performance with limited computing resources is challenging. To address this issue, this paper proposes DFFNet, a lightweight fusion network, for fast and accurate steel surface defect detection. Firstly, a lightweight backbone network called LDD is introduced, utilizing partial convolution to reduce computational complexity and extract spatial features efficiently. Then, PANet is enhanced using the Efficient Feature-Optimized Converged Network and a Feature Enhancement Aggregation Module (FEAM) to improve feature fusion. FEAM combines the Efficient Layer Aggregation Network and reparameterization techniques to extend the receptive field for defect perception, and reduce information loss for small defects. Finally, a WIOU loss function with a dynamic non-monotonic mechanism is designed to improve defect localization in complex scenes. Evaluation results on the NEU-DET dataset demonstrate that the proposed DFFNet achieves competitive accuracy with lower computational complexity, with a detection speed of 101 FPS, meeting real-time performance requirements in industrial settings. Furthermore, experimental results on the PASCAL VOC and MS COCO datasets demonstrate the strong generalization capability of DFFNet for object detection in diverse scenarios.
Keyword ：
Deep learning Deep learning Defect detection Defect detection Feature fusion Feature fusion Lightweight network Lightweight network Tiny target Tiny target
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| GB/T 7714 | Hu, Xianming , Lin, Shouying . DFFNet: a lightweight approach for efficient feature-optimized fusion in steel strip surface defect detection [J]. | COMPLEX & INTELLIGENT SYSTEMS , 2024 , 10 (5) : 6705-6723 . |
| MLA | Hu, Xianming et al. "DFFNet: a lightweight approach for efficient feature-optimized fusion in steel strip surface defect detection" . | COMPLEX & INTELLIGENT SYSTEMS 10 . 5 (2024) : 6705-6723 . |
| APA | Hu, Xianming , Lin, Shouying . DFFNet: a lightweight approach for efficient feature-optimized fusion in steel strip surface defect detection . | COMPLEX & INTELLIGENT SYSTEMS , 2024 , 10 (5) , 6705-6723 . |
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春季雨季土壤湿度高，土壤电阻率降低，系统接地电阻变小；夏秋季高温干旱，土壤电阻率升高；冬季温度太低形成冻土层，土壤电阻率急速增加，系统接地电阻变大。建立了配电网终端农场的电网模型，研究不同气候环境下，接地电阻的变化对具体农业用户电能质量的影响。计算表明，当接地电阻变大时，农场电压幅值、有效视在功率变化量超过允许范围，电压总谐波畸变率、HRU_(h vs d, CP99)、THD_(U vs d, CP99)超过限值范围，极端气候时会更严重。当湿度增加、接地电阻变小时，农户电压会发生电压暂降，波形会随湿度变化而波动。
Keyword ：
HRU HRU THD THD 土壤电阻率 土壤电阻率 接地电阻 接地电阻 有效视在功率 有效视在功率 电压暂降 电压暂降 电能质量 电能质量
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| GB/T 7714 | 熊志伟 , 林寿英 , 兰建容 . 不同气候环境下接地电阻对农业电网电能质量的影响 [J]. | 电工技术 , 2023 , 4 (17) : 217-220 . |
| MLA | 熊志伟 et al. "不同气候环境下接地电阻对农业电网电能质量的影响" . | 电工技术 4 . 17 (2023) : 217-220 . |
| APA | 熊志伟 , 林寿英 , 兰建容 . 不同气候环境下接地电阻对农业电网电能质量的影响 . | 电工技术 , 2023 , 4 (17) , 217-220 . |
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Target detection at the hive gate of a beehive can be used to effectively monitor invasive beehive species. However, in the natural environment, there is often a multi-target and multi-scale problem at the hive gate, making it difficult for beekeepers to accurately detect the internal state of the hive. (1) To solve the above problems, this paper proposes an improved RetinaNet target detection network, DY-RetinaNet, for the identification of common species at the hive doors of beehives in natural environments, i.e., Chinese bees, wasps, and cockroaches. (2) First, to solve the multi-target multi-scale problem presented in this paper, we propose replacing the FPN layer in the initial model RetinaNet with a symmetric structure BiFPN layer consisting of a feature pyramid, which allows the model to better balance the feature information of different scales. Then, for the loss function, using CIOU loss instead of smooth L1 loss makes the network more accurate for small target localization at multiple scales. Finally, the dynamic head framework is added after the model backbone network, due to the benefits of its multi-attention mechanism, which makes the model more concerned with multi-scale recognition in a multi-target scenario. (3) The experimental results of the homemade dataset show that DY-RetinaNet has the best network performance, compared to the initial model RetinaNet, when the backbone network is ResNet-101-BiFPN, and the mAP value of DY-RetinaNet is 97.38%. Compared with the initial model, the accuracy is improved by 6.77%. The experimental results from the public dataset MSCOCO 2017 show that DY-RetinaNet is better than the existing commonly used target-detection algorithms, such as SSD, YOLOV3, Faster R-CNN, Mask R-CNN, FCOS, and ExtremeNet. These results verify that the model has strong recognition accuracy and generalization ability for multi-target multi-scale detection.
Keyword ：
attention mechanism attention mechanism multi-target and multi-scale multi-target and multi-scale object detection object detection RetinaNet RetinaNet
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| GB/T 7714 | Hu, Xianming , Liu, Chao , Lin, Shouying . DY-RetinaNet Based Identification of Common Species at Beehive Nest Gates [J]. | SYMMETRY-BASEL , 2022 , 14 (6) . |
| MLA | Hu, Xianming et al. "DY-RetinaNet Based Identification of Common Species at Beehive Nest Gates" . | SYMMETRY-BASEL 14 . 6 (2022) . |
| APA | Hu, Xianming , Liu, Chao , Lin, Shouying . DY-RetinaNet Based Identification of Common Species at Beehive Nest Gates . | SYMMETRY-BASEL , 2022 , 14 (6) . |
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At present, the quality of material life and richness of the Chinese residents have been improved. Some people have high requirements for quality of their personal lives and put forward the problem of health preservation. Honey, as a nutritious food, is deeply loved by people. There are a large number of trace elements in honey, such as VC, VA, VD, VB1, and VB2. The honey from the Chinese honeybee has a very high nutritional value and plays an important role in the pollination and reproduction of some plants. Therefore, the Chinese honeybee plays a very significant role in the ecological environment. Moreover, it is protected as the main species resource of the country, which also fully proves the importance of the Chinese honeybee. Chinese bees can survive in various ecological and geographical environments in China and have strong heat as well as cold resistance. They can survive in the hot environment in the south and withstand the dynamics of severe cold in the north. At the same time, they can make full use of a small number of honey sources and have strong resistance to a variety of diseases and pests. In fact, there will be a variety of insect invasion problems in the beehive culture of Chinese bees, and it is necessary to accurately detect various diseases and pests during the breeding of Chinese bees. However, there are a large amount of insect invasion and various disease sources in the breeding stage of the Chinese bees. Therefore, in this paper, we use a deep learning algorithm to detect the insect invasion of the Chinese beehive culture and analyze the bee colonies in six bee farms in the province of Sichuan. In addition, we measure the common insect and disease indexes of the Chinese bee and analyze the parasitism rate, microsporidia infection rate, virus infection rate, and virus infection titer of bee colonies in overwintering and spring breeding. The experimental results show that the anti-insect invasion situation of bees in the six bee farms is significantly different; however, the antimite ability is basically the same.
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| GB/T 7714 | Liu, Chao , Lin, Shouying . A Pest Intrusion Detection in Chinese Beehive Culture Using Deep Learning [J]. | SCIENTIFIC PROGRAMMING , 2022 , 2022 . |
| MLA | Liu, Chao et al. "A Pest Intrusion Detection in Chinese Beehive Culture Using Deep Learning" . | SCIENTIFIC PROGRAMMING 2022 (2022) . |
| APA | Liu, Chao , Lin, Shouying . A Pest Intrusion Detection in Chinese Beehive Culture Using Deep Learning . | SCIENTIFIC PROGRAMMING , 2022 , 2022 . |
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In this research paper, the Chinese beehive culture in Fujian Province, China is used as the carrier, and the Mini-EfficientDet deep neural network after migration is used to identify common species at the hive door, that is, the identification of Chinese bees, wasps and cockroaches in the form of nymphs. In this paper, we define the modified model as Mini-EfficientDet by compressing the initial EfficientDet model and adding the category imbalance function, which makes it focus more on the recognition and classification of small targets while ensuring the recognition accuracy. Through the test on the MSCOCO2017 data set, it is concluded that when the backbone network adopts EfficientNet B7, it shows strong detection accuracy in detecting targets of various scales, which confirms the role of the category imbalance function proposed in this paper and the efficiency of the improved EfficientDet model. Detection accuracy. The pre-trained model is transferred to the field of beehive species detection through migration learning, that is, after the post-training of the self-collected data set, the detection accuracy of Chinese bee, cockroach, and wasp is 98.66%, 83.71%, and 82.06%. It has made sufficient algorithmic preparations for the later detection and early warning system of beehive species invasion. © Published under licence by IOP Publishing Ltd.
Keyword ：
Deep neural networks Deep neural networks Statistical tests Statistical tests
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| GB/T 7714 | Liu, Chao , Lin, Shouying . Research on Mini-EfficientDet Identification Algorithm Based on Transfer Learning [J]. | Journal of Physics: Conference Series , 2022 , 2218 (1) . |
| MLA | Liu, Chao et al. "Research on Mini-EfficientDet Identification Algorithm Based on Transfer Learning" . | Journal of Physics: Conference Series 2218 . 1 (2022) . |
| APA | Liu, Chao , Lin, Shouying . Research on Mini-EfficientDet Identification Algorithm Based on Transfer Learning . | Journal of Physics: Conference Series , 2022 , 2218 (1) . |
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Chinese bee breeding in Fujian Province, China adopts the beehive culture method. Due to the influence of the natural environment in this area, outdoor beehive breeding is often invaded by cockroaches and wasps, causing great damage to the bee population inside the beehive. Based on the above situation, this research establishes a data set through field image collection and data expansion, and proposes a beehive entrance and exit monitoring system based on the Faster R-CNN deep neural network combining ResNet101 and BiFPN network. By adjusting the size of the candidate frame of the RPN layer and comparing the implementation, it is concluded that the mAP value based on the ResNet101 and BiFPN models reaches 85.67%. The realization of this system can effectively detect the invasion of the Chinese bee and other species on the basis of ensuring the identification of the Chinese bee population. © 2022, The Authors. All rights reserved.
Keyword ：
Deep neural networks Deep neural networks
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| GB/T 7714 | Hu, Xianming , Lin, Shou Ying , Liu, Chao . Recognition of Chinese Bee Based on Faster R-Cnn [J]. | SSRN , 2022 . |
| MLA | Hu, Xianming et al. "Recognition of Chinese Bee Based on Faster R-Cnn" . | SSRN (2022) . |
| APA | Hu, Xianming , Lin, Shou Ying , Liu, Chao . Recognition of Chinese Bee Based on Faster R-Cnn . | SSRN , 2022 . |
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In this paper, we propose a novel entropy minimization based semi-supervised method for semantic segmentation. Entropy minimization has proven to be an effective semi-supervised method for realizing the cluster assumption, where the decision boundary should lie in low-density regions. Inspired by the existing consistency training semi-supervised segmentation networks with encoder-decoder architecture, we found that there tend to be more large gradient values at the object edges than other positions in the feature map of the encoder, and therefore propose a feature gradient map regularization to enlarge inter-class distance in the feature space for low-entropy of segmentation prediction. Additionally, we introduce an adaptive sharpening scheme with aleatoric uncertainty, and a class consistency constraint regularization, to alleviate the interference of noise with pseudo labels. Extensive experiments on PASCAL VOC, PASCAL-Context, and Leukocyte datasets show that the proposed method achieves state-of-the-art semi-supervised semantic segmentation performance without almost additional calculations and network structures. © 2021 IEEE Computer Society. All rights reserved.
Keyword ：
Computer vision Computer vision Entropy Entropy Semantics Semantics Semantic Segmentation Semantic Segmentation Signal encoding Signal encoding
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| GB/T 7714 | Wu, Jiawei , Fan, Haoyi , Zhang, Xiaoqing et al. SEMI-SUPERVISED SEMANTIC SEGMENTATION VIA ENTROPY MINIMIZATION [J]. | Proceedings - IEEE International Conference on Multimedia and Expo , 2021 . |
| MLA | Wu, Jiawei et al. "SEMI-SUPERVISED SEMANTIC SEGMENTATION VIA ENTROPY MINIMIZATION" . | Proceedings - IEEE International Conference on Multimedia and Expo (2021) . |
| APA | Wu, Jiawei , Fan, Haoyi , Zhang, Xiaoqing , Lin, Shouying , Li, Zuoyong . SEMI-SUPERVISED SEMANTIC SEGMENTATION VIA ENTROPY MINIMIZATION . | Proceedings - IEEE International Conference on Multimedia and Expo , 2021 . |
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