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397 results on '"Wang, Jiakun"'

35. Remote sensing identification of shallow landslide based on improved otsu algorithm and multi feature threshold.

Author

Ren, Jing, Wang, Jiakun, Chen, Rui, Li, Hong, Xu, Dongli, Yan, Lihua, Song, Jingyuan, Xu, Linjuan, and He, Na

Subjects
REMOTE sensing, IMAGE segmentation, LANDSLIDES, ALGORITHMS, DISASTERS, RECOGNITION (Psychology)
Abstract

In low-resolution remote sensing images under complex lighting conditions, there is a similarity in spectral characteristics between non-landslide areas and landslide bodies, which increases the probability of misjudgment in the identification process of shallow landslide bodies. In order to further improve the accuracy of landslide identification, a shallow landslide remote sensing identification method based on an improved Otsu algorithm and multi-feature threshold is proposed for the temporary treatment project of the Yangjunba disaster site in Leshan City. Using Retinex theory, remote sensing images are enhanced with local linear models and guided filtering; then, multi-feature scales and sliding window calculations of opening and closing transformations identify potential landslide areas, which are finally segmented using the Otsu algorithm. Through experimental verification, the method proposed in this article can clearly segment the target object and background after binary segmentation of remote sensing images. The recognition rate of shallow landslide bodies is not less than 95%, indicating that the method proposed in this article is relatively accurate in identifying shallow landslide bodies in the research area and has good application effects. [ABSTRACT FROM AUTHOR]

Published
2024
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42. Laparoscopic versus open major liver resection for hepatocellular carcinoma: A case-matched analysis of short- and long-term outcomes

Author

He Aoxiao, Huang Zhihao, Wang Jiakun, Feng Qian, Zhang Rongguiyi, Lu Hongcheng, Peng Long, and Wu Linquan

Subjects
lapaproscopic, open, hepatocellular carcinoma, case-matched analysis, liver resection, Medicine
Abstract

The feasibility and safety of laparoscopic major hepatectomy (LMH) are still uncertain. The purpose of the present study is to compare the short- and long-term outcomes of LMH with those of open major hepatectomy (OMH) for hepatocellular carcinoma (HCC).

Published
2021
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44. Transfer Learning-Based Specific Emitter Identification for ADS-B over Satellite System.

Author

Liu, Mingqian, Chai, Yae, Li, Ming, Wang, Jiakun, and Zhao, Nan

Subjects
CONVOLUTIONAL neural networks, LOW earth orbit satellites, AUTOMATIC dependent surveillance-broadcast, HUMAN fingerprints, FEATURE extraction, DISTRIBUTED sensors
Abstract

In future aviation surveillance, the demand for higher real-time updates for global flights can be met by deploying automatic dependent surveillance–broadcast (ADS-B) receivers on low Earth orbit satellites, capitalizing on their global coverage and terrain-independent capabilities for seamless monitoring. Specific emitter identification (SEI) leverages the distinctive features of ADS-B data. High data collection and annotation costs, along with limited dataset size, can lead to overfitting during training and low model recognition accuracy. Transfer learning, which does not require source and target domain data to share the same distribution, significantly reduces the sensitivity of traditional models to data volume and distribution. It can also address issues related to the incompleteness and inadequacy of communication emitter datasets. This paper proposes a distributed sensor system based on transfer learning to address the specific emitter identification. Firstly, signal fingerprint features are extracted using a bispectrum transform (BST) to train a convolutional neural network (CNN) preliminarily. Decision fusion is employed to tackle the challenges of the distributed system. Subsequently, a transfer learning strategy is employed, incorporating frozen model parameters, maximum mean discrepancy (MMD), and classification error measures to reduce the disparity between the target and source domains. A hyperbolic space module is introduced before the output layer to enhance the expressive capacity and data information extraction. After iterative training, the transfer learning model is obtained. Simulation results confirm that this method enhances model generalization, addresses the issue of slow convergence, and leads to improved training accuracy. [ABSTRACT FROM AUTHOR]

Published
2024
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