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6th International Conference on intelligent Computing, Communication, and Devices, ICCD 2023

作者： Ding, Haiying Guo, Lei Zhang, Zhiwen Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Gansu Lanzhou730000 China Gansu Provincial Key Laboratory of Intelligent Processing of National Languages Northwest Minzu University Gansu Lanzhou730000 China

ISBN: (数字)9781510666269

ISBN: (纸本)9781510666252

Tianzhu Tibetan Autonomous County is located at the eastern end of the Hexi Corridor. Huazangsi Town is the seat of the current Tianzhu County. According to the atlas of Chinese dialects, Huazangsi dialect belongs to the Hexi section of Lanyin Mandarin. Its tone system has some distinctive tonal characteristics in the three aspects of single tone, continuous tone sandhi and soft tone. This paper analyzes the characteristics of single tone in Tianzhu Huazang Temple dialect through the research method of experimental phonetics, and summarizes the tonal types, values and areas of single tone in Tianzhu Huazang Temple dialect, providing a teaching basis for the acquisition of Mandarin in this region. At the same time, it explores the development and change of the tone of Tianzhu Huazang Temple dialect under the contact with multi-national languages, analyzes the influence of Mandarin tone on the tone of Tianzhu Huazang Temple dialect, and makes a simple prediction of the future development trend of the tone of Tianzhu Huazang Temple dialect. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Linguistics

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Viterbi Decoding of Directed Acyclic Transformer for Non-Autoregressive Machine Translation

Viterbi Decoding of Directed Acyclic Transformer for Non-Aut...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Shao, Chenze Ma, Zhengrui Feng, Yang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Non-autoregressive models achieve significant decoding speedup in neural machine translation but lack the ability to capture sequential dependency. Directed Acyclic Transformer (DA-Transformer) was recently proposed to model sequential dependency with a directed acyclic graph. Consequently, it has to apply a sequential decision process at inference time, which harms the global translation accuracy. In this paper, we present a Viterbi decoding framework for DA-Transformer, which guarantees to find the joint optimal solution for the translation and decoding path under any length constraint. Experimental results demonstrate that our approach consistently improves the performance of DA-Transformer while maintaining a similar decoding speedup. © 2022 Association for Computational Linguistics.

关键词： Decoding

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A Triplet Multi-Task Learning Network for Semantic Change Detection

A Triplet Multi-Task Learning Network for Semantic Change De...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Shan Dong Huazhe Guo He Chen Beijing Institute of Technology Beijing China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Semantic change detection (SCD) aims to provide the change locations and extend the detailed semantic change categories before and after the observation intervals, being a pivotal task in remote sensing community. Recent studies indicate that multi-task learning paradigm is an efficient solution for modeling the SCD. However, the temporal dependency information and semantic feature separability are not efficiently explored. To overcome the above limitations, this paper proposes a new Triplet Multi-task learning Network (TMNet) for SCD, in which a differential image branch is introduced to extract the temporal information reflecting the difference in original image pairs and two temporal branch to extract the semantic representation of each image. Then, a class-wise contrastive loss is employed to deal with the change types imbalance and improve the category discrimination. Finally, experiments are carried on a public SCD dataset to demonstrated the effectiveness of the proposed method.

关键词： Accuracy Semantics Self-supervised learning Multitasking Feature extraction Sensors Data mining

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An Improved Spatial Context Based Sparse Unmixing of Hyperspectral Image

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第4期6卷 893-907页

作者： Li, Fan Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang330099 China

Sparse unmixing aims to find a set of endmembers from a known spectral li-brary that can optimally model mixed pixels taking the fractional abundance as the weight, which is widely applied to hyperspectral image interpretation. It is generally an ill-posed inverse problem and sparse regularization is conducive to obtaining a unique solution. In recent years, spatial information has been regarded as important prior knowledge for unmixing and the exploration of structured information has greatly improved the performance of sparse unmixing. However, most spatial regularization strategies assume that the abundance vectors of neighboring pixels are straightforward smoothness, while ignoring the possible sharp changes between the abundance vectors. To overcome this drawback, we propose an improved spatial context based sparse unmixing algorithm to smooth the estimated abundance map while preserving the edges in the map. Specifically, the bilateral filter is incorporated into the traditional TV regularization to further en-hance piecewise smooth transitions in estimated fractional abundances, and the weight based on spatial context information is integrated into the classic 1 norm regularizer to induce the abundance vectors of neighboring pixels to be correlated. We conduct two sets of simulated data and a set of real data experiments to verify the performance of the proposed algorithm. Experimental results show that our algorithm achieves higher accuracy on abundance estimation than several advanced spatial regularization sparse unmixing algorithms, due to better preservation of spatial texture and edge information. © 2021, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Pixels

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Photovoltaic Array Fault Detection Based on Manifold Learning and Neural Network 5

Photovoltaic Array Fault Detection Based on Manifold Learnin...

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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Wang, Kai Zhong, Yanru Luo, Xiaonan Guilin University of Electronic Science and Technology Guangxi Key Laboratory of Intelligent Processing of Computer Images and Graphic Guilin China Guilin University of Electronic Science and Technology Satellite Navigation Positioning and Location Services National and Local Joint Engineering Research Center Guilin China

ISBN: (纸本)9798350373820

This paper endeavors to devise an ingenious methodology rooted in artificial intelligence, which, in turn, can be effectively employed to identify sundry categories of faults that are closely associated with photovoltaic arrays. This novel approach amalgamates the manifold learning algorithm with a formidable multilayer perceptron neural network (MLP). Intriguingly, it substantiates that the initial reduction in data dimensionality is achieved through the remarkable Isomap algorithm. Consequently, this compressed data is seamlessly channeled into the MLP, thereby yielding remarkable outcomes concerning fault detection. To validate and scrutinize the aforementioned proposition, a quintessential circuit representing a photovoltaic array is initially simulated utilizing the venerable Matlab/Simulink tool. During this simulation, an extensive corpus of both normal and faulty data is meticulously generated. Subsequently, the manifold learning algorithm is artfully applied to drastically diminish the dimensionality of this extensive dataset The resultant compressed data is then deftly fed into the MLP, allowing it to discern the salient characteristics pertaining to the sundry fault types. Through incessant iterations of intensive training, these faults can be astutely detected and discerned with remarkable accuracy. The results conclusively demonstrate the efficacy of combining manifold-based learning and multilayer perceptron neural network in detecting faults in photovoltaic arrays. The proposed method achieves high precision and effectiveness, surpassing the traditional approach by 2.52%. © 2024 IEEE.

关键词： Fault detection

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Lightweight 6-bit S-boxes with DPA Resistance

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IEEE Transactions on Network Science and Engineering 2025年

作者： Yan, Liuyan Li, Lang Song, Qingling Hengyang Normal University College of Computer Science and Technology Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

Lightweight S-boxes have a significant impact on cryptographic algorithms for resource-constrained devices in the Internet of Things (IoT). Previous research on such S-oxes has focused on low area and good cryptographic properties, which neglects the resistance to side-channel analysis, especially differential power analysis (DPA). The reVisited transparency order (VTO) is one of the best indicators for evaluating S-boxes against DPA attacks so far. Therefore, this paper presents a scheme for designing 6-bit S-boxes. The designed S-boxes are suitable for lightweight block ciphers and have a certain ability to resist DPA attacks. Specifically, we first identify 23 3-bit S-boxes with the optimal cryptographic properties and the smallest hardware area through systematic screening. Then, the paper investigates the constructions of lightweight 6-bit S-boxes based on traditional algorithm structures. Finally, a novel structure named F-LM-F is proposed for designing lightweight 6-bit S-boxes by combining Feistel structure and Lai-Massey structure. It has been proven through comparison that the S-boxes under F-LM-F structure achieve fewer fixed points and lower VTO than the 6-bit S-box of BipBip cipher, with a 17.61% reduction in hardware area and a 13.58% decrease in CPU cycles © 2013 IEEE.

关键词： Side channel attack

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Efficient On-board Remote Sensing Scene Classification Using FPGA With Ternary Weight

Efficient On-board Remote Sensing Scene Classification Using...

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Signal, Information and Data processing (ICSIDP), IEEE International Conference on

作者： Guijie Qi Tingting Qiao Jingchi Yu Yizhuang Xie National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Remote Sensing Scene Classification (RSSC) is essential for applications such as environmental monitoring and catastrophe management, which often have stringent time constraints requiring real-time processing. On-board processing significantly enhances real-time performance but is challenged by limited computational resources and strict power requirements. To address these challenges, this study proposes an FPGA-based accelerator designed for Ternary Weight Networks (TWNs), which use ternary values {+1, 0, -1} for weights. By adopting TWNs, the multiplication operations in convolution are eliminated, resulting in a significant reduction in computing and power needs. Experimental results show that TWNs significantly reduce network parameters while retaining classification accuracy comparable to Full-precision Weight Networks (FPWNs). The FPGA-based accelerator achieves an energy-efficiency ratio of 434.11 GOP/W, outperforming most existing CNN accelerators, hence meeting the requirements for on-board RSSC.

关键词： Scene classification Accuracy Neural networks Memory management Real-time systems Energy efficiency Hardware Time factors Remote sensing Field programmable gate arrays

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Image Editing based on Diffusion Model for Remote Sensing Image Change Captioning

Image Editing based on Diffusion Model for Remote Sensing Im...

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Signal, Information and Data processing (ICSIDP), IEEE International Conference on

作者： Miaoxin Cai He Chen Can Li Shuyu Gan Liang Chen Yin Zhuang National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Remote Sensing Image Change Captioning (RSICC) is a task that utilizes natural language to describe changes in remote sensing images of the same area captured at different times. However, the significant temporal intervals between multi-temporal images, the infrequency of observable changes, and the limitations on observation locations make it difficult to acquire and annotate a large and diverse dataset for analyzing change in multi-temporal images. The scarcity of labeled data hinders the training of RSICC models, leading to poor generalization. Compared to annotated registered bi-temporal image, single-temporal data is easier to obtain. Therefore, to tackle the issue of poor generalization of RSICC models under limited annotated sample conditions, a text-guided image pairs generation (TGIPG) method is proposed to create synthetic RSICC datasets from single-temporal data and randomly sampled text instructions via a diffusion-based controllable image editing model. This approach generates more valid pairs of multi-temporal samples to address the constraints of limited change information. Specifically, this method utilizes language instructions to introduce change information into the diffusion process, gradually transforming the pre-phase image into the post-phase image. Our experiments on the LEVIR-CC dataset show that synthetic data can significantly enhance the performance of any RSICC model, with a restricted number of training samples, by employing this plug-and-play TGIPG method.

关键词： Training Semantics Natural languages Diffusion processes Diffusion models Data models Spatial databases Sensors Remote sensing Synthetic data

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Review on application progress of federated learning model and security hazard protection

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Digital Communications and Networks 2023年第1期9卷 146-158页

作者： Aimin Yang Zezhong Ma Chunying Zhang Yang Han Zhibin Hu Wei Zhang Xiangdong Huang Yafeng Wu Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and TechnologyTangshanHebeiChina Hebei Key Laboratory of Data Science and Application North China University of Science and TechnologyTangshanHebeiChina The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and TechnologyTangshanHebeiChina Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and TechnologyTangshanHebeiChina College of Science North China University of Science and TechnologyTangshanHebeiChina College of Artificial Intelligence North China University of Science and TechnologyTangshanHebeiChina

Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data *** data privacy becomes more important,it becomes difficult to collect data from multiple data owners to make machine learning predictions due to the lack of data *** is forced to be stored independently between companies,creating“data silos”.With the goal of safeguarding data privacy and security,the federated learning framework greatly expands the amount of training data,effectively improving the shortcomings of traditional machine learning and deep learning,and bringing AI algorithms closer to our *** the context of the current international data security issues,federated learning is developing rapidly and has gradually moved from the theoretical to the applied *** paper first introduces the federated learning framework,analyzes its advantages,reviews the results of federated learning applications in industries such as communication and healthcare,then analyzes the pitfalls of federated learning and discusses the security issues that should be considered in applications,and finally looks into the future of federated learning and the application layer.

关键词： Data silos Machine learning Federated learning Privacy protection Learning framework

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Carbon emission reduction enabled by informatization construction: an analysis of spatial effects based on China’s experience

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Environmental Science and Pollution Research 2024年第24期31卷 35595-35608页

作者： He, Zhuo Ma, Shaojuan Deng, Zixin Meng, Ying College of Business Administration Nanchang Institute of Technology Jiangxi Nanchang330032 China School of Mathematics and Information Science Northern Minzu University Ningxia Yinchuan750021 China Ningxia Key Laboratory of Intelligent Information and Big Data Processing Ningxia Yinchuan750021 China School of Statistics Capital University of Economics and Business Beijing100071 China

The "dual-carbon" objective presents a huge challenge for China and the world, with profound implications for the advancement of China’s eco-friendly economy. Additionally, informatization development has a significant impact on the level of carbon emissions in both local and neighbouring regions. Therefore, we employ panel data from 30 provinces in China spanning the years 2012 to 2021, and use the Kernel density estimate and Moran’s index to explore informatization level and carbon emissions space agglomeration characteristics. We elucidate the nonlinear relationship and heterogeneity between informatization improvement and carbon emissions based on the spatial Durbin model. The primary findings are as follows. Firstly, we discover a distinct spatial clustering phenomenon which the informatization level is high in coastal areas and low in inland areas, whereas carbon emissions are low in the south and high in the north. Secondly, the effect of the informatization level on carbon emissions is shown as a U-shaped and non-linear correlation, signifying inhibitory and subsequently promoting phases. Thirdly, we reveal the negative influence on carbon emissions caused by spatial lag terms of the informatization level, and find that a higher local informatization level will have an inhibitory effect on carbon emissions in neighbouring areas. Finally, there is a spatial heterogeneity in the impact of the informatization level on carbon emissions, which presents the U-shaped relation between informatization level and carbon emissions varies across the North-South subregion and the three major economic subregion of China. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Informatization

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