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PSSNet: An Optimized High-Accuracy Method for Forest Fire Smoke Detection

IEEE Internet of Things Journal 2025年

作者： Lin, Shuqi Li, Ziming Xu, Zhuonong Sun, Lixiang Zhou, Guoxiong Han, Guangjie Central South University of Forestry and Technology Institute of Applied Artificial Intelligence Hunan Changsha410004 China Department of Forest and Grassland Fire Prevention Monitoring and Dispatching Evaluation Center of Hunan Province Changsha410004 China Hohai University Department of Internet of Things Engineering Changzhou213000 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

In the field of early automatic detection of smoke from forest fires, there is the issue of the small size and interference of smoke detection by clouds. The conventional NMS (Non-Maximum Suppression) requires manual adjustment of the threshold, which may result in missed or erroneous detection. This paper proposes a high-accuracy anti-interference forest fire smoke detection network for small objects. Firstly, a window feature extractor based on singular value decomposition (SVD-STR) is designed. This extractor is capable of extracting more representative features, of capturing small and inconspicuous features in the image, and of reducing the complexity and computation of the model. Secondly, a SinThreshold Screening Attention Mechanism (SinAttention) is proposed, which can filter interference information and enhance the discriminative power of the features, thereby facilitating the accurate recognition and distinction of smoke and clouds. Subsequently, a variational particle swarm soft suppression optimization (PGS) is proposed as a means of further enhancing the optimization effect. This is achieved by adjusting the suppression strategy and incorporating a Gaussian variational particle swarm algorithm. In conclusion, an IoT forest fire detection system based on PSSNet has been constructed. The experimental results demonstrate that the mAP50 value of the method is 98.2%, the value of mAP50-95 is 80.4%, and the FPS value is 35.7. These values are superior to those of current forest fire smoke detection methods and can be utilized for the precise detection of forest fire smoke, thereby providing technical support for forest ecological protection. © 2014 IEEE.

关键词： Smoke detectors

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Optimizing Word Embedding for Fine-Grained Sentiment Analysis 1

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5th International Conference on Artificial Intelligence and Security, ICAIS 2019

作者： Zhang, Wei Zhang, Yue Yang, Kehua Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

ISBN: (数字)9783030242657

ISBN: (纸本)9783030242640

Word embeddings have been extensively used for various Natural Language Processing tasks. However, word vectors trained based on corpus context information fail to distinguish words with the same context but different semantics, which may lead to a similar word vector with opposite semantic terms. This will affect some Natural Language Processing tasks, such as fine-grained sentiment analysis tasks. In this paper, a new word vectors optimization model is proposed. This model can be applied to any pre-trained word vectors. Within a certain range, it can make the opposite semantic words away from each other, and the same semantic words are close to each other. The experimental results show that our model can improve the traditional word embedding in the fine-grained emotional analysis task of Chinese Weibo, and the optimized word vector using our model outperforms the unoptimized word vector. © 2019, Springer Nature Switzerland AG.

关键词： Embeddings

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TMI: Two-dimensional maintainability index for automotive software maintainability measurement

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Journal of Systems Architecture 2025年 166卷

作者： Xie, Jiao Yang, Jialing Zhao, Sirong Lei, Jianmei Fan, Kaiwei Xie, Guoqi Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Hunan Changsha410082 China Center of Convergence of Automobile and Cyberspace Research Institute of Hunan University in Chongqing Chongqing401120 China China Automotive Engineering Research Institute Co Ltd Electronic Communication and Software Evaluation Research Center Chongqing401122 China

The mass adoption of autonomous driving and Advanced Driver-Assistance System (ADAS) technologies increases the complexity and maintenance cost of automotive-embedded software during model-based development. Through model-based development tools (e.g., MATLAB/Simulink), the code is automatically generated by architectural transformation;however, unreasonable software architecture design could make the final generated code be difficult to maintain. Currently, both Microsoft Maintainability Index (MMI) and Bosch Maintainability Index (BMI) are used to measure the maintainability of code, but neither software architecture. We establish a four-layer maintainability index model by analyzing the characteristics of automotive software architecture, software quality model, and industry-related standards. We then propose a Two-dimensional Maintainability Index (TMI), meaning the code and architecture dimensions are simultaneously considered. This is the first work of maintainability measurement to combine code and architecture dimensions while ensuring low redundancy. TMI utilizes nine index metrics to guide architecture refactoring and code optimization, significantly improving software maintainability. © 2025 Elsevier B.V.

关键词： MATLAB

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Real-time depth estimation with an optimized encoder-decoder architecture on embedded devices 21

Real-time depth estimation with an optimized encoder-decoder...

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21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Tu, Xiaohan Xu, Cheng Liu, Siping Xie, Guoqi Li, Renfa Key Laboratory for Embedded and Network Computing of Hunan Province China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

ISBN: (纸本)9781728120584

As a strategy to obtain dense depth maps from a single image, sparse depths, or the fusion of both (RGBd), depth estimation receives much attention. Usually relying on high-performance workstations, existing depth prediction methods, are not particularly suitable for embedded devices. Moreover, the accuracy of depth estimation needs to be improved. Towards real-time and reliable depth prediction on portable embedded platforms, we propose an encoder-decoder architecture (EDA). Specifically, these are the three contributions of this work. (1) We present a light-weight encoder, which takes three modalities of data as inputs, namely, RGB images, sparse depths, or RGBd. The encoder is efficient to extract features on portable devices. (2) A novel decoder is designed to obtain accurate and high-resolution depth maps. (3) We use the existing TVM to deploy, compile, and optimize EDA on embedded platforms, for further improvements in the computational speed and power of depth estimation. In real scenarios, the optimized EDA achieves remarkable results with low latency. Through extensive experimental evaluation on two public datasets, we show that the optimized EDA outperforms the state of the arts in accuracy and computational resources. To the best of our knowledge, this is the first paper that provides an efficient encoder, learnable decoder, and end-to-end optimized encoder-decoder architecture for precise depth prediction. © 2019 IEEE.

关键词： Forecasting

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BGRA-Net: Boundary-Guided and Region-Aware Convolutional Neural network for the Segmentation of Breast Ultrasound Images

BGRA-Net: Boundary-Guided and Region-Aware Convolutional Neu...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiang Zhang Xuanya Li Kai Hu Xieping Gao Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China Baidu Inc. Beijing China Key Laboratory of Medical Imaging and Artifical Intelligence of Hunan Province Xiangnan University Chenzhou China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China

ISBN: (纸本)9781665429825

In this paper, we propose a novel convolutional neural network based on boundary-guided and region-aware (BGRA-Net) for breast tumor segmentation in ultrasound images. In particular, in the encoding stage, we propose a boundary-guided module (BGM) to guide the learning of boundary features in the decoding stage by explicitly strengthening the extracted boundary information. Meanwhile, in the decoding stage, we propose a region-aware module (RAM) to integrate different levels of detailed and semantic features to improve the comprehensive representation of tumor regional features. Besides, a scale-adaptive module (SAM) is further proposed to capture the characteristics of tumors with different sizes between the encoding and decoding stages. To evaluate the effectiveness of our BGRA-Net, we conduct extensive experiments on the UDIAT dataset and compare it with eight state-of-the-art methods. The experimental results show that our BGRA-Net outperforms the state-of-the-art methods and can achieve accurate segmentation of breast tumors with ambiguous boundaries.

关键词： Deep learning Image segmentation Breast tumors Ultrasonic imaging Semantics Feature extraction Visual effects

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Multimode optomechanical cooling via general dark-mode control

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Physical Review A 2022年第1期106卷 013526-013526页

作者： Jian Huang Deng-Gao Lai Cheng Liu Jin-Feng Huang Franco Nori Jie-Qiao Liao Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan Province Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha 410081 China Theoretical Quantum Physics Laboratory RIKEN Cluster for Pioneering Research Wako-shi Saitama 351-0198 Japan RIKEN Center for Quantum Computing 2-1 Hirosawa Wako-shi Saitama 351-0198 Japan Physics Department University of Michigan Ann Arbor Michigan 48109-1040 USA

The dark-mode effect is a stubborn obstacle for ground-state cooling of multiple degenerate mechanical modes optomechanically coupled to a common cavity-field mode. Here we propose an auxiliary-cavity-mode method for simultaneous ground-state cooling of two degenerate or near-degenerate mechanical modes by breaking the dark mode. We find that the introduction of the auxiliary cavity mode not only breaks the dark-mode effect, but also provides a new cooling channel to extract the thermal excitations stored in the dark mode. Moreover, we study the general physical-coupling configurations for breaking the dark mode in a generalized network-coupled four-mode optomechanical system consisting of two cavity modes and two mechanical modes. We find the analytical dark-mode-breaking condition in this system. This method is general and it can be generalized to break the dark-mode effect and to realize the simultaneous ground-state cooling in a multiple-mechanical-mode optomechanical system. We also demonstrate the physical mechanism behind the dark-mode breaking by studying the breaking of dark-state effect in the N−type four-level atomic system. Our results not only provide a general method to control various dark-mode and dark-state effects in physics, but also present an opportunity to the study of macroscopic quantum phenomena and applications in multiple-mechanical-resonator systems.

关键词： Optomechanics Quantum optics

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ACWB: Access-characteristic write buffer management scheme for flash memory 21

ACWB: Access-characteristic write buffer management scheme f...

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作者： Zhu, Lei Liu, Yan Zhang, Zaimei Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha Hunan410082 China School of Economics and Management Changsha University of Science and Technology Changsha Hunan410076 China

ISBN: (纸本)9781728120584

Compared with traditional hard disk drives (HDD), solid state drives (SDD) have distinct characteristics, such as efficient continuous access and random reading capability. Using of SSD is greatly limited due to the out-of-place update and endurance problem. Many flash-aware buffer schemes have been proposed to reduce write and erase operations. However, a good buffer management scheme should reduce the effects of random writes and suit access patterns. In this paper, we propose an access characteristic write buffer management scheme (ACWB) on flash memory that integrates write buffer and cost regulation based on access characteristic. Experimental results show that ACWB increases the response time by 35%, reduces erase times by 25%, and improves the hit ratio by 27% on the average compared with current flash-aware write buffer schemes. © 2019 IEEE.

关键词： Flash memory

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A survey of prototype and experiment for UAV communications

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Science China(Information Sciences) 2021年第4期64卷 5-25页

作者： Qingheng SONG Yong ZENG Jie XU Shi JIN College of Electrical and Information Engineeing Huaihua University Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province National Mobile Communications Research Laboratory Southeast University Future Network of Intelligence Institute (FNii) and School of Science and Engineering The Chinese University of Hong Kong (Shenzhen)

Unmanned aerial vehicle(UAV) communications have attracted significant attention from both academia and industry. To facilitate the large-scale usage of UAVs for various applications in practice,we provide a comprehensive survey on the prototype and experiment for UAV communications. To this end, we first provide an overview on the general architecture of the prototype and experiment for UAV communications, and then present experimental verification for air-to-ground channel models and UAV energy consumption models. Next, we discuss measurement experiments on two promising paradigms of UAV communications, namely cellular-connected UAVs and UAV-enabled aerial communication platforms. For the former, we focus on the feasibility study and address the interference mitigation issue. For UAV-enabled aerial communication platforms, we present three scenarios, namely UAV-enabled aerial base stations, UAVenabled aerial relays and UAV-enabled aerial data collection/dissemination. Finally, we point out some promising future directions for prototype and experimental measurements of UAV communications.

关键词： UAV communications prototype development channel model energy efficiency UAV-enabled communications platforms cellular-connected UAVs

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A deep hashing method based on attention module for image retrieval 13

A deep hashing method based on attention module for image re...

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13th International Conference on Intelligent Computation Technology and Automation, ICICTA 2020

作者： Long, Jun Wei, Xiangxiang Qi, Qianqian Wang, Ye School of Computer Science and Engineering Central South University Changsha410083 China Network Resources Management and Trust Evaluation Key Laboratory of Hunan Province Changsha410083 China

ISBN: (纸本)9781728186665

Hashing retrieval technique has received widespread attention since its low storage consumption and fast retrieval speed. Image retrieval based on deep hashing method uses image features with high-level semantic information and discriminative expression capabilities. However, there are still some limitations: (1) Compared with the traditional feature vector, the length of the learned hash codes is short, and each bit of the code is -1 or 1, the discrimination capability of feature representation is limited;(2) the existing deep hash algorithms cannot directly learn the discrete hash codes using sign activation function, therefore, they use relaxation-based scheme to learn the hash codes, resulting in a large quantization error. In order to solve the above problems, a deep hashing retrieval algorithm incorporate with attention model is proposed. This algorithm embeds spatial and channel attention models in the feature extraction network, and uses a novel activation function in the hash layer. In addition, a novel triple loss function is further proposed to improve the learning capability of the network. Finally, experimental results on the two benchmark databases, i.e., CIFAR-10 and MINIST datasets, show the effectiveness and superiority compared with the baselines. © 2020 IEEE.

关键词： Image retrieval

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Hexagonal Convolutional Neural network for Spatial Transcriptomics Classification

Hexagonal Convolutional Neural Network for Spatial Transcrip...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jing Gao Fa Zhang Kai Hu Xuefeng Cui Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China Institute of Computing Technology Chinese Academy of Sciences Beijing China Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Xiangnan University Chenzhou China School of Medical Technology Beijing Institute of Technology Beijing China School of Computer Science and Technology Shandong University Qingdao China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Recent advances in spatial transcriptomics have enabled the comprehensive measurement of transcriptional profiles while retaining the spatial contextual information. Identifying spatial domains is a critical step in the analysis of spatially resolved transcriptomics. Existing unsupervised methods perform poorly on this task owing to the large amount of noise and dropout events in the transcriptomic profiles. To address this problem, we first extend an unsupervised algorithm to a supervised learning method that can identify useful features and reduce noise hindrance. Second, inspired by the classical convolution in convolutional neural networks (CNNs), we designed a regular hexagonal convolution to compensate for the missing gene expression patterns from adjacent nodes. Compared with the graph convolution in graph neural networks (GNNs), our hexagonal convolution can preserve the relative spatial location information of different nodes in graph-structured data. Third, based on the hexagonal convolution, a novel hexagonal Convolutional Neural network (hexCNN) is proposed for spatial transcriptomics classification. Finally, we compared the proposed hexCNN with existing methods on the DLPFC dataset. The results show that hexCNN achieves a classification accuracy of 87.2% and an average Rand index (ARI) of 78.2% (1.9% and 3.3% higher than those of GNNs).

关键词： Visualization Convolution Supervised learning Interference Spatial databases Graph neural networks Convolutional neural networks

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