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Fire Detection Method Based on Improved Fruit Fly Optimization-Based SVM

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Computers, Materials & Continua 2020年第1期62卷 199-216页

作者： Fangming Bi Xuanyi Fu Wei Chen Weidong Fang Xuzhi Miao Biruk Assefa College of Computer Science and Technology China University of Mining and TechnologyXuzhou221116China College of Computer Science and Technology Mine Digitization Engineering Research Center of the Ministry of EducationChina University of Mining and TechnologyXuzhou221116China College of Computer Science and Technology Xi’an University of Science and TechnologyXi’an710054China Key Laboratory of Wireless Sensor Network&Communication Shanghai Institute of Micro-System and Information TechnologyChinese Academy of SciencesShanghai201899China Infromation Communication Technology Department Wollo UniversityDessie EthiopiaP.o Box 1145Ethiopia

Aiming at the defects of the traditional fire detection methods,which are caused by false positives and false negatives in large space buildings,a fire identification detection method based on video images is *** algorithm first uses the hybrid Gaussian background modeling method and the RGB color model to perform fire prejudgment on the video image,which can eliminate most non-fire ***,the traditional regional growth algorithm is improved and the fire image segmentation effect is effectively ***,based on the segmented image,the dynamic and static features of the fire flame are further analyzed and extracted in the area of the suspected fire ***,the dynamic features of the extracted fire flame images were fused and classified by improved fruit fly optimization support vector machine,and the recognition results were *** video-based fire detection method proposed in this paper greatly improves the accuracy of fire detection and is suitable for fire detection and identification in large space scenarios.

关键词： Fire detection image segmentation feature extraction fruit fly optimization support vector machine

The Emergence, Advancement and Future of Textual Answer Triggering

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The Emergence, Advancement and Future of Textual Answer Trig...

science and information Conference, SAI 2020

作者： Acheampong, Kingsley Nketia Tian, Wenhong Sifah, Emmanuel Boateng Opuni-Boachie, Kwame Obour-Agyekum Information Intelligence Tech Lab School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Chongqing Institute of Green and Intelligent Technology The Chinese Academy of Sciences Beijing China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9783030522452

The recent emergence of Answer Triggering has been annexed to the task of Answer Sentence Selection. Fundamentally, Answer Triggering challenges Question Answering systems to be capable of handling Answer Retrieval with two clauses: detecting whether a candidate answer set contains an answer or not;and if yes, retrieve that answer to the user. Till now, this task has proved itself comparatively harder, with most Answer Triggering models achieving F1 scores below 0.44. Meanwhile, due to little literature on the task, the potentials of this task in unlocking vital contributions to Natural Language Processing is hindered. Insights of this "new" NLP task assuredly needs to be known to researchers for immense contributions to kickstart again. This survey presents extensive details and analyses of a myriad of methods used by benchmark models on the task of Answer Triggering. Additionally, it discusses the datasets that emerged concurrently with the task. It again identifies the challenges, discusses solutions and provides recommendations for the advancement of such current systems and future ones. © 2020, Springer Nature Switzerland AG.

关键词： Natural language processing systems

Analysis of Age of information for A Discrete-time Dual-Queue system

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arXiv 2024年

作者： Chen, Zhengchuan Qu, Yi Pappas, Nikolaos Tang, Chaowei Wang, Min Quek, Tony Q.S. School of Microelectronics and Communication Engineering Chongqing University Chongqing China Department of Computer and Information Science Linköping University Linköping58183 Sweden School of Optoelectronics Engineering Chongqing University of Posts and Telecommunications Chongqing China Singapore University of Technology and Design Singapore487372 Singapore Yonsei Frontier Lab Yonsei University Korea Republic of

Using multiple sensors to update the status process of interest is promising in improving the information freshness. The unordered arrival of status updates at the monitor end poses a significant challenge in analyzing the timeliness performance of parallel updating systems. This work investigates the age of information (AoI) of a discrete-time dual-sensor status updating system. Specifically, the status update is generated following the zero-waiting policy. The two sensors are modeled as a geometrically distributed service time queue and a deterministic service time queue in parallel. We derive the analytical expressions for the average AoI and peak AoI using the graphical analysis method. Moreover, the connection of average AoI between discrete-time and continuous-time systems is also explored. It is shown that the AoI result of the continuous-time system is a limit case of that of the corresponding discrete-time system. Hence, the AoI result of the discrete-time system is more general than the continuous one. Numerical results validate the effectiveness of our analysis and further show that randomness of service time contributes more AoI reduction than determinacy of service time in dual-queue systems in most cases, which is different from what is known about the single-queue system. © 2024, CC BY.

关键词： Continuous time systems

DenoDet: Attention as Deformable Multi-Subspace Feature Denoising for Target Detection in SAR Images

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arXiv 2024年

作者： Dai, Yimian Zou, Minrui Li, Yuxuan Li, Xiang Ni, Kang Yang, Jian The PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China Nanjing University of Posts and Telecommunications Nanjing China Key Laboratory of Radar Imaging and Microwave Photonics Nanjing University of Aeronautics and Astronautics Ministry of Education Nanjing China VCIP CS Nankai University China

Synthetic Aperture Radar (SAR) target detection has long been impeded by inherent speckle noise and the prevalence of diminutive, ambiguous targets. While deep neural networks have advanced SAR target detection, their intrinsic low-frequency bias and static post-training weights falter with coherent noise and preserving subtle details across heterogeneous terrains. Motivated by traditional SAR image denoising, we propose DenoDet, a network aided by explicit frequency domain transform to calibrate convolutional biases and pay more attention to high-frequencies, forming a natural multiscale subspace representation to detect targets from the perspective of multi-subspace denoising. We design TransDeno, a dynamic frequency domain attention module that performs as a transform domain soft thresholding operation, dynamically denoising across subspaces by preserving salient target signals and attenuating noise. To adaptively adjust the granularity of subspace processing, we also propose a deformable group fully-connected layer (DeGroFC) that dynamically varies the group conditioned on the input features. Without bells and whistles, our plug-and-play TransDeno sets state-of-the-art scores on multiple SAR target detection datasets. The code is available at https://***/GrokCV/GrokSAR. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

Coded Caching Schemes for Multiaccess Topologies via Combinatorial Design

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arXiv 2023年

作者： Cheng, Minquan Wan, Kai Elia, Petros Caire, Giuseppe Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin541004 China The School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China The Communication Systems Department EURECOM Sophia Antipolis06410 France Electrical Engineering and Computer Science Department Technische Universität Berlin Berlin10587 Germany

This paper studies a multiaccess coded caching (MACC) where the connectivity topology between the users and the caches can be described by a class of combinatorial designs. Our model includes as special cases several MACC topologies considered in previous works. The considered MACC network includes a server containing N files, Γ cache nodes and K cacheless users, where each user can access L cache nodes. The server is connected to the users via an error-free shared link, while the users can retrieve the cache content of the connected cache-nodes while the users can directly access the content in their connected cache-nodes. Our goal is to minimise the worst-case transmission load on the shared link in the delivery phase. The main limitation of the existing MACC works is that only some specific access topologies are considered, and thus the number of users K should be either linear or exponential to Γ. We overcome this limitation by formulating a new access topology derived from two classical combinatorial structures, referred to as the t-design and the t-group divisible design. In these topologies, K scales linearly, polynomially, or even exponentially with Γ. By leveraging the properties of the considered combinatorial structures, we propose two classes of coded caching schemes for a flexible number of users, where the number of users can scale linearly, polynomially or exponentially with the number of cache nodes. In addition, our schemes can unify most schemes for the shared link network and unify many schemes for the multi-access network except for the cyclic wrap-around topology. © 2023, CC0.

关键词： Topology

A Machine Learning-Based Approach to Model Highly-Thermally Robust Metasurface Absorber

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A Machine Learning-Based Approach to Model Highly-Thermally ...

Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Sumbel Ijaz Sadia Noureen Bacha Rehman Muhammad Zubair Muhammad Qasim Mehmood Yehia Massoud Electrical Engineering Department MicroNano Lab Information Technology University of the Punjab Lahore Pakistan Department of Computer Science Namal University Mianwali Punjab Pakistan Innovative Technologies Laboratories (ITL) Electrical and Computer Engineering King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

For accelerating design procedure of compact and efficient on-chip nano-photonics and to aid computationally expensive, time-exhaustive state-of-the-art iterative simulation schemes, regression-based machine-learning models are demonstrated that predict the optical response and structural parameters of the meta-atoms.

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Improving the trainability of VQE on NISQ computers for solving portfolio optimization using convex interpolation

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arXiv 2024年

作者： Wang, Shengbin Li, Guihui Chen, Zhaoyun Wang, Peng Dou, Menghan Zheng, Haiyong Wang, Zhimin Gu, Yongjian Wu, Yu-Chun Guo, Guo-Ping Origin Quantum Computing Company Limited Anhui Hefei230026 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Anhui Hefei230026 China Intelligent Information Sensing and Processing Lab College of Electronic Engineering Ocean University of China Shandong Qingdao266000 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230088 China College of Physics and Optoelectronic Engineering Ocean University of China Shandong Qingdao266100 China

Solving combinatorial optimization problems using variational quantum algorithms (VQAs) represents one of the most promising applications in the NISQ era. However, the limited trainability of VQAs could hinder their scalability to large problem sizes. In this paper, we improve the trainability of variational quantum eigensolver (VQE) by utilizing convex interpolation to solve portfolio optimization. The idea is inspired by the observation that the Dicke state possesses an inherent clustering property. Consequently, the energy of a state with a larger Hamming distance from the ground state intuitively results in a greater energy gap away from the ground state energy in the overall distribution trend. Based on convex interpolation, the location of the ground state can be evaluated by learning the property of a small subset of basis states in the Hilbert space. This enlightens naturally the proposals of the strategies of close-to-solution initialization, regular cost function landscape, and recursive ansatz equilibrium partition. The successfully implementation of a 40-qubit experiment using only 10 superconducting qubits demonstrates the effectiveness of our proposals. Furthermore, the quantum inspiration has also spurred the development of a prototype greedy algorithm. Extensive numerical simulations indicate that the hybridization of VQE and greedy algorithms achieves a mutual complementarity, combining the advantages of both global and local optimization methods. Our proposals can be extended to improve the trainability for solving other large-scale combinatorial optimization problems that are widely used in real applications, paving the way to unleash quantum advantages of NISQ computers in the near future. Copyright © 2024, The Authors. All rights reserved.

关键词： Ground state

Research on Compression Optimization Algorithm for Super-resolution Reconstruction Network

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Research on Compression Optimization Algorithm for Super-res...

Electrical engineering and Automation (IFEEA), International Forum on

作者： Xiaodong Zhao Yanfang Fu Feng Tian Xunying Zhang Unmanned System Research Institute Northwestern Polytechnical University Xi’an China School of Computer Science and Engineering Xi’an Technological University Xi’an China Faculty of Science & Technology Bournemouth University UK School of Electronics and Information Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9781665464222

Under the condition of limited resources of embedded systems, the paper proposes a compression optimization algorithm based on pruning and quantization, so that the computational requirements of the super-resolution reconstruction algorithm based on a Convolutional Neural Network (CNN) can be met. First, a multiple regularization pruning optimization algorithm based on an attention module and a BatchNorm layer is proposed. Then, a coordination optimization algorithm of INT8 training and quantization for FPGA architecture is proposed. The performance of the pruning optimization algorithm was verified for the Super-Resolution CNN (SRCNN), the Fast Super-Resolution CNN (FSRCNN), and the Very Deep Super-resolution CNN (VDSRCNN). As for SRCNN, the performance of the quantization optimization algorithm was verified on the FPGA EC2 hardware simulation platform. The results show that the proposed compression optimization algorithm can achieve a good balance between network accuracy and inference speed.

关键词： Training Quantization (signal) Embedded systems Superresolution Reconstruction algorithms Inference algorithms Hardware

Optical phase conjugation characterization in a polarization-selected orthogonal-pumped SOA subsystem 26

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Optical phase conjugation characterization in a polarization...

26th Optoelectronics and Communications Conference, OECC 2021

作者： Sun, Fan Wen, Feng Yang, Feng Wu, Baojian Ling, Yun Qiu, Kun Key Lab of Optical Fiber Sensing and Communication Networks Ministry of Education School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China Lab of Holographic Optical Sensing Marolabs Co.Ltd. Chengdu610041 China

We thoughtfully investigate the optical phase conjugation (OPC) process in a polarization-selected orthogonal-pump semiconductor optical amplifier (PSOP-SOA) subsystem, enabling error-free signal conversion with the o... 详细信息

ISBN: (纸本)9781557528209

关键词： Signal to noise ratio