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R2F-UGCGAN: a regional fusion factor-based union gradient and contrast generative adversarial network for infrared and visible image fusion

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Optica Acta: International Journal of Optics 2023年第1期70卷 52-68页

作者： Yuning Xie Gang Liu Rui Xu Durga Prasad Bavirisetti Haojie Tang Mengliang Xing a School of Automation Engineering Shanghai University of Electric Power Shanghai People's Republic of China b Faculty of Information Technology and Electrical Engineering Department of Computer Science Norwegian University of Science and Technology Trondheim Norway

ABSTRACTABSTRACTTo efficiently preserve texture and target information in source images, an image fusion algorithm of Regional Fusion Factor-Based Union Gradient and Contrast Generative Adversarial Network (R2F-UGCGAN) is proposed. Firstly, an adaptive gradient diffusion (AGD) decomposition algorithm is designed to extract representative features. A pair of infrared (IR) and visible (VIS) images are decomposed by AGD to obtain low-frequency components with salient targets and high-frequency components with rich edge gradient information. Secondly, In the high-frequency components, principal component analysis (PCA) is used for fusion to obtain more detailed images with texture gradients. R2F-UGCGAN is used to fuse the low-frequency components, which can effectively ensure good consistency between the target region and the background region. Therefore, a fused image is produced, which inherits more thermal radiation information and important texture details. Finally, subjective and objective comparison experiments are performed on TNO and RoadScene datasets with state-of-the-art image fusion methods. The experimental results of R2F-UGCGAN are prominent and consistent compared to these fusion algorithms in terms of both subjective and objective evaluation.

关键词： Image fusion infrared visible generative adversarial network PCA adaptive gradient diffusion

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PillCrop: the Solution for the Correct Administration of Medicine

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IFAC-PapersOnLine 2021年第15期54卷 328-333页

作者： Alexandru G. Berciu Eva H. Dulf Iulia A. Stefan Faculty of Automation and Computer Science Automation Department Technical University of Cluj-Napoca Romania

The present work brings to the reader’s attention the benefits and facilities of the PillCrop application. The application enwraps a complete solution for improving the weight accuracy of each dose of medicine given to patients. The application recognizes, using artificial intelligence, the dose recommended by the medical specialist and the standard weight of each pill. If the medicinal drug can be divided it generates two surfaces that represent the necessary division of the pill to fit the weight indicated by the dose. This recommendation uses augmented reality to illustrate the part of the medicine that represents the recommended dose to the patient and where the pill should be divided.

关键词： medicine dose patient augmented reality artificial intelligence

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BREAKING FREE FROM MMI: A NEW FRONTIER IN RATIONALIZATION BY PROBING INPUT UTILIZATION

arXiv

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arXiv 2025年

作者： Liu, Wei Deng, Zhiying Niu, Zhongyu Wang, Jun Wang, Haozhao Zeng, Zhigang Li, Ruixuan School of Computer Science and Technology HUST China Faculty of Artificial Intelligence in Education Central China Normal University China iWudao Tech School of Artificial Intelligence and Automation HUST China

Extracting a small subset of crucial rationales from the full input is a key problem in explainability research. The most widely used fundamental criterion for rationale extraction is the maximum mutual information (MMI) criterion. In this paper, we first demonstrate that MMI suffers from diminishing marginal returns. Once part of the rationale has been identified, finding the remaining portions contributes only marginally to increasing the mutual information, making it difficult to use MMI to locate the rest. In contrast to MMI that aims to reproduce the prediction, we seek to identify the parts of the input that the network can actually utilize. This is achieved by comparing how different rationale candidates match the capability space of the weight matrix. The weight matrix of a neural network is typically low-rank, meaning that the linear combinations of its column vectors can only cover part of the directions in a high-dimensional space (high-dimension: the dimensions of an input vector). If an input is fully utilized by the network, it generally matches these directions (e.g., a portion of a hypersphere), resulting in a representation with a high norm. Conversely, if an input primarily falls outside (orthogonal to) these directions, its representation norm will approach zero, behaving like noise that the network cannot effectively utilize. Building on this, we propose using the norms of rationale candidates as an alternative objective to MMI. Through experiments on four text classification datasets and one graph classification dataset using three network architectures (GRUs, BERT, and GCN), we show that our method outperforms MMI and its improved variants in identifying better rationales. We also compare our method with a representative LLM (llama-3.1-8b-instruct) and find that our simple method gets comparable results to it and can sometimes even outperform it. Code: https://***/jugechengzi/Rationalization-N2R. Copyright © 2025, The Authors. All rights

关键词： Vector spaces

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Accelerating Point-Voxel Representation of 3-D Object Detection for Automatic Driving

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 254-266页

作者： Cao, JieCheng Tao, Chongben Zhang, Zufeng Gao, Zhen Luo, Xizhao Zheng, Sifa Zhu, Yuan Suzhou University of Science and Technology School of Electronic and Information Engineering Suzhou215009 China Tsinghua University Suzhou Automobile Research Institute Suzhou215134 China College of Mechanical Engineering Tongji University Shanghai200092 China Tsinghua University Department of Automation Beijing100084 China Wuhan Electronic Information Institute Wuhan430019 China McMaster University Faculty of Engineering HamiltonONL8S 0A Canada Soochow University School of Computer Science and Technology Suzhou215009 China Tongji University College of Automotive Studies Shanghai200092 China

Current point-voxel fusion methods for 3-D object detection could not make full use of complementary information in the field of autonomous driving. Therefore, a novel two-stage 3-D object detection method, called accelerating point-voxel representation (APVR), is proposed. The advantages of point-based feature and voxel-based feature can be integrated into a single 3-D representation. Thereby, the proposed method retains more fine-grained information of an object while maintaining high efficiency. Specifically, the computational cost is reduced by adding offsets to query neighboring voxels of key-points. More fine-grained information can be obtained by calculating the matching probability between neighboring voxels and key-points. During the optimization of the prediction boxes, virtual grid points are set to capture the spatial information between key-points. The constraint of minimum enclosing rectangle is also added to optimize the directions of the prediction boxes. A large number of experiments on the KITTI, NuScenes, and Waymo datasets demonstrate great generalizability and portability of the proposed approach. The effectiveness and efficiency of APVR have been proved by comparisons with the state-of-the-art methods. APVR makes the real-time processing frame rate reach 40.4 Hz while ensuring high prediction accuracy. © 2020 IEEE.

关键词： Computational efficiency

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RAC-Based One-Stop Professional Course Teaching Model Exploration and Practice - Taking the Course of "Microcomputer Principles and Interface Technology" as an Example 6th

RAC-Based One-Stop Professional Course Teaching Model Explor...

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6th International Conference on computer science and Educational Informatization, CSEI 2024

作者： Wan, Xiaorong Li, Yingna Luo, Chuan Li, Chuan You, Jinguo Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Computer Technology Applications Kunming650500 China

ISBN: (纸本)9789819637379

In the context of the country’s requirement for academic disciplines to be aligned and mutually reinforcing with industry chains, innovation chains, and talent chains, the integration of education and industry has become an important direction for the reform of higher education. In response to the iterative demand for edge intelligence in the Intelligent Internet of Things (AIOT), the course "Microcomputer Principles and Interface Technology" is deeply integrated with industry and education, establishing a hierarchical model of resource, capability, and commercialization (RAC) layers. It adopts a "one-stop" teaching mainline that starts with microcomputer theory knowledge, relies on hardware development boards, project orientation, competition innovation, and product commercialization training "five points and one line", connects knowledge product channels, integrates ideological and political education in courses, and constructs a multi-dimensional and comprehensive evaluation system to achieve the integration of industry and education from universities to enterprises, classrooms to markets, and from knowledge to products. The innovative teaching mode of this course has achieved significant results in innovative practice. This innovative teaching mode can effectively achieve the knowledge, ability, and quality goals of cultivating new engineering talents, and effectively improve the quality of talent cultivation and teaching. This teaching reform and innovation provide a reference and idea for further exploring the talent cultivation mode of schools and enterprises. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Teaching

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LOCALLY FREE WEIGHT SHARING FOR NETWORK WIDTH SEARCH 9

LOCALLY FREE WEIGHT SHARING FOR NETWORK WIDTH SEARCH

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9th International Conference on Learning Representations, ICLR 2021

作者： Su, Xiu You, Shan Huang, Tao Wang, Fei Qian, Chen Zhang, Changshui Xu, Chang School of Computer Science Faculty of Engineering The University of Sydney Australia SenseTime Research Department of Automation Tsinghua University Beijing China

Searching for network width is an effective way to slim deep neural networks with hardware budgets. With this aim, a one-shot supernet is usually leveraged as a performance evaluator to rank the performance w.r.t. different width. Nevertheless, current methods mainly follow a manually fixed weight sharing pattern, which is limited to distinguish the performance gap of different width. In this paper, to better evaluate each width, we propose a loCAlly FrEe weight sharing strategy (CafeNet) accordingly. In CafeNet, weights are more freely shared, and each width is jointly indicated by its base channels and free channels, where free channels are supposed to locate freely in a local zone to better represent each width. Besides, we propose to further reduce the search space by leveraging our introduced FLOPs-sensitive bins. As a result, our CafeNet can be trained stochastically and get optimized within a min-min strategy. Extensive experiments on ImageNet, CIFAR-10, CelebA and MS COCO dataset have verified our superiority comparing to other state-of-the-art baselines. For example, our method can further boost the benchmark NAS network EfficientNet-BO by 0.41% via searching its width more delicately. © 2021 ICLR 2021 - 9th International Conference on Learning Representations. All rights reserved.

关键词： Deep neural networks

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Context-aware music recommender system based on automatic detection of the user’s physical activity 17th

Context-aware music recommender system based on automatic de...

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17th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2020

作者： Ospina-Bohórquez, Alejandra Gil-González, Ana B. Moreno-García, María N. de Luis-Reboredo, Ana Department of Computer Science and Automation Science Faculty University of Salamanca Plaza de los Caídos. s/n Salamanca37008 Spain

ISBN: (纸本)9783030530358

The large amount of music that can be accessed in streaming nowadays has led to the development of more reliable music recommendation systems. To this end, context-aware music recommendation systems, capable of suggesting music taking into account contextual information, have emerged. Studies have shown that music helps to improve mood while can change the focus of attention of users during the performance of some activity, helping to make this activity more bearable. This work presents a music Context Aware Recommender System in order to motivate users in their daily activities. Its main purpose is to suggest to the user the most appropriate music to improve the performance of the physical activity at recommending time. The conducted experiments along a case study prove that this system is useful and satisfactory when the activity does not require a great deal of concentration. During activities that required movement, most users indicated that the perceived effort decreases when using the recommendation system proposed. They also indicated that their mood had improved after using this system. This demonstrates the usefulness of this recommender system while doing physical activities. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Recommender systems

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A Generative Adversarial Network for infrared and visible image fusion using adaptive dense generator and Markovian discriminator

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Optik 2023年第1期288卷

作者： Liu, Gang Liu, Yang Tang, Lili Bavirisetti, Durga Prasad Wang, Xiao School of Automation Engineering Shanghai University of Electric Power Shanghai200090 China Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim7034 Norway

Existing infrared and visible image fusion algorithms have problems with reduced saliency for infrared targets and a lack of background texture information. To address this problem, a generative adversarial network for image fusion based on an attention mechanism is proposed in this study. This network consists of a generator, a discriminator, and an adaptive decision block. The generator employs a DenseNet-based cascade approach, which substantially lowers the information loss during the convolution process. A Markovian discriminator, unlike global discriminators, is embedded in the network. It divides images into patches, causing the network to focus on local regions and distinguishing the generated images as a whole. In addition, an adaptive decision block is included in the model to build an intensity dynamic mapping mechanism. To guide the computation for a certain content loss, a weighted map is created based on the intensity information of infrared images. Unlike the other loss functions, the weighted parameters of each component of the loss function are adaptively evaluated. To demonstrate the effectiveness of the proposed method, qualitative and quantitative experiments on TNO and RoadScene datasets are carried out. The experimental findings suggest that the proposed technique retains more significant characteristics in source images and produces fused images with rich texture details. Furthermore, the system noise resistance of each approach is examined. Under the condition of different noise, the fusion results of our approach still exhibit less distortion and a higher peak signal-to-noise ratio when compared to other state-of-the-art fusion methods. © 2023 Elsevier GmbH

关键词： Generative adversarial networks

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Research and Analysis of the Method for Measuring the Moisture Content of Cottonmaterials at Ultrahigh Frequencies

Research and Analysis of the Method for Measuring the Moistu...

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Automatics and Informatics (ICAI), International Conference

作者： Palvan I. Kalandarov Khasan E. Turkmenov Shakhnoza R. Ubaydullayeva Nikola N. Nikolov Mariela I. Alexandrova Department “Automation and control of technology process in production” «Tashkent institute of irrigation and agricultural mechanization engineers» National Research University Tashkent Uzbekistan Department of Automation Faculty of Computer Science and Automation Technical University of Varna Varna Bulgaria

The article provides a theoretical analysis of the ultrahigh frequency (microwave) method of humidity measurement, discusses the problems of instrumentation of the method in question, and proposes a scheme of an experimental installation. Bulk cotton materials were selected as the objects of research. The electromagnetic wave radiation is analyzed during measurement and the evaluation of electrophysical characteristics requires the study of the functional dependence of the amplitude and phase shift on the mass ratio of humidity, mathematical models for various bulk cotton materials describing the joint effect of humidity, material density on the attenuation and phase shift of the passing wave are obtained.

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automation of Technological Processes for Controlling Grain Moisture in the Flowu

Automation of Technological Processes for Controlling Grain ...

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Automatics and Informatics (ICAI), International Conference

作者： Palvan I. Kalandarov Shakhnoza R. Ubaydullayeva Rano T. Gaziyeva Nikola N. Nikolov Mariela I. Alexandrova Department “Automation and control of technology process in production” «Tashkent institute of irrigation and agricultural mechanization engineers» National Research University Tashkent Uzbekistan Department of Automation Faculty of Computer Science and Automation Technical University of Varna Varna Bulgaria

The paper deals with the problems of grain wounding requires large energy costs, requiring a multi-stage process of grain processing, installation and perfect and reliable automation systems for the storage and processing of raw materials, theoretical and experimental studies and the results of the use of the microwave method in measuring the moisture content of grain and grain materials. The method under consideration is based on the principle of selective absorption of microwave radiation energy by water in materials and its derivatives. By the degree of absorption of radiation, the concentration of moisture is judged. The paper investigates the amplitude and phase methods of the passing wave in free space. The influence of the elastic properties of the material under study on the electrical characteristics of the microwave wave, suggesting fundamentally new ways of measuring the attenuation and phase shift of the electromagnetic wave, including discrete conversion of the signal of the primary converter, which does not require rigid metrological support and the use of a phase shift, is substantiated. Information on the values of the thickness of the material is analyzed in order to correct the influence of this value, consisting of two parameters: the signal of the passing wave, the attenuation and phase shift which characterizes the humidity and the additional signal - the phase wave reflected from the front surface of the materials and carrying information about its thickness.

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