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Consensus-Agent deep Reinforcement learning for Face Aging

IEEE TRANSACTIONS ON image processing 2024年 33卷 1795-1809页

作者： Lin, Ling Liu, Hao Liang, Jinqiao Li, Zhendong Feng, Jiao Han, Hu Ningxia Univ Sch Informat Engn Yinchuan 750021 Peoples R China Ningxia Key Lab Artificial Intelligence & Informat Yinchuan 750021 Peoples R China Ningxia Univ Sch Innovat & Entrepreneurship Yinchuan 750021 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Face aging tasks aim to simulate changes in the appearance of faces over time. However, due to the lack of data on different ages under the same identity, existing models are commonly trained using mapping between age groups. This makes it difficult for most existing aging methods to accurately capture the correspondence between individual identities and aging features, leading to generating faces that do not match the real aging appearance. In this paper, we re-annotate the CACD2000 dataset and propose a consensus-agent deep reinforcement learning method to solve the aforementioned problem. Specifically, we define two agents, the aging process agent and the aging personalization agent, and model the task of matching aging features as a Markov decision process. The aging process agent simulates the aging process of an individual, while the aging personalization agent calculates the difference between the aging appearance of an individual and the average aging appearance. The two agents iteratively adjust the matching degree between the target aging feature and the current identity through a form of synergistic cooperation. Extensive experimental results on four face aging datasets show that our model achieves convincing performance compared to the current state-of-the-art methods.

关键词： Face aging deep reinforcement learning Markov decision process

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Configuration-Adaptive Wireless Visual Sensing System With deep Reinforcement learning

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第9期22卷 5078-5091页

作者： Zhou, Siyuan Le, Duc Van Tan, Rui Yang, Joy Qiping Ho, Daren Nanyang Technol Univ HP NTU Digital Mfg Corp Lab Singapore 639798 Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore HP Inc Palo Alto CA 94304 USA

Visual sensing has been increasingly employed in various industrial applications including manufacturing process monitoring and worker safety monitoring. This paper presents the design and implementation of a wireless camera system, namely, EFCam, which uses low-power wireless communications and edge-fog computing to achieve cordless and energy-efficient visual sensing. The camera performs image pre-processing and offloads the data to a resourceful fog node for advanced processing using deep models. EFCam admits dynamic configurations of several parameters that form a configuration space. It aims to adapt the configuration to maintain desired visual sensing performance of the deep model at the fog node with minimum energy consumption of the camera in image capture, pre-processing, and data communications, under dynamic variations of the monitored process, the application requirement, and wireless channel conditions. However, the adaptation is challenging due to the complex relationships among the involved factors. To address the complexity, we apply deep reinforcement learning to learn the optimal adaptation policy when a fog node supports one or more wireless cameras. Extensive evaluation based on trace-driven simulations and experiments show that EFCam complies with the accuracy and latency requirements with lower energy consumption for a real industrial product object tracking application, compared with five baseline approaches incorporating hysteresis-based and event-triggered adaptation.

关键词： Wireless visual sensing fog computing computation offloading deep reinforcement learning

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Integrating image-based LLMs on edge-devices for Underwater Robotics

Integrating Image-based LLMs on edge-devices for Underwater ...

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Conference on real-time image processing and deep learning

作者： Sundaravadivel, Prabha Roselyn, Preetha J. Narayanaswamy, Vedachalam Jeyaraj, Vincent I. Ramesh, Aishree Khanal, Aaditya Univ Texas Tyler Dept Elect & Comp Engn Tyler TX 75701 USA SRMIST Dept Elect & Elect Engn Chennai TN India Natl Inst Ocean Technol Chennai Tamil Nadu India Saveetha Engn Coll Dept AI & Data Sci Chennai Tamil Nadu India Univ Texas Tyler Dept Chem Engn Tyler TX 75701 USA

ISBN: (纸本)9781510673878;9781510673861

image-based Large Language Models (LLMs) are AI models that can understand the captured images and generate textual content based on the analysis of images or visual data. Incorporating the LLMs for assessing water quality, pressure, and environmental conditions can help analyze historical data and predict potential risks and threats in underwater environments. This can improve the intervention of autonomous underwater vehicles (AUV) and remotely operated vehicles (ROV) during emergencies where the visual data must be interpreted to make informed decisions. While LLMs are primarily associated with processing and generating text, they can be integrated with images through a process known as multimodal learning, where text and images are combined for tasks that involve both modalities. Implementing such frameworks is challenging when deployed in low-power microcontrollers primarily used in monitoring systems. This research proposes evaluating multimodal tokens to enable edge computing in bio-inspired robots to monitor the underwater environment. This can help break down large real-time videos into tokens of text-based instructions associated with the description of images. The mini-robots will transmit the collected "tokens" to the nearest AUV or ROV, where the image-based LLM will be deployed. We propose to evaluate this image-based LLM in our NVIDIA Jetson Nano-based AUV. In the proposed architecture, the mini-robots can move along the length of the water column to capture images of the underwater environment. Our proposed model is evaluated to generate texts for boat and fish images. This proposed framework with integrated image-based tokens can significantly reduce the response time and data traffic in underwater real-time monitoring systems.

关键词： times Roman image area acronyms references

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real-time Batch Detection System for Defects in Highly Curved Spiral Cylindrical Coils

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IEEE ACCESS 2024年 12卷 153370-153384页

作者： Zhuo, Qinghang Kong, Linghua Zheng, Jishi Ding, Zhigang Li, Wenguang Yi, Dingrong Lian, Guofu Fujian Univ Technol Sch Mech & Automot Engn Fuzhou 350118 Peoples R China Fujian Univ Technol Digital Fujian Ind Mfg Internet Things Lab Fuzhou 350118 Peoples R China Fujian Univ Technol Sch Transportat Fuzhou 350118 Peoples R China Huaqiao Univ Sch Mech Engn & Automat Xiamen 361021 Peoples R China

Tungsten coil is one of the important components of the magnetron, and its surface quality and geometry directly affect the service life of the magnetron. The focus of this article is to apply machine vision and deep learning algorithms to study a surface defect detection and geometric measurement system for tungsten coils. Firstly, in order to solve the problem of difficulty, high cost, and low efficiency in manual detection caused by small and random surface cracks on tungsten coils, this paper proposes a surface crack detection method based on deep learning technology. By constructing the dataset and training the semantic segmentation model, the surface cracks were effectively detected and identified. Secondly, a sub-pixel edge detection method based on image processing technology and the Canny algorithm is proposed to address the issue of difficulty in measuring the geometric dimensions of tungsten coils due to their high bending spiral structure. The method accurately measures the length, outer diameter, and the maximum error is only 0.023mm. The results of the verification experiment indicate that the developed system can detect an average of 70 tungsten coil samples in one minute, with sensitivity and accuracy of 99.66% and 98.52%, respectively. This system not only has high robustness and efficiency, but also reduces 95.23% of manual workload, meeting the requirements of production lines for surface defect detection and geometric dimension measurement of tungsten coils, and solving the limitations of traditional manual detection.

关键词： Coils Tungsten Surface cracks Accuracy Defect detection Magnetic field measurement Inspection Machine vision image processing Tungsten coil machine vision defect detection dimensional measurement image processing

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ELoran signal message prediction algorithm based on Alexnet-ECA

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IET RADAR SONAR AND NAVIGATION 2024年第3期18卷 500-513页

作者： Zhang, Kai Yang, Fan Wang, Weidong Ye, Xiaotao Henan Polytech Univ Sch Phys & Elect Informat Engn Jiaozuo Peoples R China

Enhanced Loran (eLoran) is the most important backup system for Global Navigation Satellite System (GNSS). However, most current eLoran demodulation methods are based on digital signal processing, which may produce large errors under the influence of strong interference or noise. The authors propose an eLoran signal message prediction algorithm based on deep learning. Using time-frequency (TF) analysis convert one-dimensional signal into two-dimensional high-resolution image. It improves Alexnet by adjusting the convolutional layer structure, adding an attention module, and reducing the pooling layer window to construct the Alexnet-ECA network. The network is used to identify TF image features and predict signal message. The experimental results show that novel method for eLoran signal message prediction based on Alexnet-ECA can achieve high accuracy and robustness in various SNR scenarios. The accuracy is more than 90% at SNR greater than 10db and not less than 80% even in the complex environments. Compared with Resnet34 and VGG16 networks, Alexnet-ECA demonstrates its efficiency and effectiveness using real measurement data. This method can enhance the performance and reliability of eLoran receivers and applications. A deep learning algorithm for predicting eLoran signal message is proposed. The algorithm utilises time-frequency analysis to convert one-dimensional signals into high-resolution two-dimensional images. To effectively leverage these images and extract their distinctive features, an enhanced variant of Alexnet called Alexnet-ECA is devised. Subsequently, the proposed network utilises these extracted features to predict the corresponding signal ***

关键词： Loran neural networks signal processing time-frequency analysis

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real-time generation of realistic defective wafer maps via deep learning network of CycleGAN

Real-time generation of realistic defective wafer maps via d...

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Conference on real-time image processing and deep learning

作者： Alam, Lamia Kehtarnavaz, Nasser Univ Texas Dallas Dept Elect & Comp Engn Richardson TX 75080 USA

ISBN: (数字)9781510661714

ISBN: (纸本)9781510661707;9781510661714

Defective dies on a silicon wafer form a pattern that is called a wafer map. In order to adequately train a deep learning-based automated optical inspection system to detect such defective patterns, a large number of defective patterns or wafer maps are needed. In practice, on an actual production line, defective patterns occur infrequently and thus are difficult and time consuming to collect. A computationally efficient defective pattern generation solution is developed in this paper by using the deep learning network of CycleGAN which is a variant of the generative adversarial network. The public domain WM-811K wafer dataset was used to generate or synthesize defective patterns or wafer maps. The two metrics of Frechet inception distance and kernel inception distance were utilized to evaluate the resemblance of the generated defective images to the real defective images. The results obtained indicate that the developed defective pattern generation method produces realistic wafer maps at a computationally efficient rate of 3 synthesized images per second.

关键词： Defective die patterns or wafer maps Generation of synthesized wafer maps CycleGAN

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A real-time and high-performance MobileNet accelerator based on adaptive dataflow scheduling for image classification

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JOURNAL OF real-time image processing 2024年第1期21卷 4-4页

作者： Sang, Xiaoting Ruan, Tao Li, Chunlei Li, Huanyu Yang, Ruimin Liu, Zhoufeng Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Peoples R China China Patent Informat Ctr Beijing 100088 Peoples R China China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China

Convolutional neural network (CNN) models equipped with depth separable convolution (DSC) promise a lower spatial complexity while retaining high model accuracy. However, little attention has been paid to their hardware architecture. Previous studies on DSC-based CNN accelerators typically use fixed computational models for various models, leading to an imbalance between power, efficiency, and performance. To address this problem, a novel, real-time DSC-based CNN accelerator that can accommodate field-programmable gate arrays (FPGAs) of different capacities and CNNs of different sizes is proposed in this paper. Attractively, a dynamically reconfigurable computing engine and block-convolution-based adaptive dataflow scheduling mode strike a trade-off between hardware resources and the processing speed in industrial processes. The proposed MobileNet accelerator was implemented and evaluated on the Xilinx XC7020 platform. Compared to previous FPGA-based accelerators, the experimental results showed that our approach can provide 10.86 GOPS of computational performance for full HD RGB images, meeting the needs of real industrial real-time applications.

关键词： DSC-based CNN FPGA real-time accelerator Reconfigurable computing engine

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deep learning for parameter estimation of supermassive binary black holes with simulated LISA data

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CHINESE JOURNAL OF PHYSICS 2024年 88卷 301-310页

作者： Tang, Qiao Yang, Nan Li, Jin Chongqing Univ Coll Phys Chongqing 401331 Peoples R China Xingtai Univ Dept Elect Informat Sci & Technol Xingtai 054001 Peoples R China

Previous studies have demonstrated that deep learning is an effective approach for processing gravitational wave (GW) data obtained from ground-based detectors, as it can enhance the efficiency of data processing and offer great potential for real -time parameter estimation. In this paper, we explore three different deep learning architectures (MCD, TFP, and VI) for inferring the chirp mass (Mc) and the luminosity distance (DL) of supermassive binary black holes (SMBBHs) using simulated data from the Laser Interferometer Space Antenna (LISA). We train the neural networks with the simulated data and evaluate their performance on predicting the parameters (Mc, DL). The results show that more than 97.5% of the true values fall within the 3o- confidence interval of the predicted values with the optimal network. To verify the accuracy of the network on parameter estimation, we also calculate the estimation error of the parameters using the Fisher Information Matrix (FIM) with the same simulated data. By comparing the root-mean-square error (RMSE) between deep learning and FIM, we find that the two methods are comparable, which implies that deep learning can be reliable for GW parameter estimation.

关键词： Gravitational waves Parameter estimation deep learning Black Hole LISA

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A natural gas consumption forecasting system for continual learning scenarios based on Hoeffding trees with change point detection mechanism

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KNOWLEDGE-BASED SYSTEMS 2024年 304卷

作者： Svoboda, Radek Basterrech, Sebastian Kozal, Jedrzej Platos, Jan Wozniak, Michal VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci 17 Listopadu 15-2172 Ostrava 70833 Czech Republic Tech Univ Denmark Dept Appl Math & Comp Sci Kongens Lyngby Denmark Wroclaw Univ Sci & Technol Dept Syst & Comp Networks Wroclaw Poland

Forecasting natural gas consumption, considering seasonality and trends, is crucial in planning its supply and consumption and optimizing the cost of obtaining it, mainly by industrial entities. However, in times of threats to its supply, it is also a critical element that guarantees the supply of this raw material to meet individual consumers' needs, ensuring society's energy security. This article introduces a novel multistep forecasting of natural gas consumption with change point detection integration for model collection selection with continual learning capabilities using data stream processing. The performance of the forecasting models based on the proposed approach is evaluated in a complex real-world use case of natural gas consumption forecasting. Furthermore, the methodology generability was verified in an electricity load forecasting task. We employed Hoeffding tree predictors as forecasting models and the Pruned Exact Linear time (PELT) algorithm for the change point detection procedure. The change point detection integration enables the selection of a different model collection for successive time frames. Thus, three model collection selection procedures are defined and evaluated for forecasting scenarios with various densities of detected change points. These models were compared with change point agnostic baseline approaches and deep learning models. Our experiments show that the proposed approach provides superior results to deep learning models for both datasets and that fewer change points result in a lower forecasting error regardless of the model collection selection procedure employed.

关键词： Incremental learning time series forecasting Multivariate time series Change point detection Machine learning Data stream processing

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Intra voxel analysis in magnetic resonance imaging via deep learning

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2024年第1期34卷 e22977-e22977页

作者： Autorino, Maria Maddalena Franceschini, Stefano Ambrosanio, Michele Pascazio, Vito Baselice, Fabio Univ Napoli Parthenope Dept Engn Naples Italy Univ Napoli Parthenope DiSEGIM Dept Nola Italy

Magnetic Resonance Imaging (MRI) is a useful diagnostic method for producing anatomical images of the body. It differentiates tissues thanks to the measure of their proton density rho$$ \rho $$ and relaxation times T1$$ {T}_1 $$ and T2$$ {T}_2 $$. Since pathological tissues often present altered rho$$ \rho $$, T1$$ {T}_1 $$ and T2$$ {T}_2 $$ with respect to the physiological ones, MRI is largely used for the detection of large number of conditions. In addition, MRI scan presents an effective resolution able to detect very small anatomical elements, making the imaging system well suitable in contexts like prevention and early diagnosis. Nevertheless, system resolution imposes pathological volume to be larger than the voxel dimension. Since in some pathologies and conditions it could be very helpful to find tissues even smaller than the voxel dimension, this manuscript proposes an algorithm able to analyze voxel content and detect which one presents more than one tissue in its volume (i.e., which one is heterogeneous). More in detail, a machine learning algorithm is proposed, able to highlight, in the MR image, which pixel corresponds to an heterogeneous voxel. Method shows to be promising in combining good results and near real-time processing in both simulated and real scenario.

关键词： deep learning intra voxel analysis magnetic resonance imaging neural network tissues discrimination

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