Relevant bibliographies by topics / LSGAN

Academic literature on the topic 'LSGAN'

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Published: 10 March 2023

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Journal articles on the topic "LSGAN"

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Nie, Jianghua, Yongsheng Xiao, Lizhen Huang, and Feng Lv. "Time-Frequency Analysis and Target Recognition of HRRP Based on CN-LSGAN, STFT, and CNN." Complexity 2021 (April 12, 2021): 1–10. http://dx.doi.org/10.1155/2021/6664530.

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Aiming at the problem of radar target recognition of High-Resolution Range Profile (HRRP) under low signal-to-noise ratio conditions, a recognition method based on the Constrained Naive Least-Squares Generative Adversarial Network (CN-LSGAN), Short-time Fourier Transform (STFT), and Convolutional Neural Network (CNN) is proposed. Combining the Least-Squares Generative Adversarial Network (LSGAN) with the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), the CN-LSGAN is presented and applied to the HRRP denoise. The frequency domain and phase features of HRRP are gained by STFT in order to facilitate feature learning and also match the input data format of the CNN. These experimental results show that the CN-LSGAN has better data augmentation performance and can effectively avoid the model collapse compared to the generative adversarial network (GAN) and LSGAN. Also, the method has better recognition performance than the one-dimensional CNN method and the Long Short-Term Memory (LSTM) network method.
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Yue, Yunpeng, Hai Liu, Xu Meng, Yinguang Li, and Yanliang Du. "Generation of High-Precision Ground Penetrating Radar Images Using Improved Least Square Generative Adversarial Networks." Remote Sensing 13, no. 22 (November 15, 2021): 4590. http://dx.doi.org/10.3390/rs13224590.

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Deep learning models have achieved success in image recognition and have shown great potential for interpretation of ground penetrating radar (GPR) data. However, training reliable deep learning models requires massive labeled data, which are usually not easy to obtain due to the high costs of data acquisition and field validation. This paper proposes an improved least square generative adversarial networks (LSGAN) model which employs the loss functions of LSGAN and convolutional neural networks (CNN) to generate GPR images. This model can generate high-precision GPR data to address the scarcity of labelled GPR data. We evaluate the proposed model using Frechet Inception Distance (FID) evaluation index and compare it with other existing GAN models and find it outperforms the other two models on a lower FID score. In addition, the adaptability of the LSGAN-generated images for GPR data augmentation is investigated by YOLOv4 model, which is employed to detect rebars in field GPR images. It is verified that inclusion of LSGAN-generated images in the training GPR dataset can increase the target diversity and improve the detection precision by 10%, compared with the model trained on the dataset containing 500 field GPR images.
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Aguirre, Nicolas, Leandro J. Cymberknop, Edith Grall-Maës, Eugenia Ipar, and Ricardo L. Armentano. "Central Arterial Dynamic Evaluation from Peripheral Blood Pressure Waveforms Using CycleGAN: An In Silico Approach." Sensors 23, no. 3 (February 1, 2023): 1559. http://dx.doi.org/10.3390/s23031559.

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Arterial stiffness is a major condition related to many cardiovascular diseases. Traditional approaches in the assessment of arterial stiffness supported by machine learning techniques are limited to the pulse wave velocity (PWV) estimation based on pressure signals from the peripheral arteries. Nevertheless, arterial stiffness can be assessed based on the pressure–strain relationship by analyzing its hysteresis loop. In this work, the capacity of deep learning models based on generative adversarial networks (GANs) to transfer pressure signals from the peripheral arterial region to pressure and area signals located in the central arterial region is explored. The studied signals are from a public and validated virtual database. Compared to other works in which the assessment of arterial stiffness was performed via PWV, in the present work the pressure–strain hysteresis loop is reconstructed and evaluated in terms of classical machine learning metrics and clinical parameters. Least-square GAN (LSGAN) and Wasserstein GAN with gradient penalty (WGAN-GP) adversarial losses are compared, yielding better results with LSGAN. LSGAN mean ± standard deviation of error for pressure and area pulse waveforms are 0.8 ± 0.4 mmHg and 0.1 ± 0.1 cm2, respectively. Regarding the pressure–strain elastic modulus, it is achieved a mean absolute percentage error of 6.5 ± 5.1%. GAN-based deep learning models can recover the pressure–strain loop of central arteries while observing pressure signals from peripheral arteries.
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Dewi, Christine, Rung-Ching Chen, Yan-Ting Liu, and Hui Yu. "Various Generative Adversarial Networks Model for Synthetic Prohibitory Sign Image Generation." Applied Sciences 11, no. 7 (March 24, 2021): 2913. http://dx.doi.org/10.3390/app11072913.

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A synthetic image is a critical issue for computer vision. Traffic sign images synthesized from standard models are commonly used to build computer recognition algorithms for acquiring more knowledge on various and low-cost research issues. Convolutional Neural Network (CNN) achieves excellent detection and recognition of traffic signs with sufficient annotated training data. The consistency of the entire vision system is dependent on neural networks. However, locating traffic sign datasets from most countries in the world is complicated. This work uses various generative adversarial networks (GAN) models to construct intricate images, such as Least Squares Generative Adversarial Networks (LSGAN), Deep Convolutional Generative Adversarial Networks (DCGAN), and Wasserstein Generative Adversarial Networks (WGAN). This paper also discusses, in particular, the quality of the images produced by various GANs with different parameters. For processing, we use a picture with a specific number and scale. The Structural Similarity Index (SSIM) and Mean Squared Error (MSE) will be used to measure image consistency. Between the generated image and the corresponding real image, the SSIM values will be compared. As a result, the images display a strong similarity to the real image when using more training images. LSGAN outperformed other GAN models in the experiment with maximum SSIM values achieved using 200 images as inputs, 2000 epochs, and size 32 × 32.
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Man, Zhenlong, Jinqing Li, Xiaoqiang Di, Xu Liu, Jian Zhou, Jia Wang, and Xingxu Zhang. "A novel image encryption algorithm based on least squares generative adversarial network random number generator." Multimedia Tools and Applications 80, no. 18 (May 20, 2021): 27445–69. http://dx.doi.org/10.1007/s11042-021-10979-w.

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AbstractIn cryptosystems, the generation of random keys is crucial. The random number generator is required to have a sufficiently fast generation speed to ensure the size of the keyspace. At the same time, the randomness of the key is an important indicator to ensure the security of the encryption system. The chaotic random number generator has been widely used in cryptosystems due to the uncertainty, non-repeatability, and unpredictability of chaotic systems. However, chaotic systems, especially high-dimensional chaotic systems, have slow calculation speed and long iteration time. This caused a conflict between the number of random keys and the speed of generation. In this paper, we introduce the Least Squares Generative Adversarial Networks(LSGAN)into random number generation. Using LSGAN’s powerful learning ability, a novel learning random number generator is constructed. Six chaotic systems with different structures and different dimensions are used as training sets to realize the rapid and efficient generation of random numbers. Experimental results prove that the encryption key generated by this scheme can pass all randomness tests of the National Institute of Standards and Technology (NIST). Hence, our result shows that LSGAN has the potential to improve the quality of the random number generators. Finally, the results are successfully applied to the image encryption scheme based on selective scrambling and overlay diffusion, and good results are achieved.
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Bhatia, Himesh, William Paul, Fady Alajaji, Bahman Gharesifard, and Philippe Burlina. "Least kth-Order and Rényi Generative Adversarial Networks." Neural Computation 33, no. 9 (August 19, 2021): 2473–510. http://dx.doi.org/10.1162/neco_a_01416.

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Abstract We investigate the use of parameterized families of information-theoretic measures to generalize the loss functions of generative adversarial networks (GANs) with the objective of improving performance. A new generator loss function, least kth-order GAN (LkGAN), is introduced, generalizing the least squares GANs (LSGANs) by using a kth-order absolute error distortion measure with k≥1 (which recovers the LSGAN loss function when k=2). It is shown that minimizing this generalized loss function under an (unconstrained) optimal discriminator is equivalent to minimizing the kth-order Pearson-Vajda divergence. Another novel GAN generator loss function is next proposed in terms of Rényi cross-entropy functionals with order α>0, α≠1. It is demonstrated that this Rényi-centric generalized loss function, which provably reduces to the original GAN loss function as α→1, preserves the equilibrium point satisfied by the original GAN based on the Jensen-Rényi divergence, a natural extension of the Jensen-Shannon divergence. Experimental results indicate that the proposed loss functions, applied to the MNIST and CelebA data sets, under both DCGAN and StyleGAN architectures, confer performance benefits by virtue of the extra degrees of freedom provided by the parameters k and α, respectively. More specifically, experiments show improvements with regard to the quality of the generated images as measured by the Fréchet inception distance score and training stability. While it was applied to GANs in this study, the proposed approach is generic and can be used in other applications of information theory to deep learning, for example, the issues of fairness or privacy in artificial intelligence.
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Jia-Li Li, Jia-Li Li, Xing-Guo Jiang Jia-Li Li, Li He Xing-Guo Jiang, and De-Cai Li Li He. "Face Age Feature Analysis Based on Improved Conditional Adversarial Auto-encoder (I-CAAE)." 電腦學刊 34, no. 1 (February 2023): 063–73. http://dx.doi.org/10.53106/199115992023023401005.

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<p>In recent years, the research of face age features has achieved rapid development driven by deep learning. The faces generated by the Conditional Adversarial Auto-encoder (CAAE) model are not only highly credible, but also closer to the target age. However, there are many problems, such as low resolution of human face image generation and poor local feature retention effect of human face features. To this end, this paper improves on the CAAE network. Firstly, referring to the LSGAN network structure, the 4 convolution layers of the encoder are added to 5 layers and the 4 convolution layers of the generator are added to 7 layers. Secondly, on the basis of the original loss function, the image gradient difference loss function is added to ensure the output face image quality. Meanwhile, the data set were preprocessed for face correction. Finally, this paper performs face similarity analysis on the Eye-key platform and contrasts the generated image quality using structural similarity and peak signal to noise ratio metrics. In addition, the generated results were tested for their robustness. The experimental results show that the average similarity of faces generated by the Improved Conditional Adversarial Auto-encoder (I-CAAE) network was increased by 3.9. And the average peak signal to noise ratio of the generated pictures was reduced by 1.8. Confirming the superiority of the proposed method.</p> <p>&nbsp;</p>
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Tataryn, T., D. Savytskii, L. Vasylechko, C. Paulmann, and U. Bismayer. "Crystal and twin structure in LSGMn crystals." Acta Crystallographica Section A Foundations of Crystallography 68, a1 (August 7, 2012): s181. http://dx.doi.org/10.1107/s0108767312096511.

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Liu, Jia-Bao, and Ali Zafari. "Computing Minimal Doubly Resolving Sets and the Strong Metric Dimension of the Layer Sun Graph and the Line Graph of the Layer Sun Graph." Complexity 2020 (September 24, 2020): 1–8. http://dx.doi.org/10.1155/2020/6267072.

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Let G be a finite, connected graph of order of, at least, 2 with vertex set VG and edge set EG. A set S of vertices of the graph G is a doubly resolving set for G if every two distinct vertices of G are doubly resolved by some two vertices of S. The minimal doubly resolving set of vertices of graph G is a doubly resolving set with minimum cardinality and is denoted by ψG. In this paper, first, we construct a class of graphs of order 2n+Σr=1k−2nmr, denoted by LSGn,m,k, and call these graphs as the layer Sun graphs with parameters n, m, and k. Moreover, we compute minimal doubly resolving sets and the strong metric dimension of the layer Sun graph LSGn,m,k and the line graph of the layer Sun graph LSGn,m,k.
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Yang, Cheng-Hong, Sin-Hua Moi, Yu-Da Lin, and Li-Yeh Chuang. "Genetic Algorithm Combined with a Local Search Method for Identifying Susceptibility Genes." Journal of Artificial Intelligence and Soft Computing Research 6, no. 3 (July 1, 2016): 203–12. http://dx.doi.org/10.1515/jaiscr-2016-0015.

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Abstract Detecting genetic association models between single nucleotide polymorphisms (SNPs) in various disease-related genes can help to understand susceptibility to disease. Statistical tools have been widely used to detect significant genetic association models, according to their related statistical values, including odds ratio (OR), chi-square test (χ2), p-value, etc. However, the high number of computations entailed in such operations may limit the capacity of such statistical tools to detect high-order genetic associations. In this study, we propose lsGA algorithm, a genetic algorithm based on local search method, to detect significant genetic association models amongst large numbers of SNP combinations. We used two disease models to simulate the large data sets considering the minor allele frequency (MAF), number of SNPs, and number of samples. The three-order epistasis models were evaluated by chi-square test (χ2) to evaluate the significance (P-value < 0.05). Analysis results showed that lsGA provided higher chi-square test values than that of GA. Simple linear regression indicated that lsGA provides a significant advantage over GA, providing the highest β values and significant p-value.
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Dissertations / Theses on the topic "LSGAN"

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My, Kieu. "Deep Domain Adaptation for Pedestrian Detection in Thermal Imagery." Doctoral thesis, 2021. http://hdl.handle.net/2158/1238097.

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Pedestrian detection is a core problem in computer vision due to its centrality to a range of applications such as robotics, video surveillance, and advanced driving assistance systems. Despite its broad application and interest, it remains a challenging problem in part due to the vast range of conditions under which it must be robust. In particular, pedestrian detectors must be robust and reliable at nighttime and in adverse weather conditions, which are some reasons why thermal and multispectral approaches have become popular in recent years. Moreover, thermal imagery offers more privacy-preserving affordances than visible-spectrum surveillance images. However, pedestrian detection in the thermal domain remains a non-trivial task with much room for improvement. Thermal detection helps ameliorate some of the disadvantages of RGB detectors -- such as illumination variation and the various complications of detection at nighttime. However, detection using only thermal imagery still faces numerous challenges and overall lack of information in thermal images. Thermal images are typically low-resolution, which in turn leads to more challenging detection of small pedestrians. Finally, there is a general lack of thermal imagery for training state-of-the-art detectors for thermal detection. The best pedestrian detectors available today work in the visible spectrum. In this thesis, we present three new types of domain adaptation approaches for pedestrian detection in thermal imagery and demonstrate how we can mitigate the above challenges such as privacy-preserving, illumination, lacking thermal data for training, and lacking feature information in thermal images and advance the state-of-the-art. Our first contribution is two \emph{bottom-up domain adaptation} approaches. We first show that simple bottom-up domain adaptation strategies with a pre-trained \emph{adapter} segment can better preserve features from source domains when doing transfer learning of pre-trained models to the thermal domain. In a similar vein, we then show that bottom-up and \emph{layer-wise} adaptation consistently results in more effective domain transfer. Experimental results demonstrate efficiency, flexibility, as well as the potential of both bottom-up domain adaptation approaches. Our second contribution, which addresses some limitations of domain adaptation to thermal imagery, is an approach based on task-conditioned networks that simultaneously solve two related tasks. A detection network is augmented with an auxiliary classification pipeline, which is tasked with classifying whether an input image was acquired during the day or at nighttime. The feature representation learned to solve this auxiliary classification task is then used to \emph{condition} convolutional layers in the main detector network. The experimental results of task-conditioned domain adaptation indicate that task conditioning is an effective way to balance the trade-off between the effectiveness of thermal imagery at night and its weaknesses during the day. Finally, our third contribution addresses the acute lack of training data for thermal domain pedestrian detection. We propose an approach using GANs to generate synthetic thermal imagery as a type of generative data augmentation. Our experimental results demonstrate that synthetically generated thermal imagery can be used to significantly reduce the need for massive amounts of annotated thermal pedestrian data. Pedestrian detection in thermal imagery remains challenging. However, in this thesis, we have shown that our bottom-up and layer-wise domain adaptation methods -- especially the proposed task-conditioned network -- can lead to robust pedestrian detection results via using thermal-only representations at detection time. This shows the potential of our proposed methods not only for domain adaptation of pedestrian detectors but also for other tasks. Moreover, our results using generated synthetic thermal images also illustrate the potential of generative data augmentation for domain adaptation to thermal imagery.
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Books on the topic "LSGAN"

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Lai, Chu. Ba lsan và muot lsan: Titeu thuyret. Hà Nuoi: NXB Huoi nhà văn, 2004.

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Long, Tùng. Muot lsan lsam lzo. TP. HCM [i.e. Thành phro Hso Chí Minh]: NXB Văn nghue, 2008.

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Trung, Sĩ. Muot lsan lsam lzo: Truyuen. Paris: Nam Á, 1986.

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Nguynen, Ngọc Ngạn. Muot lsan rsoi thôi: Truyuen ngoan. Los Alamitos, Calif: Xuân Thu, 1987.

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Nguynen, Thị Hsong Ngát. Hai lsan srong muot mình: Titeu thuyret. Hà Nuoi: NXB Huoi nhà văn, 2003.

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Phan, Bá Kỳ. Muot lsan đã đren: Lịch syu titeu thuyret. California, USA: Nhà xurat bkan Nam Văn, 2007.

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Nguynen, Quroc Trụ. Lsan curoi, Sài Gòn: Thơ, truyuen, tạp luuan. Los Angeles, CA: Văn Mwoi, 1998.

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Thao, Song. Curoi ngày, muot lsan ngsoi lại: Tuap truyuen. Los Angeles, CA: Văn Mwoi, 2001.

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Belker, Loren B. Ctam nang cho ngưxoi lsan đsau làm qukan lý. TP. Hso Chí Minh: NXB Trke, 1999.

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Phạm, Thị Quang Ninh. Anh mwoi biret yêu lsan đsau: Tuyten tuap truyuen ngoan. Garden Grove, CA: Tvu Lvuc, 2005.

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Book chapters on the topic "LSGAN"

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Anas, Essa R., Ahmed Onsy, and Bogdan J. Matuszewski. "CT Scan Registration with 3D Dense Motion Field Estimation Using LSGAN." In Communications in Computer and Information Science, 195–207. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52791-4_16.

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Conference papers on the topic "LSGAN"

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Sun, Degang, Kun Yang, Zhixin Shi, and Chao Chen. "A New Mimicking Attack by LSGAN." In 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2017. http://dx.doi.org/10.1109/ictai.2017.00074.

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Wang, Jianhua, Xiaolin Chang, Jelena Misic, Vojislav B. Misic, Yixiang Wang, and Jianan Zhang. "Mal-LSGAN: An Effective Adversarial Malware Example Generation Model." In GLOBECOM 2021 - 2021 IEEE Global Communications Conference. IEEE, 2021. http://dx.doi.org/10.1109/globecom46510.2021.9685442.

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Huang, Rongzhou, Chuyin Huang, Yubao Liu, Genan Dai, and Weiyang Kong. "LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/326.

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Traffic prediction is a classical spatial-temporal prediction problem with many real-world applications such as intelligent route planning, dynamic traffic management, and smart location-based applications. Due to the high nonlinearity and complexity of traffic data, deep learning approaches have attracted much interest in recent years. However, few methods are satisfied with both long and short-term prediction tasks. Target at the shortcomings of existing studies, in this paper, we propose a novel deep learning framework called Long Short-term Graph Convolutional Networks (LSGCN) to tackle both traffic prediction tasks. In our framework, we propose a new graph attention network called cosAtt, and integrate both cosAtt and graph convolution networks (GCN) into a spatial gated block. By the spatial gated block and gated linear units convolution (GLU), LSGCN can efficiently capture complex spatial-temporal features and obtain stable prediction results. Experiments with three real-world traffic datasets verify the effectiveness of LSGCN.
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Wang, Yilin, and Caidan Zhao. "LSRGAN: an RFF denoising recognition network based on adversarial autoencoder." In 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022), edited by Deqiang Cheng and Omer Deperlioglu. SPIE, 2022. http://dx.doi.org/10.1117/12.2643691.

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Tataryn, T., L. Vasylechko, D. Savytskii, M. Berkowski, C. Paulmann, and U. Bismayer. "Crystal and twin structure of LSGMn-05 anode material for SOFC." In 2012 IEEE International Conference on Oxide Materials for Electronic Engineering (OMEE). IEEE, 2012. http://dx.doi.org/10.1109/omee.2012.6464775.

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Ma, Yu, Yan Liang, WanYing Zhang, and Shi Yan. "SAR Target Recognition Based on Transfer Learning and Data Augmentation with LSGANs." In 2019 Chinese Automation Congress (CAC). IEEE, 2019. http://dx.doi.org/10.1109/cac48633.2019.8996717.

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Sinclair, Martin, and Ioannis Raptis. "Implementation of a Large-Scale Actuator Network for Distributed Manipulation." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6106.

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A class of cyber-physical systems that is gradually attracting increased scientific attention is Large-Scale Actuator Networks (LSAN). A prospective application of actuator networks is distributed manipulation. Distributed manipulation has the potential to become a game-changing technology in the area of industrial automation. To examine this class of systems, this paper presents a reactive elastic surface that autonomously morphs its shape by using a grid of linear actuators to transport an object into a target location. The combined action of the actuator grid overcomes the limitations of individual actuators, resulting in a system with multiple degrees-of-freedom. Experimental results illustrate the applicability of the platform.
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Ye, Muchao, Junyu Luo, Cao Xiao, and Fenglong Ma. "LSAN: Modeling Long-term Dependencies and Short-term Correlations with Hierarchical Attention for Risk Prediction." In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3340531.3411864.

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