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Yun Sha, Zhaoyu Chen, Xuejun Liu, Yong Yan, Chenchen Du, Jiayi Liu and Ranran Han
The scarcity of attack samples is the bottleneck problem of anomaly detection of underlying business data in the industrial control system. Predecessors have done a lot of research on temporal data generation, but most of them are not suitable for indust...
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Zhirui Luo, Qingqing Li and Jun Zheng
Transfer learning using pre-trained deep neural networks (DNNs) has been widely used for plant disease identification recently. However, pre-trained DNNs are susceptible to adversarial attacks which generate adversarial samples causing DNN models to make...
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Shilin Qiu, Qihe Liu, Shijie Zhou and Chunjiang Wu
In recent years, artificial intelligence technologies have been widely used in computer vision, natural language processing, automatic driving, and other fields. However, artificial intelligence systems are vulnerable to adversarial attacks, which limit ...
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Linke Zhang, Na Wei, Xuhao Du and Shuping Wang
Identifying changes in the properties of acoustical sources based on a small number of sample data from measurements has been a challenge for decades. Typical problems are the increasing sound power from a vibrating source, decreasing transmission loss o...
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Wenhao Sun, Yidong Zou, Yunhe Wang, Boyi Xiao, Haichuan Zhang and Zhihuai Xiao
In the practical production environment, the complexity and variability of hydroelectric units often result in a need for more fault data, leading to inadequate accuracy in fault identification for data-driven intelligent diagnostic models. To address th...
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Fabrizio Cara, Michele Scalas, Giorgio Giacinto and Davide Maiorca
Due to its popularity, the Android operating system is a critical target for malware attacks. Multiple security efforts have been made on the design of malware detection systems to identify potentially harmful applications. In this sense, machine learnin...
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Meng Bi, Xianyun Yu, Zhida Jin and Jian Xu
In this paper, we propose an Iterative Greedy-Universal Adversarial Perturbations (IGUAP) approach based on an iterative greedy algorithm to create universal adversarial perturbations for acoustic prints. A thorough, objective account of the IG-UAP metho...
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Weimin Zhao, Sanaa Alwidian and Qusay H. Mahmoud
Deep neural networks are exposed to the risk of adversarial attacks via the fast gradient sign method (FGSM), projected gradient descent (PGD) attacks, and other attack algorithms. Adversarial training is one of the methods used to defend against the thr...
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Shijing Liu, Cheng Qian, Xueying Tu, Haojun Zheng, Lin Zhu, Huang Liu and Jun Chen
Variable-condition fish recognition is a type of cross-scene and cross-camera fish re-identification (re-ID) technology. Due to the difference in the domain distribution of fish images collected under different culture conditions, the available training ...
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Hyunkyung Shin, Hyeonung Shin, Wonje Choi, Jaesung Park, Minjae Park, Euiyul Koh and Honguk Woo
The automatic analysis of medical data and images to help diagnosis has recently become a major area in the application of deep learning. In general, deep learning techniques can be effective when a large high-quality dataset is available for model train...
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Wenkuan Huang, Hongbin Chen and Qiyang Zhao
The main research focus of this paper is to explore the use of the cycle-generative adversarial network (GAN) method to address the inter-turn fault issue in permanent magnet-synchronous motors (PMSMs). Specifically, this study aims to overcome the chall...
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Zhiwu Chen, Wenjing Wang, QingE Wu, Yingbo Lu, Lintao Zhou and Hu Chen
In order to solve the problem that steel surface defects are easily covered or submerged by other objects or noise, this paper proposes an open?closed transformation algorithm which can eliminate or weaken multiple noises. In the case of a small number o...
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Dmitry Namiot,Eugene Ilyushin
Pág. 101 - 118
This article, written for the Robust Machine Learning Curriculum, discusses the so-called Generative Models in Machine Learning. Generative models learn the distribution of data from some sample data set and then can generate (create) new data instances....
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Dmitry Namiot,Eugene Ilyushin
Pág. 101 - 118
This article, written for the Robust Machine Learning Curriculum, discusses the so-called Generative Models in Machine Learning. Generative models learn the distribution of data from some sample data set and then can generate (create) new data instances....
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Liang Jiang, Ying Nan, Yu Zhang and Zhihan Li
Anti-interception guidance can enhance a hypersonic glide vehicle (HGV) compard to multiple interceptors. In general, anti-interception guidance for aircraft can be divided into procedural guidance, fly-around guidance and active evading guidance. Howeve...
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Shui Jiang, Yanning Ge, Xu Yang, Wencheng Yang and Hui Cui
Reinforcement learning (RL) is pivotal in empowering Unmanned Aerial Vehicles (UAVs) to navigate and make decisions efficiently and intelligently within complex and dynamic surroundings. Despite its significance, RL is hampered by inherent limitations su...
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Rokaya Eltehewy, Ahmed Abouelfarag and Sherine Nagy Saleh
Rapid damage identification and classification in disastrous situations and natural disasters are crucial for efficiently directing aid and resources. With the development of deep learning techniques and the availability of imagery content on social medi...
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Yuwen Fu, E. Xia, Duan Huang and Yumei Jing
Machine learning has been applied in continuous-variable quantum key distribution (CVQKD) systems to address the growing threat of quantum hacking attacks. However, the use of machine learning algorithms for detecting these attacks has uncovered a vulner...
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Maryam Omar, Hafeez Ur Rehman, Omar Bin Samin, Moutaz Alazab, Gianfranco Politano and Alfredo Benso
Text-to-image synthesis is one of the most critical and challenging problems of generative modeling. It is of substantial importance in the area of automatic learning, especially for image creation, modification, analysis and optimization. A number of wo...
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Xinyu Jia, Xueqin Jiang, Zhiyong Li, Jiong Mu, Yuchao Wang and Yupeng Niu
The occurrence of pests at high frequencies has been identified as a major cause of reduced citrus yields, and early detection and prevention are of great significance to pest control. At present, studies related to citrus pest identification using deep ...
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