Review
Automation & Control Systems
M. A. Ganaie, Minghui Hu, A. K. Malik, M. Tanveer, P. N. Suganthan
Summary: This paper provides a comprehensive review of state-of-art deep ensemble models, their applications in different domains, and potential research directions.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Review
Engineering, Electrical & Electronic
Azhar Fakharuddin, Mahesh K. Gangishetty, Mojtaba Abdi-Jalebi, Sang-Hyun Chin, Abd Rashid bin Mohd Yusoff, Daniel N. Congreve, Wolfgang Tress, Felix Deschler, Maria Vasilopoulou, Henk J. Bolink
Summary: Light-emitting diodes based on halide perovskites have achieved significant progress in recent years, with external quantum efficiencies surpassing 23%. However, their practical application is still limited due to factors such as low efficiency in blue-emitting devices, inability to access emission wavelengths above 800 nm, decreased external quantum efficiency at high current density, lack of understanding of the electric field's effect on mobile ions in perovskite materials, and short device lifetimes. This review explores the development of perovskite light-emitting diodes, examining the key challenges in creating efficient and stable devices.
NATURE ELECTRONICS
(2022)
Article
Computer Science, Information Systems
N. N. Misra, Yash Dixit, Ahmad Al-Mallahi, Manreet Singh Bhullar, Rohit Upadhyay, Alex Martynenko
Summary: The Internet of Things (IoT), big data, and artificial intelligence (AI) have significantly impacted the agricultural and food industry, providing opportunities for monitoring, supply chain modernization, social media applications, food quality assessment, and food safety.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Ziwei Zhang, Peng Cui, Wenwu Zhu
Summary: This survey comprehensively reviews the application of deep learning methods on graph data. The existing methods are categorized into five types, and their development history, differences, and compositions are covered in a systematic manner. Potential future research directions are also discussed.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Information Systems
Ahmed Imteaj, Urmish Thakker, Shiqiang Wang, Jian Li, M. Hadi Amini
Summary: This article investigates the methods and challenges of training distributed machine learning models in resource-constrained IoT environments, discusses the limitations of existing research, and identifies future research directions.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Yingnan Pan, Qi Li, Hongjing Liang, Hak-Keung Lam
Summary: This article focuses on the L-2 - L-infinity/H-infinity optimization control issue for a family of nonlinear plants by Takagi-Sugeno (T-S) fuzzy approach with actuator failure. First, considering unmeasurable system states, sufficient criteria for devising fuzzy imperfect premise matching dynamic output feedback controller to maintain asymptotic stability while guaranteeing a mixed performance for T-S fuzzy systems are provided. Therewith, in the light of feasible areas of dynamic output feedback controller membership functions (MFs), a new MFs online learning policy using gradient descent algorithm is proposed to learn the real-time values of MFs to acquire a better L-2 - L-infinity/H-infinity control effect. Different from the traditional method using an imperfect premise matching scheme, under the proposed optimization algorithm, the trajectory of mixed performance index is lowered effectively. Afterward, a sufficient criterion is presented for assuring the convergence of the error of the cost function. Finally, the superiority of this online optimization learning policy is confirmed via simulations.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Hwanjun Song, Minseok Kim, Dongmin Park, Yooju Shin, Jae-Gil Lee
Summary: The lack of high-quality labels in real-world scenarios is a concern in deep learning. This survey provides a comprehensive review of robust training methods and compares their superiority. It also analyzes noise rate estimation and evaluation methodology.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Geochemistry & Geophysics
Xintong Dong, Yue Li, Tie Zhong, Ning Wu, Hongzhou Wang
Summary: Distributed fiber-optical acoustic sensing (DAS) is a promising technology in seismic exploration, but the quality of DAS-VSP data is often affected by random and coherent noises. To improve the signal-to-noise ratio, a CNN model based on L-FM-CNN is proposed, utilizing leaky ReLU as the activation function. By constructing a high-authenticity theoretical seismic data set and using a new loss function ERM, the proposed method proves effective in denoising DAS-VSP data with different SNRs.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
Senzhang Wang, Jiannong Cao, Philip S. Yu
Summary: This paper provides a comprehensive review of recent progress in applying deep learning techniques for spatial-temporal data mining (STDM). It categorizes the types of spatial-temporal data and introduces widely used deep learning models in STDM. The paper classifies existing literature based on the types of spatial-temporal data, data mining tasks, and deep learning models, and discusses the applications of deep learning for STDM in different domains. The limitations of current research and future research directions are also summarized.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Geochemistry & Geophysics
Weiwei Cai, Zhanguo Wei
Summary: In this letter, a novel cross-attention mechanism and graph convolution integration algorithm are proposed for hyperspectral data classification. Experimental results show that the proposed algorithm achieves better performances than other algorithms using different methods of training set division.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Engineering, Electrical & Electronic
Dennis Christensen, Regina Dittmann, Bernabe Linares-Barranco, Abu Sebastian, Manuel Le Gallo, Andrea Redaelli, Stefan Slesazeck, Thomas Mikolajick, Sabina Spiga, Stephan Menzel, Ilia Valov, Gianluca Milano, Carlo Ricciardi, Shi-Jun Liang, Feng Miao, Mario Lanza, Tyler J. Quill, Scott T. Keene, Alberto Salleo, Julie Grollier, Danijela Markovic, Alice Mizrahi, Peng Yao, J. Joshua Yang, Giacomo Indiveri, John Paul Strachan, Suman Datta, Elisa Vianello, Alexandre Valentian, Johannes Feldmann, Xuan Li, Wolfram H. P. Pernice, Harish Bhaskaran, Steve Furber, Emre Neftci, Franz Scherr, Wolfgang Maass, Srikanth Ramaswamy, Jonathan Tapson, Priyadarshini Panda, Youngeun Kim, Gouhei Tanaka, Simon Thorpe, Chiara Bartolozzi, Thomas A. Cleland, Christoph Posch, Shihchii Liu, Gabriella Panuccio, Mufti Mahmud, Arnab Neelim Mazumder, Morteza Hosseini, Tinoosh Mohsenin, Elisa Donati, Silvia Tolu, Roberto Galeazzi, Martin Ejsing Christensen, Sune Holm, Daniele Ielmini, N. Pryds
Summary: This article introduces the characteristics and advantages of von Neumann architecture and neuromorphic computing systems. While traditional von Neumann architecture is powerful, it has high power consumption and cannot handle complex data. Neuromorphic computing systems, inspired by biological concepts, can achieve lower power consumption for storing and processing large amounts of digital information. The aim of this article is to provide perspectives on the current state and future challenges in the field of neuromorphic technology, and to provide a concise yet comprehensive introduction and future outlook for readers.
NEUROMORPHIC COMPUTING AND ENGINEERING
(2022)
Article
Chemistry, Analytical
Pavel Trojovsky, Mohammad Dehghani
Summary: This paper introduces a new stochastic nature-inspired optimization algorithm called Pelican Optimization Algorithm (POA) to solve optimization problems in various scientific disciplines. By simulating the natural behavior of pelicans during hunting, the POA demonstrates high performance in approaching optimal solutions for unimodal functions and exploring the main optimal area for multimodal functions. Comparison with eight well-known metaheuristic algorithms confirms the competitiveness of POA in providing optimal solutions for optimization problems.
Article
Engineering, Electrical & Electronic
Fuyou Liao, Zheng Zhou, Beom Jin Kim, Jiewei Chen, Jingli Wang, Tianqing Wan, Yue Zhou, Anh Tuan Hoang, Cong Wang, Jinfeng Kang, Jong-Hyun Ahn, Yang Chai
Summary: This study presents bioinspired vision sensors based on molybdenum disulfide phototransistors that mimic the adaptive behavior of human eyes. By introducing charge trap states, the photosensitivity of the devices can be dynamically modulated under different lighting conditions, providing highly localized and dynamic modulation of photosensitivity at the pixel level and creating an effective perception range of up to 199 dB.
NATURE ELECTRONICS
(2022)
Article
Geochemistry & Geophysics
Xin Wu, Danfeng Hong, Jocelyn Chanussot
Summary: This paper proposes a new framework for multimodal remote sensing data classification, using deep learning and a cross-channel reconstruction module to learn compact fusion representations of different data sources. Extensive experiments on two multimodal RS datasets demonstrate the effectiveness and superiority of the proposed method.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Chemistry, Analytical
Mamoona Majid, Shaista Habib, Abdul Rehman Javed, Muhammad Rizwan, Gautam Srivastava, Thippa Reddy Gadekallu, Jerry Chun-Wei Lin
Summary: This article introduces the fourth industrial revolution (Industry 4.0) and its applications in the fields of Internet of Things (IoT) and wireless sensor networks (WSN), discusses the various issues and future directions in this area, and provides a systematic literature review of relevant research in recent years.
Article
Geochemistry & Geophysics
Qian Shi, Mengxi Liu, Shengchen Li, Xiaoping Liu, Fei Wang, Liangpei Zhang
Summary: The article proposes a deeply supervised attention metric-based network (DSAMNet) to address the challenges in change detection. The network uses a metric module for deep metric learning, integrated with convolutional block attention modules (CBAM), and a DS module to enhance feature extraction and generate more useful features. A new CD dataset, Sun Yat-Sen University (SYSU)-CD, containing 20,000 aerial image pairs, is also created for bitemporal image CD. Experimental results show that the network achieves the highest accuracy on both datasets.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Review
Computer Science, Information Systems
Sukhpal Singh Gill, Minxian Xu, Carlo Ottaviani, Panos Patros, Rami Bahsoon, Arash Shaghaghi, Muhammed Golec, Vlado Stankovski, Huaming Wu, Ajith Abraham, Manmeet Singh, Harshit Mehta, Soumya K. Ghosh, Thar Baker, Ajith Kumar Parlikad, Hanan Lutfiyya, Salil S. Kanhere, Rizos Sakellariou, Schahram Dustdar, Omer Rana, Ivona Brandic, Steve Uhlig
Summary: Autonomic computing investigates how systems can achieve specified control outcomes on their own. Integrating AI/ML to improve resource autonomy and performance remains a fundamental challenge. Experts in the field discuss current research, potential future directions, and challenges and opportunities for leveraging AI and ML in emerging computing paradigms.
INTERNET OF THINGS
(2022)
Article
Engineering, Electrical & Electronic
Nianyin Zeng, Peishu Wu, Zidong Wang, Han Li, Weibo Liu, Xiaohui Liu
Summary: In this article, a novel enhanced multiscale feature fusion method called ABFPN is proposed to improve the detection performance of small objects. It is evaluated on benchmark datasets and applied to detect surface defects on printed circuit boards. Experimental results demonstrate its reliability and efficiency.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Computer Science, Artificial Intelligence
Yan-Jun Liu, Wei Zhao, Lei Liu, Dapeng Li, Shaocheng Tong, C. L. Philip Chen
Summary: This article investigates the problem of tracking control for a class of nonlinear time-varying full state constrained systems. The intelligent controller and adaptive law are developed by constructing the time-varying asymmetric barrier Lyapunov function (BLF) and combining it with the backstepping algorithm. Neural networks (NNs) are used to approximate the uncertain function. This article considers constraint boundaries that are both related to state and time, making the design of the control algorithm more complex and difficult. The effectiveness of the control algorithm is verified through numerical simulation.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Review
Computer Science, Artificial Intelligence
Sicheng Zhao, Xiangyu Yue, Shanghang Zhang, Bo Li, Han Zhao, Bichen Wu, Ravi Krishna, Joseph E. Gonzalez, Alberto L. Sangiovanni-Vincentelli, Sanjit A. Seshia, Kurt Keutzer
Summary: This article reviews the latest single-source deep unsupervised domain adaptation (DA) methods for visual tasks and discusses new perspectives for future research. The article starts with the definitions of different DA strategies and descriptions of existing benchmark datasets, then summarizes and compares different categories of methods, and finally discusses future research directions.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
