Resource Type

Journal Article 1329

Year

2024 4

2023 158

2022 130

2021 127

2020 97

2019 120

2018 103

2017 91

2016 67

2015 30

2014 23

2013 19

2012 17

2011 18

2010 27

2009 21

2008 29

2007 34

2006 29

2005 36

open ︾

Keywords

Machine learning 43

Deep learning 34

Artificial intelligence 19

Reinforcement learning 14

disruptive technology 11

COVID-19 9

reliability 9

the Belt and Road 9

Additive manufacturing 7

Safety 6

Sustainability 6

disruptive technologies 6

robustness 5

strategic emerging industries 5

Big data 4

Blockchain 4

Efficacy 4

Gut microbiota 4

Active learning 3

open ︾

Search scope:

排序： Display mode:

New directions for artificial intelligence: human, machine, biological, and quantum intelligence Comment

Li WEIGANG,Liriam Michi ENAMOTO,Denise Leyi LI,Geraldo Pereira ROCHA FILHO

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 6,   Pages 984-990 doi: 10.1631/FITEE.2100227

Abstract:

This comment reviews the “once learning” mechanism (OLM) that was proposed byWeigang (1998), the subsequent success of “one-shot learning” in object categories (Li FF et al., 2003), and “you only look once” (YOLO) in objective detection (Redmon et al., 2016). Upon analyzing the current state of research in artificial intelligence (AI), we propose to divide AI into the following basic theory categories: artificial human intelligence (AHI), artificial machine intelligence (AMI), artificial biological intelligence (ABI), and artificial quantum intelligence (AQI). These can also be considered as the main directions of research and development (R&D) within AI, and distinguished by the following classification standards and methods: (1) human-, machine-, biological-, and quantum-oriented AI R&D; (2) information input processed by dimensionality increase or reduction; (3) the use of one/a few or a large number of samples for knowledge learning.

Keywords： 人工智能；机器学习；一次性学习；一瞥学习；量子计算    

HTML PDF Collect

Advanced Bioprocess Development and Manufacturing Technologies in High-Throughput Miniature Bioreactors

Jiao Peng, Chen Biqiang

Strategic Study of CAE 2016, Volume 18, Issue 4,   Pages 44-50 doi: 10.15302/J-SSCAE-2016.04.007

Abstract:

In recent years, the importance of rapid response time to virulent infectious disease is widely recognized. However, the traditional technologies for bioprocess development and manufacturing will not succeed in winning this war. US healthcare companies and related organizations including US Department of Human Health and Service, proposed and started working on the next generation of technology platforms for advanced bioprocess development and manufacturing (ABDM).The development of ABDM is to significantly reduce the timeframe for bioprocess development and manufacturing, making it possible to promptly respond to outburst of pandemic influenza and prevent it from outspreading. Meanwhile, the development of precision medicine also presents new demands to the biopharmaceutical industry. With the demand of small-scale production and fast turn-around time of precision medicine, the duration of development and manufacturing needs to be accelerated significantly. At the same time, the number of sub-groups of the product would increase, and the batch sizes and amount of final product would decrease, as well. To satisfy the demands of small-scale production and fast turnaround time of ABDM, micro- and mini-bioreactor is the key equipment. The major technologies of ABDM include high throughput screening and process development based on micro- and mini-bioreactors, disposable technology, modular unit operations and flexible manufacturing. The development of ABDM will directly strengthen the National Security, improve the welfare of the people, and it also provides great social and economic values. The impact of this platform will radiate to the entire biomanufacture industry, and open a whole new era for the bioprocess development and bio-manufacturing.

Keywords： advance bioprocess development and biomanufacturing     virulent infectious disease     precision medicine     miniature bioreactor     high throughput technology     disposable technology    

HTML PDF Collect

Reliability analysis of corroded oil and gas pipeline

Xu Wei,Liu Mao

Strategic Study of CAE 2010, Volume 12, Issue 9,   Pages 69-72

Abstract:

ASME B31G is the international criteria to assess the failure stress of corroded pipeline, taking into account its conservatism, this paper studies the failure stress of corroded pipeline based on the modified B31G, and considers some random variables that contains pipeline wall thickness, corrosion rate, operating pressure, defect depth and so on, so a limit state function of corroded carrying Oil/Gas is established. Afterwards, the first order and second moment method is employed for research on the reliability of corroded pipeline, and calculation on reliability index, failure probability and remaining life. In addition, to further standardize the management of corrosion of pipelines, taking into account the relevant provisions of American Petroleum Institute, different failure probability of pipeline were graded. In the final, a sensitivity analysis was carried out on random variables involved in the problem. The results of sensitivity analysis indicate that the failure probability is the most sensitive to the coefficient of variation of wall thickness.

Keywords： corroded pipeline     reliability analysis     FORM     failure probability    

HTML PDF Collect

Does Global Agriculture Need Another Green Revolution?

Danny Llewellyn

Engineering 2018, Volume 4, Issue 4,   Pages 449-451 doi: 10.1016/j.eng.2018.07.017

HTML PDF Collect

Spatial and Temporal Variations in the Atmospheric Age Distribution of Primary and Secondary Inorganic Aerosols in China Article

Xiaodong Xie, Qi Ying, Hongliang Zhang, Jianlin Hu,

Engineering 2023, Volume 28, Issue 9,   Pages 117-129 doi: 10.1016/j.eng.2022.03.013

Abstract:

The aging timescale of particles is a key parameter in determining their impacts on air quality, human health, and climate. In this study, a one-year simulation of the age distributions of the primary and secondary inorganic fine particulate matter (PM2.5) components was conducted over China using an age-resolved Community Multiscale Air Quality (CMAQ) model. The results indicate that primary PM2.5 (PPM) and ammonium mainly originate from fresh local emissions, with approximately 60%–80% concentrated in 0–24 h age bins in most of China throughout the year. The average age is 15–25 h in most regions in summer, but increases to 40–50 h in southern region of China and the Sichuan Basin (SCB) in winter. Sulfate is more aged than PPM, indicating an enhanced contribution from regional transport. Aged sulfate with atmospheric age > 48 h account for 30%–50% of total sulfate in most regions and seasons, and the concentrations in the > 96 h age bin can reach up to 15 µg·m−3 in SCB during winter. Dramatic seasonal variations occur in the Yangtze River Delta, Pearl River Delta, and SCB, with highest average age of 60–70 h in winter and lowest of 40–45 h in summer. The average age of nitrate is 20–30 h in summer and increases to 40–50 h in winter. The enhanced deposition rate of nitric acid vapor combined with the faster chemical reaction rate of nitrogen oxides leads to a lower atmospheric age in summer. Additionally, on pollution days, the contributions of old age bins (> 24 h) increase notably for both PPM and secondary inorganic aerosols in most cities and seasons, suggesting that regional transport plays a vital role during haze events. The age information of PM2.5, provided by the age-resolved CMAQ model, can help policymakers design effective emergent emission control measures to eliminate severe haze episodes.

Keywords： Atmospheric age     PM2.5     CMAQ model     Control strategy    

HTML PDF Collect

A Local Quadratic Embedding Learning Algorithm and Applications for Soft Sensing Article

Yaoyao Bao, Yuanming Zhu, Feng Qian

Engineering 2022, Volume 18, Issue 11,   Pages 186-196 doi: 10.1016/j.eng.2022.04.025

Abstract:

Inspired by the tremendous achievements of meta-learning in various fields, this paper proposes the local quadratic embedding learning (LQEL) algorithm for regression problems based on metric learning and neural networks (NNs). First, Mahalanobis metric learning is improved by optimizing the global consistency of the metrics between instances in the input and output space. Then, we further prove that the improved metric learning problem is equivalent to a convex programming problem by relaxing the constraints. Based on the hypothesis of local quadratic interpolation, the algorithm introduces two lightweight NNs; one is used to learn the coefficient matrix in the local quadratic model, and the other is implemented for weight assignment for the prediction results obtained from different local neighbors. Finally, the two sub-models are embedded in a unified regression framework, and the parameters are learned by means of a stochastic gradient descent (SGD) algorithm. The proposed algorithm can make full use of the information implied in target labels to find more reliable reference instances. Moreover, it prevents the model degradation caused by sensor drift and unmeasurable variables by modeling variable differences with the LQEL algorithm. Simulation results on multiple benchmark datasets and two practical industrial applications show that the proposed method outperforms several popular regression methods.

Keywords： Local quadratic embedding     Metric learning     Regression machine     Soft sensor    

HTML PDF Collect

A Real-time Monitoring Network and Fault Diagnosis Expert System for Compressors and Pumps

Gao Jinji

Strategic Study of CAE 2001, Volume 3, Issue 9,   Pages 41-47

Abstract:

Using modern information technology and artificial intelligence to achieve the condition based maintenance and predictive maintenance is one of the important ways to reduce the production cost in the process industries. The real-time monitoring network and artificial intelligent diagnosis technology for mechanical-electric plant was outlined in this paper. The Ethernet and FDDI based real-time monitoring network developed for compressors and pumps in petrochemical plants was introduced briefly. The black-gray-white gathering diagnosis method was given for the first time on the bases of approach to fault mechanism and distinctive symptoms. The mechanical fault diagnosis expert system based on black-gray-white gathering distinguishing sieve method developed in this work yields satisfactory results in the engineering practice.

Keywords： plant diagnosis engineering     real-time monitoring network     artificial intelligent diagnosis     first reason analysis method     black-gray-white gathering     sieving method    

HTML PDF Collect

Optimal synchronization control for multi-agent systems with input saturation: a nonzero-sum game Research Article

Hongyang LI, Qinglai WEI

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 7,   Pages 1010-1019 doi: 10.1631/FITEE.2200010

Abstract: This paper presents a novel method for with . The multi-agent game theory is introduced to transform the problem into a multi-agent . Then, the Nash equilibrium can be achieved by solving the coupled Hamilton–Jacobi–Bellman (HJB) equations with nonquadratic input energy terms. A novel method is presented to obtain the Nash equilibrium solution without the system models, and the critic neural networks (NNs) and actor NNs are introduced to implement the presented method. Theoretical analysis is provided, which shows that the iterative control laws converge to the Nash equilibrium. Simulation results show the good performance of the presented method.

Keywords： Optimal synchronization control     Multi-agent systems     Nonzero-sum game     Adaptive dynamic programming     Input saturation     Off-policy reinforcement learning     Policy iteration    

HTML PDF Collect

Visual interpretability for deep learning: a survey Review

Quan-shi ZHANG, Song-chun ZHU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1,   Pages 27-39 doi: 10.1631/FITEE.1700808

Abstract: This paper reviews recent studies in understanding neural-network representations and learning neural networks with interpretable/disentangled middle-layer representations. Although deep neural networks have exhibited superior performance in various tasks, interpretability is always Achilles’ heel of deep neural networks. At present, deep neural networks obtain high discrimination power at the cost of a low interpretability of their black-box representations. We believe that high model interpretability may help people break several bottlenecks of deep learning, e.g., learning from a few annotations, learning via human–computer communications at the semantic level, and semantically debugging network representations. We focus on convolutional neural networks (CNNs), and revisit the visualization of CNN representations, methods of diagnosing representations of pre-trained CNNs, approaches for disentangling pre-trained CNN representations, learning of CNNs with disentangled representations, and middle-to-end learning based on model interpretability. Finally, we discuss prospective trends in explainable artificial intelligence.

Keywords： Artificial intelligence     Deep learning     Interpretable model    

HTML PDF Collect

Decentralized multi-agent reinforcement learning with networked agents: recent advances Review Article

Kaiqing Zhang, Zhuoran Yang, Tamer Başar,kzhang66@illinois.edu,zy6@princeton.edu,basar1@illinois.edu

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 6,   Pages 802-814 doi: 10.1631/FITEE.1900661

Abstract: Multi-agent (MARL) has long been a significant research topic in both machine learning and control systems. Recent development of (single-agent) deep has created a resurgence of interest in developing new MARL algorithms, especially those founded on theoretical analysis. In this paper, we review recent advances on a sub-area of this topic: decentralized MARL with networked agents. In this scenario, multiple agents perform sequential decision-making in a common environment, and without the coordination of any central controller, while being allowed to exchange information with their neighbors over a communication network. Such a setting finds broad applications in the control and operation of robots, unmanned vehicles, mobile sensor networks, and the smart grid. This review covers several of our research endeavors in this direction, as well as progress made by other researchers along the line. We hope that this review promotes additional research efforts in this exciting yet challenging area.

Keywords： 强化学习；多智能体系统；网络系统；一致性优化；分布式优化；博弈论    

HTML PDF Collect

Communicative Learning: A Unified Learning Formalism Review

Luyao Yuan, Song-Chun Zhu

Engineering 2023, Volume 25, Issue 6,   Pages 77-100 doi: 10.1016/j.eng.2022.10.017

Abstract:

In this article, we propose a communicative learning (CL) formalism that unifies existing machine learning paradigms, such as passive learning, active learning, algorithmic teaching, and so forth, and facilitates the development of new learning methods. Arising from human cooperative communication, this formalism poses learning as a communicative process and combines pedagogy with the burgeoning field of machine learning. The pedagogical insight facilitates the adoption of alternative information sources in machine learning besides randomly sampled data, such as intentional messages given by a helpful teacher. More specifically, in CL, a teacher and a student exchange information with each other collaboratively to transmit and acquire certain knowledge. Each agent has a mind, which includes the agent's knowledge, utility, and mental dynamics. To establish effective communication, each agent also needs an estimation of its partner's mind. We define expressive mental representations and learning formulation sufficient for such recursive modeling, which endows CL with human-comparable learning efficiency. We demonstrate the application of CL to several prototypical collaboration tasks and illustrate that this formalism allows learning protocols to go beyond Shannon's communication limit. Finally, we present our contribution to the foundations of learning by putting forth hierarchies in learning and defining the halting problem of learning.

Keywords： Artificial intelligencehine     Cooperative communication     Machine learning     Pedagogy     Theory of mind    

HTML PDF Collect

Flight control for air-breathing hypersonic vehicles using linear quadratic regulator design based on stochastic robustness analysis Article

Lin CAO, Shuo TANG, Dong ZHANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 7,   Pages 882-897 doi: 10.1631/FITEE.1601363

Abstract: The flight dynamics model of air-breathing hypersonic vehicles (AHVs) is highly nonlinear and multivariable cou-pling, and includes inertial uncertainties and external disturbances that require strong, robust, and high-accuracy controllers. In this paper, we propose a linear-quadratic regulator (LQR) design method based on stochastic robustness analysis for the longitudinal dynamics of AHVs. First, input/output feedback linearization is used to design LQRs. Second, subject to various system parameter uncertainties, system robustness is characterized by the probability of stability and desired performance. Then, the mapping rela-tionship between system robustness and LQR parameters is established. Particularly, to maximize system robustness, a novel hybrid particle swarm optimization algorithm is proposed to search for the optimal LQR parameters. During the search iteration, a Chernoff bound algorithm is applied to determine the finite sample size of Monte Carlo evaluation with the given probability levels. Finally, simulation results show that the optimization algorithm can effectively find the optimal solution to the LQR parameters.

Keywords： Air-breathing hypersonic vehicles (AHVs)     Stochastic robustness analysis     Linear-quadratic regulator (LQR)     Particle swarm optimization (PSO)     Improved hybrid PSO algorithm    

HTML PDF Collect

Soft-HGRNs: soft hierarchical graph recurrent networks for multi-agent partially observable environments Research Article

Yixiang REN, Zhenhui YE, Yining CHEN, Xiaohong JIANG, Guanghua SONG,yixiangren@zju.edu.cn,zhenhuiye@zju.edu.cn,ch19930611@zju.edu.cn,ghsong@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 1,   Pages 117-130 doi: 10.1631/FITEE.2200073

Abstract: The recent progress in multi-agent (MADRL) makes it more practical in real-world tasks, but its relatively poor scalability and the partially observable constraint raise more challenges for its performance and deployment. Based on our intuitive observation that human society could be regarded as a large-scale partially observable environment, where everyone has the functions of communicating with neighbors and remembering his/her own experience, we propose a novel network structure called the hierarchical graph recurrent network (HGRN) for multi-agent cooperation under . Specifically, we construct the multi-agent system as a graph, use a novel graph convolution structure to achieve communication between heterogeneous neighboring agents, and adopt a recurrent unit to enable agents to record historical information. To encourage exploration and improve robustness, we design a method that can learn stochastic policies of a configurable target action entropy. Based on the above technologies, we propose a value-based MADRL algorithm called Soft-HGRN and its actor-critic variant called SAC-HGRN. Experimental results based on three homogeneous tasks and one heterogeneous environment not only show that our approach achieves clear improvements compared with four MADRL baselines, but also demonstrate the interpretability, scalability, and transferability of the proposed model.

Keywords： Deep reinforcement learning     Graph-based communication     Maximum-entropy learning     Partial observability     Heterogeneous settings    

HTML PDF Collect

Quantum security analysis of a lattice-basedoblivious transfer protocol Article

Mo-meng LIU, Juliane KRÄMER, Yu-pu HU, Johannes BUCHMANN

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 9,   Pages 1348-1369 doi: 10.1631/FITEE.1700039

Abstract: Because of the concise functionality of oblivious transfer (OT)protocols, they have been widely used as building blocks in securemultiparty computation and high-level protocols. The security of OTprotocols built upon classical number theoretic problems, such asthe discrete logarithm and factoring, however, is threatened as aresult of the huge progress in quantum computing. Therefore, post-quantumcryptography is needed for protocols based on classical problems,and several proposals for post-quantum OT protocols exist. However,most post-quantum cryptosystems present their security proof onlyin the context of classical adversaries, not in the quantum setting.In this paper, we close this gap and prove the security of the lattice-basedOT protocol proposed by Peikert . (CRYPTO, 2008), which is universally composably secure under theassumption of learning with errors hardness, in the quantum setting.We apply three general quantum security analysis frameworks. First,we apply the quantum lifting theorem proposed by Unruh (EUROCRYPT,2010) to prove that the security of the lattice-based OT protocolcan be lifted into the quantum world. Then, we apply two more securityanalysis frameworks specified for post-quantum cryptographic primitives,i.e., simple hybrid arguments (CRYPTO, 2011) and game-preserving reduction(PQCrypto, 2014).

Keywords： Oblivious transfer     Post-quantum     Lattice-based     Learning with errors     Universally composable    

HTML PDF Collect

Coherence analysis and Laplacian energy of recursive trees with controlled initial states Research Articles

Mei-du Hong, Wei-gang Sun, Su-yu Liu, Teng-fei Xuan,wgsun@hdu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 6,   Pages 809-962 doi: 10.1631/FITEE.1900133

Abstract: We study the of a family of recursive trees with novel features that include the initial states controlled by a parameter. The problem in a linear system with additive noises is characterized as , which is defined by a Laplacian spectrum. Based on the structures of our recursive treelike model, we obtain the recursive relationships for Laplacian eigenvalues in two successive steps and further derive the exact solutions of first- and second-order coherences, which are calculated by the sum and square sum of the reciprocal of all nonzero Laplacian eigenvalues. For a large network size , the scalings of the first- and second-order coherences are ln and $, respectively. The smaller the number of initial nodes, the better the bears. Finally, we numerically investigate the relationship between and , showing that the first- and second-order coherences increase with the increase of at approximately exponential and linear rates, respectively.

Keywords： 一致性；网络一致性；拉普拉斯能量    

HTML PDF Collect

Title Author Date Type Operation

New directions for artificial intelligence: human, machine, biological, and quantum intelligence

Li WEIGANG,Liriam Michi ENAMOTO,Denise Leyi LI,Geraldo Pereira ROCHA FILHO

Journal Article

Advanced Bioprocess Development and Manufacturing Technologies in High-Throughput Miniature Bioreactors

Jiao Peng, Chen Biqiang

Journal Article

Reliability analysis of corroded oil and gas pipeline

Xu Wei,Liu Mao

Journal Article

Does Global Agriculture Need Another Green Revolution?

Danny Llewellyn

Journal Article

Spatial and Temporal Variations in the Atmospheric Age Distribution of Primary and Secondary Inorganic Aerosols in China

Xiaodong Xie, Qi Ying, Hongliang Zhang, Jianlin Hu,

Journal Article

A Local Quadratic Embedding Learning Algorithm and Applications for Soft Sensing

Yaoyao Bao, Yuanming Zhu, Feng Qian

Journal Article

A Real-time Monitoring Network and Fault Diagnosis Expert System for Compressors and Pumps

Gao Jinji

Journal Article

Optimal synchronization control for multi-agent systems with input saturation: a nonzero-sum game

Hongyang LI, Qinglai WEI

Journal Article

Visual interpretability for deep learning: a survey

Quan-shi ZHANG, Song-chun ZHU

Journal Article

Decentralized multi-agent reinforcement learning with networked agents: recent advances

Kaiqing Zhang, Zhuoran Yang, Tamer Başar,kzhang66@illinois.edu,zy6@princeton.edu,basar1@illinois.edu

Journal Article

Communicative Learning: A Unified Learning Formalism

Luyao Yuan, Song-Chun Zhu

Journal Article

Flight control for air-breathing hypersonic vehicles using linear quadratic regulator design based on stochastic robustness analysis

Lin CAO, Shuo TANG, Dong ZHANG

Journal Article

Soft-HGRNs: soft hierarchical graph recurrent networks for multi-agent partially observable environments

Yixiang REN, Zhenhui YE, Yining CHEN, Xiaohong JIANG, Guanghua SONG,yixiangren@zju.edu.cn,zhenhuiye@zju.edu.cn,ch19930611@zju.edu.cn,ghsong@zju.edu.cn

Journal Article

Quantum security analysis of a lattice-basedoblivious transfer protocol

Mo-meng LIU, Juliane KRÄMER, Yu-pu HU, Johannes BUCHMANN

Journal Article

Coherence analysis and Laplacian energy of recursive trees with controlled initial states

Mei-du Hong, Wei-gang Sun, Su-yu Liu, Teng-fei Xuan,wgsun@hdu.edu.cn

Journal Article