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混合-增强智能：协作与认知 Review

南宁 郑,子熠 刘,鹏举 任,永强 马,仕韬 陈,思雨 余,建儒 薛,霸东 陈,飞跃 王

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 2,   Pages 153-179 doi: 10.1631/FITEE.1700053

Abstract: 人工智能追求的长期目标是使机器能像人一样学习和思考。由于人类面临的许多问题具有不确定性、脆弱性和开放性，任何智能程度的机器都无法完全取代人类，这就需要将人的作用或人的认知模型引入到人工智能系统中，形成混合-增强智能的形态，这种形态是人工智能或机器智能的可行的混合-增强智能可以分为两类基本形式：一类是人在回路的人机协同混合增强智能，另一类是将认知模型嵌入机器学习系统中，形成基于认知计算的混合智能。本文讨论人机协同的混合-增强智能的基本框架，以及基于认知计算的混合-增强智能的基本要素：直觉推理与因果模型、记忆和知识演化；特别论述了直觉推理在复杂问题求解中的作用和基本原理，以及基于记忆与推理的视觉场景理解的认知学习网络；阐述了竞争-对抗式认知学习方法，并讨论了其在自动驾驶方面的应用；最后给出混合-增强智能在相关领域的典型应用。

Keywords： 人-机协同；混合增强智能；认知计算；直觉推理；因果模型；认知映射；视觉场景理解；自主驾驶汽车    

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Human-machine augmented intelligence: research and applications Editorial

Jianru XUE, Bin HU, Lingxi LI, Junping ZHANG,jrxue@mail.xjtu.edu.cn,bh@lzu.edu.cn,LL7@iupui.edu,jpzhang@fudan.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 8,   Pages 1139-1141 doi: 10.1631/FITEE.2250000

Abstract: Current research on artificial intelligence (AI) has been entering a new era, with AI technologies and AI-enabled applications emerging in almost every aspect of human life. Meanwhile, avoiding the risk caused by limitations of AI technologies has become a grand challenge. The main idea of human-machine augmented intelligence (HAI) is to adopt the role of humans or to embed human-like cognitive abilities into intelligent machines. Increasing attention and efforts from academia, industry, and governments are attracted by the HAI idea, whose effects are far-reaching. Two fundamental formulations of HAI include human-in-the-loop HAI (HITL-HAI) and cognitive computing based HAI (CC-HAI), which have become hot and fundamental frontiers of AI, and an increasing amount of original research has emerged in recent years. Recent existing research activities on HITL-HAI include theories for human-machine collaboration, human-brain interfaces, human-machine coordination and teaming, and advanced perception and smart environments for human-machine collaboration. In particular, HITL-HAI has been widely used in interactive simulation models in aviation, driving, and robotics. In such simulations, humans play an important role because they influence the simulated environment with their own actions. Brain-computer interfaces have become increasingly important among communication channels for human-machine collaboration. CC-HAI aims to develop computational models to mimic the mechanism or function of the human brain and improve a machine’s capabilities of perception, reasoning, and decision-making. We have witnessed an increasing amount of research work on casual models, intuitive reasoning models, and associative memories that are proposed with the forms of deep neural networks.
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Mutually trustworthy human-machine knowledge automation and hybrid augmented intelligence: mechanisms and applications of cognition, management, and control for complex systems Research Article

Fei-Yue WANG, Jianbo GUO, Guangquan BU, Jun Jason ZHANG,jun.zhang.ee@whu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 8,   Pages 1142-1157 doi: 10.1631/FITEE.2100418

Abstract: In this paper, we aim to illustrate the concept of mutually trustworthy (HM-KA) as the technical mechanism of hybrid augmented intelligence (HAI) based complex system cognition, management, and control (CMC). We describe the historical development of complex system science and analyze the limitations of human intelligence and machine intelligence. The need for using human-machine HAI in is then explained in detail. The concept of “mutually trustworthy HM-KA” mechanism is proposed to tackle the CMC challenge, and its technical procedure and pathway are demonstrated using an example of corrective control in . It is expected that the proposed mutually trustworthy HM-KA concept can provide a novel and canonical mechanism and benefit real-world practices of complex system CMC.

Keywords： Complex systems     Human-machine knowledge automation     Parallel systems     Bulk power grid dispatch     Artificialintelligence     Internet of Minds (IoM)    

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Enhanced solution to the surface–volume–surface EFIE for arbitrary metal–dielectric composite objects Research Article

Han WANG, Mingjie PANG, Hai LIN

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 7,   Pages 1098-1109 doi: 10.1631/FITEE.2100387

Abstract: The surface–volume–surface electric field (SVS-EFIE) can lead to complex equations, laborious implementation, and unacceptable computational complexity in the . Therefore, a general matrix equation (GME) is proposed for electromagnetic scattering from arbitrary metal–dielectric s, and its enhanced solution is presented in this paper. In previous works, MoM solution formulation of SVS-EFIE considering only three-region metal–dielectric composite scatters was presented, and the two-stage process resulted in two integral operators in SVS-EFIE, which is arduous to implement and is incapable of reducing computational complexity. To address these difficulties, GME, which is versatile for homogeneous objects and s consisting of more than three sub-regions, is proposed for the first time. Accelerated solving policies are proposed for GME based on coupling degree concerning the spacing between sub-regions, and the coupling degree standard can be adaptively set to balance the accuracy and efficiency. In this paper, the reformed is applied for the strong coupling case, and the is presented for the weak coupling case. Parallelism can be easily applied in the enhanced solution. Numerical results demonstrate that the proposed method requires only 11.6% memory and 11.8% CPU time on average compared to the previous direct solution.

Keywords： Composite object     Integral equation     Method of moments (MoM)     Addition theorem     Iterative method    

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MSSSA: a multi-strategy enhanced sparrow search algorithm for global optimization Research Article

Kai MENG, Chen CHEN, Bin XIN

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12,   Pages 1828-1847 doi: 10.1631/FITEE.2200237

Abstract: The (SSA) is a recent meta-heuristic optimization approach with the advantages of simplicity and flexibility. However, SSA still faces challenges of premature convergence and imbalance between exploration and exploitation, especially when tackling multimodal . Aiming to deal with the above problems, we propose an enhanced variant of SSA called the multi-strategy enhanced (MSSSA) in this paper. First, a chaotic map is introduced to obtain a high-quality initial population for SSA, and the opposition-based learning strategy is employed to increase the population diversity. Then, an is designed to accommodate an adequate balance between exploration and exploitation. Finally, a is embedded in the individual update stage to avoid falling into local optima. To validate the effectiveness of the proposed MSSSA, a large number of experiments are implemented, including 40 complex functions from the IEEE CEC2014 and IEEE CEC2019 test suites and 10 classical functions with different dimensions. Experimental results show that the MSSSA achieves competitive performance compared with several state-of-the-art optimization algorithms. The proposed MSSSA is also successfully applied to solve two engineering . The results demonstrate the superiority of the MSSSA in addressing practical problems.

Keywords： Swarm intelligence     Sparrow search algorithm     Adaptive parameter control strategy     Hybrid disturbance mechanism     Optimization problems    

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Heading toward Artificial Intelligence 2.0

Yunhe Pan

Engineering 2016, Volume 2, Issue 4,   Pages 409-413 doi: 10.1016/J.ENG.2016.04.018

Abstract:

With the popularization of the Internet, permeation of sensor networks, emergence of big data, increase in size of the information community, and interlinking and fusion of data and information throughout human society, physical space, and cyberspace, the information environment related to the current development of artificial intelligence (AI) has profoundly changed. AI faces important adjustments, and scientific foundations are confronted with new breakthroughs, as AI enters a new stage: AI 2.0. This paper briefly reviews the 60-year developmental history of AI, analyzes the external environment promoting the formation of AI 2.0 along with changes in goals, and describes both the beginning of the technology and the core idea behind AI 2.0 development. Furthermore, based on combined social demands and the information environment that exists in relation to Chinese development, suggestions on the development of AI 2.0 are given.

Keywords： Artificial intelligence 2.0     Big data     Crowd intelligence     Cross-media     Human-machine     hybrid-augmented     intelligence     Autonomous-intelligent system    

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Media Enhanced by Artificial Intelligence: Can We Believe Anything Anymore?

Ramin Skibba

Engineering 2020, Volume 6, Issue 7,   Pages 723-724 doi: 10.1016/j.eng.2020.05.011

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Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised fault classification Research Article

Weijun WANG, Yun WANG, Jun WANG, Xinyun FANG, Yuchen HE

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12,   Pages 1814-1827 doi: 10.1631/FITEE.2200053

Abstract: As an indispensable part of process monitoring, the performance of relies heavily on the sufficiency of process knowledge. However, data labels are always difficult to acquire because of the limited sampling condition or expensive laboratory analysis, which may lead to deterioration of classification performance. To handle this dilemma, a new strategy is performed in which enhanced is employed to evaluate the value of each unlabeled sample with respect to a specific labeled dataset. Unlabeled samples with large values will serve as supplementary information for the training dataset. In addition, we introduce several reasonable indexes and criteria, and thus human labeling interference is greatly reduced. Finally, the effectiveness of the proposed method is evaluated using a numerical example and the Tennessee Eastman process.

Keywords： Semi-supervised     Active learning     Ensemble learning     Mixture discriminant analysis     Fault classification    

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A Novel MILP Model Based on the Topology of a Network Graph for Process Planning in an Intelligent Manufacturing System Article

Qihao Liu, Xinyu Li, Liang Gao

Engineering 2021, Volume 7, Issue 6,   Pages 807-817 doi: 10.1016/j.eng.2021.04.011

Abstract:

Intelligent process planning (PP) is one of the most important components in an intelligent manufacturing system and acts as a bridge between product designing and practical manufacturing. PP is a nondeterministic polynomial-time (NP)-hard problem and, as existing mathematical models are not formulated in linear forms, they cannot be solved well to achieve exact solutions for PP problems. This paper proposes a novel mixed-integer linear programming (MILP) mathematical model by considering the network topology structure and the OR nodes that represent a type of OR logic inside the network. Precedence relationships between operations are discussed by raising three types of precedence relationship matrices. Furthermore, the proposed model can be programmed in commonly-used mathematical programming solvers, such as CPLEX, Gurobi, and so forth, to search for optimal solutions for most open problems. To verify the effectiveness and generality of the proposed model, five groups of numerical experiments are conducted on well-known benchmarks. The results show that the proposed model can solve PP problems effectively and can obtain better solutions than those obtained by the state-ofthe- art algorithms.

Keywords： Process planning     Network     Mixed-integer linear programming     CPLEX    

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Parallel cognition: hybrid intelligence for human-machine interaction and management Research Article

Peijun YE, Xiao WANG, Wenbo ZHENG, Qinglai WEI, Fei-Yue WANG

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12,   Pages 1765-1779 doi: 10.1631/FITEE.2100335

Abstract: As an interdisciplinary research approach, traditional cognitive science adopts mainly the experiment, induction, modeling, and validation paradigm. Such models are sometimes not applicable in cyber-physical-social-systems (CPSSs), where the large number of human users involves severe heterogeneity and dynamics. To reduce the decision-making conflicts between people and machines in human-centered systems, we propose a new research paradigm called parallel cognition that uses the system of intelligent techniques to investigate cognitive activities and functionals in three stages: descriptive cognition based on artificial cognitive systems (ACSs), predictive cognition with computational deliberation experiments, and prescriptive cognition via parallel . To make iteration of these stages constantly on-line, a hybrid learning method based on both a psychological model and user behavioral data is further proposed to adaptively learn an individual's cognitive knowledge. Preliminary experiments on two representative scenarios, urban travel and cognitive visual reasoning, indicate that our parallel cognition learning is effective and feasible for human , and can thus facilitate human-machine cooperation in both complex engineering and social systems.

Keywords： Cognitive learning     Artificial intelligence     Behavioral prescription    

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Sampled data based containment control of second-order multi-agent systems under intermittent communications Research Articles

Fuyong Wang, Zhongxin Liu, Zengqiang Chen,wangfy@nankai.edu.cn,lzhx@nankai.edu.cn,chenzq@nankai.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 8,   Pages 1059-1067 doi: 10.1631/FITEE.2000204

Abstract: This paper studies the sampled data based problem of s with s, where velocity measurements for each agent are unavailable. A novel controller for second-order containment is put forward via intermittent measurement. Several necessary and sufficient conditions are derived to achieve intermittent sampled by means of analyzing the relationship among control gains, eigenvalues of the Laplacian matrix, the sampling period, and the . Finally, several simulation examples are used to testify the correctness and effectiveness of the theoretical results.

Keywords： 包含控制；二阶多智能体系统；采样位置数据；间歇通信；通信宽度    

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Wasserstein GAN-Based Small-Sample Augmentation for New-Generation Artificial Intelligence: A Case Study of Cancer-Staging Data in Biology Article

Yufei Liu, Yuan Zhou, Xin Liu, Fang Dong, Chang Wang, Zihong Wang

Engineering 2019, Volume 5, Issue 1,   Pages 156-163 doi: 10.1016/j.eng.2018.11.018

Abstract:

It is essential to utilize deep-learning algorithms based on big data for the implementation of the new generation of artificial intelligence. Effective utilization of deep learning relies considerably on the number of labeled samples, which restricts the application of deep learning in an environment with a small sample size. In this paper, we propose an approach based on a generative adversarial network (GAN) combined with a deep neural network (DNN). First, the original samples were divided into a training set and a test set. The GAN was trained with the training set to generate synthetic sample data, which enlarged the training set. Next, the DNN classifier was trained with the synthetic samples. Finally, the classifier was tested with the test set, and the effectiveness of the approach for multi-classification with a small sample size was validated by the indicators. As an empirical case, the approach was then applied to identify the stages of cancers with a small labeled sample size. The experimental results verified that the proposed approach achieved a greater accuracy than traditional methods. This research was an attempt to transform the classical statistical machine-learning classification method based on original samples into a deep-learning classification method based on data augmentation. The use of this approach will contribute to an expansion of application scenarios for the new generation of artificial intelligence based on deep learning, and to an increase in application effectiveness. This research is also expected to contribute to the comprehensive promotion of new-generation artificial intelligence.

Keywords： Artificial intelligence     Generative adversarial network     Deep neural network     Small sample size     Cancer    

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Artificial intelligence algorithms for cyberspace security applications: a technological and status review Review

Jie CHEN, Dandan WU, Ruiyun XIE,chenjie1900@mail.nwpu.edu.cn,wudd@cetcsc.com

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 8,   Pages 1117-1142 doi: 10.1631/FITEE.2200314

Abstract: Three technical problems should be solved urgently in : the timeliness and accuracy of network attack detection, the credibility assessment and prediction of the security situation, and the effectiveness of security defense strategy optimization. algorithms have become the core means to increase the chance of security and improve the network attack and defense ability in the application of . Recently, the breakthrough and application of AI technology have provided a series of advanced approaches for further enhancing network defense ability. This work presents a comprehensive review of AI technology articles for applications, mainly from 2017 to 2022. The papers are selected from a variety of journals and conferences: 52.68% are from Elsevier, Springer, and IEEE journals and 25% are from international conferences. With a specific focus on the latest approaches in , , and some popular s, the characteristics of the algorithmic models, performance results, datasets, potential benefits, and limitations are analyzed, and some of the existing challenges are highlighted. This work is intended to provide technical guidance for researchers who would like to obtain the potential of AI technical methods for and to provide tips for the later resolution of specific issues, and a mastery of the current development trends of technology and application and hot issues in the field of network security. It also indicates certain existing challenges and gives directions for addressing them effectively.

Keywords： Artificial intelligence (AI)     Machine learning (ML)     Deep learning (DL)     Optimization algorithm     Hybrid algorithm     Cyberspace security    

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Finite-time formation control for first-order multi-agent systems with region constraints Research Articles

Zhengquan Yang, Xiaofang Pan, Qing Zhang, Zengqiang Chen,zquanyang@163.com,1219006322@qq.com,qz120168@hotmail.com,chenzq@nankai.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 1,   Pages 1-140 doi: 10.1631/FITEE.2000177

Abstract: In this study, the control of s with region constraints is studied. Multiple agents have first-order dynamics and a common target area. A novel control algorithm is proposed using local information and interaction. If the communication graph is undirected and connected and the desired framework is rigid, it is proved that the controller can be used to solve the formation problem with a target area. That is, all agents can enter the desired region in finite time while reaching and maintaining the desired formation shapes. Finally, a numerical example is given to illustrate the results.

Keywords： Finite-time formation     Multi-agent system     Asymptotic convergence     Set constraint     Lyapunov theorem    

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Engineered Hybrid Materials with Smart Surfaces for Effective Mitigation of Petroleum-originated Pollutants Review

Nisar Ali, Muhammad Bilal, Adnan Khan,  Farman Ali, Mohamad Nasir Mohamad Ibrahim, Xiaoyan Gao, Shizhong Zhang, Kun Hong,  Hafiz M. N. Iqbal

Engineering 2021, Volume 7, Issue 10,   Pages 1494-1505 doi: 10.1016/j.eng.2020.07.024

Abstract:

The generation and controlled or uncontrolled release of hydrocarbon-contaminated industrial wastewater effluents to water matrices are a major environmental concern. The contaminated water comes to surface in the form of stable emulsions, which sometimes require different techniques to mitigate or separate effectively. Both the crude emulsions and hydrocarbon-contaminated wastewater effluents contain suspended solids, oil/grease, organic matter, toxic elements, salts, and recalcitrant chemicals. Suitable treatment of crude oil emulsions has been one of the most important challenges due to the complex nature and the substantial amount of generated waste. Moreover, the recovery of oil from waste will help meet the increasing demand for oil and its derivatives. In this context, functional nanostructured materials with smart surfaces and switchable wettability properties have gained increasing attention because of their excellent performance in the separation of oil–water emulsions. Recent improvements in the design, composition, morphology, and fine-tuning of polymeric nanostructured materials have resulted in enhanced demulsification functionalities. Herein, we reviewed the environmental impacts of crude oil emulsions and hydrocarbon-contaminated wastewater effluents. Their effective treatments by smart polymeric nanostructured materials with wettability properties have been stated with suitable examples. The fundamental mechanisms underpinning the efficient separation of oil–water emulsions are discussed with suitable examples along with the future perspectives of smart materials.

Keywords： Emulsion     Hydrocarbon-contamination     Environment impacts     Hybrid nanomaterials     Oil–water separation     Wastewater treatment    

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Title Author Date Type Operation

混合-增强智能：协作与认知

南宁 郑,子熠 刘,鹏举 任,永强 马,仕韬 陈,思雨 余,建儒 薛,霸东 陈,飞跃 王

Journal Article

Human-machine augmented intelligence: research and applications

Jianru XUE, Bin HU, Lingxi LI, Junping ZHANG,jrxue@mail.xjtu.edu.cn,bh@lzu.edu.cn,LL7@iupui.edu,jpzhang@fudan.edu.cn

Journal Article

Mutually trustworthy human-machine knowledge automation and hybrid augmented intelligence: mechanisms and applications of cognition, management, and control for complex systems

Fei-Yue WANG, Jianbo GUO, Guangquan BU, Jun Jason ZHANG,jun.zhang.ee@whu.edu.cn

Journal Article

Enhanced solution to the surface–volume–surface EFIE for arbitrary metal–dielectric composite objects

Han WANG, Mingjie PANG, Hai LIN

Journal Article

MSSSA: a multi-strategy enhanced sparrow search algorithm for global optimization

Kai MENG, Chen CHEN, Bin XIN

Journal Article

Heading toward Artificial Intelligence 2.0

Yunhe Pan

Journal Article

Media Enhanced by Artificial Intelligence: Can We Believe Anything Anymore?

Ramin Skibba

Journal Article

Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised fault classification

Weijun WANG, Yun WANG, Jun WANG, Xinyun FANG, Yuchen HE

Journal Article

A Novel MILP Model Based on the Topology of a Network Graph for Process Planning in an Intelligent Manufacturing System

Qihao Liu, Xinyu Li, Liang Gao

Journal Article

Parallel cognition: hybrid intelligence for human-machine interaction and management

Peijun YE, Xiao WANG, Wenbo ZHENG, Qinglai WEI, Fei-Yue WANG

Journal Article

Sampled data based containment control of second-order multi-agent systems under intermittent communications

Fuyong Wang, Zhongxin Liu, Zengqiang Chen,wangfy@nankai.edu.cn,lzhx@nankai.edu.cn,chenzq@nankai.edu.cn

Journal Article

Wasserstein GAN-Based Small-Sample Augmentation for New-Generation Artificial Intelligence: A Case Study of Cancer-Staging Data in Biology

Yufei Liu, Yuan Zhou, Xin Liu, Fang Dong, Chang Wang, Zihong Wang

Journal Article

Artificial intelligence algorithms for cyberspace security applications: a technological and status review

Jie CHEN, Dandan WU, Ruiyun XIE,chenjie1900@mail.nwpu.edu.cn,wudd@cetcsc.com

Journal Article

Finite-time formation control for first-order multi-agent systems with region constraints

Zhengquan Yang, Xiaofang Pan, Qing Zhang, Zengqiang Chen,zquanyang@163.com,1219006322@qq.com,qz120168@hotmail.com,chenzq@nankai.edu.cn

Journal Article

Engineered Hybrid Materials with Smart Surfaces for Effective Mitigation of Petroleum-originated Pollutants

Nisar Ali, Muhammad Bilal, Adnan Khan,  Farman Ali, Mohamad Nasir Mohamad Ibrahim, Xiaoyan Gao, Shizhong Zhang, Kun Hong,  Hafiz M. N. Iqbal

Journal Article