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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: 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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认知中继三跳网络联合优化 Article

澄 赵,万良 王,信威 姚,双华 杨

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 2,   Pages 253-261 doi: 10.1631/FITEE.1601414

Abstract: 认知中继网络中，传输的吞吐量和传输距离一直是衡量性能的重要指标。现有的研究多数都集中在两跳网络的优化，但其也存在着传输距离不长，只能进行单项传输等缺点。本文提出了一种新的使用认知中继的三跳网络传输方案，通过三阶段的传输过程，实现了次级用户之间的双向传输。同时，引入了叠加编码技术来处理网络中双接收节点的情况。

Keywords： 解码转发；三跳；认知中继网络；时间功率分配；叠加编码    

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Featurematching using quasi-conformalmaps Article

Chun-xue WANG, Li-gang LIU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 5,   Pages 644-657 doi: 10.1631/FITEE.1500411

Abstract: We present a fully automatic method for finding geometrically consistent correspondences while discarding outliers from the candidate point matches in two images. Given a set of candidate matches provided by scale-invariant feature transform (SIFT) descriptors, which may contain many outliers, our goal is to select a subset of these matches retaining much more geometric information constructed by a mapping searched in the space of all diffeomorphisms. This problem can be formulated as a constrained optimization involving both the Beltrami coefficient (BC) term and quasi-conformal map, and solved by an efficient iterative algorithm based on the variable splitting method. In each iteration, we solve two subproblems, namely a linear system and linearly constrained convex quadratic programming. Our algorithm is simple and robust to outliers. We show that our algorithm enables producing more correct correspondences experimentally compared with state-of-the-art approaches.

Keywords： Feature correspondence     Quasi-conformal map     Splitting method    

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A forwarding graph embedding algorithm exploiting regional topology information Article

Hong-chao HU, Fan ZHANG, Yu-xing MAO, Zhen-peng WANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1854-1866 doi: 10.1631/FITEE.1601404

Abstract: Network function virtualization (NFV) is a newly proposed technique designed to construct and manage network functions dynamically and efficiently. Allocating physical resources to the virtual network function forwarding graph is a critical issue in NFV. We formulate the forwarding graph embedding (FGE) problem as a binary integer programming problem, which aims to increase the revenue and decrease the cost to a service provider (SP) while considering limited network resources and the requirements of virtual functions. We then design a novel regional resource clustering metric to quantify the embedding potential of each substrate node and propose a topology-aware FGE algorithm called ‘regional resource clustering FGE’ (RRC-FGE). After implementing our algorithms in C++, simulation results showed that the total revenue was increased by more than 50 units and the acceptance ratio by more than 15%, and the cost of the service provider was decreased by more than 60 units.

Keywords： Network function virtualization     Virtual network function     Forwarding graph embedding    

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Achieving Cognitive Mass Personalization via the Self-X Cognitive Manufacturing Network: An Industrial Knowledge Graph- and Graph Embedding-Enabled Pathway

Xinyu Li, Pai Zheng, Jinsong Bao, Liang Gao, Xun Xu

Engineering 2023, Volume 22, Issue 3,   Pages 14-19 doi: 10.1016/j.eng.2021.08.018

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A Co-Point Mapping-Based Approach to Drivable Area Detection for Self-Driving Cars Article

Ziyi Liu,Siyu Yu,Nanning Zheng

Engineering 2018, Volume 4, Issue 4,   Pages 479-490 doi: 10.1016/j.eng.2018.07.010

Abstract:

The randomness and complexity of urban traffic scenes make it a difficult task for self-driving cars to detect drivable areas. Inspired by human driving behaviors, we propose a novel method of drivable area detection for self-driving cars based on fusing pixel information from a monocular camera with spatial information from a light detection and ranging (LIDAR) scanner. Similar to the bijection of collineation, a new concept called co-point mapping, which is a bijection that maps points from the LIDAR scanner to points on the edge of the image segmentation, is introduced in the proposed method. Our method positions candidate drivable areas through self-learning models based on the initial drivable areas that are obtained by fusing obstacle information with superpixels. In addition, a fusion of four features is applied in order to achieve a more robust performance. In particular, a feature called drivable degree (DD) is proposed to characterize the drivable degree of the LIDAR points. After the initial drivable area is characterized by the features obtained through self-learning, a Bayesian framework is utilized to calculate the final probability map of the drivable area. Our approach introduces no common hypothesis and requires no training steps; yet it yields a state-of-art performance when tested on the ROAD-KITTI benchmark. Experimental results demonstrate that the proposed method is a general and efficient approach for detecting drivable area.

Keywords： Drivable area     Self-driving     Data fusion     Co-point mapping    

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Passive millimeter-wave target recognition based on Laplacian eigenmaps

Luo Lei,Li Yuehua,Luan Yinghong

Strategic Study of CAE 2010, Volume 12, Issue 3,   Pages 77-81

Abstract:

Aiming at the disadvantages of feature extraction and selection in the traditional method for passive millimeter-wave (MMW) metal target recognition, the existence and characteristics of low dimensional manifold of the short-time Fourier spectrum of metal target echo signal are explored using manifold learning algorithm, Laplacian eigenmaps. Target classification is performed through comparing the similarity of the test samples and the positive class in terms of the low dimensional manifold. The experiments show that the method gets higher recognition rate than other linear and kernel-based nonlinear dimensionality reduction algorithm, and is robust to data aliasing.

Keywords： manifold learning     Laplacian eigenmaps     nonlinear dimensionality reduction     low dimensional manifold     MMW    

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Multi-user rate and power analysis in a cognitive radio network with massive multi-input multi-output None

Shang LIU, Ishtiaq AHMAD, Ping ZHANG, Zhi ZHANG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 5,   Pages 674-684 doi: 10.1631/FITEE.1700081

Abstract: This paper discusses transmission performance and power allocation strategies in an underlay cognitive radio (CR) network that contains relay and massive multi-input multi-output (MIMO). The downlink transmission performance of a relay-aided massive MIMO network without CR is derived. By using the power distribution criteria, the kth user’s asymptotic signal to interference and noise ratio (SINR) is independent of fast fading. When the ratio between the base station (BS) antennas and the relay antennas becomes large enough, the transmission performance of the whole system is independent of BS-to-relay channel parameters and relates only to the relay-to-users stage. Then cognitive transmission performances of primary users (PUs) and secondary users (SUs) in an underlay CR network with massive MIMO are derived under perfect and imperfect channel state information (CSI), including the end-to-end SINR and achievable sum rate. When the numbers of primary base station (PBS) antennas, secondary base station (SBS) antennas, and relay antennas become infinite, the asymptotic SINR of the PU and SU is independent of fast fading. The interference between the primary network and secondary network can be canceled asymptotically. Transmission performance does not include the interference temperature. The secondary network can use its peak power to transmit signals without causing any interference to the primary network. Interestingly, when the antenna ratio becomes large enough, the asymptotic sum rate equals half of the rate of a single-hop single-antenna K-user system without fast fading. Next, the PUs’ utility function is defined. The optimal relay power is derived to maximize the utility function. The numerical results verify our analysis. The relationships between the transmission rate and the antenna number, relay power, and antenna ratio are simulated. We show that the massive MIMO with linear pre-coding can mitigate asymptotically the interference in a multi-user underlay CR network. The primary and secondary networks can operate independently.

Keywords： Massive multi-input multi-output     Cognitive radio     Relay network     Transmission rate     Power analysis    

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A chaotic coverage path planner for the mobilerobot based on the Chebyshev map for special missions Article

Cai-hong LI, Yong SONG, Feng-ying WANG, Zhi-qiang WANG, Yi-bin LI

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 9,   Pages 1305-1319 doi: 10.1631/FITEE.1601253

Abstract: We introduce a novel strategy of designing a chaotic coveragepath planner for the mobile robot based on the Chebyshev map for achievingspecial missions. The designed chaotic path planner consists of atwo-dimensional Chebyshev map which is constructed by two one-dimensionalChebyshev maps. The performance of the time sequences which are generatedby the planner is improved by arcsine transformation to enhance thechaotic characteristics and uniform distribution. Then the coveragerate and randomness for achieving the special missions of the robotare enhanced. The chaotic Chebyshev system is mapped into the feasibleregion of the robot workplace by affine transformation. Then a universalalgorithm of coverage path planning is designed for environments withobstacles. Simulation results show that the constructed chaotic pathplanner can avoid detection of the obstacles and the workplace boundaries,and runs safely in the feasible areas. The designed strategy is ableto satisfy the requirements of randomness, coverage, and high efficiencyfor special missions.

Keywords： Mobile robot     Chebyshev map     Chaotic     Affine transformation     Coverage path planning    

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Framework and case study of cognitive maintenance in Industry 4.0 Special Feature on Industrial Internet

Bao-rui Li, Yi Wang, Guo-hong Dai, Ke-sheng Wang,kesheng.wang@ntnu.no

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 11,   Pages 1493-1504 doi: 10.1631/FITEE.1900193

Abstract: We present a new framework for (CM) based on cyber-physical systems and advanced artificial intelligence techniques. These CM systems integrate intelligent approaches and intelligent decision-making techniques, which can be used by maintenance professionals who are working with . The systems will provide technical solutions to real-time online maintenance tasks, avoid outages due to equipment failures, and ensure the continuous and healthy operation of equipment and manufacturing assets. The implementation framework of CM consists of four modules, i.e., cyber-physical system, Internet of Things, data mining, and Internet of Services. In the data mining module, fault diagnosis and prediction are realized by methods. In the case study, the backlash error of cutting-edge machine tools is taken as an example. We use a deep belief network to predict the backlash of the machine tool, so as to predict the possible failure of the machine tool, and realize the strategy of CM. Through the case study, we discuss the significance of implementing CM for cutting- edge equipment, and the framework of CM implementation has been verified. Some CM system applications in manufacturing enterprises are summarized.

Keywords： 认知维护；工业4.0；尖端设备；深度学习；绿色监视器；智能制造工厂    

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Physical layer security of underlay cognitive radio using maximal ratio combining Article

Hui ZHAO,Dan-yang WANG,Chao-qing TANG,Ya-ping LIU,Gao-feng PAN,Ting-ting LI,Yun-fei CHEN

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 9,   Pages 929-937 doi: 10.1631/FITEE.1500351

Abstract: We investigate the secrecy outage performance of maximal ratio combining (MRC) in cognitive radio networks over Rayleigh fading channels. In a single-input multiple-output wiretap system, we consider a secondary user (SU-TX) that transmits confidential messages to another secondary user (SU-RX) equipped with ( 1) antennas where the MRC technique is adopted to improve its received signal-to-noise ratio. Meanwhile, an eavesdropper equipped with ( 1) antennas adopts the MRC scheme to overhear the information between SU-TX and SU-RX. SU-TX adopts the underlay strategy to guarantee the service quality of the primary user without spectrum sensing. We derive the closed-form expressions for an exact and asymptotic secrecy outage probability.

Keywords： Cognitive radio networks     Maximal ratio combining     Secrecy outage probability     Single-input multiple-output    

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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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Intrinsic feature extraction using discriminant diffusion mapping analysis for automated tool wear evaluation None

Yi-xiang HUANG, Xiao LIU, Cheng-liang LIU, Yan-ming LI

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 11,   Pages 1352-1361 doi: 10.1631/FITEE.1601512

Abstract:

We present a method of discriminant diffusion maps analysis (DDMA) for evaluating tool wear during milling processes. As a dimensionality reduction technique, the DDMA method is used to fuse and reduce the original features extracted from both the time and frequency domains, by preserving the diffusion distances within the intrinsic feature space and coupling the features to a discriminant kernel to refine the information from the high-dimensional feature space. The proposed DDMA method consists of three main steps: (1) signal processing and feature extraction; (2) intrinsic dimensionality estimation; (3) feature fusion implementation through feature space mapping with diffusion distance preservation. DDMA has been applied to current signals measured from the spindle in a machine center during a milling experiment to evaluate the tool wear status. Compared with the popular principle component analysis method, DDMA can better preserve the useful intrinsic information related to tool wear status. Thus, two important aspects are highlighted in this study: the benefits of the significantly lower dimension of the intrinsic features that are sensitive to tool wear, and the convenient availability of current signals in most industrial machine centers.

Keywords： Tool condition monitoring     Manifold learning     Dimensionality reduction     Diffusion mapping analysis     Intrinsic feature extraction    

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RCDS: a right-confirmable data-sharing model based on symbol mapping coding and blockchain Research Article

Liang WANG, Shunjiu HUANG, Lina ZUO, Jun LI, Wenyuan LIU,wangl@hbu.edu.cn,sjhuang1120@stumail.hbu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 8,   Pages 1194-1213 doi: 10.1631/FITEE.2200659

Abstract: The problem of is a long-term bottleneck in . Existing methods for confirming data rights lack credibility owing to poor supervision, and work only with specific data types because of their technical limitations. The emergence of is followed by some new data-sharing models that may provide improved data security. However, few of these models perform well enough in confirming data rights because the data access could not be fully under the control of the facility. In view of this, we propose a right-confirmable data-sharing model named RCDS that features (SMC) and . With SMC, each party encodes its digital identity into the byte sequence of the shared data by generating a unique symbol mapping table, whereby declaration of data rights can be content-independent for any type and any volume of data. With , all data-sharing participants jointly supervise the delivery and the access to shared data, so that granting of data rights can be openly verified. The evaluation results show that RCDS is effective and practical in data-sharing applications that are conscientious about .

Keywords： Data right confirmation     Symbol mapping coding     Blockchain     Data sharing     Traitor tracing     Access control    

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On detecting primary user emulation attack using channel impulse response in the cognitive radio network Article

Qiao-mu JIANG, Hui-fang CHEN, Lei XIE, Kuang WANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 10,   Pages 1665-1676 doi: 10.1631/FITEE.1700203

Abstract: Cognitive radio is an effective technology to alleviate the spectrum resource scarcity problem by opportunistically allocating the spare spectrum to unauthorized users. However, a serious denial-of-service (DoS) attack, named the ‘primary user emulation attack (PUEA)’, exists in the network to deteriorate the system performance. In this paper, we propose a PUEA detection method that exploits the radio channel information to detect the PUEA in the cognitive radio network. In the proposed method, the uniqueness of the channel impulse response (CIR) between the secondary user (SU) and the signal source is used to determine whether the received signal is transmitted by the primary user (PU) or the primary user emulator (PUE). The closed-form expressions for the false-alarm probability and the detection probability of the proposed PUEA detection method are derived. In addition, a modified subspace-based blind channel estimation method is presented to estimate the CIR, in order for the proposed PUEA detection method to work in the scenario where the SU has no prior knowledge about the structure and content of the PU signal. Numerical results show that the proposed PUEA detection method performs well although the difference in channel characteristics between the PU and PUE is small.

Keywords： Cognitive radio network     Primary user emulation attack     Subspace-based blind channel estimation     Channel impulse response    

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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

认知中继三跳网络联合优化

澄 赵,万良 王,信威 姚,双华 杨

Journal Article

Featurematching using quasi-conformalmaps

Chun-xue WANG, Li-gang LIU

Journal Article

A forwarding graph embedding algorithm exploiting regional topology information

Hong-chao HU, Fan ZHANG, Yu-xing MAO, Zhen-peng WANG

Journal Article

Achieving Cognitive Mass Personalization via the Self-X Cognitive Manufacturing Network: An Industrial Knowledge Graph- and Graph Embedding-Enabled Pathway

Xinyu Li, Pai Zheng, Jinsong Bao, Liang Gao, Xun Xu

Journal Article

A Co-Point Mapping-Based Approach to Drivable Area Detection for Self-Driving Cars

Ziyi Liu,Siyu Yu,Nanning Zheng

Journal Article

Passive millimeter-wave target recognition based on Laplacian eigenmaps

Luo Lei,Li Yuehua,Luan Yinghong

Journal Article

Multi-user rate and power analysis in a cognitive radio network with massive multi-input multi-output

Shang LIU, Ishtiaq AHMAD, Ping ZHANG, Zhi ZHANG

Journal Article

A chaotic coverage path planner for the mobilerobot based on the Chebyshev map for special missions

Cai-hong LI, Yong SONG, Feng-ying WANG, Zhi-qiang WANG, Yi-bin LI

Journal Article

Framework and case study of cognitive maintenance in Industry 4.0

Bao-rui Li, Yi Wang, Guo-hong Dai, Ke-sheng Wang,kesheng.wang@ntnu.no

Journal Article

Physical layer security of underlay cognitive radio using maximal ratio combining

Hui ZHAO,Dan-yang WANG,Chao-qing TANG,Ya-ping LIU,Gao-feng PAN,Ting-ting LI,Yun-fei CHEN

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

Intrinsic feature extraction using discriminant diffusion mapping analysis for automated tool wear evaluation

Yi-xiang HUANG, Xiao LIU, Cheng-liang LIU, Yan-ming LI

Journal Article

RCDS: a right-confirmable data-sharing model based on symbol mapping coding and blockchain

Liang WANG, Shunjiu HUANG, Lina ZUO, Jun LI, Wenyuan LIU,wangl@hbu.edu.cn,sjhuang1120@stumail.hbu.edu.cn

Journal Article

On detecting primary user emulation attack using channel impulse response in the cognitive radio network

Qiao-mu JIANG, Hui-fang CHEN, Lei XIE, Kuang WANG

Journal Article