This page has only limited features, please log in for full access.

Unclaimed
H.B. Yu
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, China

Basic Info

Basic Info is private.

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 11 February 2021 in IEEE Transactions on Pattern Analysis and Machine Intelligence
Reads 0
Downloads 0

Spectral clustering has become one of the most effective clustering algorithms. We in this work explore the problem of spectral clustering in a lifelong learning framework termed as Generalized Lifelong Spectral Clustering (GL $^2$ SC). Different from most current studies, which concentrate on a fixed spectral clustering task set and cannot efficiently incorporate a new clustering task, the goal of our work is to establish a generalized model for new spectral clustering task by What and How to lifelong learn from past tasks. For what to lifelong learn, our GL $^2$ SC framework contains a dual memory mechanism with a deep orthogonal factorization manner: an orthogonal basis memory stores hidden and hierarchical clustering centers among learned tasks, and a feature embedding memory captures deep manifold representation common across multiple related tasks. When a new clustering task arrives, the intuition here for how to lifelong learn is that GL $^2$ SC can transfer intrinsic knowledge from dual memory mechanism to obtain task-specific encoding matrix. Then the encoding matrix can redefine the dual memory over time to provide maximal benefits when learning future tasks. To the end, empirical comparisons on several benchmark datasets show the effectiveness of our GL $^2$ SC, in comparison with several state-of-the-art spectral clustering models.

ACS Style

Gan Sun; Yang Cong; Jiahua Dong; Yuyang Liu; Zhengming Ding; Haibin Yu. What and How: Generalized Lifelong Spectral Clustering via Dual Memory. IEEE Transactions on Pattern Analysis and Machine Intelligence 2021, PP, 1 -1.

AMA Style

Gan Sun, Yang Cong, Jiahua Dong, Yuyang Liu, Zhengming Ding, Haibin Yu. What and How: Generalized Lifelong Spectral Clustering via Dual Memory. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2021; PP (99):1-1.

Chicago/Turabian Style

Gan Sun; Yang Cong; Jiahua Dong; Yuyang Liu; Zhengming Ding; Haibin Yu. 2021. "What and How: Generalized Lifelong Spectral Clustering via Dual Memory." IEEE Transactions on Pattern Analysis and Machine Intelligence PP, no. 99: 1-1.

Journal article
Published: 13 January 2021 in IEEE Transactions on Industrial Informatics
Reads 0
Downloads 0

WIA-FA and IEEE 802.11 are two most widely adopted industrial wireless standards in discrete manufacturing. However, comprehensive performance comparisons between WIA-FA and IEEE 802.11 are still missing and industrial applications urgently need experimental methods to guide the selection of appropriate wireless technologies. To this end, this paper performs extensive experiments between WIA-FA and IEEE 802.11 in two practical industrial scenarios, with one ordered scenario defining the transmission order of devices and the other order-free scenario imposing no order constraints to the transmission order of devices. Network performance indices of the WIA-FA and IEEE 802.11 networks, including reliability, delay, delay jitter, and disorder rate are compared for different network sizes and data generation periods. Experimental results show that the WIA-FA protocol provides stable network performance, while the network performance of the IEEE 802.11 protocol is random. Additionally, we perform preliminary comparisons of WIA-FA with IEEE 802.11ax and 5G New Radio.

ACS Style

Wei Liang; Jialin Zhang; Huaguang Shi; Ke Wang; Qi Wang; Meng Zheng; Haibin Yu. An Experimental Evaluation of WIA-FA and IEEE 802.11 Networks for Discrete Manufacturing. IEEE Transactions on Industrial Informatics 2021, 17, 6260 -6271.

AMA Style

Wei Liang, Jialin Zhang, Huaguang Shi, Ke Wang, Qi Wang, Meng Zheng, Haibin Yu. An Experimental Evaluation of WIA-FA and IEEE 802.11 Networks for Discrete Manufacturing. IEEE Transactions on Industrial Informatics. 2021; 17 (9):6260-6271.

Chicago/Turabian Style

Wei Liang; Jialin Zhang; Huaguang Shi; Ke Wang; Qi Wang; Meng Zheng; Haibin Yu. 2021. "An Experimental Evaluation of WIA-FA and IEEE 802.11 Networks for Discrete Manufacturing." IEEE Transactions on Industrial Informatics 17, no. 9: 6260-6271.

Letter
Published: 03 October 2020 in Sensors
Reads 0
Downloads 0

Sucker-rod pumping systems are the most widely applied artificial lift equipment in the oil and gas industry. Accurate and intelligent working condition recognition of pumping systems imposes major impacts on oilfield production benefits and efficiency. The shape of dynamometer card reflects the working conditions of sucker-rod pumping systems, and different conditions can be indicated by their typical card characteristics. In traditional identification methods, however, features are manually extracted based on specialist experience and domain knowledge. In this paper, an automatic fault diagnosis method is proposed to recognize the working conditions of sucker-rod pumping systems with massive dynamometer card data collected by sensors. Firstly, AlexNet-based transfer learning is adopted to automatically extract representative features from various dynamometer cards. Secondly, with the extracted features, error-correcting output codes model-based SVM is designed to identify the working conditions and improve the fault diagnosis accuracy and efficiency. The proposed AlexNet-SVM algorithm is validated against a real dataset from an oilfield. The results reveal that the proposed method reduces the need for human labor and improves the recognition accuracy.

ACS Style

Haibo Cheng; Haibin Yu; Peng Zeng; Evgeny Osipov; Shichao Li; Valeriy Vyatkin. Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM. Sensors 2020, 20, 5659 .

AMA Style

Haibo Cheng, Haibin Yu, Peng Zeng, Evgeny Osipov, Shichao Li, Valeriy Vyatkin. Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM. Sensors. 2020; 20 (19):5659.

Chicago/Turabian Style

Haibo Cheng; Haibin Yu; Peng Zeng; Evgeny Osipov; Shichao Li; Valeriy Vyatkin. 2020. "Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM." Sensors 20, no. 19: 5659.

Journal article
Published: 29 September 2020 in Applied Sciences
Reads 0
Downloads 0

Non‐dominated sorting, used to find pareto solutions or assign solutions to different fronts, is a key but time‐consuming process in multi‐objective evolutionary algorithms (MOEAs). The best‐case and worst‐case time complexity of non‐dominated sorting algorithms currently known are O(MNlogN) and O(MN2); M and N represent the number of objectives and the population size, respectively. In this paper, a more efficient SET‐based non‐dominated sorting algorithm, shorted to SETNDS, is proposed. The proposed algorithm can greatly reduce the number of comparisons on the promise of ensuring a shorter running time. In SETNDS, the rank of a solution to be sorted is determined by only comparing with the one with the highest rank degree in its dominant set. This algorithm is compared with six generally existing non‐dominated sorting algorithms—fast non‐dominated sorting, the arena’s principle sort, the deductive sort, the corner sort, the efficient non‐dominated sort and the best order sort on several kinds of datasets. The compared results show that the proposed algorithm is feasible and effective and its computational efficiency outperforms other existing algorithms.

ACS Style

Lingling Xue; Peng Zeng; Haibin Yu. SETNDS: A SET‐Based Non‐Dominated Sorting Algorithm for Multi‐Objective Optimization Problems. Applied Sciences 2020, 10, 6858 .

AMA Style

Lingling Xue, Peng Zeng, Haibin Yu. SETNDS: A SET‐Based Non‐Dominated Sorting Algorithm for Multi‐Objective Optimization Problems. Applied Sciences. 2020; 10 (19):6858.

Chicago/Turabian Style

Lingling Xue; Peng Zeng; Haibin Yu. 2020. "SETNDS: A SET‐Based Non‐Dominated Sorting Algorithm for Multi‐Objective Optimization Problems." Applied Sciences 10, no. 19: 6858.

Journal article
Published: 09 September 2020 in IEEE Access
Reads 0
Downloads 0

As vision and language processing techniques have made great progress, mapless-visual navigation is occupying uppermost position in domestic robot field. However, most current end-to-end navigation models tend to be strictly trained and tested on identical datasets with stationary structure, which leads to great performance degradation when dealing with unseen targets and environments. Since the targets of same category could possess quite diverse features, generalization ability of these models is also limited by their visualized task description. In this paper we propose a model-agnostic metalearning based textdriven visual navigation model to achieve generalization to untrained tasks. Based on meta-reinforcement learning approach, the agent is capable of accumulating navigation experience from existing targets and environments. When applied to finding a new object or exploring in a new scene, the agent will quickly learn how to fulfill this unfamiliar task through relatively few recursive trials. To improve learning efficiency and accuracy, we introduce fully convolutional instance-aware semantic segmentation and Word2vec into our DRL network to respectively extract visual and semantic features according to object class, creating more direct and concise linkage between targets and their surroundings. Several experiments have been conducted on realistic dataset Matterport3D to evaluate its target-driven navigation performance and generalization ability. The results demonstrate that our adaptive navigation model could navigate to text-defined targets and achieve fast adaption to untrained tasks, outperforming other state-of-the-art navigation approaches.

ACS Style

Tianfang Xue; Haibin Yu. Model-Agnostic Metalearning-Based Text-Driven Visual Navigation Model for Unfamiliar Tasks. IEEE Access 2020, 8, 166742 -166752.

AMA Style

Tianfang Xue, Haibin Yu. Model-Agnostic Metalearning-Based Text-Driven Visual Navigation Model for Unfamiliar Tasks. IEEE Access. 2020; 8 (99):166742-166752.

Chicago/Turabian Style

Tianfang Xue; Haibin Yu. 2020. "Model-Agnostic Metalearning-Based Text-Driven Visual Navigation Model for Unfamiliar Tasks." IEEE Access 8, no. 99: 166742-166752.

Journal article
Published: 15 November 2019 in IEEE Transactions on Industrial Electronics
Reads 0
Downloads 0

In a large-scale structure assembly, a highly accurate normal direction measurement for robotic drilling is required. However, the robustness and accuracy of the existing normal direction measurement method with a range sensor would dramatically decrease for a high-curvature surface or the surface edge, due to the low resolution of the range sensor. To this end, a high-resolution fringe projection sensor is proposed to improve the measurement robustness and accuracy. In this method, different from the control scheme design with a reconstructed three-dimensional (3D) point cloud in fringe projection profilometry, which involves a high computational complexity, a novel two-dimensional (2D) phase-map-based two-stage control scheme, including coarse and refined normal direction adjustments, is designed to improve the robustness and accuracy. The coarse normal direction adjustment uses a phase map to estimate the normal direction, which is used to synthesize a position-based control scheme, leading to high robustness; the refined normal direction adjustment uses a phase map to synthesize an image-based control scheme, resulting in high accuracy. The proposed method is validated by simulations and experiments, which indicate that the proposed method is effective.

ACS Style

Gang Rao; Xiangdong Yang; Haibin Yu; Ken Chen; Jing Xu. Fringe-Projection-Based Normal Direction Measurement and Adjustment for Robotic Drilling. IEEE Transactions on Industrial Electronics 2019, 67, 9560 -9570.

AMA Style

Gang Rao, Xiangdong Yang, Haibin Yu, Ken Chen, Jing Xu. Fringe-Projection-Based Normal Direction Measurement and Adjustment for Robotic Drilling. IEEE Transactions on Industrial Electronics. 2019; 67 (11):9560-9570.

Chicago/Turabian Style

Gang Rao; Xiangdong Yang; Haibin Yu; Ken Chen; Jing Xu. 2019. "Fringe-Projection-Based Normal Direction Measurement and Adjustment for Robotic Drilling." IEEE Transactions on Industrial Electronics 67, no. 11: 9560-9570.

Journal article
Published: 01 April 2019 in IEEE Sensors Journal
Reads 0
Downloads 0

Cognitive radio provides a promising solution to reliable and time-efficient wireless sensor networks. However, cognitive capability brings a quite challenging issue to energy-constrained cognitive radio sensor networks (CRSNs) since large energy consumption is required for spectrum sensing and opportunistic access. Medium access control (MAC) is critical for cognitive sensors due to its influence on energy-consuming transceivers. This work proposes a short preamble cognitive MAC (SPC-MAC) protocol for CRSNs. The major contribution of SPC-MAC is the smart combination of short preamble sampling and opportunistic forwarding. As a result, SPC-MAC could support reliable and fast spectrum access while reducing energy consumption. Furthermore, SPC-MAC is a distributed cognitive MAC protocol without requiring any common control channels. The protocol modeling for SPC-MAC is performed rigorously. Analytical and simulation results validate the superior performance of SPC-MAC.

ACS Style

Meng Zheng; Chuqing Wang; Manyi Du; Lin Chen; Wei Liang; Haibin Yu. A Short Preamble Cognitive MAC Protocol in Cognitive Radio Sensor Networks. IEEE Sensors Journal 2019, 19, 6530 -6538.

AMA Style

Meng Zheng, Chuqing Wang, Manyi Du, Lin Chen, Wei Liang, Haibin Yu. A Short Preamble Cognitive MAC Protocol in Cognitive Radio Sensor Networks. IEEE Sensors Journal. 2019; 19 (15):6530-6538.

Chicago/Turabian Style

Meng Zheng; Chuqing Wang; Manyi Du; Lin Chen; Wei Liang; Haibin Yu. 2019. "A Short Preamble Cognitive MAC Protocol in Cognitive Radio Sensor Networks." IEEE Sensors Journal 19, no. 15: 6530-6538.

Conference paper
Published: 22 February 2019 in Proceedings of the IEEE
Reads 0
Downloads 0
ACS Style

Wei Liang; Meng Zheng; Jialin Zhang; Huaguang Shi; Haibin Yu; Yutuo Yang; Shuai Liu; Wenhua Yang; Xuefeng Zhao. WIA-FA and Its Applications to Digital Factory: A Wireless Network Solution for Factory Automation. Proceedings of the IEEE 2019, 107, 1053 -1073.

AMA Style

Wei Liang, Meng Zheng, Jialin Zhang, Huaguang Shi, Haibin Yu, Yutuo Yang, Shuai Liu, Wenhua Yang, Xuefeng Zhao. WIA-FA and Its Applications to Digital Factory: A Wireless Network Solution for Factory Automation. Proceedings of the IEEE. 2019; 107 (6):1053-1073.

Chicago/Turabian Style

Wei Liang; Meng Zheng; Jialin Zhang; Huaguang Shi; Haibin Yu; Yutuo Yang; Shuai Liu; Wenhua Yang; Xuefeng Zhao. 2019. "WIA-FA and Its Applications to Digital Factory: A Wireless Network Solution for Factory Automation." Proceedings of the IEEE 107, no. 6: 1053-1073.

Journal article
Published: 21 June 2018 in IEEE Transactions on Cybernetics
Reads 0
Downloads 0

The state-of-the-art online learning approaches are only capable of learning the metric for predefined tasks. In this paper, we consider a lifelong learning problem to mimic ``human learning,'' i.e., endowing a new capability to the learned metric for a new task from new online samples and incorporating the previous experiences. Therefore, we propose a new metric learning framework: lifelong metric learning (LML), which only utilizes the data of the new task to train the metric model while preserving the original capabilities. More specifically, the proposed LML maintains a common subspace for all learned metrics, named lifelong dictionary, transfers knowledge from the common subspace to learn each new metric learning task with task-specific idiosyncrasy, and redefines the common subspace over time to maximize performance across all metric tasks. For model optimization, we apply online passive aggressive optimization algorithm to achieve lifelong metric task learning, where the lifelong dictionary and task-specific partition are optimized alternatively and consecutively. Finally, we evaluate our approach by analyzing several multitask metric learning datasets. Extensive experimental results demonstrate effectiveness and efficiency of the proposed framework.

ACS Style

Gan Sun; Cong Yang; Ji Liu; Lianqing Liu; Xiaowei Xu; Haibin Yu. Lifelong Metric Learning. IEEE Transactions on Cybernetics 2018, 49, 3168 -3179.

AMA Style

Gan Sun, Cong Yang, Ji Liu, Lianqing Liu, Xiaowei Xu, Haibin Yu. Lifelong Metric Learning. IEEE Transactions on Cybernetics. 2018; 49 (8):3168-3179.

Chicago/Turabian Style

Gan Sun; Cong Yang; Ji Liu; Lianqing Liu; Xiaowei Xu; Haibin Yu. 2018. "Lifelong Metric Learning." IEEE Transactions on Cybernetics 49, no. 8: 3168-3179.

Conference paper
Published: 01 April 2018 in 2018 IEEE Wireless Communications and Networking Conference (WCNC)
Reads 0
Downloads 0

Cognitive radio has been widely recognized as a promising solution to reliable and time-efficient wireless sensor networks. However, cognitive capability requires an extra energy consumption in spectrum sensing and spectrum access, which imposes a rather challenging problem to low cost sensors. This paper proposes a short preamble cognitive medium access control (SPC-MAC) protocol which supports reliable and fast spectrum access while addressing the energy conservation problem in cognitive radio sensor networks (CRSNs). The novelty of SPC-MAC lies in the combination of short preamble sampling (for supporting low duty cycling in CRSNs) and the opportunistic forwarding (for reliable and fast transmission). Because of the self-organizing nature, SPC-MAC does not require a common control channel. Extensive simulations demonstrate the advantage of SPC-MAC over existing works in terms of energy consumption and throughput.

ACS Style

Meng Zheng; Manyi Du; Lin Chen; Wei Liang; Haibin Yu. SPC-MAC: A short preamble cognitive MAC protocol for cognitive radio sensor networks. 2018 IEEE Wireless Communications and Networking Conference (WCNC) 2018, 1 -6.

AMA Style

Meng Zheng, Manyi Du, Lin Chen, Wei Liang, Haibin Yu. SPC-MAC: A short preamble cognitive MAC protocol for cognitive radio sensor networks. 2018 IEEE Wireless Communications and Networking Conference (WCNC). 2018; ():1-6.

Chicago/Turabian Style

Meng Zheng; Manyi Du; Lin Chen; Wei Liang; Haibin Yu. 2018. "SPC-MAC: A short preamble cognitive MAC protocol for cognitive radio sensor networks." 2018 IEEE Wireless Communications and Networking Conference (WCNC) , no. : 1-6.

Journal article
Published: 19 March 2018 in IEEE Systems Journal
Reads 0
Downloads 0

This paper investigates the joint optimization of time allocation and transmit power in wireless-powered communication networks (WPCNs), where the downlink wireless energy transfer happens in the first phase, and then uplink wireless information transfer takes place in the second phase. Considering the rate fairness among users, we formulate the resource allocation in WPCNs as a network utility maximization (NUM) problem that aims to maximize the sum of utilities over all users subject to the energy causality constraint, the quality of service constraint, and the physical constraint. We further prove that the NUM problems with different channel access schemes (the time-division multiple access scheme, the concurrent transmission scheme, and the nonorthogonal multiple access scheme) can be equivalently transformed to convex problems by deliberated math transformations. Simulations demonstrate the efficiency of this paper.

ACS Style

Meng Zheng; Wei Liang; Haibin Yu. Utility-Based Resource Allocation in Wireless-Powered Communication Networks. IEEE Systems Journal 2018, 12, 3881 -3884.

AMA Style

Meng Zheng, Wei Liang, Haibin Yu. Utility-Based Resource Allocation in Wireless-Powered Communication Networks. IEEE Systems Journal. 2018; 12 (4):3881-3884.

Chicago/Turabian Style

Meng Zheng; Wei Liang; Haibin Yu. 2018. "Utility-Based Resource Allocation in Wireless-Powered Communication Networks." IEEE Systems Journal 12, no. 4: 3881-3884.

Journal article
Published: 15 January 2018 in IEEE Transactions on Industrial Informatics
Reads 0
Downloads 0

Continuous body temperature measurement (CBTM) is of great significance for human health state monitoring. To avoid interfering with users' daily activities, CBTM is usually achieved using wearable noninvasive thermometers. Current wearable noninvasive thermometers employ steady-state models used in nonwearable thermometers; as a result, the reaction time is long and the measurement can be disturbed by users' activities. However, there is no work to solve these issues. In this paper, first, differences between wearable and nonwearable temperature measurement are analyzed. Second, the relationship among the human body temperature, the skin temperature, and the device temperature is modeled based on artificial neural networks (ANNs). Third, this paper proposes a novel multiple ANNs-based wearable CBTM method. Experiments show that the reaction time of the proposed method is about one-tenth of that of other popular wearable noninvasive CBTM methods, while the accuracy and the robustness are improved.

ACS Style

Chunhe Song; Peng Zeng; Zhongfeng Wang; Hai Zhao; Haibin Yu. Wearable Continuous Body Temperature Measurement Using Multiple Artificial Neural Networks. IEEE Transactions on Industrial Informatics 2018, 14, 4395 -4406.

AMA Style

Chunhe Song, Peng Zeng, Zhongfeng Wang, Hai Zhao, Haibin Yu. Wearable Continuous Body Temperature Measurement Using Multiple Artificial Neural Networks. IEEE Transactions on Industrial Informatics. 2018; 14 (10):4395-4406.

Chicago/Turabian Style

Chunhe Song; Peng Zeng; Zhongfeng Wang; Hai Zhao; Haibin Yu. 2018. "Wearable Continuous Body Temperature Measurement Using Multiple Artificial Neural Networks." IEEE Transactions on Industrial Informatics 14, no. 10: 4395-4406.

Journal article
Published: 29 November 2017 in IEEE Transactions on Smart Grid
Reads 0
Downloads 0

Predicting specific household characteristics (e.g., age of person, household income, cooking style, etc) from their everyday electricity consumption (i.e., smart meter data) enables energy provider to develop many intelligent business applications or help consumers to reduce their energy consumption. However, most existing works intend to predict single household characteristic via smart meter data independently, and ignore the joint analysis of different characteristics. In this paper, we consider each characteristic as an independent task and intend to predict multiple household characteristics simultaneously by designing a new multi-task learning formulation: Discriminative Multi- Task Relationship Learning (DisMTRL). Specifically, two main challenges need to be handled: 1) task relationship, that is the embedded structure of relationships among different characteristics; 2) feature learning, there exist redundant features in original training data. To achieve these, our DisMTRL model aims to obtain a simple but robust weight matrix through capturing the intrinsic relatedness among different characteristics by task covariance matrix (MTRL) and incorporating the discriminative features via feature covariance matrix (Dis). For model optimization, we employ an alternating minimization strategy to learn the optimal weight matrix as well as the relationship between tasks by converting feature learning regularization as trace minimization problem. For evaluation, we adopt a smart meter dataset collected from 4232 households in Ireland at a 30min granularity over an interval of 1.5 years. The experimental results justify the effectiveness of our proposed model.

ACS Style

Gan Sun; Yang Cong; Dongdong Hou; Huijie Fan; Xiaowei Xu; Haibin Yu. Joint Household Characteristic Prediction via Smart Meter Data. IEEE Transactions on Smart Grid 2017, 10, 1834 -1844.

AMA Style

Gan Sun, Yang Cong, Dongdong Hou, Huijie Fan, Xiaowei Xu, Haibin Yu. Joint Household Characteristic Prediction via Smart Meter Data. IEEE Transactions on Smart Grid. 2017; 10 (2):1834-1844.

Chicago/Turabian Style

Gan Sun; Yang Cong; Dongdong Hou; Huijie Fan; Xiaowei Xu; Haibin Yu. 2017. "Joint Household Characteristic Prediction via Smart Meter Data." IEEE Transactions on Smart Grid 10, no. 2: 1834-1844.

Letter
Published: 20 November 2017 in Science China Information Sciences
Reads 0
Downloads 0
ACS Style

Chi Xu; Peng Zeng; Wei Liang; Haibin Yu. Secure resource allocation for green and cognitive device-to-device communication. Science China Information Sciences 2017, 61, 1 .

AMA Style

Chi Xu, Peng Zeng, Wei Liang, Haibin Yu. Secure resource allocation for green and cognitive device-to-device communication. Science China Information Sciences. 2017; 61 (2):1.

Chicago/Turabian Style

Chi Xu; Peng Zeng; Wei Liang; Haibin Yu. 2017. "Secure resource allocation for green and cognitive device-to-device communication." Science China Information Sciences 61, no. 2: 1.

Journal article
Published: 20 July 2017 in Pattern Recognition
Reads 0
Downloads 0

In this paper, we focus on user attribute analysis by recasting such a problem as a multi-task learning issue, where each attribute is considered as an independent task. In comparison with traditional data analysis, the missing labels problem broadly presents for smart sensor data due to some objective / subjective factors, where the label incompleteness increases the difficulty significantly. Therefore, we design a semi-supervised multi-task learning model (S2MTL) to handle the missing labels issue. For modeling, we integrate the matrix factorization to learn the mapping feature dictionary and attribute space information simultaneously, and adopt the pairwise affinity similarity to incorporate the unlabeled data information, where the low rank property and model efficiency can be well controlled. For model optimization, we convert our model as two individual convex subproblems with one non-smooth, and implement an alternating direction method to generate an efficient optimal solution. State-of-the-art models have validated the effectiveness and efficiency of our proposed model via extensive experiments and comparisons, on two public datasets and our new smart building dataset.

ACS Style

Yang Cong; Gan Sun; Ji Liu; Haibin Yu; Jiebo Luo. User attribute discovery with missing labels. Pattern Recognition 2017, 73, 33 -46.

AMA Style

Yang Cong, Gan Sun, Ji Liu, Haibin Yu, Jiebo Luo. User attribute discovery with missing labels. Pattern Recognition. 2017; 73 ():33-46.

Chicago/Turabian Style

Yang Cong; Gan Sun; Ji Liu; Haibin Yu; Jiebo Luo. 2017. "User attribute discovery with missing labels." Pattern Recognition 73, no. : 33-46.

Journal article
Published: 17 July 2017 in Sustainability
Reads 0
Downloads 0

This paper presents an integrative demand response (DR) mechanism for energy management of appliances, an energy storage system and an electric vehicle (EV) within a home. The paper considers vehicle-to-home (V2H) and vehicle-to-grid (V2G) functions for energy management of EVs and the degradation cost of the EV battery caused by the V2H/V2G operation in developing the proposed DR mechanism. An efficient optimization algorithm is developed based on approximate dynamic programming, which overcomes the challenges of solving high dimensional optimization problems for the integrative home energy system. To investigate how the participation of different home appliances affects the DR efficiency, several DR scenarios are designed. Then, a detailed simulation study is conducted to investigate and compare home energy management efficiency under different scenarios.

ACS Style

Hepeng Li; Peng Zeng; Chuanzhi Zang; Haibin Yu; Shuhui Li. An Integrative DR Study for Optimal Home Energy Management Based on Approximate Dynamic Programming. Sustainability 2017, 9, 1248 .

AMA Style

Hepeng Li, Peng Zeng, Chuanzhi Zang, Haibin Yu, Shuhui Li. An Integrative DR Study for Optimal Home Energy Management Based on Approximate Dynamic Programming. Sustainability. 2017; 9 (7):1248.

Chicago/Turabian Style

Hepeng Li; Peng Zeng; Chuanzhi Zang; Haibin Yu; Shuhui Li. 2017. "An Integrative DR Study for Optimal Home Energy Management Based on Approximate Dynamic Programming." Sustainability 9, no. 7: 1248.

Journal article
Published: 17 March 2017 in IEEE Transactions on Wireless Communications
Reads 0
Downloads 0

This paper studies a green paradigm for the underlay coexistence of primary users (PUs) and secondary users (SUs) in energy harvesting cognitive radio networks (EH-CRNs), wherein battery-free SUs capture both the spectrum and the energy of PUs to enhance spectrum efficiency and green energy utilization. To lower the transmit powers of SUs, we employ multi-hop transmission with time division multiple access, by which SUs first harvest energy from the RF signals of PUs and then transmit data in the allocated time concurrently with PUs, all in the licensed spectrum. In this way, the available transmit energy of each SU mainly depends on the harvested energy before the turn to transmit, namely energy causality. Meanwhile, the transmit powers of SUs must be strictly controlled to protect PUs from harmful interference. Thus, subject to the energy causality constraint and the interference power constraint, we study the end-to-end throughput maximization problem for optimal time and power allocation. To solve this nonconvex problem, we first equivalently transform it into a convex optimization problem and then propose the joint optimal time and power allocation (JOTPA) algorithm that iteratively solves a series of feasibility problems until convergence. Extensive simulations evaluate the performance of EH-CRNs with JOTPA in three typical deployment scenarios and validate the superiority of JOTPA by making comparisons with two other resource allocation algorithms.

ACS Style

Chi Xu; Meng Zheng; Wei Liang; Haibin Yu; Ying-Chang Liang. End-to-End Throughput Maximization for Underlay Multi-Hop Cognitive Radio Networks With RF Energy Harvesting. IEEE Transactions on Wireless Communications 2017, 16, 3561 -3572.

AMA Style

Chi Xu, Meng Zheng, Wei Liang, Haibin Yu, Ying-Chang Liang. End-to-End Throughput Maximization for Underlay Multi-Hop Cognitive Radio Networks With RF Energy Harvesting. IEEE Transactions on Wireless Communications. 2017; 16 (6):3561-3572.

Chicago/Turabian Style

Chi Xu; Meng Zheng; Wei Liang; Haibin Yu; Ying-Chang Liang. 2017. "End-to-End Throughput Maximization for Underlay Multi-Hop Cognitive Radio Networks With RF Energy Harvesting." IEEE Transactions on Wireless Communications 16, no. 6: 3561-3572.

Article
Published: 06 February 2017 in Wireless Personal Communications
Reads 0
Downloads 0

This paper investigates the cooperative multihop cognitive relay networks (CRNs) under a novel two-dimensional spatial-temporal opportunity model, in which free opportunity and sharing opportunity are defined to enhance the spectrum efficiency. Correspondingly, a joint spatial-temporal access scheme (JSTAS) is proposed to realize successive spectrum access for continuous data transmission. The multihop CRNs with fixed relaying employ decode-and-forward with and without signal-to-noise ratio selection, which are named as SDF and DF, respectively. Then, considering the interference constraints from multiple primary receivers, the interference of one primary transmitter and the maximum transmit powers of cognitive users, we study the outage performance of multihop CRNs with JSTAS over Nakagami-m fading. To comprehensively evaluate JSTAS, we further present the pure spatial access scheme (SAS) and the pure temporal access scheme (TAS) under the spatial-temporal opportunity model, and calculate their average network outage probabilities. Simulation results demonstrate that SDF outperforms DF, while JSTAS outperforms SAS and TAS under all considered scenarios.

ACS Style

Chi Xu; Meng Zheng; Wei Liang; Haibin Yu. Joint Spatial-Temporal Access Scheme for Multihop Cognitive Relay Networks Over Nakagami-m Fading. Wireless Personal Communications 2017, 95, 3097 -3117.

AMA Style

Chi Xu, Meng Zheng, Wei Liang, Haibin Yu. Joint Spatial-Temporal Access Scheme for Multihop Cognitive Relay Networks Over Nakagami-m Fading. Wireless Personal Communications. 2017; 95 (3):3097-3117.

Chicago/Turabian Style

Chi Xu; Meng Zheng; Wei Liang; Haibin Yu. 2017. "Joint Spatial-Temporal Access Scheme for Multihop Cognitive Relay Networks Over Nakagami-m Fading." Wireless Personal Communications 95, no. 3: 3097-3117.

Journal article
Published: 01 January 2017 in International Journal of Sensor Networks
Reads 0
Downloads 0

This paper studies a novel channel access scheme for delay-sensitive multi-hop cognitive radio sensor networks (CRSNs) with fixed decode-and-forward relaying. We first formulate a two-dimensional spatial-temporal opportunity model wherein free and sharing opportunities are defined, and then propose a joint spatial-temporal access scheme (JSTAS) to achieve successive channel access for continuous data transmission. By spatial sensing and temporal sensing, the cognitive sensors with JSTAS control the transmission powers by fully considering their maximum transmission powers, the interference constraints of multiple primary receivers and the interference from one primary transmitter. To evaluate JSTAS, we derive the closed-form outage probability under Rayleigh fading channels and make comparisons among five different schemes. Simulation results demonstrate that JSTAS has better outage performance than that of pure spatial or temporal access scheme under all considered scenarios.

ACS Style

Chi Xu; Meng Zheng; Wei Liang; Haibin Yu; Youjian Zhao. Joint spatial-temporal access scheme of multi-hop cognitive radio sensor networks. International Journal of Sensor Networks 2017, 25, 1 .

AMA Style

Chi Xu, Meng Zheng, Wei Liang, Haibin Yu, Youjian Zhao. Joint spatial-temporal access scheme of multi-hop cognitive radio sensor networks. International Journal of Sensor Networks. 2017; 25 (1):1.

Chicago/Turabian Style

Chi Xu; Meng Zheng; Wei Liang; Haibin Yu; Youjian Zhao. 2017. "Joint spatial-temporal access scheme of multi-hop cognitive radio sensor networks." International Journal of Sensor Networks 25, no. 1: 1.

Journal article
Published: 01 January 2017 in Computer Networks
Reads 0
Downloads 0
ACS Style

Meng Zheng; Chi Xu; Wei Liang; Haibin Yu; Lin Chen. Time-efficient cooperative spectrum sensing via analog computation over multiple-access channel. Computer Networks 2017, 112, 84 -94.

AMA Style

Meng Zheng, Chi Xu, Wei Liang, Haibin Yu, Lin Chen. Time-efficient cooperative spectrum sensing via analog computation over multiple-access channel. Computer Networks. 2017; 112 ():84-94.

Chicago/Turabian Style

Meng Zheng; Chi Xu; Wei Liang; Haibin Yu; Lin Chen. 2017. "Time-efficient cooperative spectrum sensing via analog computation over multiple-access channel." Computer Networks 112, no. : 84-94.