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Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning

REMOTE SENSING 2023年第17期15卷 4165页

作者： Yan, Suqing Su, Yalan Luo, Xiaonan Sun, Anqing Ji, Yuanfa Ghazali, Kamarul Hawari bin Guilin Univ Elect Technol Guangxi Key Lab Precis Nav Technol & Applicat Guilin 541004 Peoples R China Guilin Univ Elect Technol Sch Informat & Commun Guilin 541004 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China Natl & Local Joint Engn Res Ctr Satellite Nav Loca Guilin 541004 Peoples R China GUET Nanning E Tech Res Inst Co Ltd Nanning 530031 Peoples R China Univ Malaysia Pahang Fac Elect & Elect Engn Pekan 25200 Malaysia

Accurate location information has significant commercial and economic value as they are widely used in intelligent manufacturing, material localization and smart homes. Magnetic sequence-based approaches show great promise mainly due to their pervasiveness and stability. However, existing geomagnetic indoor localization methods are facing the problems of location ambiguity and feature extraction deficiency, which will lead to large localization errors. To address these issues, we propose a coarse-to-fine geomagnetic indoor localization method based on deep learning. First, a multidimensional geomagnetic feature extraction method is presented which can extract magnetic features from spatial and temporal aspects. Then, a hierarchical deep neural network model is devised to extract more accurate geomagnetic information and corresponding location clues for more accurate localization. Finally, localization is achieved through a particle filter combined with IMU localization. To evaluate the performance of the proposed methods, we carried out several experiments at three trial paths with two heterogeneous devices, Vivo X30 and Huawei Mate30. Experimental results demonstrate that the proposed algorithm can achieve more accurate localization performance than the state-of-the-art methods. Meanwhile, the proposed algorithm has low cost and good pervasiveness for different devices.

关键词： indoor localization geomagnetic signals neural networks pedestrian dead reckoning particle filter

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Stability of networked control systems under malicious attacks and bit rate constraints

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2024年第18期361卷

作者： Wang, Wen-Hui Wang, Yan-Wu Hu, Meng-Jie Yang, Wu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221000 Peoples R China Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China

This article investigates the stability of networked control systems under two types of malicious attacks and bit rate constraints. The malicious attacks may lead to parameter mismatches, including scaling variable mismatches between the encoder and decoder sides and discrepancies between the data sent by the encoder and received by the decoder, causing system instability. To address these issues, scaling variable update rules are proposed for the encoder and decoder sides separately to mitigate the adverse effects of malicious attacks. A control strategy featuring a compensation term is designed to counteract the detrimental impacts of the mismatch. A lower bound on the bit rate is established to prevent quantization saturation and achieve exponential stability of the system. Finally, confirmatory and comparative simulations showcase the effectiveness of the theoretical insights and the merits of the proposed strategies, respectively.

关键词： Networked control system Malicious attack Bit rate constraint

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Cascading and Enhanced Residual Networks for Accurate Single-image Super-Resolution

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第1期51卷 115-125页

作者： Lan, Rushi Sun, Long Liu, Zhenbing Lu, Huimin Su, Zhixun Pang, Cheng Luo, Xiaonan Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Kyushu Inst Technol Dept Mech & Control Engn Kitakyushu Fukuoka 8048550 Japan Dalian Univ Technol Sch Math Sci Dalian 116024 Peoples R China Guilin Univ Elect Technol Natl Local Joint Engn Res Ctr Satellite Nav & Loc Guilin 541004 Peoples R China

Deep convolutional neural networks (CNNs) have contributed to the significant progress of the single-image super-resolution (SISR) field. However, the majority of existing CNN-based models maintain high performance with massive parameters and exceedingly deeper structures. Moreover, several algorithms essentially have underused the low-level features, thus causing relatively low performance. In this article, we address these problems by exploring two strategies based on novel local wider residual blocks (LWRBs) to effectively extract the image features for SISR. We propose a cascading residual network (CRN) that contains several locally sharing groups (LSGs), in which the cascading mechanism not only promotes the propagation of features and the gradient but also eases the model training. Besides, we present another enhanced residual network (ERN) for image resolution enhancement. ERN employs a dual global pathway structure that incorporates nonlocal operations to catch long-distance spatial features from the the original low-resolution (LR) input. To obtain the feature representation of the input at different scales, we further introduce a multiscale block (MSB) to directly detect low-level features from the LR image. The experimental results on four benchmark datasets have demonstrated that our models outperform most of the advanced methods while still retaining a reasonable number of parameters.

关键词： Feature extraction image reconstruction Spatial resolution Convolutional neural networks Training graphics Convolutional neural network multiscale learning residual learning single-image super-resolution (SISR)

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Real-Time 3D Reconstruction Method Based on Monocular Vision

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SENSORS 2021年第17期21卷 5909-5909页

作者： Jia, Qingyu Chang, Liang Qiang, Baohua Zhang, Shihao Xie, Wu Yang, Xianyi Sun, Yangchang Yang, Minghao Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China Chinese Acad Sci CASIA Res Ctr Brain Inspired Intelligence BII Inst Automat Beijing 100190 Peoples R China

Real-time 3D reconstruction is one of the current popular research directions of computer vision, and it has become the core technology in the fields of virtual reality, industrialized automatic systems, and mobile robot path planning. Currently, there are three main problems in the real-time 3D reconstruction field. Firstly, it is expensive. It requires more varied sensors, so it is less convenient. Secondly, the reconstruction speed is slow, and the 3D model cannot be established accurately in real time. Thirdly, the reconstruction error is large, which cannot meet the requirements of scenes with accuracy. For this reason, we propose a real-time 3D reconstruction method based on monocular vision in this paper. Firstly, a single RGB-D camera is used to collect visual information in real time, and the YOLACT++ network is used to identify and segment the visual information to extract part of the important visual information. Secondly, we combine the three stages of depth recovery, depth optimization, and deep fusion to propose a three-dimensional position estimation method based on deep learning for joint coding of visual information. It can reduce the depth error caused by the depth measurement process, and the accurate 3D point values of the segmented image can be obtained directly. Finally, we propose a method based on the limited outlier adjustment of the cluster center distance to optimize the three-dimensional point values obtained above. It improves the real-time reconstruction accuracy and obtains the three-dimensional model of the object in real time. Experimental results show that this method only needs a single RGB-D camera, which is not only low cost and convenient to use, but also significantly improves the speed and accuracy of 3D reconstruction.

关键词： monocular vision YOLACT plus plus deep optimization real-time 3D reconstruction

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Virtual Reality Aided High-Quality 3D Reconstruction by Remote Drones

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ACM TRANSACTIONS ON INTERNET TECHNOLOGY 2022年第1期22卷 1-20页

作者： Zhang, Di Xu, Feng Pun, Chi-Man Yang, Yang Lan, Rushi Wang, Liejun Li, Yujie Gao, Hao Nanjing Univ Posts & Telecommun Coll Automat 9 Wenyuan Rd Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence 9 Wenyuan Rd Nanjing 210023 Peoples R China Tsinghua Univ Sch Software 30 Shuangqing Rd Beijing 100084 Peoples R China Univ Macau Dept Comp & Informat Sci Taipa Macao Peoples R China Nanjing Univ Posts & Telecommun Nanjing Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc 1 Jinji Rd Guilin 541004 Peoples R China Xinjiang Univ Coll Software Engn 666 Shengli Rd Urumqi 830046 Peoples R China Kyushu Inst Technol Kitakyushu Fukuoka Japan

Artificial intelligence including deep learning and 3D reconstruction methods is changing the daily life of people. Now, an unmanned aerial vehicle that can move freely in the air and avoid harsh ground conditions has been commonly adopted as a suitable tool for 3D reconstruction. The traditional 3D reconstruction mission based on drones usually consists of two steps: image collection and offline post-processing. But there are two problems: one is the uncertainty of whether all parts of the target object are covered, and another is the tedious post-processing time. Inspired by modern deep learning methods, we build a telexistence drone system with an onboard deep learning computation module and a wireless data transmission module that perform incremental real-time dense reconstruction of urban cities by itself. Two technical contributions are proposed to solve the preceding issues. First, based on the popular depth fusion surface reconstruction framework, we combine it with a visual-inertial odometry estimator that integrates the inertial measurement unit and allows for robust camera tracking as well as high-accuracy online 3D scan. Second, the capability of real-time 3D reconstruction enables a new rendering technique that can visualize the reconstructed geometry of the target as navigation guidance in the HMD. Therefore, it turns the traditional path-planning-based modeling process into an interactive one, leading to a higher level of scan completeness. The experiments in the simulation system and our real prototype demonstrate an improved quality of the 3D model using our artificial intelligence leveraged drone system.

关键词： 3D reconstruction virtual reality unmanned aerial vehicle telexistence human-robot-interaction

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Multi-strategy particle swarm and ant colony hybrid optimization for airport taxiway planning problem

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INFORMATION SCIENCES 2022年 612卷 576-593页

作者： Deng, Wu Zhang, Lirong Zhou, Xiangbing Zhou, Yongquan Sun, Yuzhu Zhu, Weihong Chen, Huayue Deng, Wuquan Chen, Huiling Zhao, Huimin Civil Aviat Univ China Sch Elect Informat & Automat Tianjin 300300 Peoples R China Sichuan Tourism Univ Sch Informat & Engn Chengdu 610100 Peoples R China Shenzhen Airlines Co Ltd Shenzhen 518128 Peoples R China Guangxi Univ Nationalities Coll Artificial Intelligence Nanning 530006 Peoples R China China West Normal Univ Sch Comp Sci Nanchong 637002 Peoples R China Chongqing Univ Cent Hosp Dept Endocrinol Key Lab Biorheol Sci & TechnolMinist Educ Chongqing 400014 Peoples R China Wenzhou Univ Key Lab Intelligent Image Proc & Anal Wenzhou 325035 Zhejiang Peoples R China

As the connecting hub of the airport runways and gates, the taxiway plays a very important role in the rational allocation and utilization of the airport resources. In this paper, a multi -strategy particle swarm and ant colony hybrid optimization algorithm, namely MPSACO is proposed to solve the airport runway planning problem and avoid taxiway conflicts and conflict propagation. Firstly, a reasonable mathematical model of airport taxiway planning is constructed. Secondly, the multi-strategy particle swarm optimization algorithm (CWBPSO) is employed to propose a new pheromone initialization approach for ACO. And a new pheromone allocation mechanism is designed and a new pheromone update strategy based on the principle of wolf predation is developed, which are combined to design a new pheromone hybrid strategy to enhance the pheromone influence of the opti-mal solution, dynamically adjust the search direction, and avoid to decline the best search ability, so as to greatly improve the optimization performance of the algorithm. Finally, an airport taxiway planning approach based on MPSACO is proposed, and a conflict adjust-ment strategy based on speed priority and the idea of first come and first serve (FCFS) is designed to effectively optimize the airport taxiway path. In order to prove the effective-ness of the proposed algorithm/method, 10 traveling salesman problems (TSP) with differ-ent scales and an actual airport taxiway planning problem are selected in here. The experiment results show that the proposed MPSACO can effectively solve TSP and obtain the better optimal solutions, and the proposed airport taxiway planning approach can effectively plan the airport taxiing path, avoid the airport taxiing conflicts, and improve the utilization rate of taxiway resources.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Ant colony optimozation Particle swarm optimozation Pheromone hybrid strategy First come and first serve Multi-strategy Taxiway planning

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A Low-Cost and Efficient Indoor Fusion Localization Method

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SENSORS 2022年第15期22卷 5505-5505页

作者： Yan, Suqing Wu, Chunping Deng, Honggao Luo, Xiaonan Ji, Yuanfa Xiao, Jianming Guilin Univ Elect Technol Guangxi Key Lab Precis Nav Technol & Applicat Guilin 541004 Peoples R China Guilin Univ Elect Technol Sch Informat & Commun Guilin 541004 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China Natl & Local Joint Engn Res Ctr Satellite Nav Pos Guilin 541004 Peoples R China Guilin Univ Dept Sci & Engn Guilin 541004 Peoples R China

Accurate indoor location information has considerable social and economic value in applications, such as pedestrian heatmapping and indoor navigation. Ultrasonic-based approaches have received significant attention mainly since they have advantages in terms of positioning with temporal correlation. However, it is a great challenge to gain accurate indoor localization due to complex indoor environments such as non-uniform indoor facilities. To address this problem, we propose a fusion localization method in the indoor environment that integrates the localization information of inertial sensors and acoustic signals. Meanwhile, the threshold scheme is used to eliminate outliers during the positioning process. In this paper, the estimated location is fused by the adaptive distance weight for the time difference of arrival (TDOA) estimation and improved pedestrian dead reckoning (PDR) estimation. Three experimental scenes have been developed. The experimental results demonstrate that the proposed method has higher localization accuracy in determining the pedestrian location than the state-of-the-art methods. It resolves the problem of outliers in indoor acoustic signal localization and cumulative errors in inertial sensors. The proposed method achieves better performance in the trade-off between localization accuracy and low cost.

关键词： indoor localization step length estimation acoustic signal improved pedestrian dead reckon

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Chinese Emotional Dialogue Response Generation via Reinforcement Learning

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ACM TRANSACTIONS ON INTERNET TECHNOLOGY 2021年第4期21卷 1–17页

作者： Lan, Rushi Wang, Jing Huang, Wenming Deng, Zhenrong Sun, Xiyan Chen, Zhuo Luo, Xiaonan Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc 1 Jinji Rd Guilin 541004 Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin Peoples R China Guilin Univ Elect Technol Natl Local Joint Engn Res Ctr Satellite Nav & Loc 1 Jinji Rd Guilin 541004 Peoples R China Univ British Columbia Dept Mech Engn Vancouver Campus2329 West Mall Vancouver BC V6T 1Z4 Canada

In an open-domain dialogue system, recognition and expression of emotions are the key factors for success. Most of the existing research related to Chinese dialogue systems aims at improving the quality of content but ignores the expression of human emotions. In this article, we propose a Chinese emotional dialogue response generation algorithm based on reinforcement learning that can generate responses not only according to content but also according to emotion. In the proposed method, a multi-emotion classification model is first used to add emotion labels to the corpus of post-response pairs. Then, with the help of reinforcement learning, the reward function is constructed based on two aspects, namely, emotion and content. Among the generated candidates, the system selects the one with long-term success as the best reply. At the same time, to avoid safe responses and diversify dialogue, a diversity beam search algorithm is applied in the decoding process. The comparative experiments demonstrate that the proposed model achieves satisfactory results according to both automatic and human evaluations.

关键词： Dialogue generation emotion classification reinforcement learning safe response reward function

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Deep Neural Network-Based Fusion Localization Using Smartphones

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SENSORS 2023年第21期23卷 8680-8680页

作者： Yan, Suqing Su, Yalan Xiao, Jianming Luo, Xiaonan Ji, Yuanfa Bin Ghazali, Kamarul Hawari Guilin Univ Elect Technol Guangxi Key Lab Precis Nav Technol & Applicat Guilin 541004 Peoples R China Guilin Univ Elect Technol Sch Informat & Commun Guilin 541004 Peoples R China Guilin Univ Dept Sci & Engn Guilin 541006 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China Natl & Local Joint Engn Res Ctr Satellite Nav Loca Guilin 541004 Peoples R China GUET Nanning Etech Res Inst Co Ltd Nanning 530031 Peoples R China Univ Malaysia Pahang Fac Elect & Elect Engn Pekan 25200 Malaysia

Indoor location-based services (LBS) have tremendous practical and social value in intelligent life due to the pervasiveness of smartphones. The magnetic field-based localization method has been an interesting research hotspot because of its temporal stability, ubiquitousness, infrastructure-free nature, and good compatibility with smartphones. However, utilizing discrete magnetic signals may result in ambiguous localization features caused by random noise and similar magnetic signals in complex symmetric and large-scale indoor environments. To address this issue, we propose a deep neural network-based fusion indoor localization system that integrates magnetic and pedestrian dead reckoning (PDR). In this system, we first propose a ResNet-GRU-LSTM neural network model to achieve magnetic localization more accurately. Afterward, we put forward a multifeatured-driven step length estimation. A hierarchy GRU (H-GRU) neural network model is proposed, and a multidimensional dataset using acceleration and a gyroscope is constructed to extract more valid characteristics. Finally, more reliable and accurate pedestrian localization can be achieved under the particle filter framework. Experiments were conducted at two trial sites with two pedestrians and four smartphones. Results demonstrate that the proposed system achieves better accuracy and robustness than other traditional localization algorithms. Moreover, the proposed system exhibits good generality and practicality in real-time localization with low cost and low computational complexity.

关键词： indoor localization deep neural networks magnetic dead reckoning step length estimation smartphone

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A local dense decision-making method for solving the maximum clique problem in large-scale undirected graphs

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 102卷

作者： Yu, Zhuo Wang, Xiaofeng Zhao, Jian North MinZu Univ Sch Comp Sci & Engn Yinchuan 750021 Peoples R China North MinZu Univ Key Lab Image Graph Intelligent Proc State Ethn Af Yinchuan 750021 Peoples R China Northwest Univ Sch Informat Sci & Technol Xian 710127 Peoples R China

The maximum clique problem is a classic difficult problem. Most traditional solutions are branch and bound-based exact algorithms that perform well in terms of both accuracy and time at small legend sizes. However, in recent years, the scale of legend models in practical applications has expanded rapidly, and the traditional undirected graphical model solving methods are no longer applicable. At larger scales, a heuristic decision-making method for local density is proposed, a density index function is constructed, and a decision-making inference for finding maximum cliques is established. Our algorithm avoids random and disorderly traversal solutions, and it ensures the accuracy of the solution process. At the same time, a fast search threshold is added to the algorithm to improve the solution efficiency and local optimization ability. The experimental comparison shows that this algorithm and its improvements have better solution accuracy and solution time compared with the maximum clique accuracy algorithm.

关键词： The maximum clique Decision function Combination optimization Locally dense Branch and Bound

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