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Indoor Human Activity Perception Based on Infrared Thermopile Array Sensor
2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA)
Year: 2024 | Conference Paper |
Human activity perception systems play a significant role in “SENSEable City”, smart homes and medical care, etc. Thermopile infrared array sensor (TPAS) is a new type of sensor to detect human targets without privacy invasion. This paper focuses on the perception of static and dynamic human activities. A hardware platform is built based on TPAS MLX90640 with the output of 24 \times 32 pixels, t...Show More
Non-Contact Respiratory and Heart Rate Detection Using IR-UWB Radars Under Random Body Activity
In recent years, more and more attention has been paid to the daily detection of vital signs. Using impulse radio ultrawideband (IR-UWB) radars to detect vital signs [respiratory rate (RR) and heart rate (HR)] has become a research hotspot. In healthcare, long-term continual monitoring of vital signs is required. However, most research works on vital signs detection assume that the subjects always...Show More
Occluded Multi-Human Target Segmentation in Dynamic Indoor Scenes Based on Thermopile Array Sensor
Low-resolution thermopile infrared array sensor (TPAS) has gained increasing attention for real-time perception and monitoring of indoor human targets. Most recent researches focus on single individual just because of its low resolution of TPAS. In addition, there are many dynamically changing heat sources besides human targets in indoor environments. The dynamic background removal and occluded hu...Show More
A Cloud-Edge-Based Multi-Label Learning and Fast Clustering Method for Emerging New PV Faults
2023 IEEE 13th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)
Year: 2023 | Conference Paper |
Aiming at accurate identification of emerging new faults in PV systems, a novel PV fault diagnosis framework based on cloud-edge collaboration is designed. Model updates are deployed on the cloud servers, while real-time diagnosis is implemented at the edge devices. A multi-label learning model is developed to extract fault features efficiently, and a threshold scheme is further designed for featu...Show More
Skeleton-based Human Action Recognition in Low-Resolution Infrared Images
2023 IEEE 18th Conference on Industrial Electronics and Applications (ICIEA)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
This paper proposes a recognition method of human action based on human skeleton extraction in low resolution infrared images, which are collected by two thermopile infrared array deployed on the side. Each frame of image is processed sequentially through quantification, interpolation and background removal operations, and the original human skeleton data is then extracted using a thinning algorit...Show More
Cross-domain Person Re-Identification in Low Resolution Non-visual Sensor Network
2023 IEEE 18th Conference on Industrial Electronics and Applications (ICIEA)
Year: 2023 | Conference Paper |
Person Re-identification (RE-ID) is a computer vision technique for detecting specific persons in image or video sequences, enabling the recognition of target persons in non-overlapping camera views. This paper focuses on person re-identification under the FOVs of two infrared thermopile array sensors, which are similar to the low-resolution infrared cameras. A convolutional-recurrent neural netwo...Show More
Spatiotemporal Transformer for Data Inference and Long Prediction in Sparse Mobile CrowdSensing
En Wang;Weiting Liu;Wenbin Liu;Chaocan Xiang;Bo Yang;Yongjian Yang
IEEE INFOCOM 2023 - IEEE Conference on Computer Communications
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Mobile CrowdSensing (MCS) is a data sensing paradigm that recruits users carrying mobile terminals to collect data. As its variant, Sparse MCS has been further proposed for large-scale and fine-grained sensing task with the advantage of collecting only a few data to infer unsensed data. However, in many real-world scenarios, such as early prevention of epidemic, people are interested in not only t...Show More
QVDDPG: QV Learning with Balanced Constraint in Actor-Critic Framework
Jiao Huang;Jifeng Hu;Luheng Yang;Zhihang Ren;Hechang Chen;Bo Yang
2023 International Joint Conference on Neural Networks (IJCNN)
Year: 2023 | Conference Paper |
Actor-critic framework has achieved tremendous success in a great many of decision-making scenarios. Nevertheless, when updating the value of new states and actions in the long-term scene, these methods suffer from misestimate problem and gradient variance problem, significantly reducing convergence speed and robustness of the policy. These problems severely limit the application scope of these me...Show More
Collaborate Global and Local: An Efficient PV Compound Fault Diagnosis Scheme With Multilabel Learning and Model Fusion
Fast and accurate fault diagnosis of photovoltaic (PV) modules has been the focus of intelligent operation and maintenance for PV systems. Considering efficiency and cost, many machine learning (ML)-based methods are widely used in practice. Due to the increasing scale of PV stations, the series–parallel topology of the system becomes more complicated, and multiple faults may inevitably occur toge...Show More
Distributed Online Generalized Nash Equilibrium Tracking for Prosumer Energy Trading Games
Yongkai Xie;Zhaojian Wang;John Z.F. Pang;Bo Yang;Xinping Guan
With the proliferation of distributed generations, traditional passive consumers in distribution networks are evolving into "prosumers", which can both produce and consume energy. Energy trading with the main grid or between prosumers is inevitable if the energy surplus and shortage exist. To this end, this paper investigates the peer-to-peer (P2P) energy trading market, which is formulated as a g...Show More
Online Optimization in Power Systems With High Penetration of Renewable Generation: Advances and Prospects
Zhaojian Wang;Wei Wei;John Zhen Fu Pang;Feng Liu;Bo Yang;Xinping Guan;Shengwei Mei
Traditionally, offline optimization of power systems is acceptable due to the largely predictable loads and reliable generation. The increasing penetration of fluctuating renewable generation and internet-of-things devices allowing for fine-grained controllability of loads have led to the diminishing applicability of offline optimization in the power systems domain, and have redirected attention t...Show More
Hyperbolic Neural Collaborative Recommender
Anchen Li;Bo Yang;Huan Huo;Hongxu Chen;Guandong Xu;Zhen Wang
Recently, deep learning techniques have yielded immense success on recommender systems. However, one weakness of most deep methods is that, users/items mutual semantic relationships, which are latent in the user-item interactions, are not distilled out explicitly. Moreover, most methods have been primarily focused on representation learning in euclidean geometry. Since recent studies have shown th...Show More
Distributed Power Sharing Control Based on Adaptive Virtual Impedance in Seaport Microgrids With Cold Ironing
Peng Zhao;Kai Ma;Jie Yang;Bo Yang;Josep M. Guerrero;Chunxia Dou;Xinping Guan
In a seaport microgrid (SMG), power sharing among distributed generation (DG) units is hindered by power coupling, line changes, and frequent power fluctuations caused by ship charging and discharging through cold ironing, which, in turn, threatens system stability and inverter security. This article proposes a delay-tolerant distributed adaptive virtual impedance control strategy for assigned act...Show More
Outlier-Concerned Data Completion Exploiting Intra- and Inter-Data Correlations in Sparse CrowdSensing
En Wang;Mijia Zhang;Wenbin Liu;Haoyi Xiong;Bo Yang;Yongjian Yang;Jie Wu
Mobile CrowdSensing (MCS) is a popular data collection paradigm which usually faces the problem of sparse sensed data because of the limited sensing cost. In order to address the situation of sparse data, sparse MCS recruits users to sense important areas and infers completed data by data completion, which is crucial in sparse MCS for urban sensing applications (e.g. enhancing data expression, imp...Show More
Spatiotemporal Urban Inference and Prediction in Sparse Mobile CrowdSensing: A Graph Neural Network Approach
Mobile CrowdSensing (MCS) has recently become a promising data acquisition paradigm, which recruits a large number of users to collect data from the target sensing areas. Obviously, with the increase of sensing scale and the decrease of sensing granularity, traditional MCS cannot fully cover the required sensing areas especially the inaccessible areas. As a variant, Sparse MCS can utilize the spat...Show More
An Optimization Strategy of Price and Conversion Factor Considering the Coupling of Electricity and Gas Based on Three-Stage Game
In order to improve the profits of electricity utility company (EUC) and gas utility company (GUC) and reduce the electricity and heat cost of users, an energy trading and pricing scheme based on three-stage game is proposed. Firstly, a three-stage optimization problem is established, and the conversion between electricity and gas is considered. Meanwhile, the conversion factor is introduced and c...Show More
In the field of image anomaly detection, self-supervised learning using only normal samples has become a hot research topic since it is difficult to acquire sufficient abnormal data. However, existing self-supervised methods usually consider the impact of the superficial information rather than the semantics of images, resulting in limited generalisation performance. Furthermore, the small amount ...Show More
In smart factory, the number of sensors and industrial applications is increasing exponentially. The data transfer relationship between sensor data and applications has also become intricate. Message queuing telemetry transport (MQTT) has become the de-facto standard in the IoT field due to its decoupling of data transfer in time and space. However, even though existing MQTT message servers have b...Show More
Building Temperature and Humidity Adaptive Control for a Multi-Zone HVAC System Using Hybrid Modeling Method
2022 IEEE International Conference on Industrial Technology (ICIT)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
In this paper, the heating, ventilation and air conditioning (HVAC) system as the contributor of building climate management is studied on multi-zone climate dynamics modelling and control for occupancy comfort. The first-principles model together with data-driven model as the hybrid model is proposed for comprehensive multi-zone hygro-thermal dynamics. The unmodeled climate dynamics such as unkno...Show More
Microservice Dynamic Migration based on Age of Service for Edge Computing
2022 IEEE International Conference on Industrial Technology (ICIT)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
In the production process of a smart factory, the change of orders will cause adjustment or movement of devices, leading to prolonging the service response time. Therefore, microservice dynamic migration is necessary to optimize service freshness for industrial applications. We propose an evaluation index called Age of Service (AoS) based on the concept of Age of Information (AoI) to evaluate the ...Show More
Virtual Platoon based CAVs Cooperative Driving at Unsignalized Intersection
2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)
Year: 2022 | Conference Paper |
Cited by: Papers (8)
The emergence of connected and automated vehicles (CAVs) poses a promising future of transportation systems. Complicated traffic conditions at intersections lead to challenges for cooperative driving of CAVs while ensuring safety and efficiency. This paper proposes a conflict-free cooperative control method for CAVs at unsignalized intersections. Based on the conflict points inside the intersectio...Show More
Trustworthy and Efficient Crowdsensed Data Trading on Sharding Blockchain
En Wang;Jiatong Cai;Yongjian Yang;Wenbin Liu;Hengzhi Wang;Bo Yang;Jie Wu
With the development of communications, networking, and information technology, Crowdsensed Data Trading (CDT) becomes a novel data trading paradigm. In CDT, the data requesters publish crowdsensing tasks with specific data requirements, and then workers complete these tasks, upload the data and obtain corresponding rewards. To efficiently deal with data trading, most of the existing CDT systems a...Show More
Inter-vehicle communication is crucial in many severe weather or natural disaster situations. However, the wireless link quality is worse than the wired in that the network topology between vehicles changes frequently as vehicles move at high speeds. This also seriously affects the quality of inter-vehicle communication and corresponding services. Emerging computing-intensive service tasks like au...Show More
A Probabilistic Graphical Model Based on Neural-symbolic Reasoning for Visual Relationship Detection
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2022 | Conference Paper |
Cited by: Papers (8)
This paper aims to leverage symbolic knowledge to improve the performance and interpretability of the Visual Relationship Detection (VRD) models. Existing VRD methods based on deep learning suffer from the problems of poor performance on insufficient labeled examples and lack of interpretability. To overcome the aforementioned weaknesses, we integrate symbolic knowledge into deep learning models a...Show More
Dependency-aware Task Scheduling and Cache Placement in Vehicular Networks
2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
Mobile Edge Computing (MEC) enables vehicles to flexibly obtain computing, content storage and other services by deploying at the network edge, which is of great research significance. The application tasks in vehicular networks can be distributed to MEC or task vehicle for collaborative processing. Aiming at the situation that the task dependency graph with multiple corresponding relationships is...Show More
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