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In modern healthcare, sensing technologies such as IoT empower the quality of assisted living service by knowing what a resident is doing in real-time. Using extreme connectivity and cloud computing in a smart home, where a collection of sensors is installed, the sensors sample continuously from the movements of the resident as well as ambient data from the surrounding inside the house. Automatic human activity recognition of the resident's activities is one of the key components of assisted living in smart home. For monitoring in-home safety, the ability in recognizing abnormal activities such as accident, falling, acute disease attack (e.g. asthma, stroke, etc.), fainting, wobbling, is particularly important. The detection and machine learning process must be both accurate and fast, to cope with the real-time activity recognition. To this end, a novel streamlined sensor data processing method is proposed called Evolutionary Expand-and-Contract Instance-based Learning algorithm (EEAC-IBL). The multivariate data stream is first expanded into many subspaces, then the subspaces which are corresponding to the characteristics of the features are selected and condensed into a significant feature subset. The selection operates scholastically instead of deterministically by evolutionary optimization which approximates the best subgroup. Followed by data stream mining, the machine learning for activity recognition is done on the fly. This approach is unique and suitable for such extreme connectivity scenario where precise feature selection is not required, and the relative importance of each feature among the sensor data changes over time. This stochastic approximation method is fast and accurate, offering an alternative to traditional machine learning method for smart home activity recognition application. Our experimental results show computing advantages over other classical approaches.
Shimin Hu; Simon Fong; Wei Song; Kyungeun Cho; Richard C. Millham; Jinan Fiaidhi. Novel evolutionary-EAC instance-learning-based algorithm for fast data stream mining in assisted living with extreme connectivity. Computing 2021, 103, 1519 -1543.
AMA StyleShimin Hu, Simon Fong, Wei Song, Kyungeun Cho, Richard C. Millham, Jinan Fiaidhi. Novel evolutionary-EAC instance-learning-based algorithm for fast data stream mining in assisted living with extreme connectivity. Computing. 2021; 103 (7):1519-1543.
Chicago/Turabian StyleShimin Hu; Simon Fong; Wei Song; Kyungeun Cho; Richard C. Millham; Jinan Fiaidhi. 2021. "Novel evolutionary-EAC instance-learning-based algorithm for fast data stream mining in assisted living with extreme connectivity." Computing 103, no. 7: 1519-1543.
Posture recognition technologies based on the Internet of Things (IoT) are widely required to care industry, such as fall detection of elder persons. In order to realize the low-power transformation of motion information in wide-area, Long Range (LoRa) is used in this paper to develop a human posture recognition system. The system is integrated by an mpu-9250 sensor, a LoRa Shield board and an Arduino Mega master control board, which collect human posture data and transmit them to the cloud server remotely. Combined with a random forest algorithm, real-time human posture movement data is carried out to recognize and classify human posture movement. The posture recognizing accuracy calculated by random forest algorithm is the higher than that of other classic machine learning algorithms. This way, our proposed real-time human posture recognition system is able to assist care industry to automatically monitor real-time posture situations of elder persons.
Wei Song; Jinqiao Liao; Jinkun Han. A Real-Time Human Posture Recognition System Using Internet of Things (IoT) Based on LoRa Wireless Network. Lecture Notes in Electrical Engineering 2021, 379 -385.
AMA StyleWei Song, Jinqiao Liao, Jinkun Han. A Real-Time Human Posture Recognition System Using Internet of Things (IoT) Based on LoRa Wireless Network. Lecture Notes in Electrical Engineering. 2021; ():379-385.
Chicago/Turabian StyleWei Song; Jinqiao Liao; Jinkun Han. 2021. "A Real-Time Human Posture Recognition System Using Internet of Things (IoT) Based on LoRa Wireless Network." Lecture Notes in Electrical Engineering , no. : 379-385.
3D (3-Dimensional) object recognition is a hot research topic that benefits environment perception, disease diagnosis, and the mobile robot industry. Point clouds collected by range sensors are a popular data structure to represent a 3D object model. This paper proposed a 3D object recognition method named Dynamic Graph Convolutional Broad Network (DGCB-Net) to realize feature extraction and 3D object recognition from the point cloud. DGCB-Net adopts edge convolutional layers constructed by weight-shared multiple-layer perceptrons (MLPs) to extract local features from the point cloud graph structure automatically. Features obtained from all edge convolutional layers are concatenated together to form a feature aggregation. Unlike stacking many layers in-depth, our DGCB-Net employs a broad architecture to extend point cloud feature aggregation flatly. The broad architecture is structured utilizing a flat combining architecture with multiple feature layers and enhancement layers. Both feature layers and enhancement layers concatenate together to further enrich the features’ information of the point cloud. All features work on the object recognition results thus that our DGCB-Net show better recognition performance than other 3D object recognition algorithms on ModelNet10/40 and our scanning point cloud dataset.
Yifei Tian; Long Chen; Wei Song; Yunsick Sung; Sangchul Woo. DGCB-Net: Dynamic Graph Convolutional Broad Network for 3D Object Recognition in Point Cloud. Remote Sensing 2020, 13, 66 .
AMA StyleYifei Tian, Long Chen, Wei Song, Yunsick Sung, Sangchul Woo. DGCB-Net: Dynamic Graph Convolutional Broad Network for 3D Object Recognition in Point Cloud. Remote Sensing. 2020; 13 (1):66.
Chicago/Turabian StyleYifei Tian; Long Chen; Wei Song; Yunsick Sung; Sangchul Woo. 2020. "DGCB-Net: Dynamic Graph Convolutional Broad Network for 3D Object Recognition in Point Cloud." Remote Sensing 13, no. 1: 66.
Point clouds have been widely used in three-dimensional (3D) object classification tasks, i.e., people recognition in unmanned ground vehicles. However, the irregular data format of point clouds and the large number of parameters in deep learning networks affect the performance of object classification. This paper develops a 3D object classification system using a broad learning system (BLS) with a feature extractor called VB-Net. First, raw point clouds are voxelized into voxels. Through this step, irregular point clouds are converted into regular voxels which are easily processed by the feature extractor. Then, a pre-trained VoxNet is employed as a feature extractor to extract features from voxels. Finally, those features are used for object classification by the applied BLS. The proposed system is tested on the ModelNet40 dataset and ModelNet10 dataset. The average recognition accuracy was 83.99% and 90.08%, respectively. Compared to deep learning networks, the time consumption of the proposed system is significantly decreased.
Zishu Liu; Wei Song; Yifei Tian; Sumi Ji; Yunsick Sung; Long Wen; Tao Zhang; Liangliang Song; Amanda Gozho. VB-Net: Voxel-Based Broad Learning Network for 3D Object Classification. Applied Sciences 2020, 10, 6735 .
AMA StyleZishu Liu, Wei Song, Yifei Tian, Sumi Ji, Yunsick Sung, Long Wen, Tao Zhang, Liangliang Song, Amanda Gozho. VB-Net: Voxel-Based Broad Learning Network for 3D Object Classification. Applied Sciences. 2020; 10 (19):6735.
Chicago/Turabian StyleZishu Liu; Wei Song; Yifei Tian; Sumi Ji; Yunsick Sung; Long Wen; Tao Zhang; Liangliang Song; Amanda Gozho. 2020. "VB-Net: Voxel-Based Broad Learning Network for 3D Object Classification." Applied Sciences 10, no. 19: 6735.
Scientists have explored the human body for hundreds of years, and yet more relationships between the behaviors and health are still to be discovered. With the development of data mining, artificial intelligence technology, and human posture detection, it is much more possible to figure out how behaviors and movements influence people’s health and life and how to adjust the relationship between work and rest, which is needed urgently for modern people against this high-speed lifestyle. Using smart technology and daily behaviors to supervise or predict people’s health is a key part of a smart city. In a smart city, these applications involve large groups and high-frequency use, so the system must have low energy consumption, a portable system, and a low cost for long-term detection. To meet these requirements, this paper proposes a posture recognition method based on multisensor and using LoRa technology to build a long-term posture detection system. LoRa WAN technology has the advantages of low cost and long transmission distances. Combining the LoRa transmitting module and sensors, this paper designs wearable clothing to make people comfortable in any given posture. Aiming at LoRa’s low transmitting frequency and small size of data transmission, this paper proposes a multiprocessing method, including data denoising, data enlarging based on sliding windows, feature extraction, and feature selection using Random Forest, to make 4 values retain the most information about 125 data from 9 axes of sensors. The result shows an accuracy of 99.38% of extracted features and 95.06% of selected features with the training of 3239 groups of datasets. To verify the performance of the proposed algorithm, three testers created 500 groups of datasets and the results showed good performance. Hence, due to the energy sustainability of LoRa and the accuracy of recognition, this proposed posture recognition using multisensor and LoRa can work well when facing long-term detection and LoRa fits smart city well when facing long-distance transmission.
Jinkun Han; Wei Song; Amanda Gozho; Yunsick Sung; Sumi Ji; Liangliang Song; Long Wen; Qi Zhang. LoRa-Based Smart IoT Application for Smart City: An Example of Human Posture Detection. Wireless Communications and Mobile Computing 2020, 2020, 1 -15.
AMA StyleJinkun Han, Wei Song, Amanda Gozho, Yunsick Sung, Sumi Ji, Liangliang Song, Long Wen, Qi Zhang. LoRa-Based Smart IoT Application for Smart City: An Example of Human Posture Detection. Wireless Communications and Mobile Computing. 2020; 2020 ():1-15.
Chicago/Turabian StyleJinkun Han; Wei Song; Amanda Gozho; Yunsick Sung; Sumi Ji; Liangliang Song; Long Wen; Qi Zhang. 2020. "LoRa-Based Smart IoT Application for Smart City: An Example of Human Posture Detection." Wireless Communications and Mobile Computing 2020, no. : 1-15.
With the wide application of Light Detection and Ranging (LiDAR) in the collection of high-precision environmental point cloud information, three-dimensional (3D) object classification from point clouds has become an important research topic. However, the characteristics of LiDAR point clouds, such as unstructured distribution, disordered arrangement, and large amounts of data, typically result in high computational complexity and make it very difficult to classify 3D objects. Thus, this paper proposes a Convolutional Neural Network (CNN)-based 3D object classification method using the Hough space of LiDAR point clouds to overcome these problems. First, object point clouds are transformed into Hough space using a Hough transform algorithm, and then the Hough space is rasterized into a series of uniformly sized grids. The accumulator count in each grid is then computed and input to a CNN model to classify 3D objects. In addition, a semi-automatic 3D object labeling tool is developed to build a LiDAR point clouds object labeling library for four types of objects (wall, bush, pedestrian, and tree). After initializing the CNN model, we apply a dataset from the above object labeling library to train the neural network model offline through a large number of iterations. Experimental results demonstrate that the proposed method achieves object classification accuracy of up to 93.3% on average.
Wei Song; Lingfeng Zhang; Yifei Tian; Simon Fong; Jinming Liu; Amanda Gozho. CNN-based 3D object classification using Hough space of LiDAR point clouds. Human-centric Computing and Information Sciences 2020, 10, 1 -14.
AMA StyleWei Song, Lingfeng Zhang, Yifei Tian, Simon Fong, Jinming Liu, Amanda Gozho. CNN-based 3D object classification using Hough space of LiDAR point clouds. Human-centric Computing and Information Sciences. 2020; 10 (1):1-14.
Chicago/Turabian StyleWei Song; Lingfeng Zhang; Yifei Tian; Simon Fong; Jinming Liu; Amanda Gozho. 2020. "CNN-based 3D object classification using Hough space of LiDAR point clouds." Human-centric Computing and Information Sciences 10, no. 1: 1-14.
Fast and accurate obstacle detection is essential for accurate perception of mobile vehicles’ environment. Because point clouds sensed by light detection and ranging (LiDAR) sensors are sparse and unstructured, traditional obstacle clustering on raw point clouds are inaccurate and time consuming. Thus, to achieve fast obstacle clustering in an unknown terrain, this paper proposes an elevation-reference connected component labeling (ER-CCL) algorithm using graphic processing unit (GPU) programing. LiDAR points are first projected onto a rasterized x–z plane so that sparse points are mapped into a series of regularly arranged small cells. Based on the height distribution of the LiDAR point, the ground cells are filtered out and a flag map is generated. Next, the ER-CCL algorithm is implemented on the label map generated from the flag map to mark individual clusters with unique labels. Finally, obstacle labeling results are inverse transformed from the x–z plane to 3D points to provide clustering results. For real-time 3D point cloud clustering, ER-CCL is accelerated by running it in parallel with the aid of GPU programming technology.
Yifei Tian; Wei Song; Long Chen; Yunsick Sung; Jeonghoon Kwak; Su Sun. A Fast Spatial Clustering Method for Sparse LiDAR Point Clouds Using GPU Programming. Sensors 2020, 20, 2309 .
AMA StyleYifei Tian, Wei Song, Long Chen, Yunsick Sung, Jeonghoon Kwak, Su Sun. A Fast Spatial Clustering Method for Sparse LiDAR Point Clouds Using GPU Programming. Sensors. 2020; 20 (8):2309.
Chicago/Turabian StyleYifei Tian; Wei Song; Long Chen; Yunsick Sung; Jeonghoon Kwak; Su Sun. 2020. "A Fast Spatial Clustering Method for Sparse LiDAR Point Clouds Using GPU Programming." Sensors 20, no. 8: 2309.
Plane extraction is regarded as a necessary function that supports judgment basis in many applications, including semantic digital map reconstruction and path planning for unmanned ground vehicles. Owing to the heterogeneous density and unstructured spatial distribution of three-dimensional (3D) point clouds collected by light detection and ranging (LiDAR), plane extraction from it is recently a significant challenge. This paper proposed a parallel 3D Hough transform algorithm to realize rapid and precise plane detection from 3D LiDAR point clouds. After transforming all the 3D points from a Cartesian coordinate system to a pre-defined 3D Hough space, the generated Hough space is rasterised into a series of arranged cells to store the resided point counts into individual cells. A 3D connected component labeling algorithm is developed to cluster the cells with high values in Hough space into several clusters. The peaks from these clusters are extracted so that the targeting planar surfaces are obtained in polar coordinates. Because the laser beams emitted by LiDAR sensor holds several fixed angles, the collected 3D point clouds distribute as several horizontal and parallel circles in plane surfaces. This kind of horizontal and parallel circles mislead plane detecting results from horizontal wall surfaces to parallel planes. For detecting accurate plane parameters, this paper adopts a fraction-to-fraction method to gradually transform raw point clouds into a series of sub Hough space buffers. In our proposed planar detection algorithm, a graphic processing unit (GPU) programming technology is applied to speed up the calculation of 3D Hough space updating and peaks searching.
Yifei Tian; Wei Song; Long Chen; Yunsick Sung; Jeonghoon Kwak; Su Sun. Fast Planar Detection System Using a GPU-Based 3D Hough Transform for LiDAR Point Clouds. Applied Sciences 2020, 10, 1744 .
AMA StyleYifei Tian, Wei Song, Long Chen, Yunsick Sung, Jeonghoon Kwak, Su Sun. Fast Planar Detection System Using a GPU-Based 3D Hough Transform for LiDAR Point Clouds. Applied Sciences. 2020; 10 (5):1744.
Chicago/Turabian StyleYifei Tian; Wei Song; Long Chen; Yunsick Sung; Jeonghoon Kwak; Su Sun. 2020. "Fast Planar Detection System Using a GPU-Based 3D Hough Transform for LiDAR Point Clouds." Applied Sciences 10, no. 5: 1744.
LiDAR has been widely used in 3D reconstruction due to its high resolution, wide range and tolerance towards light and weather. To realize accurate and complete environment perception and reconstruction, LiDAR point cloud registration plays a crucial role. This paper utilized an Iterative Closest Point (ICP) algorithm to register the sparse point cloud sensed by LiDAR into a whole indoor environment model. Instead of using a standard ICP algorithm, a point-to-plane ICP is adopted with point cloud selection, point pair matching and rejection. The transformation value between two point cloud data is iteratively calculated and optimized until the defined error metric reaches convergence.
Su Sun; Wei Song; Yifei Tian; Simon Fong. An ICP-Based Point Clouds Registration Method for Indoor Environment Modeling. Lecture Notes in Electrical Engineering 2019, 339 -344.
AMA StyleSu Sun, Wei Song, Yifei Tian, Simon Fong. An ICP-Based Point Clouds Registration Method for Indoor Environment Modeling. Lecture Notes in Electrical Engineering. 2019; ():339-344.
Chicago/Turabian StyleSu Sun; Wei Song; Yifei Tian; Simon Fong. 2019. "An ICP-Based Point Clouds Registration Method for Indoor Environment Modeling." Lecture Notes in Electrical Engineering , no. : 339-344.
During autonomous driving, fast and accurate object recognition supports environment perception for local path planning of unmanned ground vehicles. Feature extraction and object recognition from large-scale 3D point clouds incur massive computational and time costs. To implement fast environment perception, this paper proposes a 3D recognition system with multiple feature extraction from light detection and ranging point clouds modified by parallel computing. Effective object feature extraction is a necessary step prior to executing an object recognition procedure. In the proposed system, multiple geometry features of a point cloud that resides in corresponding voxels are computed concurrently. In addition, a scale filter is employed to convert feature vectors from uncertain count voxels to a normalized object feature matrix, which is convenient for object-recognizing classifiers. After generating the object feature matrices of all voxels, an initialized multilayer neural network (NN) model is trained offline through a large number of iterations. Using the trained NN model, real-time object recognition is realized using parallel computing technology to accelerate computation.
Yifei Tian; Wei Song; Su Sun; Simon Fong; Shuanghui Zou. 3D object recognition method with multiple feature extraction from LiDAR point clouds. The Journal of Supercomputing 2019, 75, 4430 -4442.
AMA StyleYifei Tian, Wei Song, Su Sun, Simon Fong, Shuanghui Zou. 3D object recognition method with multiple feature extraction from LiDAR point clouds. The Journal of Supercomputing. 2019; 75 (8):4430-4442.
Chicago/Turabian StyleYifei Tian; Wei Song; Su Sun; Simon Fong; Shuanghui Zou. 2019. "3D object recognition method with multiple feature extraction from LiDAR point clouds." The Journal of Supercomputing 75, no. 8: 4430-4442.
In this paper, the problem of coordinated control of multiple hovercrafts is addressed. For a single hovercraft, by using the backstepping technique, a nonlinear controller is proposed, where Radial Basis Function Neural Networks (RBFNNs) are adopted to approximate unmodeled terms. Despite the application of RBFNNs, integral terms are introduced, improving the robustness of controller. As a result, global uniformly ultimate boundedness is achieved. Regarding the communication topology, two different directed graphs are chosen under the assumption that there are no delays when they communicate with each other. In order to testify the performance of the proposed strategy, simulation results are presented, showing that vehicles can move forward in a specific formation pattern and RBFNNs are able to approximate unmodeled terms.
Kairong Duan; Simon Fong; Yan Zhuang; Wei Song. Artificial Neural Networks in Coordinated Control of Multiple Hovercrafts with Unmodeled Terms. Applied Sciences 2018, 8, 862 .
AMA StyleKairong Duan, Simon Fong, Yan Zhuang, Wei Song. Artificial Neural Networks in Coordinated Control of Multiple Hovercrafts with Unmodeled Terms. Applied Sciences. 2018; 8 (6):862.
Chicago/Turabian StyleKairong Duan; Simon Fong; Yan Zhuang; Wei Song. 2018. "Artificial Neural Networks in Coordinated Control of Multiple Hovercrafts with Unmodeled Terms." Applied Sciences 8, no. 6: 862.
Cloud computing is a new commercial model that enables customers to acquire large amounts of virtual resources on demand. Resources including hardware and software can be delivered as services and measured by specific usage of storage, processing, bandwidth, etc. In Cloud computing, task scheduling is a process of mapping cloud tasks to Virtual Machines (VMs). When binding the tasks to VMs, the scheduling strategy has an important influence on the efficiency of datacenter and related energy consumption. Although many traditional scheduling algorithms have been applied in various platforms, they may not work efficiently due to the large number of user requests, the variety of computation resources and complexity of Cloud environment. In this paper, we tackle the task scheduling problem which aims to minimize makespan by Genetic Algorithm (GA). We propose an incremental GA which has adaptive probabilities of crossover and mutation. The mutation and crossover rates change according to generations and also vary between individuals. Large numbers of tasks are randomly generated to simulate various scales of task scheduling problem in Cloud environment. Based on the instance types of Amazon EC2, we implemented virtual machines with different computing capacity on CloudSim. We compared the performance of the adaptive incremental GA with that of Standard GA, Min-Min, Max-Min , Simulated Annealing and Artificial Bee Colony Algorithm in finding the optimal scheme. Experimental results show that the proposed algorithm can achieve feasible solutions which have acceptable makespan with less computation time.
Kairong Duan; Simon Fong; Shirley W. I. Siu; Wei Song; Steven Sheng-Uei Guan. Adaptive Incremental Genetic Algorithm for Task Scheduling in Cloud Environments. Symmetry 2018, 10, 168 .
AMA StyleKairong Duan, Simon Fong, Shirley W. I. Siu, Wei Song, Steven Sheng-Uei Guan. Adaptive Incremental Genetic Algorithm for Task Scheduling in Cloud Environments. Symmetry. 2018; 10 (5):168.
Chicago/Turabian StyleKairong Duan; Simon Fong; Shirley W. I. Siu; Wei Song; Steven Sheng-Uei Guan. 2018. "Adaptive Incremental Genetic Algorithm for Task Scheduling in Cloud Environments." Symmetry 10, no. 5: 168.
The information of human occupancy plays a crucial role in building management. For instance, fewer people, less demand for heat and electricity supply, and vice versa. Moreover, when there is a fire in a building, it is convenient to know how many persons in a single room there are in order to plan a more efficient rescue strategy. However, currently most buildings have not installed adequate devices that can be used to count the number of people, and the most popular embedded fire alarm system triggers a warning only when a fire breaks out with plenty of smoke. In view of this constraint, in this paper we propose a carbon oxides gases based warning system to detect potential fire breakouts and to estimate the number of people in the proximity. In order to validate the efficiency of the devised system, we simulate its application in the Fog Computing environment. Furthermore, we also improve the iFogSim by giving data analytics capacity to it. Based on this framework, energy consumption, latency, and network usage of the designed system obtained from iFogSim are compared with those obtained from Cloud environment.
Kairong Duan; Simon Fong; Yan Zhuang; Wei Song. Carbon Oxides Gases for Occupancy Counting and Emergency Control in Fog Environment. Symmetry 2018, 10, 66 .
AMA StyleKairong Duan, Simon Fong, Yan Zhuang, Wei Song. Carbon Oxides Gases for Occupancy Counting and Emergency Control in Fog Environment. Symmetry. 2018; 10 (3):66.
Chicago/Turabian StyleKairong Duan; Simon Fong; Yan Zhuang; Wei Song. 2018. "Carbon Oxides Gases for Occupancy Counting and Emergency Control in Fog Environment." Symmetry 10, no. 3: 66.
Salvatore Galati; Wei Song; Gergely Orban; Andreas R. Luft; Alain Kaelin-Lang; Song Wei. Cortical slow wave activity correlates with striatal synaptic strength in normal but not in Parkinsonian rats. Experimental Neurology 2018, 301, 50 -58.
AMA StyleSalvatore Galati, Wei Song, Gergely Orban, Andreas R. Luft, Alain Kaelin-Lang, Song Wei. Cortical slow wave activity correlates with striatal synaptic strength in normal but not in Parkinsonian rats. Experimental Neurology. 2018; 301 ():50-58.
Chicago/Turabian StyleSalvatore Galati; Wei Song; Gergely Orban; Andreas R. Luft; Alain Kaelin-Lang; Song Wei. 2018. "Cortical slow wave activity correlates with striatal synaptic strength in normal but not in Parkinsonian rats." Experimental Neurology 301, no. : 50-58.
With the popularity and affordability of ZigBee wireless sensor technology, IoT-based smart controlling system for home appliances becomes prevalent for smart home applications. From the data analytics point of view, one important objective from analyzing such IoT data is to gain insights from the energy consumption patterns, thereby trying to fine-tune the energy efficiency of the appliance usage. The data analytics usually functions at the back-end crunching over a large archive of big data accumulated over time for learning the overall pattern from the sensor data feeds. The other objective of the analytics, which may often be more crucial, is to predict and identify whether an abnormal consumption event is about to happen. For example, a sudden draw of energy that leads to hot spot in the power grid in a city, or black-out at home. This dynamic prediction is usually done at the operational level, with moving data stream, by data stream mining methods . In this paper, an improved version of very fast decision tree (VFDT) is proposed, which learns from misclassified results for the sake of filtering the noisy data from learning and maintaining sharp classification accuracy of the induced prediction model. Specifically, a new technique called misclassified recall (MR), which is a pre-processing step for self-rectifying misclassified instances, is formulated. In energy data prediction, most misclassified instances are due to data transmission errors or faulty devices. The former case happens intermittently, and the errors from the latter cause may persist for a long time. By caching up the data at the MR pre-processor, the one-pass online model learning can be effectively shielded in case of intermitting problems at the wireless sensor network; likewise the stored data could be investigated afterwards should the problem persist for long. Simulation experiments over a dataset about predicting exceptional appliances energy use in a low energy building are conducted. The reported results validate the efficacy of the new methodology VFDT + MR, in comparison to a collection of popular data stream mining algorithms from the literature.
Simon Fong; Jiaxue Li; Wei Song; Yifei Tian; Raymond K. Wong; Nilanjan Dey. Predicting unusual energy consumption events from smart home sensor network by data stream mining with misclassified recall. Journal of Ambient Intelligence and Humanized Computing 2018, 9, 1197 -1221.
AMA StyleSimon Fong, Jiaxue Li, Wei Song, Yifei Tian, Raymond K. Wong, Nilanjan Dey. Predicting unusual energy consumption events from smart home sensor network by data stream mining with misclassified recall. Journal of Ambient Intelligence and Humanized Computing. 2018; 9 (4):1197-1221.
Chicago/Turabian StyleSimon Fong; Jiaxue Li; Wei Song; Yifei Tian; Raymond K. Wong; Nilanjan Dey. 2018. "Predicting unusual energy consumption events from smart home sensor network by data stream mining with misclassified recall." Journal of Ambient Intelligence and Humanized Computing 9, no. 4: 1197-1221.
The development of Cloud computing and data analytics technologies has made it possible to process big data faster. Distributed computing schemes, for instance, can help to reduce the time required for data analysis and thus enhance its efficiency. However, fewer researchers have paid attention to the problem of the high-energy consumption of the cluster, placing a heavy burden on the environment, especially when the number of nodes is extremely large. As a consequence, the principle of sustainable development is violated. Considering this problem, this paper proposes an approach that can be applied to remove less-efficient nodes or to migrate over-utilized nodes of the cluster so as to adjust the load of the cluster properly and thereby achieve the goal of energy conservation. Furthermore, in order to testify the performance of the proposed methodology, we present the simulation results implemented by using CloudSim.
Kairong Duan; Simon Fong; Wei Song; Athanasios V. Vasilakos; Raymond Wong. Energy-Aware Cluster Reconfiguration Algorithm for the Big Data Analytics Platform Spark. Sustainability 2017, 9, 2357 .
AMA StyleKairong Duan, Simon Fong, Wei Song, Athanasios V. Vasilakos, Raymond Wong. Energy-Aware Cluster Reconfiguration Algorithm for the Big Data Analytics Platform Spark. Sustainability. 2017; 9 (12):2357.
Chicago/Turabian StyleKairong Duan; Simon Fong; Wei Song; Athanasios V. Vasilakos; Raymond Wong. 2017. "Energy-Aware Cluster Reconfiguration Algorithm for the Big Data Analytics Platform Spark." Sustainability 9, no. 12: 2357.
Face recognition has attracted numerous research interests as a promising biometrics with many distinct advantages. However there are inevitable gaps lying between face recognition in lab condition and ubiquitous face recognition application in real word, which mainly caused by various illumination condition, random occlusion, lack of sample images and etc. To combat the influence of these impact factors, a novel dual features based sparse representation classification algorithm is proposed. It contains illumination robust feature based dictionary learning and fused sparse representation with dual features. Firstly, an enhanced center-symmetric local binary pattern (ECSLBP) derived from conducting center symmetric encoding on the fused component images is presented for dictionary construction. Then, sparse representation with dual features including both ECSLBP and CSLBP is conducted. The final recognition is derived from the fusion of both classification results according to a novel fusion scheme. Numerous experiments results on both Extended Yale B database and the AR database show that the proposed algorithm exhibits distinguished discriminative ability and state-of-the-art recognition rate compared with other existing algorithms, especially for single sample face recognition under random partial occlusion.
Chen Li; Shuai Zhao; Wei Song; Ke Xiao; Yanjie Wang. Ubiquitous single-sample face recognition under occlusion based on sparse representation with dual features. Journal of Ambient Intelligence and Humanized Computing 2017, 1 -11.
AMA StyleChen Li, Shuai Zhao, Wei Song, Ke Xiao, Yanjie Wang. Ubiquitous single-sample face recognition under occlusion based on sparse representation with dual features. Journal of Ambient Intelligence and Humanized Computing. 2017; ():1-11.
Chicago/Turabian StyleChen Li; Shuai Zhao; Wei Song; Ke Xiao; Yanjie Wang. 2017. "Ubiquitous single-sample face recognition under occlusion based on sparse representation with dual features." Journal of Ambient Intelligence and Humanized Computing , no. : 1-11.
Over the years, advanced IT technologies have facilitated the emergence of new ways of generating and gathering data rapidly, continuously, and largely and are associated with a new research and application branch, namely, data stream mining (DSM). Among those multiple scenarios of DSM, the Internet of Things (IoT) plays a significant role, with a typical meaning of a tough and challenging computational case of big data. In this paper, we describe a self-adaptive approach to the pre-processing step of data stream classification. The proposed algorithm allows different divisions with both variable numbers and lengths of sub-windows under a whole sliding window on an input stream, and clustering-based particle swarm optimization (CPSO) is adopted as the main metaheuristic search method to guarantee that its stream segmentations are effective and adaptive to itself. In order to create a more abundant search space, statistical feature extraction (SFX) is applied after variable partitions of the entire sliding window. We validate and test the effort of our algorithm with other temporal methods according to several IoT environmental sensor monitoring datasets. The experiments yield encouraging outcomes, supporting the reality that picking significant appropriate variant sub-window segmentations heuristically with an incorporated clustering technique merit would allow these to perform better than others.
Kun Lan; Simon Fong; Wei Song; Athanasios V. Vasilakos; Richard C. Millham. Self-Adaptive Pre-Processing Methodology for Big Data Stream Mining in Internet of Things Environmental Sensor Monitoring. Symmetry 2017, 9, 244 .
AMA StyleKun Lan, Simon Fong, Wei Song, Athanasios V. Vasilakos, Richard C. Millham. Self-Adaptive Pre-Processing Methodology for Big Data Stream Mining in Internet of Things Environmental Sensor Monitoring. Symmetry. 2017; 9 (10):244.
Chicago/Turabian StyleKun Lan; Simon Fong; Wei Song; Athanasios V. Vasilakos; Richard C. Millham. 2017. "Self-Adaptive Pre-Processing Methodology for Big Data Stream Mining in Internet of Things Environmental Sensor Monitoring." Symmetry 9, no. 10: 244.
Virtual Reality (VR) has recently experienced rapid development for human–computer interactions. Users wearing VR headsets gain an immersive experience when interacting with a 3-dimensional (3D) world. We utilise a light detection and ranging (LiDAR) sensor to detect a 3D point cloud from the real world. To match the scale between a virtual environment and a user’s real world, this paper develops a boundary wall detection method using the Hough transform algorithm. A connected-component-labelling (CCL) algorithm is applied to classify the Hough space into several distinguishable blocks that are segmented using a threshold. The four largest peaks among the segmented blocks are extracted as the parameters of the wall plane. The virtual environment is scaled to the size of the real environment. In order to synchronise the position of the user and his/her avatar in the virtual world, a wireless Kinect network is proposed for user localisation. Multiple Kinects are mounted in an indoor environment to sense the user’s information from different viewpoints. The proposed method supports the omnidirectional detection of the user’s position and gestures. To verify the performance of our proposed system, we developed a VR game using several Kinects and a Samsung Gear VR device.
Wei Song; Liying Liu; Yifei Tian; Guodong Sun; Simon Fong; Kyungeun Cho. A 3D localisation method in indoor environments for virtual reality applications. Human-centric Computing and Information Sciences 2017, 7, 39 .
AMA StyleWei Song, Liying Liu, Yifei Tian, Guodong Sun, Simon Fong, Kyungeun Cho. A 3D localisation method in indoor environments for virtual reality applications. Human-centric Computing and Information Sciences. 2017; 7 (1):39.
Chicago/Turabian StyleWei Song; Liying Liu; Yifei Tian; Guodong Sun; Simon Fong; Kyungeun Cho. 2017. "A 3D localisation method in indoor environments for virtual reality applications." Human-centric Computing and Information Sciences 7, no. 1: 39.
Single-crystal Sn/Cu solder joints with (001), (101), (301) and (100) plane of Sn as the interfaces were prepared for studying the relation between orientation of Sn and electromigration. The growth of interfacial intermetallic compounds was found to strongly depend on the Sn grain orientation. When the c-axis was parallel to the current direction, a severe polarity effect was observed, while the c-axis of Sn off the current direction, the polarity effect became less pronounced. For (101) and (301) orientation samples making a 28.6° and 58.6° angles with c-axis of Sn, asymmetrical IMCs layer growth at the anode interface, downward sloping serrated edges of Cu dissolution groove at the cathode and granular Cu6Sn5growth at the surface were observed, while these morphological features were not found in (001) and (100) samples making a 0° and 90° angles with c-axis of Sn. It is suggested that when the c-axis of β-Sn was off the current direction, the direction of Cu atoms diffusion is shifted to the c-axis of tin grain under the electromigration driving force. © 2017 Elsevier B.V.
Jian-Qiang Chen; Kai-Lang Liu; Jing-Dong Guo; Hui-Cai Ma; Song Wei; Jian-Ku Shang. Electromigration anisotropy introduced by tin orientation in solder joints. Journal of Alloys and Compounds 2017, 703, 264 -271.
AMA StyleJian-Qiang Chen, Kai-Lang Liu, Jing-Dong Guo, Hui-Cai Ma, Song Wei, Jian-Ku Shang. Electromigration anisotropy introduced by tin orientation in solder joints. Journal of Alloys and Compounds. 2017; 703 ():264-271.
Chicago/Turabian StyleJian-Qiang Chen; Kai-Lang Liu; Jing-Dong Guo; Hui-Cai Ma; Song Wei; Jian-Ku Shang. 2017. "Electromigration anisotropy introduced by tin orientation in solder joints." Journal of Alloys and Compounds 703, no. : 264-271.