Multimodal Sensor Fusion for Indoor Occupancy Determination

2015 ◽  
Vol 764-765 ◽  
pp. 1319-1323
Author(s):  
Rong Shue Hsiao ◽  
Ding Bing Lin ◽  
Hsin Piao Lin ◽  
Jin Wang Zhou
Keyword(s):  
Bayesian Networks ◽  
Sensor Fusion ◽  
Dynamic Bayesian Networks ◽  
Sensor Nodes ◽  
Detection Accuracy ◽  
Multisensor Fusion ◽  
Fusion Algorithm ◽  
Improve Energy Efficiency ◽  
Occupancy Detection ◽  
Automation Systems

Pyroelectric infrared (PIR) sensors can detect the presence of human without the need to carry any device, which are widely used for human presence detection in home/office automation systems in order to improve energy efficiency. However, PIR detection is based on the movement of occupants. For occupancy detection, PIR sensors have inherent limitation when occupants remain relatively still. Multisensor fusion technology takes advantage of redundant, complementary, or more timely information from different modal sensors, which is considered an effective approach for solving the uncertainty and unreliability problems of sensing. In this paper, we proposed a simple multimodal sensor fusion algorithm, which is very suitable to be manipulated by the sensor nodes of wireless sensor networks. The inference algorithm was evaluated for the sensor detection accuracy and compared to the multisensor fusion using dynamic Bayesian networks. The experimental results showed that a detection accuracy of 97% in room occupancy can be achieved. The accuracy of occupancy detection is very close to that of the dynamic Bayesian networks.

An anti-interference detection algorithm for parking monitoring systems

2019 ◽  
Vol 12 (4) ◽  
pp. 481-496
Author(s):  
Ani Dong ◽  
Zusheng Zhang ◽  
Jiaming Chen
Keyword(s):  
Data Fusion ◽  
Local Level ◽  
Detection Algorithm ◽  
Magnetic Sensors ◽  
Sensor Nodes ◽  
Detection Accuracy ◽  
Fusion Algorithm ◽  
Content Type ◽  
Occupancy Detection ◽  
Local Decision

Purpose Magnetic sensors have recently been proposed for parking occupancy detection. However, there has adjacent interference problem, i.e. the magnetic signal is easy to be interfered by the vehicles which are parking on adjacent spaces. The purpose of this paper is to propose a sensing algorithm to eliminate the adjacent interference. Design/methodology/approach The magnetic signals are converted to the pattern representation sequences, and the similarity is calculated using the pattern distance. The detection algorithm includes two levels: local decision and data fusion. In the local decision level, the sampled signals can be divided into three classes: vacant, occupied and uncertain. Then a collaborative decision is used to fusion the signals which belong to the uncertain class for the second level. Findings An experiment system included 60 sensor nodes that were deployed on bay parking spaces. Experiment results show that the proposed algorithm has better detection accuracy than existing algorithms. Originality/value This paper proposes a data fusion algorithm to eliminate adjacent interference. To balance the energy consumption and detection accuracy, the algorithm includes two levels: local decision and data fusion. In most of cases, the local decision can obtain the accurate detection result. Only the signals that cannot be correctly detected at the local level need data fusion operation.

scholarly journals Infrared–ultrasonic sensor fusion for support vector machine–based fall detection

2018 ◽  
Vol 29 (9) ◽  
pp. 2027-2039 ◽  
Author(s):  
Zhangjie Chen ◽  
Ya Wang
Keyword(s):  
Support Vector Machine ◽  
Sensor Fusion ◽  
Learning Algorithm ◽  
Fall Detection ◽  
Ultrasonic Sensor ◽  
Support Vector ◽  
Detection Accuracy ◽  
Data Recording ◽  
Fusion Algorithm ◽  
Average Accuracy

This article presents an infrared–ultrasonic sensor fusion approach for support vector machine–based fall detection, often required by elderly healthcare. Its detection algorithms and performance evaluation are detailed. The location, size, and temperature profile of the user can be estimated based on a novel sensory fusion algorithm. Different feature sets of the support vector machine–based machine learning algorithm are analyzed and their impact on fall detection accuracy is evaluated and compared empirically. Experiments study three non-fall activities, standing, sitting, and stooping, and two fall actions, forward falling and sideway falling, to simulate daily activities of the elderly. Fall detection accuracy studies are performed based on discretely and continuously (closer to reality) recorded experimental data, respectively. For the discrete data recording, an average accuracy of 92.2% is achieved when the stand-alone Grid-EYE is used and the accuracy is increased to 96.7% when sensor fusion is used. For the continuous data recording (180 training sets, 60 test sets at each distance), an average accuracy less than 70.0% is achieved when the stand-alone Grid-EYE is used and the accuracy is increased to around 90.3% after sensor fusion. New features will be explored in the next step to further increase detection accuracy.

Development and implementation of novel sensor fusion algorithm for occupancy detection and automation in energy efficient buildings

2019 ◽  
Vol 44 ◽  
pp. 85-98 ◽  
Author(s):  
J. Preetha Roselyn ◽  
R. Annie Uthra ◽  
Ajith Raj ◽  
D. Devaraj ◽  
Pranav Bharadwaj ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Energy Efficient ◽  
Fusion Algorithm ◽  
Energy Efficient Buildings ◽  
Occupancy Detection

Dempster-Shafer-Based Sensor Fusion Approach for Machinery Fault Diagnosis

2017 ◽  
Author(s):  
Kar Hoou Hui ◽  
Meng Hee Lim ◽  
Salman Leong
Keyword(s):  
Fault Diagnosis ◽  
Sensor Fusion ◽  
Evidence Theory ◽  
Detection Accuracy ◽  
Test Machine ◽  
Fusion Algorithm ◽  
Vibration Signals ◽  
Machine Vibration ◽  
Fusion Approach ◽  
Machinery Fault Diagnosis

Artificial intelligence (AI) has played an increasingly important role in condition monitoring and machinery fault diagnosis in power generation plants. However, the accuracy and reliability of any AI-based machinery fault diagnosis is highly dependent on the quality and quantity of the input data fed to the AI model. The hypothesis of this paper is that AI-based fault diagnosis can be further improved by taking into account all the available sensor inputs of the machine. In short, the more sensor inputs fed into the AI model, the more accurate and reliable the outcome of the fault diagnosis. This paper proposes an application of Dempster-Shafer (DS) evidence theory for sensor fusion to improve the accuracy of decision-making in machinery fault diagnosis, by fusing all the available vibration signals measured on different axes and locations of the test machine. Vibration signals from different axes and locations of a machinery faults simulator were collected by multiple accelerometers simulating various machinery health conditions, namely healthy, unbalance, misalignment and foundation looseness. The accuracy of fault diagnosis using a different number of sensor inputs was then investigated. Analysis results showed that by combining more sensor inputs using a DS-based algorithm can improve fault detection accuracy from an average of 63% to 83%. In conclusion, the multi-sensor fusion algorithm can be applied to increase the accuracy and reliability of AI-based fault diagnosis.

scholarly journals IoT based Sensor Fusion Algorithm for Online Smart Health Monitoring

2019 ◽  
Vol 8 (2) ◽  
pp. 6040-6046
Keyword(s):  
Sensor Fusion ◽  
Human Life ◽  
Standard Of Care ◽  
Health Condition ◽  
The Body ◽  
Sensor Nodes ◽  
Fusion Algorithm ◽  
Patient Health ◽  
Health Care Applications ◽  
Internet Of Things Technology

Emerging Internet of Things technology plays the major role in modern healthcare not only for sensing but also in recording, communication and display results. The major role of an intensive care unit (ICU) is to improve patient health such as bringing about a change in the treatment or move the patient to a step-down unit etc. Monitoring also shows the extent of observance with a formulated standard of care. In ICU, care should be taken to monitor medical parameters, such as EEG, EMG, BP etc , continuously. In recent health care applications such as real time human health condition monitoring, patient information management etc, IoT technology brings convenience of general practitioner and human, since it is applied in various medical areas, the Body Sensor Network (BSN) is one of the main technology of IoT based medical applications, where a tiny smart and lightweight wireless sensor nodes are used for monitoring patient’s health condition. Hence, this paper proposes BSN integrated with IoT based sensor fusion algorithm to save human life those who are in critical condition. Sensor fusion algorithm is used to detect the criticality of the patient’s health condition and IoT technology is used for communicating information. The testbed has been developed using Rasberry Pi controller, EMG sensor,, BP sensor etc and tested. The tested results also analyzed.

scholarly journals Research on Active Intelligent Perception Technology of Vessel Situation Based on Multisensor Fusion

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ruixin Ma ◽  
Yong Yin ◽  
Zilong Li ◽  
Jing Chen ◽  
Kexin Bao
Keyword(s):  
Target Detection ◽  
Point Cloud ◽  
Automatic Identification ◽  
Identification System ◽  
Detection Accuracy ◽  
Point Cloud Data ◽  
Multisensor Fusion ◽  
Fusion Algorithm ◽  
Monitoring Method ◽  
Cloud Data

In this paper, we focus on the safety supervision of inland vessels. This paper especially aims at studying the vessel target detection and dynamic tracking algorithm based on computer vision and the target fusion algorithm based on multisensor. For the vessel video target detection and tracking, this paper analyzes the current widely used methods and theories. Additionally, facing the application scenarios and characteristics of inland vessels, a comprehensive vessel video target detection algorithm is proposed in this paper. It is combined with a three-frame difference method based on Canny edge detection and a background subtraction method based on mixed Gaussian background modeling. Besides, for the multisensor target fusion, the processing method of laser point cloud data and automatic identification system (AIS) data is analyzed in this paper. Based on the idea of fuzzy mathematics, this paper proposes a method for calculating the fuzzy correlation matrix with normal membership function, which realizes the fusion of vessel track features of laser point cloud data and AIS data under dynamic video correction. Finally, through this method, a set of vessel situation active intelligent perception systems based on multisensor fusion was developed. Experiments show that this method has better environmental applicability and detection accuracy than traditional manual detection and any single monitoring method.

scholarly journals Edge Computing for Data Anomaly Detection of Multi-Sensors in Underground Mining

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 302
Author(s):  
Chunde Liu ◽  
Xianli Su ◽  
Chuanwen Li
Keyword(s):  
Energy Consumption ◽  
Anomaly Detection ◽  
Underground Mining ◽  
Heterogeneous Data ◽  
Edge Computing ◽  
Sensor Nodes ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Clustering Methods ◽  
Safety Warning

There is a growing interest in safety warning of underground mining due to the huge threat being faced by those working in underground mining. Data acquisition of sensors based on Internet of Things (IoT) is currently the main method, but the data anomaly detection and analysis of multi-sensors is a challenging task: firstly, the data that are collected by different sensors of underground mining are heterogeneous; secondly, real-time is required for the data anomaly detection of safety warning. Currently, there are many anomaly detection methods, such as traditional clustering methods K-means and C-means. Meanwhile, Artificial Intelligence (AI) is widely used in data analysis and prediction. However, K-means and C-means cannot directly process heterogeneous data, and AI algorithms require equipment with high computing and storage capabilities. IoT equipment of underground mining cannot perform complex calculation due to the limitation of energy consumption. Therefore, many existing methods cannot be directly used for IoT applications in underground mining. In this paper, a multi-sensors data anomaly detection method based on edge computing is proposed. Firstly, an edge computing model is designed, and according to the computing capabilities of different types of devices, anomaly detection tasks are migrated to different edge devices, which solve the problem of insufficient computing capabilities of the devices. Secondly, according to the requirements of different anomaly detection tasks, edge anomaly detection algorithms for sensor nodes and sink nodes are designed respectively. Lastly, an experimental platform is built for performance comparison analysis, and the experimental results show that the proposed algorithm has better performance in anomaly detection accuracy, delay, and energy consumption.

Multiple small objects tracking based on dynamic Bayesian networks with spatial prior

Optik ◽  
2014 ◽  
Vol 125 (10) ◽  
pp. 2243-2247 ◽  
Author(s):  
Rui Yao ◽  
Yanning Zhang ◽  
Yong Zhou ◽  
Shixiong Xia
Keyword(s):  
Bayesian Networks ◽  
Dynamic Bayesian Networks ◽  
Objects Tracking
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