scholarly journals A new framework for high-resolution pedestrian data processing using rule-based algorithms and real-time alarm systems

2020 ◽  
Vol 5 ◽  
pp. A99
Author(s):  
Michael Moos ◽  
Basil Vitins ◽  
Mirwais Tayebi ◽  
Lukas Gamper ◽  
Julia Wysling ◽  
...  
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Safety Evaluation ◽  
Processing System ◽  
Strategic Communication ◽  
Sensor Data ◽  
Shopping Centers ◽  
Pedestrian Flow ◽  
Fundamental Diagram ◽  
Technological Advances

Pedestrian flows and densities have increased in recent years within transport-related public facilities such as train stations, as well as in private buildings such as shopping centers, event halls or convention centers. Increasing flows and high densities often raise comfort, safety, operational and delay issues; and therefore, require pedestrian flow optimization, intervention or even revised regulation. Recent technological advances enhanced pedestrian sensing; however, they disregard adaptive data capture, processing, and strategic communication within reasonable time, or real-time, such as tactic occupancy or density alarms trigger rules. Content of this research is twofold. First, new data capturing and processing advances of recent technological developments are combined in an integral software and hardware-based framework. Second, applied methods highlight projects and experiences on both pedestrian research and on existing and operating pedestrian facilities. Based on the described, two-sided approach, proposed framework is able to fulfil high safety and comfort standards of facilities such as train stations, retail facilities or event halls. In this research, past semi-automatic video analysis processing of pedestrian behavioral studies is replaced with combined sensor and data processing system within proposed framework. In train stations of major operators, real-time pedestrian observation increases safety levels on station platforms. Tactic algorithms and alarm trigger schemes enable on-time surveillance, e.g. at overcrowded floor levels in shopping centers for escalator or door closure. Sensor data is used to train models for underpass pedestrian flow regarding path choice and fundamental diagram. In retail, queue length, trajectory analysis and floor occupancy are determined for economic, comfort as well as safety evaluation. Using trajectory classification, movement and dwell time is analyzed for staff and visitors separately (see Figure 1).

Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

2017 ◽  
Vol 8 (2) ◽  
pp. 88-105 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
Daphne Lopez
Keyword(s):  
Climate Change ◽  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Sensor Data ◽  
Climate Data ◽  
The Real ◽  
Disease Surveillance System

Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.

Development of a real-time data processing system for a prototype of the Tomo-e Gozen wide field CMOS camera

2016 ◽  
Author(s):  
Ryou Ohsawa ◽  
Shigeyuki Sako ◽  
Hidenori Takahashi ◽  
Yuki Kikuchi ◽  
Mamoru Doi ◽  
...  
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Processing System ◽  
Wide Field ◽  
Cmos Camera ◽  
Data Processing System ◽  
Time Data ◽  
Real Time Data ◽  
Real Time Data Processing

A Discussion about FBG Sensors Application of Crane SHM System

2013 ◽  
Vol 336-338 ◽  
pp. 185-191
Author(s):  
Xiao Peng Xie ◽  
Dong Hui Wang ◽  
Guo Jian Huang ◽  
Xin Hua Wang
Keyword(s):  
Finite Element ◽  
Real Time ◽  
Failure Modes ◽  
Safety Evaluation ◽  
Structural Condition ◽  
Sensor Data ◽  
Gantry Crane ◽  
The Real ◽  
Time Stress ◽  
Fbg Sensors

The arrangement positions and the quantities are different for different types of cranes. In order to make suitable decision, much investigate and survey was done at preliminary stage, and we know that the flange connected gate legs and turntables, the connections between load-bearing beam and rotary column under the engine room and the connections between jib and turntable are easy to lose efficient, and their mainly failure modes are cracks. By the method of finite element, 32 sensors (including 21 welding strain FBG sensors and 11 temperature FBG sensors) were used after doing much investigate and survey and finite element modeling analysis, which are arranged in different places of a gantry crane of MQ2533, for real-time structure health monitoring. This method makes the sensor data obtained more realistically reflects the crane structural condition, which provides reliable data support for crane safety monitoring and safety evaluation. Then a software platform is developed to monitor the real-time stress. If the real-time stress exceeds the allowable stress, it issues an alarm signal to the operator.

scholarly journals A Framework for Real Time Processing of Sensor Data in the Cloud

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Supun Kamburugamuve ◽  
Leif Christiansen ◽  
Geoffrey Fox
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Cloud Services ◽  
Sensor Data ◽  
Smart Devices ◽  
Time Data ◽  
Real Time Processing ◽  
Real Time Data ◽  
Autonomous Robot Navigation ◽  
Real Time Data Processing

We describe IoTCloud, a platform to connect smart devices to cloud services for real time data processing and control. A device connected to IoTCloud can communicate with real time data analysis frameworks deployed in the cloud via messaging. The platform design is scalable in connecting devices as well as transferring and processing data. With IoTCloud, a user can develop real time data processing algorithms in an abstract framework without concern for the underlying details of how the data is distributed and transferred. For this platform, we primarily consider real time robotics applications such as autonomous robot navigation, where there are strict requirements on processing latency and demand for scalable processing. To demonstrate the effectiveness of the system, a robotic application is developed on top of the framework. The system and the robotics application characteristics are measured to show that data processing in central servers is feasible for real time sensor applications.

scholarly journals Development of an On-line Real Time Data Processing System for the Ion Microanalyzer

1975 ◽  
Vol 23 (4) ◽  
pp. 271-279
Author(s):  
YOSHIAKI KATO ◽  
YOSHIO TAJIMA ◽  
HAJIME HAYAKAWA ◽  
ATSUSHI SHIBATA ◽  
KOUJI NISHIWAKI
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Processing System ◽  
Data Processing System ◽  
Time Data ◽  
Real Time Data ◽  
On Line ◽  
Real Time Data Processing

scholarly journals A Real-Time Detection Method for Abnormal Data of Internet of Things Sensors Based on Mobile Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xuguang Liu
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Data Processing ◽  
Real Time ◽  
Detection Method ◽  
Edge Computing ◽  
Sensor Data ◽  
Detection Accuracy ◽  
Spatiotemporal Correlation ◽  
Real Time Detection

Aiming at the anomaly detection problem in sensor data, traditional algorithms usually only focus on the continuity of single-source data and ignore the spatiotemporal correlation between multisource data, which reduces detection accuracy to a certain extent. Besides, due to the rapid growth of sensor data, centralized cloud computing platforms cannot meet the real-time detection needs of large-scale abnormal data. In order to solve this problem, a real-time detection method for abnormal data of IoT sensors based on edge computing is proposed. Firstly, sensor data is represented as time series; K-nearest neighbor (KNN) algorithm is further used to detect outliers and isolated groups of the data stream in time series. Secondly, an improved DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm is proposed by considering spatiotemporal correlation between multisource data. It can be set according to sample characteristics in the window and overcomes the slow convergence problem using global parameters and large samples, then makes full use of data correlation to complete anomaly detection. Moreover, this paper proposes a distributed anomaly detection model for sensor data based on edge computing. It performs data processing on computing resources close to the data source as much as possible, which improves the overall efficiency of data processing. Finally, simulation results show that the proposed method has higher computational efficiency and detection accuracy than traditional methods and has certain feasibility.

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