The Intelligent Detection System of the Machineroomless Elevator Based on WSN

2011 ◽  
Vol 339 ◽  
pp. 67-70
Author(s):  
Zhi Jun Chen ◽  
Xin Liu ◽  
Xue Qin Yan ◽  
Ru Zhang
Keyword(s):  
Data Collection ◽  
Internet Of Things ◽  
Real Time ◽  
Network Structure ◽  
Detection System ◽  
Sensor Nodes ◽  
Zigbee Technology ◽  
Intelligent Detection ◽  
System Data ◽  
Test Result

An intelligent detection system of machineroomless elevator is designed in this paper as well as the diagrams of system’s network structure and workflow. The system chooses the CC2430 chip based on ZigBee technology as controller for system data collection and transmission node, designs the circuits schematic for the elevator sensor nodes and gateway nodes and the monitoring center to realize real-time display of the elevator running state. The test result shows that the system is reliable, small in size, low in cost and consumption which means a tryout for the application of internet of things in elevators.

scholarly journals Sistem Pemantauan dan Pendeteksi Kebakaran berbasis Logika Fuzzy dan Real-time Database

2021 ◽  
Vol 9 (3) ◽  
pp. 577
Author(s):  
EVA AISAH HW ◽  
ROHMAT TULLOH ◽  
SUGONDO HADIYOSO ◽  
DADAN NUR RAMADAN
Keyword(s):  
Fuzzy Logic ◽  
Internet Of Things ◽  
Real Time ◽  
Human Error ◽  
Detection System ◽  
Environmental Parameters ◽  
Fire Detection ◽  
Smoke Detector ◽  
Web Interface ◽  
Average Transmission

ABSTRAKKebakaran rumah seringkali disebabkan oleh kelalaian manusia. Oleh karena itu diperlukan sebuah sistem yang dapat mendeteksi kebakaran secara online realtime. Pada studi ini, dirancang dan diimplementasikan sebuah sistem pendeteksi kebakaran dengan sejumlah sensor untuk mengukur beberapa parameter lingkungan. Sistem ini dilengkapi dengan pengambil keputusan menggunakan metode fuzzy logic. Parameter lingkungan yang diukur mencakup suhu ruangan, asap dan api yang kemudian dapat dimonitor secara real-time melalui web interface menggunakan Internet of Things platform. Pengujian menunjukkan bahwa detektor dapat mendeteksi api dengan jarak hingga 100 cm dengan akurasi mencapai 100%. Pengujian sensor suhu menunjukkan akurasi 98.79%, sementara itu detektor asap memperoleh akurasi 77.81%. Sistem ini mampu mengirimkan data dengan rata-rata delay transmisi 0.62 detik. Sistem usulan ini diharapkan dapat menyediakan pemantauan kondisi suatu ruangan secara real-time.Kata kunci: Kebakaran, Real-Time, Deteksi, Fuzzy, Internet Of Things ABSTRACTHouse fires are often caused by human error. Therefore, we need a system that can detect fires online real-time. In this study, a fire detection system with a number of sensors is designed and implemented to measure several environmental parameters. This system is equipped with a decision maker using the fuzzy logic method. The environmental parameters measured include room temperature, smoke and fire which can then be monitored in real time via a web interface using the Internet of Things platform. Tests show that the detector can detect fires with a distance of up to 100 cm with an accuracy of up to 100%. The temperature sensor test shows an accuracy of 98.79%, while the smoke detector generates an accuracy of 77.81%. This system is capable of sending data with an average transmission delay of 0.62 seconds. This proposed system is expected to provide realtime monitoring of the condition of a room.Keywords: Fire, Real-time, detection, Fuzzy, internet of things

scholarly journals Internet of Things: A Comprehensive Study on Machine Learning-based Intrusion Detection approaches

2021 ◽  
Author(s):  
Priyanka Gupta ◽  
Lokesh Yadav ◽  
Deepak Singh Tomar
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Real Time ◽  
Detection System ◽  
Research Work ◽  
Security And Privacy ◽  
Iot Security ◽  
Advantages And Disadvantages ◽  
Iot Devices

The Internet of Things (IoT) connects billions of interconnected devices that can exchange information with each other with minimal user intervention. The goal of IoT to become accessible to anyone, anytime, and anywhere. IoT has engaged in multiple fields, including education, healthcare, businesses, and smart home. Security and privacy issues have been significant obstacles to the widespread adoption of IoT. IoT devices cannot be entirely secure from threats; detecting attacks in real-time is essential for securing devices. In the real-time communication domain and especially in IoT, security and protection are the major issues. The resource-constrained nature of IoT devices makes traditional security techniques difficult. In this paper, the research work carried out in IoT Intrusion Detection System is presented. The Machine learning methods are explored to provide an effective security solution for IoT Intrusion Detection systems. Then discussed the advantages and disadvantages of the selected methodology. Further, the datasets used in IoT security are also discussed. Finally, the examination of the open issues and directions for future trends are also provided.

scholarly journals Real-time object locator for cryo-EM data collection --- You only navigate EM once ---

2021 ◽  
Author(s):  
Koji Yonekura ◽  
Saori Maki-Yonekura ◽  
Hisashi Naitow ◽  
Tasuku Hamaguchi ◽  
Kiyofumi Takaba
Keyword(s):  
Electron Microscopy ◽  
Machine Learning ◽  
Electron Diffraction ◽  
Data Collection ◽  
Real Time ◽  
Single Particle ◽  
Detection System ◽  
Target Object ◽  
Electron Crystallography ◽  
Cryo Electron Microscopy

In cryo-electron microscopy (cryo-EM) data collection, locating a target object is the most error-prone. Here, we present a machine learning-based approach with a real-time object locator named yoneoLocr using YOLO, a well-known object detection system. Implementation showed its effectiveness in rapidly and precisely locating carbon holes in single particle cryo-EM and for locating crystals and evaluating electron diffraction (ED) patterns in automated cryo-electron crystallography (cryo-EX) data collection.

scholarly journals Machine learning-based real-time object locator/evaluator for cryo-EM data collection

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Koji Yonekura ◽  
Saori Maki-Yonekura ◽  
Hisashi Naitow ◽  
Tasuku Hamaguchi ◽  
Kiyofumi Takaba
Keyword(s):  
Electron Microscopy ◽  
Machine Learning ◽  
Data Collection ◽  
Real Time ◽  
Detection System ◽  
Target Object ◽  
Accurate Data ◽  
Electron Crystallography ◽  
Cryo Electron Microscopy ◽  
Diffraction Patterns

AbstractIn cryo-electron microscopy (cryo-EM) data collection, locating a target object is error-prone. Here, we present a machine learning-based approach with a real-time object locator named yoneoLocr using YOLO, a well-known object detection system. Implementation shows its effectiveness in rapidly and precisely locating carbon holes in single particle cryo-EM and in locating crystals and evaluating electron diffraction (ED) patterns in automated cryo-electron crystallography (cryo-EX) data collection. The proposed approach will advance high-throughput and accurate data collection of images and diffraction patterns with minimal human operation.

Research on the Roller Intelligent Detection System Based on LabVIEW

2013 ◽  
Vol 438-439 ◽  
pp. 1084-1088
Author(s):  
Ummin Okumura ◽  
Yu Jie Qi ◽  
Yun Long ◽  
Tian Hang Zhang
Keyword(s):  
Nondestructive Testing ◽  
Real Time ◽  
Detection System ◽  
Detection Methods ◽  
Intelligent Detection ◽  
Speed Up ◽  
Compaction Control ◽  
And Control ◽  
Compaction Degree

Based on the platform of LabVIEW, a set of roller intelligent detecting system is developed. With this system, it is easy to realize functions of fast nondestructive testing of subgrade compaction degree, roller speed, rollers compaction trajectory, compaction times, GPS real-time positioning as well as saving and printing report forms. Compared with traditional detection methods, this detecting system can test and control on-site compaction quality much more easily, in order to speed up the construction progress, improve the quality of subgrade compaction, control and manage compaction work better.

Development of Denial of Service (DoS) Mitigation for Internet of Things (IoT) Sensor Node

2019 ◽  
Vol 4 (2) ◽  
pp. 26-34
Author(s):  
Abdul Fuad Abdul Rahman ◽  
Azni Ab Halim ◽  
Nurul Syazwani ◽  
Maslina Daud ◽  
Madihah Zulfa Mohamad ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Sensor Node ◽  
Detection System ◽  
Denial Of Service ◽  
Sensor Nodes ◽  
Security Threat ◽  
Smart Water ◽  
Keywords Internet ◽  
Water Sensor ◽  
Using Data

ABSTRACT Objective - The objective of this paper is to propose a lightweight IDS algorithm to secure IoT Sensor Node. Methodology/Technique - The proposed IDS algorithm for IoT Sensor Node shall prevent the abnormal energy consumption by monitoring, calculating, and evaluating energy drop from each cluster nodes based on a few conditions. Findings - The DoS attack is considered as one of security threat that may affect the quality service of IoT network and also reduce the lifespan of IoT Sensor Nodes Novelty - The approach is using data from previous experiments and translated it to develop mitigation to secure IoT Sensor Node, thus increased the lifespan of IoT Sensor Nodes. Type of Paper: Other. Keywords: Internet of Things (IoT); Intrusion Detection System (IDS); Denial of Service (DoS); Smart Water; Sensor.

IoTP an Efficient Privacy Preserving Scheme for Internet of Things Environment

2020 ◽  
Vol 14 (2) ◽  
pp. 116-142
Author(s):  
Shelendra Kumar Jain ◽  
Nishtha Kesswani
Keyword(s):  
Data Collection ◽  
Internet Of Things ◽  
Data Access ◽  
Privacy Preserving ◽  
Sensor Nodes ◽  
Sensitive Information ◽  
Time Data ◽  
Sensitive Data ◽  
Data Store ◽  
Collection Data

Many emerging fields are adopting Internet of Things technologies to incorporate smartness in respective areas. Several IoT based application area produces large volumes of real time data. Data aggregated through sensor nodes may contain highly sensitive information. An effective and successful IoT system must protect sensitive data from revealing to unauthorized persons. In this article, the authors present an efficient privacy-preserving mechanism called Internet of Things privacy (IoTp). The research simulates and analyzes the effectiveness of the proposed data aggregation and data access mechanism for a typical IoT system. Proposed IoTp scheme ensures privacy at data collection, data store and data access phases of the IoT system. The authors have compared proposed work with existing model. Results show that IoTp scheme is efficient and lightweight mechanism for data collection and data access. It is suitable for the resource constrained IoT ecosystems.

scholarly journals 112. Sick Employee Online Log System for Tracking Employee Illnesses During the 2017–2018 Influenza Season Provided Real-Time Surveillance and Early Detection of Influenza-Like Illnesses Among Employees

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S2-S3 ◽  
Author(s):  
Keith M Ramsey ◽  
M Kathy Cochran ◽  
William Cleve
Keyword(s):  
Early Detection ◽  
Real Time ◽  
Influenza Season ◽  
Detection System ◽  
Data Entry ◽  
The State ◽  
Influenza Surveillance ◽  
Influenza Like Illness ◽  
Department Of Health ◽  
System Data

Abstract Background Vidant Health is an 8-hospital, 1,542-bed system (including the 908-bed teaching hospital for The Brody School of Medicine at East Carolina University) with over 12,000 employees, and uses a sick employee online log (SEOL) to track illnesses among employees. Influenza-like illness (ILI) surveillance is collected from sentinel sites across the state of North Carolina (NC) by the Department of Health. Our goals were to determine the utility of the SEOL to monitor ILI among employees and to compare trends with the NC ILI-system for Influenza surveillance. Methods When an employee calls in sick, symptoms for ILI in both the SEOL system and NC ILI-system include fever plus cough and/or sore throat. SEOL is an internet-based system, so information is collected and analyzed in real time. The number of sick hospital employees with influenza-like illness (ILI) per week during the 2017–2018 Influenza season was compared both to those employees reporting “Flu” and to the NC ILI numbers from the sentinel sites using MS Excel. Results The data analyzed was from October 2017 to April 2018. First, while lesser actual numbers of sick employees reported “Flu,” there was a correlation value of 0.93 between those reporting “Flu” and those reporting ILI symptoms (see Figure 1). Secondly, the SEOL results are available daily, while the NC ILI data are reported 7–12 days from entry; however, the peaks in ILI paralleled those of the peaks in SEOL data for employees reporting symptoms of ILI (see Figure 2) with a correlation value of 0.79 between the two. Finally, there were no breaks in confidentiality for those employees utilizing the SEOL. Conclusion The SEOL provided a real-time tool to monitor employee illnesses due to ILI during influenza season, and without the lag time of the ILI-surveillance by the state. This system maintained confidentiality with a convenient method for data entry. These findings conclude that the SEOL system data correlated positively with the state ILI data and provided an early detection system for the appearance of influenza among our employees. Disclosures All authors: No reported disclosures.

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