scholarly journals Data-Centric Knowledge Discovery Strategy for a Safety-Critical Sensor Application

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Nilamadhab Mishra ◽  
Hsien-Tsung Chang ◽  
Chung-Chih Lin
Keyword(s):  
Knowledge Discovery ◽  
Time Frame ◽  
Industrial Applications ◽  
Sensor Data ◽  
Sensors And Actuators ◽  
Safety Critical ◽  
Sensor Applications ◽  
Hard Time ◽  
Collection Data ◽  
Time Critical

In an indoor safety-critical application, sensors and actuators are clustered together to accomplish critical actions within a limited time constraint. The cluster may be controlled by a dedicated programmed autonomous microcontroller device powered with electricity to perform in-network time critical functions, such as data collection, data processing, and knowledge production. In a data-centric sensor network, approximately 3–60% of the sensor data are faulty, and the data collected from the sensor environment are highly unstructured and ambiguous. Therefore, for safety-critical sensor applications, actuators must function intelligently within a hard time frame and have proper knowledge to perform their logical actions. This paper proposes a knowledge discovery strategy and an exploration algorithm for indoor safety-critical industrial applications. The application evidence and discussion validate that the proposed strategy and algorithm can be implemented for knowledge discovery within the operational framework.

scholarly journals Deep Convolutional Neural Network with RNNs for Complex Activity Recognition Using Wrist-Worn Wearable Sensor Data

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1685
Author(s):  
Sakorn Mekruksavanich ◽  
Anuchit Jitpattanakul
Keyword(s):  
Neural Networks ◽  
Activity Recognition ◽  
Human Activities ◽  
Recognition Performance ◽  
Confusion Matrix ◽  
Experimental Studies ◽  
Industrial Applications ◽  
Sensor Data ◽  
Complex Activity ◽  
Activity Data

Sensor-based human activity recognition (S-HAR) has become an important and high-impact topic of research within human-centered computing. In the last decade, successful applications of S-HAR have been presented through fruitful academic research and industrial applications, including for healthcare monitoring, smart home controlling, and daily sport tracking. However, the growing requirements of many current applications for recognizing complex human activities (CHA) have begun to attract the attention of the HAR research field when compared with simple human activities (SHA). S-HAR has shown that deep learning (DL), a type of machine learning based on complicated artificial neural networks, has a significant degree of recognition efficiency. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two different types of DL methods that have been successfully applied to the S-HAR challenge in recent years. In this paper, we focused on four RNN-based DL models (LSTMs, BiLSTMs, GRUs, and BiGRUs) that performed complex activity recognition tasks. The efficiency of four hybrid DL models that combine convolutional layers with the efficient RNN-based models was also studied. Experimental studies on the UTwente dataset demonstrated that the suggested hybrid RNN-based models achieved a high level of recognition performance along with a variety of performance indicators, including accuracy, F1-score, and confusion matrix. The experimental results show that the hybrid DL model called CNN-BiGRU outperformed the other DL models with a high accuracy of 98.89% when using only complex activity data. Moreover, the CNN-BiGRU model also achieved the highest recognition performance in other scenarios (99.44% by using only simple activity data and 98.78% with a combination of simple and complex activities).

scholarly journals Electronic Textiles

Encyclopedia ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 115-130
Author(s):  
Guido Ehrmann ◽  
Andrea Ehrmann
Keyword(s):  
Power Supply ◽  
Sensor Data ◽  
Electronic Textiles ◽  
Sensors And Actuators ◽  
External Communication ◽  
Data Processor ◽  
Temperature Strain

Electronic textiles belong to the broader range of smart (or “intelligent”) textiles. Their “smartness” is enabled by embedded or added electronics and allows the sensing of defined parameters of their environment as well as actuating according to these sensor data. For this purpose, different sensors (e.g., temperature, strain, light sensors) and actuators (e.g., LEDs or mechanical actuators) are embedded and connected with a power supply, a data processor, and internal/external communication.

scholarly journals Knowledge discovery from sensor data (SensorKDD)

2011 ◽  
Vol 12 (2) ◽  
pp. 50-53 ◽  
Author(s):  
Varun Chandola ◽  
Olufemi A. Omitaomu ◽  
Auroop R. Ganguly ◽  
Ranga R. Vatsavai ◽  
Nitesh V. Chawla ◽  
...  
Keyword(s):  
Knowledge Discovery ◽  
Sensor Data

scholarly journals Timing Predictability and Security in Safety-Critical Industrial Cyber-Physical Systems: A Position Paper

2020 ◽  
Vol 10 (9) ◽  
pp. 3125
Author(s):  
Saad Mubeen ◽  
Elena Lisova ◽  
Aneta Vulgarakis Feljan
Keyword(s):  
Embedded Systems ◽  
Cyber Physical Systems ◽  
Position Paper ◽  
Technological Advancement ◽  
Functional Safety ◽  
Physical Systems ◽  
Safety Critical ◽  
Secure Embedded Systems ◽  
Time Critical ◽  
Networking Technology

Cyber Physical Systems (CPSs) are systems that are developed by seamlessly integrating computational algorithms and physical components, and they are a result of the technological advancement in the embedded systems and distributed systems domains, as well as the availability of sophisticated networking technology. Many industrial CPSs are subject to timing predictability, security and functional safety requirements, due to which the developers of these systems are required to verify these requirements during the their development. This position paper starts by exploring the state of the art with respect to developing timing predictable and secure embedded systems. Thereafter, the paper extends the discussion to time-critical and secure CPSs and highlights the key issues that are faced when verifying the timing predictability requirements during the development of these systems. In this context, the paper takes the position to advocate paramount importance of security as a prerequisite for timing predictability, as well as both security and timing predictability as prerequisites for functional safety. Moreover, the paper identifies the gaps in the existing frameworks and techniques for the development of time- and safety-critical CPSs and describes our viewpoint on ensuring timing predictability and security in these systems. Finally, the paper emphasises the opportunities that artificial intelligence can provide in the development of these systems.

Knowledge Discovery for Sensor Network Comprehension

2010 ◽  
pp. 118-135 ◽  
Author(s):  
Pedro Pereira Rodrigues ◽  
João Gama ◽  
Luís Lopes
Keyword(s):  
Sensor Networks ◽  
Knowledge Discovery ◽  
Sensor Network ◽  
Data Streams ◽  
High Rate ◽  
Sensor Data ◽  
Processing Abilities ◽  
Comprehension Ability ◽  
Visualization Of Data ◽  
Different Characteristics

In this chapter we explore different characteristics of sensor networks which define new requirements for knowledge discovery, with the common goal of extracting some kind of comprehension about sensor data and sensor networks, focusing on clustering techniques which provide useful information about sensor networks as it represents the interactions between sensors. This network comprehension ability is related with sensor data clustering and clustering of the data streams produced by the sensors. A wide range of techniques already exists to assess these interactions in centralized scenarios, but the seizable processing abilities of sensors in distributed algorithms present several benefits that shall be considered in future designs. Also, sensors produce data at high rate. Often, human experts need to inspect these data streams visually in order to decide on some corrective or proactive operations (Rodrigues & Gama, 2008). Visualization of data streams, and of data mining results, is therefore extremely relevant to sensor data management, and can enhance sensor network comprehension, and should be addressed in future works.

A delay-aware spectrum handoff scheme for prioritized time-critical industrial applications with channel selection strategy

2019 ◽  
Vol 144 ◽  
pp. 112-123
Author(s):  
Stephen S. Oyewobi ◽  
Gerhard P. Hancke ◽  
Adnan M. Abu-Mahfouz ◽  
Adeiza J. Onumanyi
Keyword(s):  
Channel Selection ◽  
Industrial Applications ◽  
Selection Strategy ◽  
Spectrum Handoff ◽  
Time Critical

Sensor Fusion for Industrial Applications Using Transducer Markup Language

2006 ◽  
Author(s):  
Valerie J. Yoder ◽  
Steven W. Havens ◽  
Arthur J. Na ◽  
Rachel E. Weingrad
Keyword(s):  
Sensor Fusion ◽  
Industrial Applications ◽  
Laser Surface ◽  
Sensor Data ◽  
Manufacturing Processes ◽  
Markup Language ◽  
Coordinate Measurement ◽  
Proprietary Software ◽  
Target Attributes ◽  
Sensor Transducer

Manufacturing processes would greatly benefit from fusing data from many disparate sensors, but systems today do not fully exploit available sensor data. Disparate sensors could include Coordinate Measurement Machines (CMM), laser surface scanners, micro sensors, cameras, acoustic devices, thermocouples, or other various devices which provide measurement or visual data. Often, sensor data requires separate customized software for each type of sensor system, as opposed to having common tools for use across a wide array of sensor systems. This process of stove-piping requires proprietary software for analysis and display of each sensor type, and inhibits interoperability. There are several challenges to sensor fusion which need to be addressed. First, many sensors providing data are heterogeneous in phenomena detection and operation, providing measurements of different target attributes. This makes the measurements very difficult to fuse directly. Second, these disparate sensors are asynchronous in time. The collection, integration, buffering and transmitting time can each affect the way time is calculated and stored by the sensor. Transducer Markup Language (TML), developed by IRIS Corporation, addresses these challenges. This paper describes TML and addresses examples of industrial applications of TML-enabled transducer networks.

scholarly journals An Engineering Approach to Storage and Access in High Priority Scenarios

2019 ◽  
Vol 63 (1) ◽  
pp. 1349-1353
Author(s):  
FNU Varun Ananthasivan Srikrishnan ◽  
Richard T. Stone ◽  
Cong Xu
Keyword(s):  
Human Factors ◽  
Human Performance ◽  
Normal Operation ◽  
Aviation Industry ◽  
Critical Systems ◽  
Safety Critical ◽  
Work Spaces ◽  
Storage Room ◽  
Before And After ◽  
Time Critical

Over the past few years, an extensive amount of research has been done in the field of Human Factors. Applications range from the design of day-to-day products like cell phones to the design and development of safety-critical systems like flight displays. The highly critical aviation industry has shown time and again the importance of human-centered approach in developing systems for the safety of those operating it and the passengers. Similarly, other safety-critical industries like law enforcement have been seen to incorporate human factors in the design of weapons and exoskeletons aimed at adapting to humans and making their unit stronger. Many manufacturing firms have begun to see the importance of proper work postures for their employees to avoid musculoskeletal disorders and the financial and regulatory implications of not following proper work ethics that take care of employees’ health. Further, many organizations have started to consider team dynamics in their operations understanding the importance of healthy interaction among the employees and between employees and the management. However, there are a very few references to any studies or organizational practices that draw a connection between human performance and human-centric re-design of work places, with most designs being limited to work desks and activity-based working (ABW) work spaces. This paper focuses on the organizational engineering of storage spaces to enable easy location and retrieval of equipment, thus supporting the time-critical nature of operations at a miscellaneous storage room at the Story County Sheriff’s Office. Experiments were carried out using two familiar scenarios both before and after the redesign of the storage room. A significant improvement in the performance of the operator was observed after the redesign, as could be seen by the reduction in time taken to identify and retrieve equipment and the qualitative survey that was obtained at the end of the experiment. The wasted time was translated to a cost and the newly designed storage design saved a significant amount of money spent on actions that precluded efficient accomplishment of tasks, something that could have been used by the Sheriff’s office to purchase equipment for normal operation of the office. The results suggested such interventions in different sectors that have similar high-priority operations. The results of the study indicates that there is a need for the industry to extend research towards this field that we name “organization engineering”.

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