Intelligent Sensor Fusion in Robotic Prosthetic Eye System

2004 ◽  
Vol 8 (3) ◽  
pp. 313-323
Author(s):  
Jason J. Gu ◽  
◽  
Max Meng ◽  
Albert Cook ◽  
Peter Xiaoping Liu ◽  
...  
Keyword(s):  
Experimental Studies ◽  
Failure Detection ◽  
Sensor Data ◽  
Sensor Failure ◽  
Intelligent Sensor ◽  
Two Generations ◽  
Processing Techniques ◽  
And Control ◽  
Prosthetic Eye ◽  
Theoretical Issues

This paper is concerned with the design, sensing and control of a robotic prosthetic eye that moves horizontally in synchronization with the movement of the natural eye. Two generations of robotic prosthetic eye models have been developed. Theoretical issues on sensor failure detection and recovery, and signal processing techniques used in sensor data fusion are studied using statistical methods and artificial neural network based techniques. In addition, practical control system design and implementation using micro controllers are studied and implemented to carry out the natural eye movement detection and artificial robotic eye control tasks. Simulation and experimental studies are performed and the results are included to demonstrate the effectiveness of the research project reported in this paper.

scholarly journals Design, Sensing and Control of a Robotic Prosthetic Eye for Natural Eye Movement

2006 ◽  
Vol 3 (1) ◽  
pp. 29-41 ◽  
Author(s):  
J. J. Gu ◽  
M. Meng ◽  
A. Cook ◽  
P. X. Liu
Keyword(s):  
Control System ◽  
Eye Movement ◽  
Experimental Studies ◽  
Failure Detection ◽  
Sensor Data ◽  
Generation Model ◽  
Movement Detection ◽  
Sensor Failure ◽  
And Control ◽  
Prosthetic Eye

Loss of an eye is a tragedy for a person, who may suffer psychologically and physically. This paper is concerned with the design, sensing and control of a robotic prosthetic eye that moves horizontally in synchronization with the movement of the natural eye. Two generations of robotic prosthetic eye models have been developed. The first generation model uses an external infrared sensor array mounted on the frame of a pair of eyeglasses to detect the natural eye movement and to feed the control system to drive the artificial eye to move with the natural eye. The second generation model removes the impractical usage of the eye glass frame and uses the human brain EOG (electro-ocular-graph) signal picked up by electrodes placed on the sides of a person's temple to carry out the same eye movement detection and control tasks as mentioned above. Theoretical issues on sensor failure detection and recovery, and signal processing techniques used in sensor data fusion, are studied using statistical methods and artificial neural network based techniques. In addition, practical control system design and implementation using micro-controllers are studied and implemented to carry out the natural eye movement detection and artificial robotic eye control tasks. Simulation and experimental studies are performed, and the results are included to demonstrate the effectiveness of the research project reported in this paper.

A methodology for intelligent sensor measurement, validation, fusion, and fault detection for equipment monitoring and diagnostics

2001 ◽  
Vol 15 (4) ◽  
pp. 307-320 ◽  
Author(s):  
SATNAM ALAG ◽  
ALICE M. AGOGINO ◽  
MAHESH MORJARIA
Keyword(s):  
Fault Detection ◽  
Sensor Data ◽  
Sensor Failure ◽  
Intelligent Sensor ◽  
Equipment Monitoring ◽  
Sensor Failures ◽  
Measurement Validation ◽  
Sensor Measurement ◽  
Monitoring And Diagnostics ◽  
Gas Turbine Power Plant

In equipment monitoring and diagnostics, it is very important to distinguish between a sensor failure and a system failure. In this paper, we develop a comprehensive methodology based on a hybrid system of AI and statistical techniques. The methodology is designed for monitoring complex equipment systems, which validates the sensor data, associates a degree of validity with each measurement, isolates faulty sensors, estimates the actual values despite faulty measurements, and detects incipient sensor failures. The methodology consists of four steps: redundancy creation, state prediction, sensor measurement validation and fusion, and fault detection through residue change detection. Through these four steps we use the information that can be obtained by looking at: information from a sensor individually, information from the sensor as part of a group of sensors, and the immediate history of the process that is being monitored. The advantage of this methodology is that it can detect multiple sensor failures, both abrupt as well as incipient. It can also detect subtle sensor failures such as drift in calibration and degradation of the sensor. The four-step methodology is applied to data from a gas turbine power plant.

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 Multi-Blockchain-Based IoT Data Processing Techniques to Ensure the Integrity of IoT Data in AIoT Edge Computing Environments

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3515
Author(s):  
Sung-Ho Sim ◽  
Yoon-Su Jeong
Keyword(s):  
Low Cost ◽  
Third Party ◽  
Monitoring And Control ◽  
Constant Size ◽  
Computing Environments ◽  
Iot Devices ◽  
Cloud Servers ◽  
The Cost ◽  
Processing Techniques ◽  
And Control

As the development of IoT technologies has progressed rapidly recently, most IoT data are focused on monitoring and control to process IoT data, but the cost of collecting and linking various IoT data increases, requiring the ability to proactively integrate and analyze collected IoT data so that cloud servers (data centers) can process smartly. In this paper, we propose a blockchain-based IoT big data integrity verification technique to ensure the safety of the Third Party Auditor (TPA), which has a role in auditing the integrity of AIoT data. The proposed technique aims to minimize IoT information loss by multiple blockchain groupings of information and signature keys from IoT devices. The proposed technique allows IoT information to be effectively guaranteed the integrity of AIoT data by linking hash values designated as arbitrary, constant-size blocks with previous blocks in hierarchical chains. The proposed technique performs synchronization using location information between the central server and IoT devices to manage the cost of the integrity of IoT information at low cost. In order to easily control a large number of locations of IoT devices, we perform cross-distributed and blockchain linkage processing under constant rules to improve the load and throughput generated by IoT devices.

scholarly journals Overexpression of arginine decarboxylase in transgenic plants

1997 ◽  
Vol 325 (2) ◽  
pp. 331-337 ◽  
Author(s):  
Daniel BURTIN ◽  
Anthony J. MICHAEL
Keyword(s):  
Transgenic Plants ◽  
Arginine Decarboxylase ◽  
Polyamine Metabolism ◽  
Morphological Development ◽  
Polyamine Biosynthesis ◽  
Adenosylmethionine Decarboxylase ◽  
Two Generations ◽  
Key Enzyme ◽  
Polyamine Conjugate ◽  
And Control

The activity of arginine decarboxylase (ADC), a key enzyme in plant polyamine biosynthesis, was manipulated in two generations of transgenic tobacco plants. Second-generation transgenic plants overexpressing an oat ADC cDNA contained high levels of oat ADC transcript relative to tobacco ADC, possessed elevated ADC enzyme activity and accumulated 10–20-fold more agmatine, the direct product of ADC. In the presence of high levels of the precursor agmatine, no increase in the levels of the polyamines putrescine, spermidine and spermine was detected in the transgenic plants. Similarly, the activities of ornithine decarboxylase and S-adenosylmethionine decarboxylase were unchanged. No diversion of polyamine metabolism into the hydroxycinnamic acid–polyamine conjugate pool or into the tobacco alkaloid nicotine was detected. Activity of the catabolic enzyme diamine oxidase was the same in transgenic and control plants. The elevated ADC activity and agmatine production were subjected to a metabolic/physical block preventing increased, i.e. deregulated, polyamine accumulation. Overaccumulation of agmatine in the transgenic plants did not affect morphological development.

Elevated levels of activin A in clinical and experimental pulmonary hypertension

2009 ◽  
Vol 106 (4) ◽  
pp. 1356-1364 ◽  
Author(s):  
Arne Yndestad ◽  
Karl-Otto Larsen ◽  
Erik Øie ◽  
Thor Ueland ◽  
Camilla Smith ◽  
...  
Keyword(s):  
Pulmonary Hypertension ◽  
Smooth Muscle ◽  
Smooth Muscle Cells ◽  
Transforming Growth Factor ◽  
Experimental Studies ◽  
Plasminogen Activator Inhibitor ◽  
Muscle Cells ◽  
Activin A ◽  
Plasminogen Activator Inhibitor 1 ◽  
And Control

Activin A, a member of the transforming growth factor (TGF)-β superfamily, is involved in regulation of tissue remodeling and inflammation. Herein, we wanted to explore a role for activin A in pulmonary hypertension (PH). Circulating levels of activin A and its binding protein follistatin were measured in patients with PH ( n = 47) and control subjects ( n = 14). To investigate synthesis and localization of pulmonary activin A, we utilized an experimental model of hypoxia-induced PH. In mouse lungs, we also explored signaling pathways that can be activated by activin A, such as phosphorylation of Smads, which are mediators of TGF-β signaling. Possible pathophysiological mechanisms initiated by activin A were explored by exposing pulmonary arterial smooth muscle cells in culture to this cytokine. Elevated levels of activin A and follistatin were found in patients with PH, and activin A levels were significantly related to mortality. Immunohistochemistry of lung autopsies from PH patients and lungs with experimental PH localized activin A primarily to alveolar macrophages and bronchial epithelial cells. Mice with PH exhibited increased pulmonary levels of mRNA for activin A and follistatin in the lungs, and also elevated pulmonary levels of phosphorylated Smad2. Finally, we found that activin A increased proliferation and induced gene expression of endothelin-1 and plasminogen activator inhibitor-1 in pulmonary artery smooth muscle cells, mediators that could contribute to vascular remodeling. Our findings in both clinical and experimental studies suggest a role for activin A in the development of various types of PH.

The Achievement of Intelligent Lubrication Adjustment Function of CNC Machine Tools Based on SSAS

2014 ◽  
Vol 556-562 ◽  
pp. 1454-1459
Author(s):  
Dong Sheng You
Keyword(s):  
Machine Tools ◽  
Sensor Data ◽  
Lubricating Oil ◽  
Cnc Machine Tools ◽  
Signal Acquisition ◽  
Cnc Machine ◽  
Lubrication Mode ◽  
Free Function ◽  
And Control ◽  
The Relationship

The use of CNC machine tools signal acquisition, two-way transmission of the temperature sensor data, the ladder design and macro program guide and other methods on the implementation of a temperature sensing system of smart lubrication function. It is not only low-end CNC machine tools can compensate for deficiencies in equipment protection features and maintenance-free function, but also enhance the diversity of processing. Ultimately by analyzing the different lubrication mode, the relationship between the lubricating oil pressure and temperature and other factors, to draw the function in the lubrication in a stabilizing effect on oil pressure and control bearings and nut seat temperature. It is simple and practical, has important theoretical significance and great value.

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