scholarly journals Automatically Determining Lumbar Load during Physically Demanding Work: A Validation Study

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2476
Author(s):  
Charlotte Christina Roossien ◽  
Christian Theodoor Maria Baten ◽  
Mitchel Willem Pieter van der Waard ◽  
Michiel Felix Reneman ◽  
Gijsbertus Jacob Verkerke
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Discriminant Validity ◽  
Real Life ◽  
Working Environment ◽  
Physically Active ◽  
Perceived Workload ◽  
Artificial Neural ◽  
Lumbar Load ◽  
Physically Demanding Work

A sensor-based system using inertial magnetic measurement units and surface electromyography is suitable for objectively and automatically monitoring the lumbar load during physically demanding work. The validity and usability of this system in the uncontrolled real-life working environment of physically active workers are still unknown. The objective of this study was to test the discriminant validity of an artificial neural network-based method for load assessment during actual work. Nine physically active workers performed work-related tasks while wearing the sensor system. The main measure representing lumbar load was the net moment around the L5/S1 intervertebral body, estimated using a method that was based on artificial neural network and perceived workload. The mean differences (MDs) were tested using a paired t-test. During heavy tasks, the net moment (MD = 64.3 ± 13.5%, p = 0.028) and the perceived workload (MD = 5.1 ± 2.1, p < 0.001) observed were significantly higher than during the light tasks. The lumbar load had significantly higher variances during the dynamic tasks (MD = 33.5 ± 36.8%, p = 0.026) and the perceived workload was significantly higher (MD = 2.2 ± 1.5, p = 0.002) than during static tasks. It was concluded that the validity of this sensor-based system was supported because the differences in the lumbar load were consistent with the perceived intensity levels and character of the work tasks.

scholarly journals Recognition and Digitization of Handwritten Text using Histogram of Gradients and Artificial Neural Network

2021 ◽  
Vol 12 (6) ◽  
pp. 2555-2564
Author(s):  
Dr.S.K.Nivetha Et al.
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Real Life ◽  
Machine Learning Algorithms ◽  
Postal Code ◽  
Training Costs ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Artificial Neural

Handwriting recognition is one of the most persuasive and interesting projects as it is required in many real-life applications such as bank-check processing, postal-code recognition, handwritten notes or question paper digitization etc. Machine learning and deep learning methods are being used by developers to make computers more intelligent. A person learns how to execute a task by learning and repeating it over and over before it memorises the steps. The neurons in his brain will then be able to easily execute the task that he has mastered. This is also very close to machine learning. It employs a variety of architectures to solve various problems. Handwritten text recognition systems are models that capture and interpret handwritten numeric and character data from sources such as paper documents and photographs. For this application, a variety of machine learning algorithms were used. However, several limitations have been found, such as a large number of iterations, high training costs, and so on. Even though the other models have given impressive accuracy, it still has some drawbacks. In an unsupervised way, the Artificial Neural Network is used to learn effective data coding. For recognising real-world data, we built a model using Histogram of Oriented Gradients (HOG) and Artificial Neural Networks (ANN).

scholarly journals Fuzzy Matrix Game: A Fast Approach using Artificial Hybrid Neural-Net Logic-Gate Switching Circuit

2022 ◽  
Author(s):  
Ankan Bhaumik ◽  
Sankar Kumar Roy
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Artificial Neural Network ◽  
Logic Gate ◽  
Real Life ◽  
Neural Nets ◽  
Matrix Game ◽  
Switching Circuit ◽  
Artificial Neural

Abstract Introducing neuro -fuzzy concept in decision making problems, makes a new way in artificial intelligence and expert systems. Sometimes, neural networks are used to optimize certain performances. In general, knowledge acquisition becomes difficult when problem's variables, constraints, environment, decision maker's attitude and complex behavior are encountered with. A sense of fuzziness prevails in these situations; sometimes numerically and sometimes linguistically. Neural networks (or neural nets) help to overcome this problem. Neural networks are explicitly and implicitly hyped to draw out fuzzy rules from numerical information and linguistic information. Logic-gate and switching circuit mobilize the fuzzy data in crisp environment and can be used in artificial neural network, also. Game theory has a tremendous scope in decision making; and consequently decision makers' hesitant characters play an important role in it. In this paper, a game situation is clarified under artificial neural network through logic-gate switching circuit in hesitant fuzzy environment with a suitable example; and this concept can be applied in future for real-life situations.

Bio-Inspired Computing through Artificial Neural Network

2017 ◽  
pp. 246-274
Author(s):  
Nilamadhab Dash ◽  
Rojalina Priyadarshini ◽  
Brojo Kishore Mishra ◽  
Rachita Misra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intelligent Systems ◽  
Deductive Reasoning ◽  
Real Life ◽  
Artificial Neural ◽  
Living Organisms ◽  
Artificial Neural Network Ann ◽  
Intelligent Models ◽  
Algorithmic Model

Developing suitable mathematical or algorithmic model to solve real life complex problems is one of the major challenges faced by the researchers especially those involved in the computer science field. To a large extent Computational intelligence has been found to be effective in designing such models. Bio inspired computing is the technique which makes the machines intelligent by adapting the behavior and methods exhibited by the human beings and other living organisms while forming intelligent systems. These intelligent models include the intelligent techniques such as Artificial Neural Network (ANN), evolutionary computation, swarm intelligence, fuzzy system, artificial immune system accompanied by fuzzy logic, expert system, deductive reasoning. All these together form the area of Bio inspired computing. The chapter deals with various bio inspired technique, giving emphasis on issues, development, advances and practical implementations of ANN.

Reliability Analysis of Waste Tires Semi-Submersible Breakwater

2017 ◽  
Vol 864 ◽  
pp. 363-368 ◽  
Author(s):  
Long Qi ◽  
Zi Chang Shangguan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Complex Structure ◽  
Working Environment ◽  
Waste Tires ◽  
The Monte Carlo Method ◽  
Wide Range ◽  
Artificial Neural

With the continuous development of social economy, China's port construction scale has become saturated. As an important part of the harbor, the breakwater plays a crucial role in the safety of the working environment in the harbor, and the reliability of the breakwater is an assessment of its safety. Build on the previous studies, this paper puts forward a semi-submersible breakwater using waste tires. The reliability of this breakwater is analyzed by Monte Carlo method of artificial neural network based on Matlab, and the results are compared to those of Direct sampling Monte Carlo method and Important sampling Monte Carlo method. The results show that the Monte Carlo method is able to analyze the reliability of overturning failure of semi-submersible breakwater of waste tire. Compared with the other two consequences, the result is more accurate. The Monte Carlo method of artificial neural network based on Matlab has the obvious advantage in being devoted to the problem of complex structure and variable. Safety of the breakwater meets the relevant requirements and can be applied to the actual engineering. It can be seen that waste tires has a high degree of reliability, daptability, and a wide range of applications.

scholarly journals An ARMA Type Pi-Sigma Artificial Neural Network for Nonlinear Time Series Forecasting

2018 ◽  
Vol 8 (2) ◽  
pp. 121-132 ◽  
Author(s):  
Esra Akdeniz ◽  
Erol Egrioglu ◽  
Eren Bas ◽  
Ufuk Yolcu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Real Life ◽  
Life Time ◽  
High Order ◽  
Non Linear ◽  
Artificial Neural

Abstract Real-life time series have complex and non-linear structures. Artificial Neural Networks have been frequently used in the literature to analyze non-linear time series. High order artificial neural networks, in view of other artificial neural network types, are more adaptable to the data because of their expandable model order. In this paper, a new recurrent architecture for Pi-Sigma artificial neural networks is proposed. A learning algorithm based on particle swarm optimization is also used as a tool for the training of the proposed neural network. The proposed new high order artificial neural network is applied to three real life time series data and also a simulation study is performed for Istanbul Stock Exchange data set.

scholarly journals Application of Artificial Neural Network for Damage Detection in Planetary Gearbox of Wind Turbine

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Marcin Strączkiewicz ◽  
Tomasz Barszcz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Turbines ◽  
Estimation Error ◽  
Real Life ◽  
Sudden Increase ◽  
Operational Conditions ◽  
Planetary Gearbox ◽  
Regression Parameters ◽  
Artificial Neural

In the monitoring process of wind turbines the utmost attention should be given to gearboxes. This conclusion is derived from numerous summary papers. They reveal that, on the one hand, gearboxes are one of the most fault susceptible elements in the drive-train and, on the other, the most expensive to replace. Although state-of-the-art CMS can usually provide advanced signal processing tools for extraction of diagnostic information, there are still many installations, where the diagnosis is based simply on the averaged wideband features like root-mean-square (RMS) or peak-peak (PP). Furthermore, for machinery working in highly changing operational conditions, like wind turbines, those estimators are strongly fluctuating, and this fluctuation is not linearly correlated to operation parameters. Thus, the sudden increase of a particular feature does not necessarily have to indicate the development of fault. To overcome this obstacle, it is proposed to detect a fault development with Artificial Neural Network (ANN) and further observation of linear regression parameters calculated on the estimation error between healthy and unknown condition. The proposed reasoning is presented on the real life example of ring gear fault in wind turbine’s planetary gearbox.

scholarly journals ASSESSING COLLUSION RISKS IN MANAGING CONSTRUCTION PROJECTS USING ARTIFICIAL NEURAL NETWORK

2018 ◽  
Vol 24 (5) ◽  
pp. 2003-2025 ◽  
Author(s):  
Ming Shan ◽  
Yun Le ◽  
Kenneth T. W. Yiu ◽  
Albert P. C. Chan ◽  
Yi Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Construction Projects ◽  
Real Life ◽  
Assessment Model ◽  
Developing Strategy ◽  
Neural Network Approach ◽  
Body Of Knowledge ◽  
Artificial Neural ◽  
Artificial Neural Network Approach

Being an insidious risk to construction projects, collusion has attracted extensive attention from numerous researchers around the world. However, little effort has ever been made to assess collusion, which is important and necessary for curbing collusion in construction projects. Specific to the context of China, this paper developed an artificial neural network model to assess collusion risk in construction projects. Based on a comprehensive literature review, a total of 22 specific collusive practices were identified first, and then refined by a two-round Delphi interview with 15 experienced experts. Subsequently, using the consolidated framework of collusive practices, a questionnaire was further developed and disseminated, which received 97 valid replies. The questionnaire data were then utilized to develop and validate the collusion risk assessment model with the facilitation of artificial neural network approach. The developed model was finally applied in a real-life metro project in which its reliability and applicability were both verified. Although the model was developed under the context of Chinese construction projects, its developing strategy can be applied in other countries, especially for those emerging economies that have a significant concern of collusion in their construction sectors, and thus contributing to the global body of knowledge of collusion.

Bio-Inspired Computing through Artificial Neural Network

Fuzzy Systems ◽  
2017 ◽  
pp. 1285-1313
Author(s):  
Nilamadhab Dash ◽  
Rojalina Priyadarshini ◽  
Brojo Kishore Mishra ◽  
Rachita Misra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intelligent Systems ◽  
Deductive Reasoning ◽  
Real Life ◽  
Artificial Neural ◽  
Living Organisms ◽  
Artificial Neural Network Ann ◽  
Intelligent Models ◽  
Algorithmic Model

Developing suitable mathematical or algorithmic model to solve real life complex problems is one of the major challenges faced by the researchers especially those involved in the computer science field. To a large extent Computational intelligence has been found to be effective in designing such models. Bio inspired computing is the technique which makes the machines intelligent by adapting the behavior and methods exhibited by the human beings and other living organisms while forming intelligent systems. These intelligent models include the intelligent techniques such as Artificial Neural Network (ANN), evolutionary computation, swarm intelligence, fuzzy system, artificial immune system accompanied by fuzzy logic, expert system, deductive reasoning. All these together form the area of Bio inspired computing. The chapter deals with various bio inspired technique, giving emphasis on issues, development, advances and practical implementations of ANN.

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