ENG Transactions (ISSN 2717-3127) is a journal of advances in all fields of interest in engineering, and is published continuously. This journal publishes original and high quality papers that have passed the double-blind peer-review procedure. Researchers can submit their original research articles, review, short communications, letters and latest achievements across all areas of Engineering and their multidisciplinary applications such as but not limited to:
Electrical and Electronic Engineering, Computer Engineering, Mechanical Engineering, Civil Engineering, Mechatronics Engineering, Aerospace Engineering, Biomedical Engineering, Automotive Engineering, Robotics Engineering, Structural Engineering, Architectural Engineering, Marine Engineering, Safety Engineering, Microelectronic Engineering, Environmental Engineering, Sustainable Engineering, Engineering Management, MBA in Engineering, Systems Engineering, Manufacturing Engineering, Geological Engineering, Engineering Physics, Photonics Engineering, Nanotechnology Engineering, Mining Engineering, Ceramics Engineering, Industrial Engineering, Material Engineering, Chemical Engineering, and etc.
Papers in control, fault detection, fuzzy logic, filtering, automation, communication, optical, signal/image/video/text processing, data compression, data mining, bioinformatics, sensors, neural networks, satellite and hyperspectral remote sensing, machine/deep learning and the applications, artificial intelligence and it’s applications, computer science, electronic science, robotics, and applications of optimization and operation research in various engineering problems will also be published in this journal.
Peer Review Process
Once papers are approved in the initial evaluation stage, they will pass through double-blind peer-review process conducted in collaboration with editorial board members and independent reviewers. The first decision will be provided 15 days after submission. ENG Transactions encourages the authors to submit their manuscripts using our Journal Managing system. Reviewers can also use thereviewer’s panel.
Open Access Statement
ENG Transactions is an open access journal. The journal provides immediate open access to its content and the manuscripts are freely open access without charge for readers and researchers. For more information about copyrighit policy and licensing, please visit here.
In this short communication work, we try to answer the following question if the existing system safety and reliability analysis methods such as fault tree analysis (FTA) are robust and powerful in complex system safety, why they cannot correctly handle the system safety when there are multi number of opposing interests as conflicts. Our simple answer is that the present cannot address such critical circumstances in system maintainability, availability, reliability, and even more resilience. That is what we need to develop probabilistic methods underlying the game-theoretic context. First, the game theory result is based on an optimization model; thus, there is no requirement to validate the model as we have the optimum outcomes. Second, game theory enables decision-makers in system safety to take into account any potential conflicts, either to be direct or indirect. We provide a simple FTA example and show how the new concept can be applied initially.
Guangyu Shen 1,
Tao Liu 1,
Guangkui Song 2,
He Li 3
ENGTRANSACTIONS 2, 1-13
ABSTRACT
This paper contributes to the design of a new anthropomorphic lower extremity exoskeleton device inspired by the distribution of the lower extremity muscles. Different from traditional structures, where a single rotary actuator or a single linear actuator is installed at the joint, each leg of the designed exoskeleton device is a combination of two parallel robots. For the two parallel robots, one connects the waist, a thigh, and a shank to move the hip and knee joints, while, the other connects a thigh, a shank, and a foot to move the knee and ankle joints. In addition, a certain gap at the knee joint and the actuators with springs are also introduced as a basis to avoid the pain caused by the rigid structure. The results show that the new exoskeleton advantages in preforming higher control sensitivity, stronger bearing capacity, and better human-robot interaction performance compared with the previous prototypes.
In this paper, relationship between energy consumption and the size of the building has been presented. Electricity and fuel gases as two essential energies in the world have also been investigated by considering that the relationship is linear. The energy waste of different buildings with different sizes has been surveyed. This can broaden people’s horizon in order to find the ways of reducing the amount of energy consuming which have significant effects on reducing energy crises. It is possible to conserve energy for future generations and increase total life cycle energy use and associated environmental effects.
This paper proposes a new non-dominated-sorting method for non-dominated sorting genetic algorithm (NSGA2). This method combines crowding distance and distance-based methods for non-dominated-sorting. The distance-based method identifies concavities on the Pareto Front curve and selects the solutions located at these points. Our goal in this method is to converge faster and find more optimal solutions in the Genetic Algorithm. The Numerical example shows that the results obtained from NSGA2 method with modified non-dominated sorting are better than regular NSGA2.
Yas Malakoutian 2,
Pooria Mostafapour 3,
Sina Ziafat Dost Abed 4
ENGTRANSACTIONS 2, 1-12
ABSTRACT
This paper works with the monthly rainfall of six meteorological regions and TRNC (North Cyprus) as a whole for the hydrologic years from September 1975 to August 2014 period. In order to predict 5 years ahead of the yearly rainfall of each meteorological region and TRNC, three different time series models (Markov, Auto-regressive (AR) and Holt-Winter Multiplicative) were used. For this reason, the rainfall of hydrologic years from 1975-76 to 2003-04 were used for training and from 2004-05 to 2013-14 were used for forecasting (testing) the trained data. The best representative time-series model for each region was selected based on the standardized averages of four statistical error checking measures (MAPE, MAD, MSE and RMSE). The selected model for each region was then used to predict (estimate) the rainfall for five successive hydrologic years ahead from 2014-15 to 2018-19.
This paper proposes a hybrid diagnostic method for early detection of COVID-19 based on support vector machine (SVM) and selected deep features of chest computed tomography (CT) images. The developed method consists of four main parts including the feature extractor part, feature selection part, classifier part and optimizer part. In the feature extraction part, a convolutional neural network (ConvNet) is implemented for image preprocessing and extraction of new features from CT images. In the feature selection part, minimum Redundancy Maximum Relevance (mRMR) method is applied to select the most effective and informative features for extracted deep features by ConvNet. The selected features are fed into SVM in the classification part. Free hyper-parameters such as size and number of filters in ConvNet, and penalty factor in SVM control their accuracy and robustness. In the optimization part of the developed method, we applied the black widow optimization algorithm (BWOA) for optimal tuning of these parameters. The acquired outcomes demonstrated that the developed diagnostic method has excellent performance in the detection of COVID-19 and distinguishing it from other frequent respiratory illnesses using only small number of training data, which has huge possibility to help physicians and pulmonologist in performing a quick diagnosis. The developed diagnostic method can mitigate the enormous amount of work from professional treatment staff particularly when the healthcare system is overburdened.
About ENG Transactions
ENG Transactions (ISSN 2717-3127) is a journal of advances in all fields of interest in engineering, and is published continuously. This journal publishes original and high quality papers that have passed the double-blind peer-review procedure. Researchers can submit their original research articles, review, short communications, letters and latest achievements across all areas of Engineering and their multidisciplinary applications such as but not limited to:
Electrical and Electronic Engineering, Computer Engineering, Mechanical Engineering, Civil Engineering, Mechatronics Engineering, Aerospace Engineering, Biomedical Engineering, Automotive Engineering, Robotics Engineering, Structural Engineering, Architectural Engineering, Marine Engineering, Safety Engineering, Microelectronic Engineering, Environmental Engineering, Sustainable Engineering, Engineering Management, MBA in Engineering, Systems Engineering, Manufacturing Engineering, Geological Engineering, Engineering Physics, Photonics Engineering, Nanotechnology Engineering, Mining Engineering, Ceramics Engineering, Industrial Engineering, Material Engineering, Chemical Engineering, and etc.
Papers in control, fault detection, fuzzy logic, filtering, automation, communication, optical, signal/image/video/text processing, data compression, data mining, bioinformatics, sensors, neural networks, satellite and hyperspectral remote sensing, machine/deep learning and the applications, artificial intelligence and it’s applications, computer science, electronic science, robotics, and applications of optimization and operation research in various engineering problems will also be published in this journal.
Peer Review Process
Once papers are approved in the initial evaluation stage, they will pass through double-blind peer-review process conducted in collaboration with editorial board members and independent reviewers. The first decision will be provided 15 days after submission. ENG Transactions encourages the authors to submit their manuscripts using our Journal Managing system. Reviewers can also use the reviewer’s panel.
Open Access Statement
ENG Transactions is an open access journal. The journal provides immediate open access to its content and the manuscripts are freely open access without charge for readers and researchers. For more information about copyrighit policy and licensing, please visit here.
In this short communication work, we try to answer the following question if the existing system safety and reliability analysis methods such as fault tree analysis (FTA) are robust and powerful in complex system safety, why they cannot correctly handle the system safety when there are multi number of opposing interests as conflicts. Our simple answer is that the present cannot address such critical circumstances in system maintainability, availability, reliability, and even more resilience. That is what we need to develop probabilistic methods underlying the game-theoretic context. First, the game theory result is based on an optimization model; thus, there is no requirement to validate the model as we have the optimum outcomes. Second, game theory enables decision-makers in system safety to take into account any potential conflicts, either to be direct or indirect. We provide a simple FTA example and show how the new concept can be applied initially.
This paper contributes to the design of a new anthropomorphic lower extremity exoskeleton device inspired by the distribution of the lower extremity muscles. Different from traditional structures, where a single rotary actuator or a single linear actuator is installed at the joint, each leg of the designed exoskeleton device is a combination of two parallel robots. For the two parallel robots, one connects the waist, a thigh, and a shank to move the hip and knee joints, while, the other connects a thigh, a shank, and a foot to move the knee and ankle joints. In addition, a certain gap at the knee joint and the actuators with springs are also introduced as a basis to avoid the pain caused by the rigid structure. The results show that the new exoskeleton advantages in preforming higher control sensitivity, stronger bearing capacity, and better human-robot interaction performance compared with the previous prototypes.
In this paper, relationship between energy consumption and the size of the building has been presented. Electricity and fuel gases as two essential energies in the world have also been investigated by considering that the relationship is linear. The energy waste of different buildings with different sizes has been surveyed. This can broaden people’s horizon in order to find the ways of reducing the amount of energy consuming which have significant effects on reducing energy crises. It is possible to conserve energy for future generations and increase total life cycle energy use and associated environmental effects.
This paper proposes a new non-dominated-sorting method for non-dominated sorting genetic algorithm (NSGA2). This method combines crowding distance and distance-based methods for non-dominated-sorting. The distance-based method identifies concavities on the Pareto Front curve and selects the solutions located at these points. Our goal in this method is to converge faster and find more optimal solutions in the Genetic Algorithm. The Numerical example shows that the results obtained from NSGA2 method with modified non-dominated sorting are better than regular NSGA2.
This paper works with the monthly rainfall of six meteorological regions and TRNC (North Cyprus) as a whole for the hydrologic years from September 1975 to August 2014 period. In order to predict 5 years ahead of the yearly rainfall of each meteorological region and TRNC, three different time series models (Markov, Auto-regressive (AR) and Holt-Winter Multiplicative) were used. For this reason, the rainfall of hydrologic years from 1975-76 to 2003-04 were used for training and from 2004-05 to 2013-14 were used for forecasting (testing) the trained data. The best representative time-series model for each region was selected based on the standardized averages of four statistical error checking measures (MAPE, MAD, MSE and RMSE). The selected model for each region was then used to predict (estimate) the rainfall for five successive hydrologic years ahead from 2014-15 to 2018-19.
This paper proposes a hybrid diagnostic method for early detection of COVID-19 based on support vector machine (SVM) and selected deep features of chest computed tomography (CT) images. The developed method consists of four main parts including the feature extractor part, feature selection part, classifier part and optimizer part. In the feature extraction part, a convolutional neural network (ConvNet) is implemented for image preprocessing and extraction of new features from CT images. In the feature selection part, minimum Redundancy Maximum Relevance (mRMR) method is applied to select the most effective and informative features for extracted deep features by ConvNet. The selected features are fed into SVM in the classification part. Free hyper-parameters such as size and number of filters in ConvNet, and penalty factor in SVM control their accuracy and robustness. In the optimization part of the developed method, we applied the black widow optimization algorithm (BWOA) for optimal tuning of these parameters. The acquired outcomes demonstrated that the developed diagnostic method has excellent performance in the detection of COVID-19 and distinguishing it from other frequent respiratory illnesses using only small number of training data, which has huge possibility to help physicians and pulmonologist in performing a quick diagnosis. The developed diagnostic method can mitigate the enormous amount of work from professional treatment staff particularly when the healthcare system is overburdened.