Search results for: neural style transfer

Authors: O. P. Oladipo, O. A. Falode

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The determination of Recovery Factor is of great importance to the reservoir engineer since it relates reserves to the initial oil in place. Reserves are the producible portion of reservoirs and give an indication of the profitability of a field Development. The core objective of this project is to develop an artificial neural network model using selected reservoir data to predict Recovery Factors (RF) of hydrocarbon reservoirs and compare the model with a couple of the existing correlations. The type of Artificial Neural Network model developed was the Single Layer Feed Forward Network. MATLAB was used as the network simulator and the network was trained using the supervised learning method, Afterwards, the network was tested with input data never seen by the network. The results of the predicted values of the recovery factors of the Artificial Neural Network Model, API Correlation for water drive reservoirs (Sands and Sandstones) and Guthrie and Greenberger Correlation Equation were obtained and compared. It was noted that the coefficient of correlation of the Artificial Neural Network Model was higher than the coefficient of correlations of the other two correlation equations, thus making it a more accurate prediction tool. The Artificial Neural Network, because of its accurate prediction ability is helpful in the correct prediction of hydrocarbon reservoir factors. Artificial Neural Network could be applied in the prediction of other Petroleum Engineering parameters because it is able to recognise complex patterns of data set and establish a relationship between them.

Keywords: recovery factor, reservoir, reserves, artificial neural network, hydrocarbon, MATLAB, API, Guthrie, Greenberger

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Authors: Ala Rakhshandeh, Zahra Ashkar, Solmaz Dehghani Dolatabadi, Hossein Bayat

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The present study has been conducted to investigate the role of family emotional climate and emotional expression style in the academic well-being of students with military parents. Children, including 280 female students of Shahriar police officers, were selected by random sampling method, and they have been investigated through Alfred B. Hillburn's family emotional climate questionnaire (1964), King and Ammon's emotional expression questionnaire (1990), and Pitrinen, Sweeney, and Falto's academic well-being questionnaire (2014). The data were analyzed using statistical methods of correlation coefficient and stepwise multiple regression under the SPSS23 program. The results reveal that the variables of family emotional climate and emotional expression can explain 36.4% of the variance in academic well-being. This finding reveals that with an increase of standard deviation on the scores of family emotional climate and emotional expression, 0.513 and 0.155 standard deviations are added to the scores of academic well-being, respectively. The emotional climate of the family has a superior distinctive role in predicting the educational well-being of female students. Thus, the emotional climate of the family and the style of emotional expression play a meaningful role in the academic well-being of students with the military parent.

Keywords: emotional climate, family, emotional expression style, academic well-being

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Authors: Bader Alhumaidi Alsubaei, Zahid H. Akash, Ali Imam Sunny

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The heat transfer coefficient can be improved passively by using a dimpled surface on the tube. The contact area where heat transfer takes place can be enlarged and turbulence will be purposefully produced inside the duct; as a consequence, higher heat transfer quality will be achieved by employing an extended inner or outer surface (dimpled surface). In order to compare the rate and quality of heat transfer between a regular-shaped pipe and a dimpled pipe, a dimpled tube with a fixed dimple radius was created. Numerical analysis of the plain and dimpled pipes was performed using ANSYS. A 23% increase in Nusselt number was seen for dimpled tubes compared to plain tubes. In comparison to plain tubes, dimpled tubes' increase in thermal performance index was found to be between 8% and 10%. An increase in pressure drop of 18% was noted.

Keywords: heat transfer, dimpled tube, CFD, ANSYS

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Authors: P. Valsalal, S. Thangalakshmi

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There is a widespread changeover in the electrical power industry universally from old-style monopolistic outline towards a horizontally distributed competitive structure to come across the demand of rising consumption. When the transmission lines of derestricted system are incapable to oblige the entire service needs, the lines are overloaded or congested. The governor between customer and power producer is nominated as Independent System Operator (ISO) to lessen the congestion without obstructing transmission line restrictions. Among the existing approaches for congestion management, the frequently used approaches are reorganizing the generation and load curbing. There is a boundary for reorganizing the generators, and further loads may not be supplemented with the prevailing resources unless more private power producers are added in the system by considerably raising the cost. Hence, congestion is relaxed by appropriate Flexible AC Transmission Systems (FACTS) devices which boost the existing transfer capacity of transmission lines. The FACTs device, namely, Unified Power Flow Controller (UPFC) is preferred, and the correct placement of UPFC is more vital and should be positioned in the highly congested line. Hence, the weak line is identified by using power flow performance index with the new objective function with proposed hybrid Fish – Bee algorithm. Further, the location of UPFC at appropriate line reduces the branch loading and minimizes the voltage deviation. The power transfer capacity of lines is determined with and without UPFC in the identified congested line of IEEE 30 bus structure and the simulated results are compared with prevailing algorithms. It is observed that the transfer capacity of existing line is increased with the presented algorithm and thus alleviating the congestion.

Keywords: available line transfer capability, congestion management, FACTS device, Hybrid Fish-Bee Algorithm, ISO, UPFC

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Authors: Sajjad Baghernezhad

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Meta-heuristic and hybrid algorithms have high adeer in modeling medical problems. In this study, a neural network was used to predict covid-19 among high-risk and low-risk patients. This study was conducted to collect the applied method and its target population consisting of 550 high-risk and low-risk patients from the Kerman University of medical sciences medical center to predict the coronavirus. In this study, the memetic algorithm, which is a combination of a genetic algorithm and a local search algorithm, has been used to update the weights of the neural network and develop the accuracy of the neural network. The initial study showed that the accuracy of the neural network was 88%. After updating the weights, the memetic algorithm increased by 93%. For the proposed model, sensitivity, specificity, positive predictivity value, value/accuracy to 97.4, 92.3, 95.8, 96.2, and 0.918, respectively; for the genetic algorithm model, 87.05, 9.20 7, 89.45, 97.30 and 0.967 and for logistic regression model were 87.40, 95.20, 93.79, 0.87 and 0.916. Based on the findings of this study, neural network models have a lower error rate in the diagnosis of patients based on individual variables and vital signs compared to the regression model. The findings of this study can help planners and health care providers in signing programs and early diagnosis of COVID-19 or Corona.

Keywords: COVID-19, decision support technique, neural network, genetic algorithm, memetic algorithm

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Authors: Shreya Sirohi, Vineeta Sirohi

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In the 21st century, the nature of work has become quite complex and dynamic, and in response to this, the organizational culture continues to change and develop new perspectives. Organizational culture and leadership are important elements of any organization. Organization’s performance and success to a large extent, depend upon these two factors. The ability of a leader lies in confronting with the challenge of evolving and adapting the culture of the organization as per the situational demands. Leadership and organizational culture are conceptually intertwined. Leadership is a key ingredient for the successful transformation of any organization, and a favorable organizational culture helps to motivate the employees towards their work. Organizational culture and leadership style plays a crucial role in achieving the specified objectives of an organization. The harmony between culture and leader within organization undoubtedly affects relationships, processes, and employee performance. The present investigation aimed to study the Leadership style and Organisational Culture of private organizations and the relationship between the two. The study was carried out on a sample of 100 employees from five private organizations located in the cities of Gurgaon and Delhi in India. The data was collected by employing organisational culture profile and multifactor leadership questionnaire. The findings of the study indicate that the selected organizations had dominant transformation leadership style, whereas the organizational culture varied from one organization to another. However, technocratic culture was found to be prominent, followed by entrepreneurial organizational culture. A low positive correlation was found between leadership style and organizational culture. The transformational leaders have a positive and significant relationship with employee’s satisfaction, productivity, and organization’s culture. The leaders practicing transformational leadership style inspire their followers, are innovative and are aware of their needs as well as of their followers. Such leadership style has a positive impact both on employees and working culture. Employees of such organization are able to come up with innovative ideas and are efficient in handling situations and making effective decisions. However, low correlation is self indicative of the fact that a single leadership style or a single culture type alone cannot contribute solely towards the growth of an organization. There is a need to blend the culture types and leadership styles suiting the needs of the organization. Organisational culture represents the deeper values and beliefs of the employees and influences organizational performance; hence, the leader has a crucial role to play in creating and managing organizational culture in aligning to the requirements of the present era of competitiveness, globalization and technological advancement.

Keywords: leadership style, organizational culture, technocratic, transformational

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Authors: Jorge L. Nisperuza, Wilson Sandoval, Edward. A. Gil, Johan A. Jimenez

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In orbital mechanics, an active research topic is the calculation of interplanetary trajectories efficient in terms of energy and time. In this sense, this work concerns the calculation of the orbital elements for sending interplanetary probes in the extrasolar system TRAPPIST-1. Specifically, using the mathematical expressions of the circular and elliptical trajectory parameters, expressions for the flight time and the orbital transfer rate increase between orbits, the orbital parameters and the graphs of the trajectories of Hohmann and Hohmann bi-elliptic for sending a probe from the innermost planet to all the other planets of the studied system, are obtained. The relationship between the orbital transfer rate increments and the relationship between the flight times for the two transfer types is found. The results show that, for all cases under consideration, the Hohmann transfer results to be the least energy and temporary cost, a result according to the theory associated with Hohmann and Hohmann bi-elliptic transfers. Saving in the increase of the speed reaches up to 87% was found, and it happens for the transference between the two innermost planets, whereas the time of flight increases by a factor of up to 6.6 if one makes use of the bi-elliptic transfer, this for the case of sending a probe from the innermost planet to the outermost.

Keywords: bi-elliptic Hohmann transfer, exoplanet, extrasolar system, Hohmann transfer, TRAPPIST-1

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Authors: Jaisridhar P.

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The present study aimed to investigate preferred teaching styles of young faculties in agricultural education among 23 constituent colleges of Tamil Nadu Agricultural University (TNAU) using Staffordshire Evaluation of Teaching Styles (SETS). An onlinesurvey was conducted among 156 young faculties of 2014 Batch working in different constituent colleges of TNAU and 73 faculties respondent to the survey. The results showed that 62.53 percent preferred “The one-off teacher” stylefollowed by62.26 percent preferring “The student centered, sensitive teacher” style.“The all-round flexible and adaptable teaching style” was preferred by 61.64 percent. The Official Curriculum Teacher” with 61.23 per cent preferring this style.58.97 per cent preferred “The Big Conference Teacher” followed by 58.08 percent of the faculties preferring “The Straight Fact no Non-sense Teacher” type of teaching style. From the results, it wasconcluded that blended teaching approach can balance a teacher’s personal strengths and interest with student’s needs, and curricular requirements enables a teacher to tailor their teaching according to the student’s needs and as per subject matter.

Keywords: teaching styles, assistant professors, agriculture, tamil nadu

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Authors: Kyung-Jin You, Kiwon Rhee, Marc H. Schieber, Nitish V. Thakor, Hyun-Chool Shin

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It remains unknown whether M1 neurons encode multi-finger movements independently or as a certain neural network of single finger movements although multi-finger movements are physically a combination of single finger movements. We present an evidence of correlation between single and multi-finger movements and also attempt a challenging task of semi-blind decoding of neural data with minimum training of the neural decoder. Data were collected from 115 task-related neurons in M1 of a trained rhesus monkey performing flexion and extension of each finger and the wrist (12 single and 6 two-finger-movements). By exploiting correlation of temporal firing pattern between movements, we found that correlation coefficient for physically related movements pairs is greater than others; neurons tuned to single finger movements increased their firing rate when multi-finger commands were instructed. According to this knowledge, neural semi-blind decoding is done by choosing the greatest and the second greatest likelihood for canonical candidates. We achieved a decoding accuracy about 60% for multiple finger movement without corresponding training data set. this results suggest that only with the neural activities on single finger movements can be exploited to control dexterous multi-fingered neuroprosthetics.

Keywords: finger movement, neural activity, blind decoding, M1

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Authors: Masato Sasaki, Masayoshi Yamamoto

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The majority of wireless power transfer (WPT) systems transfer power in a directional manner. This paper describes a discrete exciting voltage control technique for WPT via magnetic resonant coupling with two orthogonal transmitter coils (2D omni-directional WPT system) which can maximize the power transfer efficiency in response to the change of coupling status. The theory allows the equations of the efficiency of the system to be determined at all the rate of the mutual inductance. The calculated results are included to confirm the advantage to one directional WPT system and the validity of the theory and the equations.

Keywords: wireless power transfer, omni-directional, orthogonal, efficiency

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Authors: Ik–Tae Im, H. M. Abdelmotalib, M. A. Youssef, S. B. Young

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In this study the flow characteristics and bed-to-wall heat transfer in a gas-solid conical fluidized bed combustor were investigated using both experimental and numerical methods. The computational fluid dynamic (CFD) simulations were carried out using a commercial software, Fluent V6.3. A two-fluid Eulerian-Eulerian model was applied in order to simulate the gas–solid flow and heat transfer in a conical sand-air bed with 30o con angle and 22 cm static bed height. Effect of different fluidizing number varying in the range of 1.5 - 2.3, drag models namely (Syamlal-O’Brien and Gidaspow), and friction viscosity on flow and bed-to-wall heat transfer were analyzed. Both bed pressure drop and heat transfer coefficient increased with increasing inlet gas velocity. The Gidaspow drag model showed a better agreement with experimental results than other drag model. The friction viscosity had no clear effect on both hydrodynamics and heat transfer.

Keywords: computational fluid dynamics, heat transfer coefficient, hydrodynamics, renewable energy

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Authors: Tingting Chen, Shouliang Zhao

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Compared with other regions, the construction for villages and towns in regions inhabited by minority nationality shall be deeply rooted in natural and cultural endowment in locality, and more importance shall be attached to building of characteristics. In this kind of area, landscape design is very important for its character and tradition. By empirical study in Shennongjia Area, some findings could be summarized as below. There are unique natural and cultural resources in Shennongjia Forestry District; during transformation on style and features of Tujia Village, Xiaguping, special style and features have been successfully shaped through 4 strategies: (1) highlighting Tujia Culture and architectural style in west region of Hubei Province; (2) merging with local natural environment; (3) introducing system of rural coordination architect; and (4) making great efforts to design and construct environmental embellishments with village and town symbols.

Keywords: rural coordination architect, special style and features, characteristic landscape, villages and towns in regions inhabited by minority nationality

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Authors: Xi Liu, Fan Yuan

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In the evolving landscape of physics-based machine learning (PBML), particularly within fluid dynamics and its applications in electromechanical engineering, robot vision, and robot learning, achieving precision and alignment with researchers' specific needs presents a formidable challenge. In response, this work proposes a methodology that integrates neural transformation with a modified smoothed particle hydrodynamics model for generating transformed 3D fluid simulations. This approach is useful for nanoscale science, where the unique and complex behaviors of viscoelastic medium demand accurate neurally-transformed simulations for materials understanding and manipulation. In electromechanical engineering, the method enhances the design and functionality of fluid-operated systems, particularly microfluidic devices, contributing to advancements in nanomaterial design, drug delivery systems, and more. The proposed approach also aligns with the principles of PBML, offering advantages such as multi-fluid stylization and consistent particle attribute transfer. This capability is valuable in various fields where the interaction of multiple fluid components is significant. Moreover, the application of neurally-transformed hydrodynamical models extends to manufacturing processes, such as the production of microelectromechanical systems, enhancing efficiency and cost-effectiveness. The system's ability to perform neural transfer on 3D fluid scenes using a deep learning algorithm alongside physical models further adds a layer of flexibility, allowing researchers to tailor simulations to specific needs across scientific and engineering disciplines.

Keywords: physics-based machine learning, robot vision, robot learning, hydrodynamics

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Authors: Oladapo Tolulope Ibitoye

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Neural networks have become prominent and widely engaged in algorithmic-based machine learning networks. They are perfect in solving day-to-day issues to a certain extent. Neural networks are computing systems with several interconnected nodes. One of the numerous areas of application of neural networks is object detection. This is a prominent area due to the coronavirus disease pandemic and the post-pandemic phases. Wearing a face mask in public slows the spread of the virus, according to experts’ submission. This calls for the development of a reliable and effective model for detecting face masks on people's faces during compliance checks. The existing neural network models for facemask detection are characterized by their black-box nature and large dataset requirement. The highlighted challenges have compromised the performance of the existing models. The proposed model utilized Faster R-CNN Model on Inception V3 backbone to reduce system complexity and dataset requirement. The model was trained and validated with very few datasets and evaluation results shows an overall accuracy of 96% regardless of skin tone.

Keywords: convolutional neural network, face detection, face mask, masked faces

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Authors: Nitin Ghule, S. G. Taji

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A mathematical model of hot-wall condensers used in refrigerators is presented. The model predicts the heat transfer characteristics of condenser and the effects of various design and operating parameters on condenser tube length and capacity. A finite element approach was used to model the condenser. The condenser tube is divided into elemental units, with each element consisting of adhesive tape, refrigerant tube and outer metal sheet. The heat transfer characteristics of each section are then analyzed by considering the heat transfer through the tube wall, tape and the outer sheet. Variations in inner heat transfer coefficient and pressure drop are considered depending on temperature, fluid phase, type of flow and orientation of tube. Variation in outer heat transfer coefficient is also taken into account. Various materials were analysed for the tube, tape and outer sheet.

Keywords: condenser, domestic refrigerator, heat transfer, mathematical model

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Authors: Grace D. Severo, Lopita U. Jung

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This study aimed to determine the educational philosophies and teaching styles of the college faculty of the University of Perpetual Help System DALTA in the campuses of Las-Piñas, Molino, and Calamba, south of Metro Manila. It sought to determine the relationships of educational philosophy and teaching styles of the college faculty vis-à-vis the university system’s educational philosophies and teaching style preferences. A hundred and five faculty members from the Colleges of Education, Arts and Sciences responded to the survey during the academic year 2014-2015. The Philosophy of Adult Education Inventory measured the faculty’s preferred educational philosophies. The Principles of Adult Learning Scale measured the faculty’s teaching style preference. Findings show that there is a similarity between the university system and the faculty members in using the progressive educational philosophy, however both contrasted in the preferred teaching style. Majority of the faculty held progressive educational philosophy but their preference for teacher-centered teaching style did not match. This implies that the majority are certain of having progressive educational philosophy but are not utilizing the learner-centered teaching styles; a high degree of support and commitment to practice a progressive and humanist philosophical orientation in education; and a high degree of support on teacher-centered teaching style promotion from the institution can strengthen a high degree of commitment for the faculty to enunciate their values and practice through these educational philosophies and teaching styles.

Keywords: educational philosophies, teaching styles, philosophy of adult education inventory, principles of adult learning scale

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Authors: M. A. Akhavan-Behabadi, M. Najafi, A. Abbasi

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An empirical investigation was performed in order to study the heat transfer characteristics of R600a flow boiling inside horizontal flattened tubes and the simultaneous effect of nanoparticles on boiling heat transfer in flattened channel. Round copper tubes of 8.7 mm I.D. were deformed into flattened shapes with different inside heights of 6.9, 5.5, and 3.4 mm as test areas. The effect of different parameters such as mass flux, vapor quality and inside height on heat transfer coefficient was studied. Flattening the tube caused a significant enhancement in heat transfer performance, so that the maximum augmentation ratio of 163% was obtained in flattened channel with lowest internal height. A new correlation was developed based on the present experimental data to predict the heat transfer coefficient in flattened tubes. This correlation estimated 90% of the entire database within ±20%. The best flat channel with the point of view of heat transfer performance was selected to study the effect of nanoparticle on heat transfer enhancement. Four homogenized mixtures containing 1% weight fraction of R600a/oil with different CuO nanoparticles concentration including 0.5%, 1% and 1.5% mass fraction of R600a/oil/CuO were studied. Observations show that heat transfer was improved by adding nanoparticles, which lead to maximum enhancement of 79% compare to the pure refrigerant at the same test condition.

Keywords: nano fluids, heat transfer, flattend tube, transport phenomena

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Authors: S. Neelima, P. S. Subramanyam

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The major tool for distribution planning is load forecasting, which is the anticipation of the load in advance. Artificial neural networks have found wide applications in load forecasting to obtain an efficient strategy for planning and management. In this paper, the application of neural networks to study the design of short term load forecasting (STLF) Systems was explored. Our work presents a pragmatic methodology for short term load forecasting (STLF) using proposed two-stage model of wavelet transform (WT) and artificial neural network (ANN). It is a two-stage prediction system which involves wavelet decomposition of input data at the first stage and the decomposed data with another input is trained using a separate neural network to forecast the load. The forecasted load is obtained by reconstruction of the decomposed data. The hybrid model has been trained and validated using load data from Telangana State Electricity Board.

Keywords: electrical distribution systems, wavelet transform (WT), short term load forecasting (STLF), artificial neural network (ANN)

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Authors: M. Amoura, M. Benmoussa, N. Zeraibi

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The flow and heat transfer is an important phenomenon in engineering systems due to its wide application in electronic cooling, heat exchangers, double pane windows etc.. The enhancement of heat transfer in these systems is an essential topic from an energy saving perspective. Lower heat transfer performance when conventional fluids, such as water, engine oil and ethylene glycol are used hinders improvements in performance and causes a consequent reduction in the size of such systems. The use of solid particles as an additive suspended into the base fluid is a technique for heat transfer enhancement. Therefore, the heat transfer enhancement in a horizontal circular tube that is maintained at a constant temperature under laminar regime has been investigated numerically. A computational code applied to the problem by use of the finite volume method was developed. Nanofluid was made by dispersion of Al2O3 nanoparticles in pure water and ethylene glycol. Results illustrate that the suspended nanoparticles increase the heat transfer with an increase in the nanoparticles volume fraction and for a considered range of Reynolds numbers. On the other hand, the heat transfer is very sensitive to the base fluid.

Keywords: Al2O3 nanoparticles, circular tube, heat transfert enhancement, numerical simulation

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Authors: Nurul Yusnita

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Many studies have been developed to help the students to get good achievement in English learning. They can be from the teaching method or psychological ones. One of the psychological studies in educational research is learning style. In some ways, learning style can affect the achievement of the students. This study aimed to examine 4 (four) learning styles and their relations to English achievement among the students learning English in Language Training Center of Universitas Muhammadiyah Yogyakarta (LTC UMY). The method of this study was descriptive analytical. The sample consisted of 39 Accounting students in LTC UMY. The data was collected through questionnaires with Likert-scale. The achievement was obtained from the grade of the students. To analyze the questionnaires and to see the relation between the learning styles and the student achievement, SPSS statistical software of correlational analysis was used. The result showed that both visual and auditory had the same percentage of 35.9% (14 students). 3 students (7.7%) had kinaesthetic learning style and 8 students (20.5%) had visual and auditory ones. Meanwhile, there were 5 students (12.8%) who had visual learning style could increase their grades. Only 1 student (2.5%) who had visual and auditory could improve his grade. Besides grade increase, there were also grade decrease. Students with visual, auditory, visual and auditory, and kinaesthetic learning styles were 3 students (7.7%), 5 students (12%), 4 students (10.2%) and 1 student (2.5%) respectively. In conclusion, there was no significant relationship between learning style and English achievement. Most of the good achievers were the students with visual and auditory learning styles and most of them preferred visual method. The implication is the teachers and material designers could improve their method through visual things to achieve effective English teaching learning.

Keywords: accounting students, English achievement, language training centre, learning styles

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Authors: Han Bing, Yin Zhenjie

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In order to analyze the status of a diesel engine, as well as conduct fault prediction, a new prediction model based on a gray system is proposed in this paper, which takes advantage of the neural network and the genetic algorithm. The proposed GBPGA prediction model builds on the GM (1.5) model and uses a neural network, which is optimized by a genetic algorithm to construct the error compensator. We verify our proposed model on the diesel faulty simulation data and the experimental results show that GBPGA has the potential to employ fault prediction on diesel.

Keywords: fault prediction, neural network, GM(1, 5) genetic algorithm, GBPGA

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Authors: Janneke K. Oostrom, Richard Ronay

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The so called “red sneakers effect” refers to people’s inclination to infer status and competence from signals of nonconformity. In the current research, we explore an untested possible boundary condition to the red sneakers effect within the context of personnel selection. In two experimental studies (total N = 156), we examined how (non)conforming dress style interacts with the quality of a job applicant’s resume to determine hirability perceptions. We found that dress style indeed impacts hirability perceptions, but that the exact impact depends on the quality of the applicant’s resume. Results revealed that applicants with a low quality resume were punished for behaving in a nonconforming way, whereas applicants with a high quality resume appeared to have the leeway to dress as they please. Importantly, the observed interaction effect was mediated by perceptions of power. These findings suggest that nonconforming dress acts as a power-signaling mechanism in the context of personnel selection. However, the signaling effects of non-conforming dress style can backfire when accompanied by evidence that such posturing is not matched by cues of actual competence.

Keywords: clothing, hirability, nonconformity, personnel selection, power

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Authors: Jie Zhao, Meng Su

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Image recognition, as one of the most critical technologies in computer vision, works to help machine-like robotics understand a scene, that is, if deployed appropriately, will trigger the revolution in remote sensing and industry automation. With the developments of AI technologies, there are many prevailing and sophisticated neural networks as technologies developed for image recognition. However, computer vision platforms as hardware, supporting neural networks for image recognition, as crucial as the neural network technologies, need to be more congruently addressed as the research subjects. In contrast, different computer vision platforms are deterministic to leverage the performance of different neural networks for recognition. In this paper, three different computer vision platforms – Jetson Nano(with 4GB), a standalone laptop(with RTX 3000s, using CUDA), and Google Colab (web-based, using GPU) are explored and four prominent neural network architectures (including AlexNet, VGG(16/19), GoogleNet, and ResNet(18/34/50)), are investigated. In the context of pairwise usage between different computer vision platforms and distinctive neural networks, with the merits of recognition accuracy and time efficiency, the performances are evaluated. In the case study using public imageNets, our findings provide a nuanced perspective on optimizing image recognition tasks across Edge-AI platforms, offering guidance on selecting appropriate neural network structures to maximize performance under hardware constraints.

Keywords: alexNet, VGG, googleNet, resNet, Jetson nano, CUDA, COCO-NET, cifar10, imageNet large scale visual recognition challenge (ILSVRC), google colab

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Authors: Abdolreza Memari

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In this article in order to estimate the viscosity of crude oil,a numerical method has been used. We use this method to measure the crude oil's viscosity for 3 states: Saturated oil's viscosity, viscosity above the bubble point and viscosity under the saturation pressure. Then the crude oil's viscosity is estimated by using KHAN model and roller ball method. After that using these data that include efficient conditions in measuring viscosity, the estimated viscosity by the presented method, a radial based neural method, is taught. This network is a kind of two layered artificial neural network that its stimulation function of hidden layer is Gaussian function and teaching algorithms are used to teach them. After teaching radial based neural network, results of experimental method and artificial intelligence are compared all together. Teaching this network, we are able to estimate crude oil's viscosity without using KHAN model and experimental conditions and under any other condition with acceptable accuracy. Results show that radial neural network has high capability of estimating crude oil saving in time and cost is another advantage of this investigation.

Keywords: viscosity, Iranian crude oil, radial based, neural network, roller ball method, KHAN model

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Authors: Dawid Sołoducha, Tomasz Borowski, Marian Kordas, Rafał Rakoczy

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The magnetic field in the past few years became an important part of many studies. Magnetic field (MF) may be used to affect the process in many ways; for example, it can be used as a factor to stabilize the system. We can use MF to steer the operation, to activate or inhibit the process, or even to affect the vital activity of microorganisms. Using various types of magnetic field generators is always connected with the delivery of some heat to the system. Heat transfer is a very important phenomenon; it can influence the process positively and negatively, so it’s necessary to measure heat stream transferred from the place of generation and prevent negative influence on the operation. The aim of the presented work was to apply various types of magnetic fields and to measure heat transfer phenomena. The results were obtained by continuous measurement at several measuring points with temperature probes. Results were compilated in the form of temperature profiles. The study investigated the undetermined heat transfer in a custom system equipped with a magnetic field generator. Experimental investigations are provided for the explanation of the influence of the various type of magnetic fields on the heat transfer process. The tested processes are described by means of the criteria which defined heat transfer intensification under the action of magnetic field.

Keywords: heat transfer, magnetic field, undetermined heat transfer, temperature profile

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Authors: Samuel Nii Tackie, Oyebade K. Oyedotun, Ebenezer O. Olaniyi, Adnan Khashman

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Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task; and the performances obtained from these networks evaluated in consideration of achieved recognition rates and training time.

Keywords: artificial neural network, data mining, classification, students’ evaluation

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Authors: Ahmed H. El-Masry, Mohamed A. Dabaon, Tarek F. El-Shafiey, Abd El-Hakim A. Khalil

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An experimental study was performed to investigate the behavior and strength of proposed technique to connect reinforced concrete (RC) beam to steel or composite columns. This approach can practically be used in several types of building construction. In this technique, the main beam of the frame consists of a transfer part (part of beam; Tr.P) and a common reinforcement concrete beam. The transfer part of the beam is connected to the column, whereas the rest of the beam is connected to the transfer part from each side. Four full-scale beam-column connections were tested under static loading. The test parameters were the length of the transfer part and the column properties. The test results show that using of the transfer part technique leads to modify the deformation capabilities for the RC beam and hence it increases its resistance against failure. Increase in length of the transfer part did not necessarily indicate an enhanced behavior. The test results contribute to the characterization of the connection behavior between RC beam - steel column and can be used to calibrate numerical models for the simulation of this type of connection.

Keywords: composite column, reinforced concrete beam, steel column, transfer part

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Authors: Oluwaponmile David Alao

Abstract:

Misdiagnosis has been the major problem in health sector. Heart attack has been one of diseases that have high level of misdiagnosis recorded on the part of physicians. In this paper, an intelligent system has been developed for diagnosis of heart attack in the health sector. Dataset of heart attack obtained from UCI repository has been used. This dataset is made up of thirteen attributes which are very vital in diagnosis of heart disease. The system is developed on the multilayer perceptron trained with back propagation neural network then simulated with feed forward neural network and a recognition rate of 87% was obtained which is a good result for diagnosis of heart attack in medical field.

Keywords: heart attack, artificial neural network, diagnosis, intelligent system

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Authors: Ghorab Sara, Meziani Lila, Rubin Harvey Stuart

Abstract:

The rapid growth of deep learning has led to intricate and resource-intensive deep neural networks widely used in computer vision tasks. However, their complexity results in high computational demands and memory usage, hindering real-time application. To address this, research focuses on model compression techniques. The paper provides an overview of recent advancements in compressing neural networks and categorizes the various methods into four main approaches: network pruning, quantization, network decomposition, and knowledge distillation. This paper aims to provide a comprehensive outline of both the advantages and limitations of each method.

Keywords: model compression, deep neural network, pruning, knowledge distillation, quantization, low-rank decomposition

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Authors: Vinai K. Singh

Abstract:

In Neural Network-based Learning techniques, there are several models of Convolutional Networks. Whenever the methods are deployed with large datasets, only then can their applicability and appropriateness be determined. Clinical and pathological pictures of lobular carcinoma are thought to exhibit a large number of random formations and textures. Working with such pictures is a difficult problem in machine learning. Focusing on wet laboratories and following the outcomes, numerous studies have been published with fresh commentaries in the investigation. In this research, we provide a framework that can operate effectively on raw photos of various resolutions while easing the issues caused by the existence of patterns and texturing. The suggested approach produces very good findings that may be used to make decisions in the diagnosis of cancer.

Keywords: lobular carcinoma, convolutional neural networks (CNN), deep learning, histopathological imagery scans

Procedia PDF Downloads 136