Search results for: hybrid models

Authors: M. Caramihai, Irina Severin

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This paper deals with the design of an intelligent control structure for a bioprocess of Hansenula polymorpha yeast cultivation. The objective of the process control is to produce biomass in a desired physiological state. The work demonstrates that the designed Hybrid Control Techniques (HCT) are able to recognize specific evolution bioprocess trajectories using neural networks trained specifically for this purpose, in order to estimate the model parameters and to adjust the overall bioprocess evolution through an expert system and a fuzzy structure. The design of the control algorithm as well as its tuning through realistic simulations is presented. Taking into consideration the synergism of different paradigms like fuzzy logic, neural network, and symbolic artificial intelligence (AI), in this paper we present a real and fulfilled intelligent control architecture with application in bioprocess control.

Keywords: bioprocess, intelligent control, neural nets, fuzzy structure, hybrid techniques

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Authors: Salahaeddine Sbai

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Air quality analysis using machine learning models is a method employed to assess and predict air pollution levels. This approach leverages the capabilities of machine learning algorithms to analyze vast amounts of air quality data and extract valuable insights. By training these models on historical air quality data, they can learn patterns and relationships between various factors such as weather conditions, pollutant emissions, and geographical features. The trained models can then be used to predict air quality levels in real-time or forecast future pollution levels. This application of machine learning in air quality analysis enables policymakers, environmental agencies, and the general public to make informed decisions regarding health, environmental impact, and mitigation strategies. By understanding the factors influencing air quality, interventions can be implemented to reduce pollution levels, mitigate health risks, and enhance overall air quality management. Climate change is having significant impacts on Morocco, affecting various aspects of the country's environment, economy, and society. In this study, we use some machine learning models under python environment to predict and analysis air quality change over North of Morocco to evaluate the climate change impact on agriculture.

Keywords: air quality, machine learning models, pollution, pollutant emissions

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Authors: Deng Zengming, Wang Mingjiang

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As 3D video is explored as a hot research topic in the last few decades, free-viewpoint TV (FTV) is no doubt a promising field for its better visual experience and incomparable interactivity. View synthesis is obviously a crucial technology for FTV; it enables to render images in unlimited numbers of virtual viewpoints with the information from limited numbers of reference view. In this paper, a novel hybrid synthesis framework is proposed and blending priority is explored. In contrast to the commonly used View Synthesis Reference Software (VSRS), the presented synthesis process is driven in consideration of the temporal correlation of image sequences. The temporal correlations will be exploited to produce fine synthesis results even near the foreground boundaries. As for the blending priority, this scheme proposed that one of the two reference views is selected to be the main reference view based on the distance between the reference views and virtual view, another view is chosen as the auxiliary viewpoint, just assist to fill the hole pixel with the help of background information. Significant improvement of the proposed approach over the state-of –the-art pixel-based virtual view synthesis method is presented, the results of the experiments show that subjective gains can be observed, and objective PSNR average gains range from 0.5 to 1.3 dB, while SSIM average gains range from 0.01 to 0.05.

Keywords: fusion method, Gaussian mixture model, hybrid framework, view synthesis

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Authors: Samuel Opeyemi Williams, Razali Bin Adul Hamid, M. S. Misnan, Taki Eddine Seghier, D. I. Ajayi

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Construction sites are characterized with occupational risks. Review of literature on construction accidents reveals that a lot of theories have been propounded over the years by different theorists, coupled with multifarious models developed by different proponents at different times. Accidents are unplanned events that are prominent in construction sites, involving materials, objects and people with attendant damages, loses and injuries. Models were developed to investigate the causations of accident with the aim of preventing its occurrence. Though, some of these theories were criticized, most especially, the Heinrich Domino theory, being mostly faulted for placing much blame on operatives rather than the management. The purpose of this paper is to unravel the significant construction accident causation theories and models for the benefit of understanding of the theories, and consequently enabling construction stakeholders identify the possible potential hazards on construction sites, as all stakeholders have significant roles to play in preventing accident. Accidents are preventable; hence, understanding the risk factors of accident and the causation theories paves way for its prevention. However, findings reveal that still some gaps missing in the existing models, while it is recommended that further research can be made in order to develop more models in order to maintain zero accident on construction sites.

Keywords: domino theory, construction site, site safety, accident causation model

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Authors: M. Wigwe, J. G Akpa, E. N Wami

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Mathematical models of the absorber of a glycol dehydration facility was developed using the principles of conservation of mass and energy. Models which predict variation of the water content of gas in mole fraction, variation of gas and liquid temperatures across the parking height were developed. These models contain contributions from bulk and diffusion flows. The effect of diffusion on the process occurring in the absorber was studied in this work. The models were validated using the initial conditions in the plant data from Company W TEG unit in Nigeria. The results obtained showed that the effect of diffusion was noticed between z=0 and z=0.004 m. A deviation from plant data of 0% was observed for the gas water content at a residence time of 20 seconds, at z=0.004 m. Similarly, deviations of 1.584% and 2.844% were observed for the gas and TEG temperatures.

Keywords: separations, absorption, simulation, dehydration, water content, triethylene glycol

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Authors: Sally A. Brown

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Over the last 30 years, researchers have developed models or frameworks to provide a more structured understanding of the reading comprehension process. Cognitive information processing models and social cognitive theories both provide frameworks to inform reading comprehension instruction. The purpose of this paper is to (a) provide an overview of the historical development of reading comprehension theory, (b) review the literature framed by cognitive information processing, social cognitive, and integrated reading comprehension theories, and (c) demonstrate how these frameworks inform instruction. As integrated models of reading can guide the interpretation of various factors related to student learning, an integrated framework designed by the researcher will be presented. Results indicated that features of cognitive processing and social cognitivism theory—represented in the integrated framework—highlight the importance of the role of the teacher. This model can aid teachers in not only improving reading comprehension instruction but in identifying areas of challenge for students.

Keywords: explicit instruction, integrated models of reading comprehension, reading comprehension, teacher’s role

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Authors: Oulad Naoui Noureddine, Cherif ELAmine, Djehiche Abdelkader

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Water resources management includes several disciplines; the modeling of rainfall-runoff relationship is the most important discipline to prevent natural risks. There are several models to study rainfall-runoff relationship in watersheds. However, the majority of these models are not applicable in all basins of the world.  In this study, a new stochastic method called The Only Corresponding Competitor method (OCC) was used for the hydrological modeling of M’ZAB   Watershed (South East of Algeria) to adapt a few empirical models for any hydrological regime.  The results obtained allow to authorize a certain number of visions, in which it would be interesting to experiment with hydrological models that improve collectively or separately the data of a catchment by the OCC method.

Keywords: modelling, optimization, rainfall-runoff relationship, empirical model, OCC

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Authors: Ahmed Khalil

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Increased energy demands around the world have led to the raise in power production which has resulted with more greenhouse gas emissions through fossil sources. These fossil sources and emissions cause deterioration in echo-system. Therefore, renewable energy sources come to the scene as echo-friendly and clean energy sourcing, whereas the electrical devices and energy needs decrease in the timeline. Each of these renewable energy sources contribute to the reduction of greenhouse gases and mitigate environmental deterioration. However, there are also some general and source-specific challenges, which influence the choice of the investors. The most prominent general challenge that effects end-users’ comfort and reliability is usually determined as the intermittence which derives from the diversions of source conditions, due to nature dynamics and uncontrolled periodic changes. Research and development professionals strive to mitigate intermittence challenge through material improvement for each renewable source whereas hybrid source mix stand as a solution. This solution prevails well, when single renewable technologies are upgraded further. On the other hand, integration of energy efficient devices and systems, raise the affirmative effect of such solution in means of less energy requirement in sustainability composition or scenario. This paper provides a glimpse on the advantages of composing renewable source mix versus single usage, with contribution of sampled e-efficient systems and devices. Accordingly it demonstrates the extended benefits, through planning and predictive estimation stages of Ahmadi Town Projects in Kuwait.

Keywords: e-efficient systems, hybrid source, intermittence challenge, renewable energy

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Authors: Habtemariam Alemu

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It has been known that the performance of position and force control is highly affected by both robot dynamic and environment stiffness uncertainties. In this paper, joint space hybrid force and position control strategy with self-selecting matrix using artificial neural network compensator is proposed. The objective of the work is to improve controller robustness by applying a neural network technique in order to compensate the effect of uncertainties in the robot model. Simulation results for a 6 degree of freedom (6-DoF) manipulator and different types of environments showed the effectiveness of the suggested approach. 6-DoF Puma 560 family robot manipulator is chosen as industrial robot and its efficient dynamic model is designed using Matlab/SimMechanics library.

Keywords: robot manipulator, force/position control, artificial neural network, Matlab/Simulink

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Authors: C. D. Ellis, H. Xia, X. Chen

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Experimental and computational studies investigating heat transfer in separated flows have been of increasing importance over the last 60 years, as efforts are being made to understand and improve the efficiency of components such as combustors, turbines, heat exchangers, nuclear reactors and cooling channels. Understanding of not only the time-mean heat transfer properties but also the unsteady properties is vital for design of these components. As computational power increases, more sophisticated methods of modelling these flows become available for use. The hybrid LES-RANS approach has been applied to a blunt leading edge flat plate, utilising a structured grid at a moderate Reynolds number of 20300 based on the plate thickness. In the region close to the wall, the RANS method is implemented for two turbulence models; the one equation Spalart-Allmaras model and Menter’s two equation SST k-ω model. The LES region occupies the flow away from the wall and is formulated without any explicit subgrid scale LES modelling. Hybridisation is achieved between the two methods by the blending of the nearest wall distance. Validation of the flow was obtained by assessing the mean velocity profiles in comparison to similar studies. Identifying the vortex structures of the flow was obtained by utilising the λ2 criterion to identify vortex cores. The qualitative structure of the flow compared with experiments of similar Reynolds number. This identified the 2D roll up of the shear layer, breaking down via the Kelvin-Helmholtz instability. Through this instability the flow progressed into hairpin like structures, elongating as they advanced downstream. Proper Orthogonal Decomposition (POD) analysis has been performed on the full flow field and upon the surface temperature of the plate. As expected, the breakdown of POD modes for the full field revealed a relatively slow decay compared to the surface temperature field. Both POD fields identified the most energetic fluctuations occurred in the separated and recirculation region of the flow. Latter modes of the surface temperature identified these levels of fluctuations to dominate the time-mean region of maximum heat transfer and flow reattachment. In addition to the current research, work will be conducted in tracking the movement of the vortex cores and the location and magnitude of temperature hot spots upon the plate. This information will support the POD and statistical analysis performed to further identify qualitative relationships between the vortex dynamics and the response of the surface heat transfer.

Keywords: heat transfer, hybrid LES-RANS, separated and reattached flow, vortex dynamics

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Authors: Candida Petrogalli, Giovanni Incerti, Luigi Solazzi

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This paper describes three lumped parameters models for the study of the dynamic behaviour of a boom crane. The models proposed here allow evaluating the fluctuations of the load arising from the rope and structure elasticity and from the type of the motion command imposed by the winch. A calculation software was developed in order to determine the actual acceleration of the lifted mass and the dynamic overload during the lifting phase. Some application examples are presented, with the aim of showing the correlation between the magnitude of the stress and the type of the employed motion command.

Keywords: crane, dynamic model, overloading condition, vibration

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Authors: Usama Shahid

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Be it written documents, news columns, or students' essays, verifying the content can be a time-consuming task. Apart from the spelling and grammar mistakes, the proofreader is also supposed to verify whether the content included in the essay or document is relevant or not. The irrelevant content in any document or essay is referred to as off-topic text and in this paper, we will address the problem of off-topic text detection from a document using machine learning techniques. Our study aims to identify the off-topic content from a document using Echo state network model and we will also compare data with other models. The previous study uses Convolutional Neural Networks and TFIDF to detect off-topic text. We will rearrange the existing datasets and take new classifiers along with new word embeddings and implement them on existing and new datasets in order to compare the results with the previously existing CNN model.

Keywords: off topic, text detection, eco state network, machine learning

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Authors: Khaled M. Alhawiti

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This research study aims to present a retrospective study about speech recognition systems and artificial intelligence. Speech recognition has become one of the widely used technologies, as it offers great opportunity to interact and communicate with automated machines. Precisely, it can be affirmed that speech recognition facilitates its users and helps them to perform their daily routine tasks, in a more convenient and effective manner. This research intends to present the illustration of recent technological advancements, which are associated with artificial intelligence. Recent researches have revealed the fact that speech recognition is found to be the utmost issue, which affects the decoding of speech. In order to overcome these issues, different statistical models were developed by the researchers. Some of the most prominent statistical models include acoustic model (AM), language model (LM), lexicon model, and hidden Markov models (HMM). The research will help in understanding all of these statistical models of speech recognition. Researchers have also formulated different decoding methods, which are being utilized for realistic decoding tasks and constrained artificial languages. These decoding methods include pattern recognition, acoustic phonetic, and artificial intelligence. It has been recognized that artificial intelligence is the most efficient and reliable methods, which are being used in speech recognition.

Keywords: speech recognition, acoustic phonetic, artificial intelligence, hidden markov models (HMM), statistical models of speech recognition, human machine performance

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Authors: Abiodun Ladanu Ajala, Wuyi Oke

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Mathematical simulation and optimization models packaged within interactive computer programs provide a common way for planners and managers to predict the behaviour of any proposed water resources system design or management policy before it is implemented. Modeling presents a principal technique of predicting the behaviour of the proposed infrastructural designs or management policies. Models can be developed and used to help identify specific alternative plans that best meet those objectives. This study discusses various types of models, their development, architecture, data requirements, and applications in the field of engineering. It also outlines the advantages and limitations of each the optimization and simulation models presented. The techniques explored in this review include; dynamic programming, linear programming, fuzzy optimization, evolutionary algorithms and finally artificial intelligence techniques. Previous studies carried out using some of the techniques mentioned above were reviewed, and most of the results from different researches showed that indeed optimization and simulation provides viable alternatives and predictions which form a basis for decision making in building engineering structures and also in engineering planning and management.

Keywords: linear programming, mutation, optimization, simulation

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Authors: Abubakar Muhammad Umar, Nur Adeela Yasid, Hassan Mohd Daud, Mohd Yunus Abd Shukor

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Glyphosate [N-(phosphonomethyl) glycine] is among the most broadly and generally recognized broad-spectrum herbicides used in agriculture due to its low cost and effectiveness in weed management. The pollution of glyphosate in the aquatic environment can be via water run-off from agricultural lands, or by spray drift, aerial spraying or due to industrial discharge, which may be seen as a threat to aquatic biota. Fish is one of the best organisms to study the toxicological aspects of glyphosate. A 49 days experiment was conducted under laboratory condition to ascertain the effects of technical grade glyphosate on behaviour and histopathological conditions in the gills of red hybrid tilapia using light inverted microscope. Air gasping, erratic swimming, fin movement, mucus secretion, hemorrhages and loss of scales were observed as behavioural changes in the exposed fish. There was no any histopathological complication observed in the gill of the control fish, but various level of alterations were seen in the gills of the fish exposed to glyphosate herbicide. These include lifting of primary lamella, congestion of secondary lamella as well as hyperplasia in both primary and secondary gill lamella and hypertrophy of secondary gill lamella. Based on the findings of this study, glyphosate herbicide exerts behavioural and histopathological changes in the gill of red hybrid tilapia, and therefore the fish is considered as good bioindicator in aquatic environment monitoring. Excessive usage of glyphosate herbicide near aquatic habitats should be discouraged.

Keywords: glyphosate, behavioral, histopathological, tilapia

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Authors: Lucie Cote, Sonia McFadden

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Mental health problems in the workplace are currently one of the main causes of absences. Research work has highlighted the importance of a collaborative process involving the stakeholders in the return-to-work process and has established the best management practices to ensure a successful return-to-work. However, very few studies have specifically explored the combination of various management models and determined whether they could satisfy the needs of the stakeholders. The objective of this study is to analyze two models for managing the return to work: the ‘medical-administrative’ and the ‘support of the worker’ in order to understand the actions and actors involved in these models. The study also aims to explore whether these models meet the needs of the actors involved in the management of the return to work. A qualitative case study was conducted in a Canadian federal organization. An abundant internal documentation and semi-directed interviews with six managers, six workers and four human resources professionals involved in the management of records of employees returning to work after a mental health problem resulted in a complete picture of the return to work management practices used in this organization. The triangulation of this data facilitated the examination of the benefits and limitations of each approach. The results suggest that the actions of management for employee return to work from both models of management ‘support of the worker’ and ‘medical-administrative’ are compatible and can meet the needs of the actors involved in the return to work. More research is needed to develop a structured model integrating best practices of the two approaches to ensure the success of the return to work.

Keywords: return to work, mental health, management models, organizations

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Authors: M. Saiful Islam, M. Mohandes, S. Rehman, S. Badran

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Increasing necessity of wind power is directing us to have precise knowledge on wind resources. Methodical investigation of potential locations is required for wind power deployment. High penetration of wind energy to the grid is leading multi megawatt installations with huge investment cost. This fact appeals to determine appropriate places for wind farm operation. For accurate assessment, detailed examination of wind speed profile, relative humidity, temperature and other geological or atmospheric parameters are required. Among all of these uncertainty factors influencing wind power estimation, vertical extrapolation of wind speed is perhaps the most difficult and critical one. Different approaches have been used for the extrapolation of wind speed to hub height which are mainly based on Log law, Power law and various modifications of the two. This paper proposes a Artificial Neural Network (ANN) and Genetic Algorithm (GA) based hybrid model, namely GA-NN for vertical extrapolation of wind speed. This model is very simple in a sense that it does not require any parametric estimations like wind shear coefficient, roughness length or atmospheric stability and also reliable compared to other methods. This model uses available measured wind speeds at 10m, 20m and 30m heights to estimate wind speeds up to 100m. A good comparison is found between measured and estimated wind speeds at 30m and 40m with approximately 3% mean absolute percentage error. Comparisons with ANN and power law, further prove the feasibility of the proposed method.

Keywords: wind profile, vertical extrapolation of wind, genetic algorithm, artificial neural network, hybrid machine learning

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Authors: Subhadip Biswas, Shivendra Maurya, Satish Chandra, Indrajit Ghosh

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Use of speed prediction models sometimes appears as a feasible alternative to laborious field measurement particularly, in case when field data cannot fulfill designer’s requirements. However, developing speed models is a challenging task specifically in the context of developing countries like India where vehicles with diverse static and dynamic characteristics use the same right of way without any segregation. Here the traffic composition plays a significant role in determining the vehicular speed. The present research was carried out to examine the effects of traffic volume and its composition on vehicular speed under mixed traffic conditions. Classified traffic volume and speed data were collected from different geometrically identical six lane divided arterials in New Delhi. Based on these field data, speed prediction models were developed for individual vehicle category adopting Kriging approximation technique, an alternative for commonly used regression. These models are validated with the data set kept aside earlier for validation purpose. The predicted speeds showed a great deal of agreement with the observed values and also the model outperforms all other existing speed models. Finally, the proposed models were utilized to evaluate the effect of traffic volume and its composition on speed.

Keywords: speed, Kriging, arterial, traffic volume

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Authors: Saeed Rauf

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Purpose: Drought stress is a major production constraint in sunflowers and causes yield losses under tropical and subtropical environments having high evapo-tranpirational losses. Given the consequences, three trials were designed to evaluate drought-resistant sunflower hybrids. Research Methods: Field trials were conducted under a split-plot arrangement with 17 hybrids and two contrasting regimes at Sargodha, Pakistan and 7 hybrids at Karj, Iran. Water stress condition was simulated by holding water in a stress regime. Hybrids were also screened against five levels of osmotic-ally induced stress, i.e. 0-15%, under a completely randomized design with 3 replications. Findings: Hybrids H1 (C.112.× RH.344) and H3 (C.112.× RSIN.82) showed the highest seed yield ha-1 and early flowering at Karj Iran. Commercial hybrid had the highest CTD (18.2°C) followed by C112 × RH.344 (17.29 °C). Hybrid C.250 × R.SIN.82 had the highest seed yield (m-2), followed by C.112 × RH.365 and C.124 × RSIN.82 under both stress and non-stress regimes at Sargodha, Pakistan. Seedling trial results showed that 6 hybrids only germinated in 5 and 7.5% PEG-induced osmotic stress, respectively. H1 (C.112 × RH.344) and H2 (C.112 × RH.347) had the highest germination% at 5% and 7.5% osmotic stress (OS). Seedling vigor index (SVI) was the highest in H1 (C.112 × RH.344) hybrids at 5% OS, H2 had the highest SVI under 7.5% OS, followed by H3 (C112 × RH344) and H4 (C116 × RH344). Originality/Value: In view of above results, it was concluded that hybrid combination H1 had the highest seed yield under stress conditions in both environments. High seed yield may be due to its better germination and vigor index under stress conditions.

Keywords: climate change, CTD, genetic variability, osmotic stress

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Authors: Shryas Bhurat, Aryan Vashistha, Sampreet Dinakar Nayak, Ayush Gupta

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As the technology is advancing more towards high computational resources, there is a paradigm shift in the usage of these resources to optimize the design process. This paper discusses the usage of ‘Generative Design using Artificial Intelligence’ to build better models that adapt the operations like selection, mutation, and crossover to generate results. The human mind thinks of the simplest approach while designing an object, but the intelligence learns from the past & designs the complex optimized CAD Models. Generative Design takes the boundary conditions and comes up with multiple solutions with iterations to come up with a sturdy design with the most optimal parameter that is given, saving huge amounts of time & resources. The new production techniques that are at our disposal allow us to use additive manufacturing, 3D printing, and other innovative manufacturing techniques to save resources and design artistically engineered CAD Models. Also, this paper discusses the Genetic Algorithm, the Non-Domination technique to choose the right results using biomimicry that has evolved for current habitation for millions of years. The computer uses parametric models to generate newer models using an iterative approach & uses cloud computing to store these iterative designs. The later part of the paper compares the topology optimization technology with Generative Design that is previously being used to generate CAD Models. Finally, this paper shows the performance of algorithms and how these algorithms help in designing resource-efficient models.

Keywords: genetic algorithm, bio mimicry, generative modeling, non-dominant techniques

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Authors: Lina Vyas, Stuti Rawat

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The COVID-19 outbreak has forced many employees to extensively adopt remote work or, widely known as work from home (WFH) arrangements. During the last two years, both employers and employees have had the opportunity to be increasingly aware of the benefits and drawbacks of WFH. Likewise, it gained the attention of academics from various schools of thought who have been interested in the future of work practices and work-life balance. Additionally, employees might also have various demands regarding their work practices after the pandemic. This study explores the potential of hybrid ways of working in the post-pandemic period by comparing first-timers who (sometimes or always) worked from home during the pandemic with those who did not, in terms of the aspects of work-life balance, work-life interference, job performance and willingness to work from home after the pandemic. The quantitative research approach was adopted. Data were collected via an online questionnaire from employees working from home in Hong Kong during the pandemic. There were one thousand three hundred and twenty-eight responses, but only 1,235 respondents experienced working from home during the pandemic. The findings reveal that 72.2% never had or hardly experienced work from home prior to the pandemic. There were statistically significant differences between first-timers and non-first-timers in work-life balance and work-life interference. The study also found that first-timers who were always working from home during the pandemic would prefer having longer WFH after the pandemic than those who were sometimes working from home. These results would serve as a basis for policy development, enabling policymakers to design appropriate HR policies and amend them to meet the current context of actual employee needs.

Keywords: hybrid working mode, remote working, work from home, work-life balance, workplace

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Authors: C. Senabre, S. Valero, M. Lopez, E. Velasco, M. Sanchez

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This paper focuses on an application of linear mixed models to short-term load forecasting. The challenge of this research is to improve a currently working model at the Spanish Transport System Operator, programmed by us, and based on linear autoregressive techniques and neural networks. The forecasting system currently forecasts each of the regions within the Spanish grid separately, even though the behavior of the load in each region is affected by the same factors in a similar way. A load forecasting system has been verified in this work by using the real data from a utility. In this research it has been used an integration of several regions into a linear mixed model as starting point to obtain the information from other regions. Firstly, the systems to learn general behaviors present in all regions, and secondly, it is identified individual deviation in each regions. The technique can be especially useful when modeling the effect of special days with scarce information from the past. The three most relevant regions of the system have been used to test the model, focusing on special day and improving the performance of both currently working models used as benchmark. A range of comparisons with different forecasting models has been conducted. The forecasting results demonstrate the superiority of the proposed methodology.

Keywords: short-term load forecasting, mixed effects models, neural networks, mixed effects models

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Authors: Shuwen Hu, Yuancheng Lou, Dongxu Ji

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Photovoltaic (PV) power generation, as one of the most commercialized methods to utilize solar power, can only convert a limited range of solar spectrum into electricity, whereas the majority of the solar energy is dissipated as heat. To address this problem, thermoelectric (TE) module is often integrated with the concentrated PV module for waste heat recovery and regeneration. In this research, a feasibility analysis is conducted for the tandem concentrated photovoltaic-thermoelectric (CPV-TE) hybrid system considering various operational parameters as well as TE material properties. Furthermore, the power output density of the CPV-TE hybrid system is maximized by selecting the optimal TE material with application of a systematic assessment methodology. In the feasibility analysis, CPV-TE is found to be more advantageous than sole CPV system except under high optical concentration ratio with low cold side convective coefficient. It is also shown that the effects of the TE material properties, including Seebeck coefficient, thermal conductivity, and electrical resistivity, on the feasibility of CPV-TE are interacted with each other and might have opposite effect on the system performance under different operational conditions. In addition, the optimal TE material selected by the proposed assessment methodology can improve the system power output density by 227 W/m2 under highly concentrated solar irradiance hence broaden the feasible range of CPV-TE considering optical concentration ratio.

Keywords: feasibility analysis, material assessment methodology, photovoltaic waste heat recovery, tandem photovoltaic-thermoelectric

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Authors: Arif Armagan Gozutok, Alper Kule, Burak Tos, Selman Demirel

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Wireless communication chain requires effective ways to keep throughput efficiency high while it suffers location-dependent, time-varying burst errors. Several techniques are developed in order to assure that the receiver recovers the transmitted information without errors. The most fundamental approaches are error checking and correction besides re-transmission of the non-acknowledged packets. In this paper, stop & wait (SAW) and chase combined (CC) hybrid automated repeat request (HARQ) protocols are compared and analyzed in terms of throughput and average delay for the usage of low earth orbit (LEO) mega-constellations case. Several assumptions and technological implementations are considered as well as usage of low-density parity check (LDPC) codes together with several constellation orbit configurations.

Keywords: HARQ, LEO, satellite constellation, throughput

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Authors: M. Govindarajan

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One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of intrusion detection. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase, and combining phase. A wide range of comparative experiments is conducted for standard datasets of intrusion detection. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, Dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of intrusion detection. 

Keywords: data mining, ensemble, radial basis function, support vector machine, accuracy

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Authors: Almagul Assainova , Dariya Abykenova, Liudmila Goncharenko, Sergey Sybachin, Saule Rakhimova, Abay Aman

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The recognition of a handwritten Kazakh text is a relevant objective today for the digitization of materials. The study presents a model of a hybrid neural network for handwriting recognition, which includes a convolutional neural network and a multi-layer perceptron. Each network includes 1024 input neurons and 42 output neurons. The model is implemented in the program, written in the Python programming language using the EMNIST database, NumPy, Keras, and Tensorflow modules. The neural network training of such specific letters of the Kazakh alphabet as ә, ғ, қ, ң, ө, ұ, ү, h, і was conducted. The neural network model and the program created on its basis can be used in electronic document management systems to digitize the Kazakh text.

Keywords: handwriting recognition system, image recognition, Kazakh font, machine learning, neural networks

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Authors: Verónica Diaz Quezada

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This research examines the teaching models used by secondary math teachers when teaching logarithmic, quadratic and exponential functions. For this, descriptive case studies have been carried out on 5 secondary teachers. These teachers have been chosen from 3 scientific-humanistic and technical schools, in Chile. Data have been obtained through non-participant class observation and the application of a questionnaire and a rubric to teachers. According to the results, the didactic model that prevails is the one that starts with an interactive strategy, moves to a more content-based structure, and ends with a reinforcement stage. Nonetheless, there is always influence from teachers, their methods, and the group of students.

Keywords: teaching models, math teachers, functions, secondary education

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Authors: Alexander G. Yushchenko

Abstract:

From a multi-science point of view, we analyze threats to security resulting from globalization of international information space and information and communication aggression of Russia. A definition of Ruschism is formulated as an ideology supporting aggressive actions of modern Russia against the Euro-Atlantic community. Stages of the hybrid war Russia is leading against Ukraine are described, including the elements of subversive activity of the special services, the activation of the military phase and the gradual shift of the focus of confrontation to the realm of information and communication technologies. We reveal an emergence of a threat for democratic states resulting from the destabilizing impact of a target state’s mass media and social networks being exploited by Russian secret services under freedom-of-speech disguise. Thus, we underline the vulnerability of cyber- and information security of the network society in regard of hybrid war. We propose to define the latter a synergetic war. Our analysis is supported with a long-term qualitative monitoring of representation of top state officials on popular TV channels and Facebook. From the memetics point of view, we have detected a destructive psycho-information technology used by the Kremlin, a kind of information catastrophe, the essence of which is explained in detail. In the conclusion, a comprehensive plan for information protection of the public consciousness and mentality of Euro-Atlantic citizens from the aggression of the enemy is proposed.

Keywords: cyber and information security, hybrid war, psycho-information technology, synergetic war, Ruschism

Procedia PDF Downloads 118

Authors: Yanshuang Zhang, Byungho Jeong

Abstract:

In many cases, there is a time lag between the consumption of inputs and the production of outputs. This time lag effect should be considered in evaluating the performance of organizations. Recently, a couple of DEA models were developed for considering time lag effect in efficiency evaluation of research activities. Multi-periods input(MpI) and Multi-periods output(MpO) models are integrated models to calculate simple efficiency considering time lag effect. However, these models can’t discriminate efficient DMUs because of the nature of basic DEA model in which efficiency scores are limited to ‘1’. That is, efficient DMUs can’t be discriminated because their efficiency scores are same. Thus, this paper suggests a super-efficiency model for efficiency evaluation under the consideration of time lag effect based on the MpO model. A case example using a long-term research project is given to compare the suggested model with the MpO model.

Keywords: DEA, super-efficiency, time lag, multi-periods input

Procedia PDF Downloads 454

Authors: E. Tandis, E. Assareh

Abstract:

Designing the optimal shape of MW wind turbine blades is provided in a number of cases through evolutionary algorithms associated with mathematical modeling (Blade Element Momentum Theory). Evolutionary algorithms, among the optimization methods, enjoy many advantages, particularly in stability. However, they usually need a large number of function evaluations. Since there are a large number of local extremes, the optimization method has to find the global extreme accurately. The present paper introduces a new population-based hybrid algorithm called Genetic-Based Bees Algorithm (GBBA). This algorithm is meant to design the optimal shape for MW wind turbine blades. The current method employs crossover and neighborhood searching operators taken from the respective Genetic Algorithm (GA) and Bees Algorithm (BA) to provide a method with good performance in accuracy and speed convergence. Different blade designs, twenty-one to be exact, were considered based on the chord length, twist angle and tip speed ratio using GA results. They were compared with BA and GBBA optimum design results targeting the power coefficient and solidity. The results suggest that the final shape, obtained by the proposed hybrid algorithm, performs better compared to either BA or GA. Furthermore, the accuracy and speed convergence increases when the GBBA is employed

Keywords: Blade Design, Optimization, Genetic Algorithm, Bees Algorithm, Genetic-Based Bees Algorithm, Large Wind Turbine

Procedia PDF Downloads 304