Search results for: wireless performance analysis

Authors: Sasan Talebnezhad, Saeed Pourmahdian, Naghi Assali

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Homogeneous α-diimine nickel (II) catalyst complexes, with and without amino para-aryl position functionality, were synthesized. These complexes were immobilized on carboxyl, hydroxyl and acyl chloride functionalized multi-walled carbon nanotubes to form five novel heterogeneous α diiminonickel catalysts. Immobilization was performed by covalent or electrostatic bonding via methylaluminoxane (MAO) linker or amide linkage. Both the nature of α-diimine ligands and the kind of interaction between anchored catalyst complexes and multi-walled carbon nanotube surface influenced the catalytic performance, microstructure, and morphology of obtained polyethylenes. The catalyst prepared by amide bonding showed lowest relative weight loss in thermogravimetry analysis and highest activities up to 5863 gr PE mmol-1Ni.hr-1. This catalyst produced polyethylene with dense botryoidal morphology.

Keywords: α-diimine nickel (II) complexes, immobilization, multi-walled carbon nanotubes, ethylene polymerization

Procedia PDF Downloads 502

Authors: Kavitha Madhu, Karthik K. Srinivasan, R. Sivanandan

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Indian traffic can be considered as mixed and heterogeneous due to the presence of various types of vehicles that operate with weak lane discipline. Consequently, vehicles can position themselves anywhere in the traffic stream depending on availability of gaps. The choice of lateral positioning is an important component in representing and characterizing mixed traffic. The field data provides evidence that the trajectory of vehicles in Indian urban roads have significantly varying longitudinal and lateral components. Further, the notion of headway which is widely used for homogeneous traffic simulation is not well defined in conditions lacking lane discipline. From field data it is clear that following is not strict as in homogeneous and lane disciplined conditions and neighbouring vehicles ahead of a given vehicle and those adjacent to it could also influence the subject vehicles choice of position, speed and acceleration. Given these empirical features, the suitability of using headway distributions to characterize mixed traffic in Indian cities is questionable, and needs to be modified appropriately. To address these issues, this paper attempts to analyze the time gap distribution between consecutive vehicles (in a time-sense) crossing a section of roadway. More specifically, to characterize the complex interactions noted above, the influence of composition, manoeuvre types, and lateral placement characteristics on time gap distribution is quantified in this paper. The developed model is used for evaluating various performance measures such as link speed, midblock delay and intersection delay which further helps to characterise the vehicular fuel consumption and emission on urban roads of India. Identifying and analyzing exact interactions between various classes of vehicles in the traffic stream is essential for increasing the accuracy and realism of microscopic traffic flow modelling. In this regard, this study aims to develop and analyze time gap distribution models and quantify it by lead lag pair, manoeuvre type and lateral position characteristics in heterogeneous non-lane based traffic. Once the modelling scheme is developed, this can be used for estimating the vehicle kilometres travelled for the entire traffic system which helps to determine the vehicular fuel consumption and emission. The approach to this objective involves: data collection, statistical modelling and parameter estimation, simulation using calibrated time-gap distribution and its validation, empirical analysis of simulation result and associated traffic flow parameters, and application to analyze illustrative traffic policies. In particular, video graphic methods are used for data extraction from urban mid-block sections in Chennai, where the data comprises of vehicle type, vehicle position (both longitudinal and lateral), speed and time gap. Statistical tests are carried out to compare the simulated data with the actual data and the model performance is evaluated. The effect of integration of above mentioned factors in vehicle generation is studied by comparing the performance measures like density, speed, flow, capacity, area occupancy etc under various traffic conditions and policies. The implications of the quantified distributions and simulation model for estimating the PCU (Passenger Car Units), capacity and level of service of the system are also discussed.

Keywords: lateral movement, mixed traffic condition, simulation modeling, vehicle following models

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Authors: M. Jamuna Rani, K. Ramanathan

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A steam power plant is housed with various types of equipments like boiler, turbine, heat exchanger etc. These equipments are mainly connected with piping systems. Such a piping layout design depends mainly on stress analysis and flexibility. It will vary with respect to pipe geometrical properties, pressure, temperature, and supports. The present paper is to analyze the presence and effect of hangers and expansion joints in the piping layout/routing using CAESAR-II software. Main aim of piping stress analysis is to provide adequate flexibility for absorbing thermal expansion, code compliance for stresses and displacement incurred in piping system. The design is said to be safe if all these are in allowable range as per code. In this study, a sample problem is considered for analysis as per power piping ASME B31.1 code and the results thus obtained are compared.

Keywords: ASTM B31.1, hanger, expansion joint, CAESAR-II

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Authors: N. Boutra, N. Ravot, J. Benoit, O. Picon

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Submarine power cables used for offshore wind farms electric energy distribution and transmission are subject to numerous threats. Some of the risks are associated with transport, installation and operating in harsh marine environment. This paper describes the feasibility of an electromagnetic low frequency sensing technique for submarine power cable failure prediction. The impact of a structural damage shape and material variability on the induced electric field is evaluated. The analysis is performed by modeling the cable using the finite element method, we use sensitivity analysis in order to identify the main damage characteristics affecting electric field variation. Lastly, we discuss the results obtained.

Keywords: electromagnetism, finite element method, sensitivity analysis, submarine power cables

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Authors: Haowei Lu, Anaya Aaron

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Understanding historical solar generation and forecasting future solar generation from interconnected Distributed Energy Resources (DER) is crucial for utility planning and interconnection studies. The existing methodology, which relies on solar radiation, weather data, and common inverter models, is becoming less accurate. Rapid advancements in DER technologies have resulted in more diverse project sites, deviating from common patterns due to various factors such as DC/AC ratio, solar panel performance, tilt angle, and the presence of DC-coupled battery energy storage systems. In this paper, the authors review 10,000 DER projects within the system and analyze the Advanced Metering Infrastructure (AMI) data for various types to demonstrate the impact of different parameters. An updated methodology is proposed for redefining historical and future solar generation in distribution feeders.

Keywords: photovoltaic system, solar energy, fluctuations, energy storage, uncertainty

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Authors: P. R. Chauhan, S. K. Tyagi

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Adsorption refrigeration technology offers a sustainable and energy-efficient cooling alternative over traditional refrigeration technologies for meeting the fast-growing cooling demands. With its ability to utilize natural refrigerants, low-grade heat sources, and modular configurations, it has the potential to revolutionize the cooling industry. Despite these benefits, the commercial viability of this technology is hampered by several fundamental limiting constraints, including its large size, low uptake capacity, and poor performance as a result of deficient heat and mass transfer characteristics. The primary cause of adequate heat and mass transfer characteristics and magnitude of exergy loss in various real processes of adsorption cooling system can be assessed by the entropy generation rate analysis, i. e. Second law of Thermodynamics. Therefore, this article presents the second law of thermodynamic-based investigation in terms of entropy generation rate (EGR) to identify the energy losses in various processes of the HPCC-based adsorption system using MATLAB R2021b software. The adsorption technology-based cooling system consists of two beds made up of silica gel and arranged in a single stage, while the water is employed as a refrigerant, coolant, and hot fluid. The variation in process-wise EGR is examined corresponding to cycle time, and a comparative analysis is also presented. Moreover, the EGR is also evaluated in the external units, such as the heat source and heat sink unit used for regeneration and heat dump, respectively. The research findings revealed that the combination of adsorber and desorber, which operates across heat reservoirs with a higher temperature gradient, shares more than half of the total amount of EGR. Moreover, the EGR caused by the heat transfer process is determined to be the highest, followed by a heat sink, heat source, and mass transfer, respectively. in case of heat transfer process, the operation of the valve is determined to be responsible for more than half (54.9%) of the overall EGR during the heat transfer. However, the combined contribution of the external units, such as the source (18.03%) and sink (21.55%), to the total EGR, is 35.59%. The analysis and findings of the present research are expected to pinpoint the source of the energy waste in HPCC based adsorption cooling systems.

Keywords: adsorption cooling cycle, heat transfer, mass transfer, entropy generation, silica gel-water

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Authors: Aya Maher, Pakinam Youssef

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The objective of this theoretical study is to explore in depth the role of leaders in managing employees’ dysfunctional behavior at workplace in an effort to recommend strategies and solutions for these destructive behaviors that affect employees’ performance. The significance of the study lies in the fact that dysfunctional behavior has been widely spread in almost all organizations, public and private, with its very destructive manifestations. Dysfunctional behavior may be classified into thefts, sabotage, sexual harassment, jealousy, envy, revenge, vulgarity all of which affect employees’ moral, self-esteem and satisfaction level drastically which will be reflected negatively on their performance and productivity. The main research question will focus on the role of leaders in managing employees’ dysfunctional behavior effectively at the workplace through the different strategies and control measures. In this study, the data will be collected from different academic literature and through some primary data by conducting interviews with some public and private employees from different managerial levels and fields.

Keywords: dysfunctional behavior, employees deviant behavior, employees moral, leaders role

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Authors: Qihang Zeng, Wei Xu, Ying Shen, Changyuan Yu

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In this paper, a highly sensitive fiber-optic curvature sensor based on four mode fiber (FMF) is presented and investigated. The proposed sensing structure is constructed by fusing a section of FMF into two standard single mode fibers (SMFs) concatenated with two no core fiber (NCF), i.e., SMF-NCF-FMF-NCF-SMF structure is fabricated. The length of the NCF is very short about 1 millimeter acting as exciting/recoupling the light from/into the core of the SMF, while the FMF is with 3 centimeters long supporting four eigenmodes including LP₀₁, LP₁₁, LP₂₁ and LP₀₂. High core modes in FMF can be effectively stimulated owing to mismatched mode field distribution and the mainly sensing principle is based on modal interferometer spectrum analysis. Different curvatures induce different strains on the FMF such that affecting the modal excitation, resulting spectrum shifts. One can get the curvature value by tracking the wavelength shifting. Experiments have been done to address the sensing performance, which is about 7.8 nm/m⁻¹ within a range of 1.90 m⁻¹~3.18 m⁻¹.

Keywords: curvature, four mode fiber, highly sensitive, modal interferometer

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Authors: Yu Zhang, Pedro Desouza

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Since big data has become substantially more accessible and manageable due to the development of powerful tools for dealing with unstructured data, people are eager to mine information from social media resources that could not be handled in the past. Sentiment analysis, as a novel branch of text mining, has in the last decade become increasingly important in marketing analysis, customer risk prediction and other fields. Scientists and researchers have undertaken significant work in creating and improving their sentiment models. In this paper, we present a concept of selecting appropriate classifiers based on the features and qualities of data sources by comparing the performances of five classifiers with three popular social media data sources: Twitter, Amazon Customer Reviews, and Movie Reviews. We introduced a couple of innovative models that outperform traditional sentiment classifiers for these data sources, and provide insights on how to further improve the predictive power of sentiment analysis. The modelling and testing work was done in R and Greenplum in-database analytic tools.

Keywords: sentiment analysis, social media, Twitter, Amazon, data mining, machine learning, text mining

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Authors: Gokcen Ogruk-Maz, Sinan Yildirim

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Introduction: Exchange rate forecasting is pivotal for informed financial decision-making, encompassing risk management, investment strategies, and international trade planning. However, traditional forecasting models often fail to capture the complexity and volatility of currency markets. This study explores the potential of ensemble learning techniques such as Random Forest, Gradient Boosting, and AdaBoost to enhance the accuracy and robustness of exchange rate predictions. Research Objectives The primary objective is to evaluate the performance of ensemble methods in comparison to traditional econometric models such as Uncovered Interest Rate Parity, Purchasing Power Parity, and Monetary Models. By integrating advanced machine learning techniques with fundamental macroeconomic indicators, this research seeks to identify optimal approaches for predicting exchange rate movements across major currency pairs. Methodology: Using historical exchange rate data and economic indicators such as interest rates, inflation, money supply, and GDP, the study develops forecasting models leveraging ensemble techniques. Comparative analysis is performed against traditional models and hybrid approaches incorporating Facebook Prophet, Artificial Neural Networks, and XGBoost. The models are evaluated using statistical metrics like Mean Squared Error, Theil Ratio, and Diebold-Mariano tests across five currency pairs (JPY to USD, AUD to USD, CAD to USD, GBP to USD, and NZD to USD). Preliminary Results: Results indicate that ensemble learning models consistently outperform traditional methods in predictive accuracy. XGBoost shows the strongest performance among the techniques evaluated, achieving significant improvements in forecast precision with consistently low p-values and Theil Ratios. Hybrid models integrating macroeconomic fundamentals into machine learning frameworks further enhance predictive accuracy. Discussion: The findings show the potential of ensemble methods to address the limitations of traditional models by capturing non-linear relationships and complex dynamics in exchange rate movements. While Random Forest and Gradient Boosting are effective, the superior performance of XGBoost suggests that its capacity for handling sparse and irregular data offers a distinct advantage in financial forecasting. Conclusion and Implications: This research demonstrates that ensemble learning techniques, particularly when combined with traditional macroeconomic fundamentals, provide a robust framework for improving exchange rate forecasting. The study offers actionable insights for financial practitioners and policymakers, emphasizing the value of integrating machine learning approaches into predictive modeling for monetary economics.

Keywords: exchange rate forecasting, ensemble learning, financial modeling, machine learning, monetary economics, XGBoost

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Authors: Shawn Michael Condon

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Current research on teaching expressivity mainly involves instrumentalists. This study focuses on choral singers’ preference of priming techniques based on four methods for teaching expressivity. 112 choral singers answered the survey about their preferred methods for priming expressivity (vocal modelling, using metaphor, tapping into felt emotions, and drawing on past experiences) in three conditions (active, passive, and instructor). Analysis revealed higher preference for drawing on past experience among more experienced singers. The most preferred technique in the passive and instructor roles was vocal modelling, with metaphors and tapping into felt emotions favoured in an active role. Priming techniques are often used in combination with other methods to enhance singing technique or expressivity and are dependent upon the situation, repertoire, and the preferences of the instructor and performer.

Keywords: emotion, expressivity, performance, singing, teaching

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Authors: Remo Waser, Simon Maranda, Anastasia Stamatiou, Ludger J. Fischer, Joerg Worlitschek

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In latent heat storage, a phase change material (PCM) is used to store thermal energy. The heat transfer rate during solidification is limited and considered as a key challenge in the development of latent heat storages. Thus, finned heat exchangers (HEX) are often utilized to increase the heat transfer rate of the storage system. In this study, a new modeling approach to calculating the heat transfer rate in latent thermal energy storages with complex HEX geometries is presented. This model allows for an optimization of the HEX design in terms of costs and thermal performance of the system. Modeling solidification processes requires the calculation of time-dependent heat conduction with moving boundaries. Commonly used computational fluid dynamic (CFD) methods enable the analysis of the heat transfer in complex HEX geometries. If applied to the entire storage, the drawback of this approach is the high computational effort due to small time steps and fine computational grids required for accurate solutions. An alternative to describe the process of solidification is the so-called temperature-based approach. In order to minimize the computational effort, a quasi-stationary assumption can be applied. This approach provides highly accurate predictions for tube heat exchangers. However, it shows unsatisfactory results for more complex geometries such as finned tube heat exchangers. The presented simulation model uses a temporal and spatial discretization of heat exchanger tube. The spatial discretization is based on the smallest possible symmetric segment of the HEX. The heat flow in each segment is calculated using finite volume method. Since the heat transfer fluid temperature can be derived using energy conservation equations, the boundary conditions at the inner tube wall is dynamically updated for each time step and segment. The model allows a prediction of the thermal performance of latent thermal energy storage systems using complex HEX geometries with considerably low computational effort.

Keywords: modelling of solidification, finned tube heat exchanger, latent thermal energy storage

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Authors: Zayar Phyo, Ei Chaw Htoon

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In a New-LSB based Steganography method, the Fisher-Yates algorithm is used to permute an existing array randomly. However, that algorithm performance became slower and occurred memory overflow problem while processing the large dimension of images. Therefore, the Text-Based Shuffling algorithm aimed to select only necessary pixels as hiding characters at the specific position of an image according to the length of the input text. In this paper, the enhanced text-based shuffling algorithm is presented with the powered of GPU to improve more excellent performance. The proposed algorithm employs the OpenCL Aparapi framework, along with XORShift Kernel including the Pseudo-Random Number Generator (PRNG) Kernel. PRNG is applied to produce random numbers inside the kernel of OpenCL. The experiment of the proposed algorithm is carried out by practicing GPU that it can perform faster-processing speed and better efficiency without getting the disruption of unnecessary operating system tasks.

Keywords: LSB based steganography, Fisher-Yates algorithm, text-based shuffling algorithm, OpenCL, XORShiftKernel

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Authors: Svetlana Ignatjeva, Jelena Slesareva

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The aim of the study is development and adaptation of the method to analyze and quantify the indicators characterizing the relationship between a company and its employees. Diagnostics of such indicators is one of the most complex and actual issues in psychology of labour. The offered method is based on the questionnaire; its indicators reflect cognitive, affective and connotative components of socio-psychological attitude of employees to be as efficient as possible in their professional activities. This approach allows measure not only the selected factors but also such parameters as cognitive and behavioural dissonances. Adaptation of the questionnaire includes factor structure analysis and suitability analysis of phenomena indicators measured in terms of internal consistency of individual factors. Structural validity of the questionnaire was tested by exploratory factor analysis. Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Factor analysis allows reduce dimension of the phenomena moving from the indicators to aggregative indexes and latent variables. Aggregative indexes are obtained as the sum of relevant indicators followed by standardization. The coefficient Cronbach's Alpha was used to assess the reliability-consistency of the questionnaire items. The two-step cluster analysis in the space of allocated factors allows classify employees according to their attitude to work in the company. The results of psychometric testing indicate possibility of using the developed technique for the analysis of employees’ attitude towards their work in companies and development of recommendations on their optimization.

Keywords: involved in the organization, loyalty, organizations, method

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Authors: Sadegh Torfi

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Realization of anticipated energy efficiency from recuperative run-around energy recovery (RER) systems requires identification of the system components influential parameters. Because simulation modeling is considered as an integral part of the design and economic evaluation of RER systems, it is essential to calibrate the developed models and validate the performance predictions by means of comparison with data from experimental measurements. Several theoretical and numerical analyses on RER systems by researchers have been done, but generally the effect of distance between hot and cold flow is ignored. The objective of this study is to develop a thermohydroulic model for a typical RER system that accounts for energy loss from the interconnecting piping and effects of interconnecting pipes length performance of run-around energy recovery systems. Numerical simulation shows that energy loss from the interconnecting piping is change linear with pipes length and if pipes are properly isolated, maximum reduction of effectiveness of RER systems is 2% in typical piping systems.

Keywords: combined heat and power, heat recovery, effectiveness, CGS

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Authors: Charmaine Lamiel, Van Hoa Nguyen, Jae-Jin Shim

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Supercapacitors are energy storage devices capable of storing more energy than conventional capacitors and have higher power density than batteries. The advantages of this method include the non-use of reducing agents and acidic medium, and no further use of a post-heat treatment unlike the conventional processes, in which calcination is generally employed after obtaining the initial product. Furthermore, it also offers a shorter reaction time at low temperatures and low power requirements, which allows low fabrication and energy cost. In this study, microwave irradiation was used for the facile and rapid synthesis of mesoporous RGO@(Co,Mn)3O4 nanosheets as an electrode material. The as-prepared electrode exhibited a high capacitance of 953 F•g^−1 at 1 A•g^−1 in a 6 M KOH electrolyte solution. Moreover, the electrode exhibited a high energy density of 76.2 Wh•kg^−1 at a power density of 720 W•kg^−1, and a high power density of 7200 W•kg^−1 at an energy density of 38 Wh•kg^−1. The successful methodology was considered to be efficient and cost-effective, thereby providing an active electrode material with very promising electrochemical performance.

Keywords: cobalt-manganese oxide, electrochemical, graphene, microwave synthesis, supercapacitor

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Authors: Ratheesh Radhakrishnan, P. C. Sreekumar, K. Krishnamoorthy

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Heat transfer augmentation techniques ultimately results in the reduction of thermal resistance in a conventional heat exchanger by generating higher convective heat transfer coefficient. It also results in reduction of size, increase in heat duty, decrease in approach temperature difference and reduction in pumping power requirements for heat exchangers. Present study deals with compound augmentation technique, which is not widely used. The study deals with the use of Alumina (Al2O3)/water nanofluid and baffled twisted tape inserts in double pipe heat exchanger as compound augmentation technique. Experiments were conducted to evaluate the heat transfer coefficient and friction factor for the flow through the inner tube of heat exchanger in turbulent flow range (8000Keywords: enhancement, heat transfer coefficient, friction factor, twisted tape, nanofluid

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Authors: S. Daqiqeh Rezaei, S. Mohajerzadeh, M. R. Sharifi

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Pedicle screws are used to stabilize vertebrae and treat several types of spinal diseases and injuries. Multiaxial pedicle screws are a type of pedicle screw that increase surgical versatility, but they also increase design complexity. Failure of multiaxial pedicle screws caused by static loading, dynamic loading and fatigue can lead to irreparable damage to the patient. Inappropriate deformation of the multiaxial pedicle screw tulip can cause system failure. Investigation of deformation and stress in these tulips can be employed to optimize multiaxial pedicle screw design. The sensitivity of this matter necessitates precise analyzing and modeling of pedicle screws. In this work, three commercial multiaxial pedicle screw tulips and a newly designed tulip are investigated using finite element analysis. Employing video measuring machine (VMM), tulips are modeled. Afterwards, utilizing ANSYS, static analysis is performed on these models. In the end, stresses and displacements of the models are compared.

Keywords: pedicle screw, multiaxial pedicle screw, finite element analysis, static analysis

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Authors: Yulan Wu

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The spread of fake news on social media has posed significant societal harm to the public and the nation, with its threats spanning various domains, including politics, economics, health, and more. News on social media often covers multiple domains, and existing models studied by researchers and relevant organizations often perform well on datasets from a single domain. However, when these methods are applied to social platforms with news spanning multiple domains, their performance significantly deteriorates. Existing research has attempted to enhance the detection performance of multi-domain datasets by adding single-domain labels to the data. However, these methods overlook the fact that a news article typically belongs to multiple domains, leading to the loss of domain knowledge information contained within the news text. To address this issue, research has found that news records in different domains often use different vocabularies to describe their content. In this paper, we propose a fake news detection framework that combines domain knowledge and expert knowledge. Firstly, it utilizes an unsupervised domain discovery module to generate a low-dimensional vector for each news article, representing domain embeddings, which can retain multi-domain knowledge of the news content. Then, a feature extraction module uses the domain embeddings discovered through unsupervised domain knowledge to guide multiple experts in extracting news knowledge for the total feature representation. Finally, a classifier is used to determine whether the news is fake or not. Experiments show that this approach can improve multi-domain fake news detection performance while reducing the cost of manually labeling domain labels.

Keywords: fake news, deep learning, natural language processing, multiple domains

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Authors: Yu Hung Chiang, Hei Chia Wang

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Nowadays, tourism has become a part of life. Before reserving hotels, customers need some information, which the most important source is online reviews, about hotels to help them make decisions. Due to the dramatic growing of online reviews, it is impossible for tourists to read all reviews manually. Therefore, designing an automatic review analysis system, which summarizes reviews, is necessary for them. The main purpose of the system is to understand the opinion of reviews, which may be positive or negative. In other words, the system would analyze whether the customers who visited the hotel like it or not. Using sentiment analysis methods will help the system achieve the purpose. In sentiment analysis methods, the targets of opinion (here they are called the feature) should be recognized to clarify the polarity of the opinion because polarity of the opinion may be ambiguous. Hence, the study proposes an unsupervised method using Part-Of-Speech pattern and multi-lexicons sentiment analysis to summarize all reviews. We expect this method can help customers search what they want information as well as make decisions efficiently.

Keywords: text mining, sentiment analysis, product feature extraction, multi-lexicons

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Authors: Edwin Christmann, John Hicks

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This meta-analysis compared the mathematics achievement of students who received either traditional instruction or traditional instruction supplemented with computer-assisted instruction (CAI). From the 27 conclusions, an overall mean effect size of 0.236 was calculated, indicating that, on average, students receiving traditional instruction supplemented with CAI attained higher mathematics achievement than did 59.48 percent of those receiving traditional instruction per se.

Keywords: CAI, science, meta-analysis, traditional

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Authors: Rahil Bahrami, Kaveh Ashenayi

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This paper presents a systematic classification and comparative analysis of non-isolated bi-directional DC-DC converters. The increasing demand for efficient energy conversion in diverse applications has spurred the development of various converter topologies. In this study, we categorize bi-directional converters into three distinct classes: Inverting, Non-Inverting, and Interleaved. Each category is characterized by its unique operational characteristics and benefits. Furthermore, a practical comparison is conducted by evaluating the results of simulation of each bi-directional converter. BDCs can be classified into isolated and non-isolated topologies. Non-isolated converters share a common ground between input and output, making them suitable for applications with minimal voltage change. They are easy to integrate, lightweight, and cost-effective but have limitations like limited voltage gain, switching losses, and no protection against high voltages. Isolated converters use transformers to separate input and output, offering safety benefits, high voltage gain, and noise reduction. They are larger and more costly but are essential for automotive designs where safety is crucial. The paper focuses on non-isolated systems.The paper discusses the classification of non-isolated bidirectional converters based on several criteria. Common factors used for classification include topology, voltage conversion, control strategy, power capacity, voltage range, and application. These factors serve as a foundation for categorizing converters, although the specific scheme might vary depending on contextual, application, or system-specific requirements. The paper presents a three-category classification for non-isolated bi-directional DC-DC converters: inverting, non-inverting, and interleaved. In the inverting category, converters produce an output voltage with reversed polarity compared to the input voltage, achieved through specific circuit configurations and control strategies. This is valuable in applications such as motor control and grid-tied solar systems. The non-inverting category consists of converters maintaining the same voltage polarity, useful in scenarios like battery equalization. Lastly, the interleaved category employs parallel converter stages to enhance power delivery and reduce current ripple. This classification framework enhances comprehension and analysis of non-isolated bi-directional DC-DC converters. The findings contribute to a deeper understanding of the trade-offs and merits associated with different converter types. As a result, this work aids researchers, practitioners, and engineers in selecting appropriate bi-directional converter solutions for specific energy conversion requirements. The proposed classification framework and experimental assessment collectively enhance the comprehension of non-isolated bi-directional DC-DC converters, fostering advancements in efficient power management and utilization.The simulation process involves the utilization of PSIM to model and simulate non-isolated bi-directional converter from both inverted and non-inverted category. The aim is to conduct a comprehensive comparative analysis of these converters, considering key performance indicators such as rise time, efficiency, ripple factor, and maximum error. This systematic evaluation provides valuable insights into the dynamic response, energy efficiency, output stability, and overall precision of the converters. The results of this comparison facilitate informed decision-making and potential optimizations, ensuring that the chosen converter configuration aligns effectively with the designated operational criteria and performance goals.

Keywords: bi-directional, DC-DC converter, non-isolated, energy conversion

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Authors: David Ming Liu, Benjamin Hirsch, Bashir Aden

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Syndromic surveillance is to detect or predict disease outbreaks through the analysis of medical sources of data. Using social media data like tweets to do syndromic surveillance becomes more and more popular with the aid of open platform to collect data and the advantage of microblogging text and mobile geographic location features. In this paper, a Syndromic Surveillance Framework is presented with machine learning kernel using tweets data analytics. Influenza and the three cities Abu Dhabi, Al Ain and Dubai of United Arabic Emirates are used as the test disease and trial areas. Hospital cases data provided by the Health Authority of Abu Dhabi (HAAD) are used for the correlation purpose. In our model, Latent Dirichlet allocation (LDA) engine is adapted to do supervised learning classification and N-Fold cross validation confusion matrix are given as the simulation results with overall system recall 85.595% performance achieved.

Keywords: Syndromic surveillance, Tweets, Machine Learning, data mining, Latent Dirichlet allocation (LDA), Influenza

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Authors: Ali A. Ukasha, Majdi F. Elbireki, Mohammad F. Abdullah

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Data security needed in data transmission, storage, and communication to ensure the security. This paper is divided into two parts. This work interests with the color image which is decomposed into red, green and blue channels. The blue and green channels are compressed using 3-levels discrete wavelet transform. The Arnold transform uses to changes the locations of red image channel pixels as image scrambling process. Then all these channels are encrypted separately using the key image that has same original size and are generating using private keys and modulo operations. Performing the X-OR and modulo operations between the encrypted channels images for image pixel values change purpose. The extracted contours from color images recovery can be obtained with accepted level of distortion using single step parallel contour extraction (SSPCE) method. Experiments have demonstrated that proposed algorithm can fully encrypt 2D Color images and completely reconstructed without any distortion. Also shown that the analyzed algorithm has extremely large security against some attacks like salt and pepper and Jpeg compression. Its proof that the color images can be protected with a higher security level. The presented method has easy hardware implementation and suitable for multimedia protection in real time applications such as wireless networks and mobile phone services.

Keywords: SSPCE method, image compression and salt and peppers attacks, bitplanes decomposition, Arnold transform, color image, wavelet transform, lossless image encryption

Procedia PDF Downloads 525

Authors: Keerti Kembhavi, M. R. Archana, V. Anjaneyappa

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Most asphalt pavement condition evaluation use rating frameworks in which asphalt pavement distress is estimated by type, extent, and severity. Rating is carried out by the pavement condition rating (PCR), which is tedious and expensive. This paper presents the development of a low-cost technique for image pavement distress analysis that permits the identification of pothole and cracks. The paper explores the application of image processing tools for the detection of potholes and cracks. Longitudinal cracking and pothole are detected using Fuzzy-C- Means (FCM) and proceeded with the Spectral Theory algorithm. The framework comprises three phases, including image acquisition, processing, and extraction of features. A digital camera (Gopro) with the holder is used to capture pavement distress images on a moving vehicle. FCM classifier and Spectral Theory algorithms are used to compute features and classify the longitudinal cracking and pothole. The Matlab2016Ra Image preparing tool kit utilizes performance analysis to identify the viability of pavement distress on selected urban stretches of Bengaluru city, India. The outcomes of image evaluation with the utilization semi-computerized image handling framework represented the features of longitudinal crack and pothole with an accuracy of about 80%. Further, the detected images are validated with the actual dimensions, and it is seen that dimension variability is about 0.46. The linear regression model y=1.171x-0.155 is obtained using the existing and experimental / image processing area. The R2 correlation square obtained from the best fit line is 0.807, which is considered in the linear regression model to be ‘large positive linear association’.

Keywords: crack detection, pothole detection, spectral clustering, fuzzy-c-means

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Authors: Ibrahim Cicek, Melike Nikbay

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Electrically propelled vehicles, either road or aerial vehicles are studied on contemporarily for their robust maneuvers and cost-efficient transport operations. The main power generating systems of such vehicles electrified by selecting proper components and assembled as e-powertrain. Generally, e-powertrain components selected considering the target performance requirements. Since the main component of propulsion is the drive unit, e-motor control system is subjected to achieve the performance targets. In this paper, the optimization of e-motor control parameters studied by Integral Squared Method (ISE). The overall aim is to minimize power consumption of such vehicles depending on mission profile and maintaining smooth maneuvers for passenger comfort. The sought-after values of control parameters are computed using the Optimal Control Theory. The system is modeled as a closed-loop linear control system with calibratable parameters.

Keywords: optimization, e-powertrain, optimal control, electric vehicles

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Authors: Liping Yang, Curran Crawford, Jr. Ren, Zhengyi Ren

Abstract:

Considering the cost evaluation and the stress analysis, a fuzzy satisfactory optimization (FSO) method has been developed for a hybrid composite flywheel. To evaluate the cost, the cost coefficients of the flywheel components are obtained through calculating the weighted sum of the scores of the material manufacturability, the structure character, and the material price. To express the satisfactory degree of the energy, the cost, and the mass, the satisfactory functions are proposed by using the decline function and introducing a satisfactory coefficient. To imply the different significance of the objectives, the object weight coefficients are defined. Based on the stress analysis of composite material, the circumferential and radial stresses are considered into the optimization formulation. The simulations of the FSO method with different weight coefficients and storage energy density optimization (SEDO) method of a flywheel are contrasted. The analysis results show that the FSO method can satisfy different requirements of the designer and the FSO method with suitable weight coefficients can replace the SEDO method.

Keywords: flywheel energy storage, fuzzy, optimization, stress analysis

Procedia PDF Downloads 353

Authors: Khader S. Al-Aidmat, Venus W. Samawi

Abstract:

Blurred vision in the misty atmosphere is essential problem which needs to be resolved. To solve this problem, we developed a technique to restore foggy degraded image from its original version using Back-propagation neural network (BP-NN). The suggested technique is based on mapping between foggy scene and its corresponding original scene. Seven different approaches are suggested based on type of features used in image restoration. Features are extracted from spatial and spatial-frequency domain (using DCT). Each of these approaches comes with its own BP-NN architecture depending on type and number of used features. The weight matrix resulted from training each BP-NN represents a fog filter. The performance of these filters are evaluated empirically (using PSNR), and perceptually. By comparing the performance of these filters, the effective features that suits BP-NN technique for restoring foggy images is recognized. This system proved its effectiveness and success in restoring moderate foggy images.

Keywords: artificial neural network, discrete cosine transform, feed forward neural network, foggy image restoration

Procedia PDF Downloads 386

Authors: Veronica Diaz Quezada

Abstract:

In this article, the performance and errors are featured and analysed in the limit problems solving of a real-valued function, in correspondence to competency-based education in engineering careers, in the south of Chile. The methodological component is contextualised in a qualitative research, with a descriptive and explorative design, with elaboration, content validation and application of quantitative instruments, consisting of two parallel forms of open answer tests, based on limit application problems. The mathematical competences and errors made by students from five engineering careers from a public University are identified and characterized. Results show better performance only to solve routine-context problem-solving competence, thus they are oriented towards a rational solution or they use a suitable problem-solving method, achieving the correct solution. Regarding errors, most of them are related to techniques and the incorrect use of theorems and definitions of real-valued function limits of real variable.

Keywords: engineering education, errors, limits, mathematics competences, problem solving

Procedia PDF Downloads 156

Authors: Mitra Arami

Abstract:

The main objective of this paper is to identify the factors that influence Kuwaiti consumers’ behavior towards online shopping. A survey was conducted among B2C e-commerce customers using a structured self-administered questionnaire. The findings of this study show that B2C e-commerce customer behavior in Kuwait is strongly influenced by customer entertainment but weakly influenced by customer trust. While the overall research project involves exploratory research using mixed methods, the focus of this paper is on a quantitative analysis of responses obtained from a survey of Kuwaiti customers, with the design of the questionnaire instrument being based on the findings of a qualitative analysis. The main findings of the analysis include a list of key factors that affect Kuwait online shoppers, and quantitative indications of the relative strengths of the various relationships. This study provides a basis for further research and more in depth studies to find the scope of online shopping in Kuwait especially, the influence of hedonic and utilitarian motivations on user engagement.

Keywords: e-commerce, online shopping, customer behavior, quantitative analysis, Kuwait

Procedia PDF Downloads 385