Search results for: performance assessment

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: 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: Mohammed A. Elhaj, Jamal S. Yassin

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Owing to the lack of synchronization between the solar energy availability and the heat demands in a specific application, the energy storing sub-system is necessary to maintain the continuity of thermal process. The present work is dealing with an active solar heating storing system in which an air solar collector is connected to storing unit where this energy is distributed and provided to the heated space in a controlled manner. The solar collector is a box type absorber where the air flows between a number of vanes attached between the collector absorber and the bottom plate. This design can improve the efficiency due to increasing the heat transfer area exposed to the flowing air, as well as the heat conduction through the metal vanes from the top absorbing surface. The storing unit is a packed bed type where the air is coming from the air collector and circulated through the bed in order to add/remove the energy through the charging / discharging processes, respectively. The major advantage of the packed bed storage is its high degree of thermal stratification. Numerical solution of the packed bed energy storage is considered through dividing the bed into a number of equal segments for the bed particles and solved the energy equation for each segment depending on the neighbor ones. The studied design and performance parameters in the developed simulation model including, particle size, void fraction, etc. The final results showed that the collector efficiency was fluctuated between 55%-61% in winter season (January) under the climatic conditions of Misurata in Libya. Maximum temperature of 52ºC is attained at the top of the bed while the lower one is 25ºC at the end of the charging process of hot air into the bed. This distribution can satisfy the required load for the most house heating in Libya.

Keywords: solar energy, thermal process, performance, collector, packed bed, numerical analysis, simulation

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Authors: Sogol Zarafshan

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The present study aimed to identify important factors affecting the effectiveness of quality control circles in a hospital, as well as rank them using a combination of fuzzy VIKOR and Grey Relational Analysis (GRA). The study population consisted of five academic members and five experts in the field of nursing working in a hospital, who were selected using a purposive sampling method. Also, a sample of 107 nurses was selected through a simple random sampling method using their employee codes and the random-number table. The required data were collected using a researcher-made questionnaire which consisted of 12 factors. The validity of this questionnaire was confirmed through giving the opinions of experts and academic members who participated in the present study, as well as performing confirmatory factor analysis. Its reliability also was verified (α=0.796). The collected data were analyzed using SPSS 22.0 and LISREL 8.8, as well as VIKOR–GRA and IPA methods. The results of ranking the factors affecting the effectiveness of quality control circles showed that the highest and lowest ranks were related to ‘Managers’ and supervisors’ support’ and ‘Group leadership’. Also, the highest hospital performance was for factors such as ‘Clear goals and objectives’ and ‘Group cohesiveness and homogeneity’, and the lowest for ‘Reward system’ and ‘Feedback system’, respectively. The results showed that although ‘Training the members’, ‘Using the right tools’ and ‘Reward system’ were factors that were of great importance, the organization’s performance for these factors was poor. Therefore, these factors should be paid more attention by the studied hospital managers and should be improved as soon as possible.

Keywords: Quality control circles, Fuzzy VIKOR, Grey Relational Analysis, Importance–Performance Analysis

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Authors: Grace O. Akinlade, Danjuma D. Maza, Oluwakemi O. Olawolu, Delight O. Babalola, John A. O. Oyekunle, Joshua O. Ojo

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The Mokuro area of Ile-Ife, South West Nigeria, was well known for gold mining in the past (about twenty years ago). However, the place has since been reclaimed and converted to residential area without any environmental risk assessment of the impact of the mining tailings on the environment. Soil, water, and plant samples were collected from 4 different locations around the mine-turned-residential area. Soil samples were pulverized and sieved into finer particles, while the plant samples were dried and pulverized. All the samples were digested and analyzed for As, Pb, Cd, and Zn using atomic absorption spectroscopy (AAS). From the analysis results, the hazard index (HI) was then calculated for the metals. The soil and plant samples were air dried and pulverized, then weighed, after which the samples were packed into special and properly sealed containers to prevent radon gas leakage. After the sealing, the samples were kept for 28 days to attain secular equilibrium. The concentrations of 40K, 238U, and 232Th in the samples were measured using a cesium iodide (CsI) spectrometer and URSA software. The AAS analysis showed that As, Pb, Cd (Toxic metals), and Zn (essential trace metals) are in concentrations lower than permissible limits in plants and soil samples, while the water samples had concentrations higher than permissible limits. The calculated health indices (HI) show that HI for water is >1 and that of plants and soil is <1. Gamma spectrometry result shows high levels of activity concentrations above the recommended limits for all the soil and plant samples collected from the area. Only the water samples have activity concentrations below the recommended limit. Consequently, the absorbed dose, annual effective dose, and excess lifetime cancer risk are all above the recommended safe limit for all the samples except for water samples. In conclusion, all the samples collected from the area are either contaminated with toxic metals or they pose radiological hazards to the consumers. Further detailed study is therefore recommended in order to be able to advise the residents appropriately.

Keywords: toxic metals, gamma spectrometry, Ile-Ife, radiological hazards, gold mining

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

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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

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Authors: Veronica Diaz Quezada

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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

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Authors: Domenico Panno, Antonino D’amico, Hamed Jafargholi

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This paper presents an experimental analysis of the performance of a system for freezing fish products equipped with a modulating vapour injection scroll compressor operating with R448A refrigerant. Freezing is a critical process for the preservation of seafood products, as it influences quality, food safety, and environmental sustainability. The use of a modulating scroll compressor with vapour injection, associated with the R448A refrigerant, is proposed as a solution to optimize the performance of the system, reducing energy consumption and mitigating the environmental impact. The stream injection modulating scroll compressor represents an advanced technology that allows you to adjust the compressor capacity based on the actual cooling needs of the system. Vapour injection allows the optimization of the refrigeration cycle, reducing the evaporation temperature and improving the overall efficiency of the system. The use of R448A refrigerant, with a low Global Warming Potential (GWP), is part of an environmental sustainability perspective, helping to reduce the climate impact of the system. The aim of this research was to evaluate the performance of the system through a series of experiments conducted on a pilot plant for the freezing of fish products. Several operational variables were monitored and recorded, including evaporation temperature, condensation temperature, energy consumption, and freezing time of seafood products. The results of the experimental analysis highlighted the benefits deriving from the use of the modulating vapour injection scroll compressor with the R448A refrigerant. In particular, a significant reduction in energy consumption was recorded compared to conventional systems. The modulating capacity of the compressor made it possible to adapt the cold production to variations in the thermal load, ensuring optimal operation of the system and reducing energy waste. Furthermore, the use of an electronic expansion valve highlighted greater precision in the control of the evaporation temperature, with minimal deviation from the desired set point. This helped ensure better quality of the final product, reducing the risk of damage due to temperature changes and ensuring uniform freezing of the fish products. The freezing time of seafood has been significantly reduced thanks to the configuration of the entire system, allowing for faster production and greater production capacity of the plant. In conclusion, the use of a modulating vapour injection scroll compressor operating with R448A has proven effective in improving the performance of a system for freezing fish products. This technology offers an optimal balance between energy efficiency, temperature control, and environmental sustainability, making it an advantageous choice for food industries.

Keywords: scroll compressor, vapor injection, refrigeration system, EER

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Authors: Fikru Fentaw Abera

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Climate changes alter regional hydrologic conditions and results in a variety of impacts on water resource systems. Such hydrologic changes will affect almost every aspect of human well-being. The goal of this paper is to assess the impact of climate change on the hydrology of Upper Guder catchment located in northwest of Ethiopia. The GCM derived scenarios (HadCM3 A2a & B2a SRES emission scenarios) experiments were used for the climate projection. The statistical downscaling model (SDSM) was used to generate future possible local meteorological variables in the study area. The down-scaled data were then used as input to the soil and water assessment tool (SWAT) model to simulate the corresponding future stream flow regime in Upper Guder catchment of the Abay River Basin. A semi distributed hydrological model, SWAT was developed and Generalized Likelihood Uncertainty Estimation (GLUE) was utilized for uncertainty analysis. GLUE is linked with SWAT in the Calibration and Uncertainty Program known as SWAT-CUP. Three benchmark periods simulated for this study were 2020s, 2050s and 2080s. The time series generated by GCM of HadCM3 A2a and B2a and Statistical Downscaling Model (SDSM) indicate a significant increasing trend in maximum and minimum temperature values and a slight increasing trend in precipitation for both A2a and B2a emission scenarios in both Gedo and Tikur Inch stations for all three bench mark periods. The hydrologic impact analysis made with the downscaled temperature and precipitation time series as input to the hydrological model SWAT suggested for both A2a and B2a emission scenarios. The model output shows that there may be an annual increase in flow volume up to 35% for both emission scenarios in three benchmark periods in the future. All seasons show an increase in flow volume for both A2a and B2a emission scenarios for all time horizons. Potential evapotranspiration in the catchment also will increase annually on average 3-15% for the 2020s and 7-25% for the 2050s and 2080s for both A2a and B2a emissions scenarios.

Keywords: climate change, Guder sub-basin, GCM, SDSM, SWAT, SWAT-CUP, GLUE

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Authors: Hyun-Woo Cho

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Earlier detection of incipient abnormal operations in terms of plant-wide process management is quite necessary in order to improve product quality and process safety. And generating warning signals or alarms for operating personnel plays an important role in process automation and intelligent plant health monitoring. Various methodologies have been developed and utilized in this area such as expert systems, mathematical model-based approaches, multivariate statistical approaches, and so on. This work presents a nonlinear empirical monitoring methodology based on the real-time analysis of massive process data. Unfortunately, the big data includes measurement noises and unwanted variations unrelated to true process behavior. Thus the elimination of such unnecessary patterns of the data is executed in data processing step to enhance detection speed and accuracy. The performance of the methodology was demonstrated using simulated process data. The case study showed that the detection speed and performance was improved significantly irrespective of the size and the location of abnormal events.

Keywords: detection, monitoring, process data, noise

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Authors: Debarag Banerjee, Pooja Singh, Arjun Avadhanam, Saksham Srivastava

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Autonomous conversational agents, i.e., chatbots, are becoming an increasingly common mechanism for enterprises to provide support to customers and partners. In order to rate chatbots, especially ones powered by Generative AI tools like Large Language Models (LLMs), we need to be able to accurately assess their performance. This is where chatbot benchmarking becomes important. In this paper, authors propose the use of a benchmark that they call the E2E (End to End) benchmark and show how the E2E benchmark can be used to evaluate the accuracy and usefulness of the answers provided by chatbots, especially ones powered by LLMs. The authors evaluate an example chatbot at different levels of sophistication based on both our E2E benchmark as well as other available metrics commonly used in the state of the art and observe that the proposed benchmark shows better results compared to others. In addition, while some metrics proved to be unpredictable, the metric associated with the E2E benchmark, which uses cosine similarity, performed well in evaluating chatbots. The performance of our best models shows that there are several benefits of using the cosine similarity score as a metric in the E2E benchmark.

Keywords: chatbot benchmarking, end-to-end (E2E) benchmarking, large language model, user centric evaluation.

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Authors: Frazer G. Thompson

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The 1990s in America ended with a mass-school-shooting (at least four killed by gunfire excluding the perpetrator(s)) at Columbine High School in Littleton, Colorado. Post-event, many demanded that government and civilian experts develop a ‘profile’ of the potential school shooter in order to identify and preempt likely future acts of violence. This grounded theory research study seeks to explore the validity of the original hypotheses proposed by the Federal Bureau of Investigation (FBI) in 2000, as it relates to the commonality of disclosure by perpetrators of mass-school-shootings, by evaluating fourteen mass-school-shooting events between 2000 and 2019 at locations around the United States. Methods: The strategy of inquiry seeks to investigate case files, public records, witness accounts, and available psychological profiles of the shooter. The research methodology is inclusive of one-on-one interviews with members of the FBI’s Critical Incident Response Group seeking perspective on commonalities between individuals; specifically, disclosure of intent pre-event. Results: The research determined that school shooters do not ‘unfailingly’ notify others of their plans. However, in nine of the fourteen mass-school-shooting events analyzed, the perpetrator did inform the third party of their intent pre-event in some form of written, oral, or electronic communication. In the remaining five instances, the so-called ‘red-flag’ indicators of the potential for an event to occur were profound, and unto themselves, might be interpreted as notification to others of an imminent deadly threat. Conclusion: Data indicates that conclusions drawn in the FBI’s threat assessment perspective published in 2000 are relevant and current. There is evidence that despite potential ‘red-flag’ indicators which may or may not include a variety of other characteristics, perpetrators of mass-school-shooting events are likely to share their intentions with others through some form of direct or indirect communication. More significantly, implications of this research might suggest that society is often informed of potential danger pre-event but lacks any equitable means by which to disseminate, prevent, intervene, or otherwise act in a meaningful way considering said revelation.

Keywords: columbine, FBI profiling, guns, mass shooting, mental health, school violence

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Authors: Vithaya Intaraphimol

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This research investigates the effect of financial accounting information and non-financial accounting reports on corporate credibility via strength of board of directors and market environment volatility as moderating effect. Data in this research is collected by questionnaire form non-financial companies listed on the Stock Exchange of Thailand. Multiple regression statistic technique is chosen for analyzing the data. The empirical results find that firms with greater financial accounting information reports and non-financial accounting information reports will gain greater corporate credibility. Therefore, the corporate reporting has the value for the firms. Moreover, the strength of board of directors will positively moderate the financial and non-financial accounting information reports and corporate credibility relationship. Whereas, market environment volatility will negatively moderate the financial and nonfinancial accounting information reports and corporate credibility relationship.

Keywords: corporate credibility, financial and non-financial reports, firms performance, economics

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Authors: Mahmoud Khamaira, Ahmed Abu-Siada, Yasser Alharbi

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Doubly Fed Induction Generators (DFIGs) are currently extensively used in variable speed wind power plants due to their superior advantages that include reduced converter rating, low cost, reduced losses, easy implementation of power factor correction schemes, variable speed operation and four quadrants active and reactive power control capabilities. On the other hand, DFIG sensitivity to grid disturbances, especially for voltage sags represents the main disadvantage of the equipment. In this paper, a coil is proposed to be integrated within the DFIG converters to improve the overall performance of a DFIG-based wind energy conversion system (WECS). The charging and discharging of the coil are controlled by controlling the duty cycle of the switches of the dc-dc chopper. Simulation results reveal the effectiveness of the proposed topology in improving the overall performance of the WECS system under study.

Keywords: doubly fed induction generator, coil, wind energy conversion system, converter topology

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Authors: Adriana Diaz Triana, Wolfgang Wimmer, Sebastian Glaser, Rainer Pamminger

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SUSTAINEXT is a pioneer initiative in Extremadura, Spain under the EU Biobased industries. SUSTANEXT will scale-up and validate an industrial facility to produce botanical extracts, based on three key pillars. First, the whole valorization of bio-based feedstocks with a zero-waste and zero-emissions ambition. SUSTAINEXT will be deployed with six feedstocks. Three medicinal and aromatic plants (Rosemary, Chamomile, and Lemon verbena) will be locally sourced from disused tobacco fields with installed agri-voltaics; and three underexploited agro-industrial side streams will be further valorized (Olive, artichoke-cardoon, and pomegranate). Second, a dynamic, analytical biorefinery (DYANA) will isolate polyphenol and tri-terpenes from feedstocks in a disruptive and circular way. SUSTAINEXT explores 12 valorization routes (VRs) to extract and purify 46 functional ingredients, of which 13 are new in the market and 12 are newly produced in Europe. Third, the integrated and versatile value chain engages all actors, from feedstocks suppliers to extract users in the industries of food, animal feed, nutraceuticals, cosmetics, chemical performance, soil enhancers and fertilizers. This paper addresses SUTAINEXT activities towards zero impacts and full regulatory compliance. A comprehensive Life Cycle Thinking approach is proposed, with four complementary assessments running iteratively along the project duration (4,5 years). These are the Life Cycle Cost (LCCA), Life Cycle (LCA), Social Life Cycle (S-LCA) and Circularity (CA) assessments. The LCA will help evaluate the feedstock suitability parameters and intrinsic characteristics that quantify the feedstock´s grade for a determined use, and the feedstock´s suitability index for a specific VR. The LCA will also study the emissions, land use change, energy generation and consumption, and other environmental aspects and impacts of the VRs, to identify the most resource efficient and less impactful distribution of products from the circular biorefinery model used in SUSTAINEXT. Challenges to complete the LCA include the definition of the system boundaries, carrying out a robust inventory, and the proper allocation of impacts to the different VRs.

Keywords: biorefinery, botanical extracts, life cycle assessment, valorization routes.

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Authors: Mehmet Savsar

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One of the most important factors affecting power plant performance is the reliability of the turbine units operated under different conditions. Reliability directly affects plant availability and performance. Therefore, it is very important to be able to analyze turbine units, as well as power plant system reliability and availability under various operational conditions. In this paper, data related to power station failures are collected and analyzed in detail for all power stations in the state of Kuwait. Failures are characterized and categorized. Reliabilities of various power plants are analyzed and availabilities are quantified. Based on calculated availabilities of all installed power plants, actual power output is estimated. Furthermore, based on the past 15 years of data, power consumption trend is determined and the demand for power in the future is forecasted. Estimated power output is compared to the forecasted demand in order to determine the need for future capacity expansion.

Keywords: power plants, reliability, availability, capacity, preventive maintenance, forecasting

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Authors: Justin Reverdi, Sixin Zhang, Serge Gratton, Said Aoues, Thomas Pellegrini

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In Vapor Cycle Systems, the flow sensor plays a key role in different monitoring and control purposes. However, physical sensors can be expensive, inaccurate, heavy, cumbersome, or highly sensitive to vibrations, which is especially problematic when embedded into an aircraft. The conception of a virtual sensor based on other standard sensors is a good alternative. In this paper, a data-driven model using a Convolutional Neural Network is proposed to estimate the flow of the compressor. To fit the model to our dataset, we tested different loss functions. We show in our application that a Dynamic Time Warping based loss function called DILATE leads to better dynamical performance than the vanilla mean squared error (MSE) loss function. DILATE allows choosing a trade-off between static and dynamic performance.

Keywords: deep learning, dynamic time warping, vapor cycle system, virtual sensor

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Authors: A. Lovascio, A. D’Orazio, V. Centonze

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From several years till now the aerospace industry is developing more and more small satellites for Low-Earth Orbit (LEO) missions. Such satellites have a low cost of making and launching since they have a size and weight smaller than other types of satellites. However, because of size limitations, small satellites need integrated electronic equipment based on digital logic. Moreover, the LEOs require telecommunication modules with high throughput to transmit to earth a big amount of data in a short time. In order to meet such requirements, in this paper we propose a Telemetry, Tracking & Command module optimized through the use of the Commercial Off-The-Shelf components. The proposed approach exploits the major flexibility offered by these components in reducing costs and optimizing the performance. The method has been applied in detail for the design of the front-end receiver, which has a low noise figure (1.5 dB) and DC power consumption (smaller than 2 W). Such a performance is particularly attractive since it allows fulfilling the energy budget stringent constraints that are typical for LEO small platforms.

Keywords: COTS, LEO, small-satellite, TT&C

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Authors: Yang Liu

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Reinforced concrete (RC) shear wall structures are one of the most popular and efficient structural forms for medium- and high-rise buildings to resist the action of earthquake loading. Thus, it is of great significance to evaluate the seismic demands of the RC shear walls. In this paper, the application of the extend spectrum-based pushover analysis (ESPA) method on the seismic evaluation of the shear wall structure is presented. The ESPA method includes a nonlinear consecutive pushover analysis procedure and a linear elastic modal response analysis procedure to consider the combination of modes in both elastic and inelastic cases. It is found from the results of case study that the ESPA method can predict the seismic performance of shear wall structures, including internal forces and deformations very well.

Keywords: reinforced concrete shear wall, seismic performance, high mode effect, nonlinear analysis

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Authors: Vinh Duy Cao, Shima Pilehvar, Anna M. Szczotok, Anna-Lena Kjøniksen

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The total energy consumption is dramatically increasing on over the world, especially for building energy consumption where a significant proportion of energy is used for heating and cooling purposes. One of the solutions to reduce the energy consumption for the building is to improve construction techniques and enhance material technology. Recently, microcapsulated phase change materials (MPCM) with high energy storage capacity within the phase transition temperature of the materials is a potential method to conserve and save energy. A new composite materials with high energy storage capacity by mixing MPCM into concrete for passive building technology is the promising candidate to reduce the energy consumption. One of the most untilized building materials for mixing with MPCM is Portland cement concrete. However, the emission of carbon dioxide (CO2) due to producing cement which plays the important role in the global warming is the main drawback of PCC. Accordingly, an environmentally friendly building material, geopolymer, which is synthesized by the reaction between the industrial waste material (aluminosilicate) and a strong alkali activator, is a potential materials to mixing with MPCM. Especially, the effect of MPCM on the thermal and mechanical properties of geopolymer concrete (GPC) is very limited. In this study, high thermal energy storage capacity materials were fabricated by mixing MPCM into geopolymer concrete. This article would investigate the effect of MPCM concentration on thermal and mechanical properties of GPC. The target is to balance the effect of MPCM on improving the thermal performance and maintaining the compressive strength of the geopolymer concrete at an acceptable level for building application.

Keywords: microencapsulated phase change materials, geopolymer concrete, energy storage capacity, thermal performance

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Authors: Ch. Sridevi, S. P. Shailender Kumar, B. Gurudayal, A. Chalapathi Rao, K. Koteswara Rao, P. Srinivasulu

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System reliability and system availability evaluation plays a crucial role in ensuring the seamless operation of complex satellite data reception system with consistent performance for longer periods. This paper presents a novel approach for the same using a case study on one of the antenna systems at satellite data reception ground station in India. The methodology involves analyzing system's components, their failure rates, system's architecture, generation of logical reliability block diagram model and estimating the reliability of the system using the component level mean time between failures considering exponential distribution to derive a baseline estimate of the system's reliability. The model is then validated with collected system level field failure data from the operational satellite data reception systems that includes failure occurred, failure time, criticality of the failure and repair times by using statistical techniques like median rank, regression and Weibull analysis to extract meaningful insights regarding failure patterns and practical reliability of the system and to assess the accuracy of the developed reliability model. The study mainly focused on identification of critical units within the system, which are prone to failures and have a significant impact on overall performance and brought out a reliability model of the identified critical unit. This model takes into account the interdependencies among system components and their impact on overall system reliability and provides valuable insights into the performance of the system to understand the Improvement or degradation of the system over a period of time and will be the vital input to arrive at the optimized design for future development. It also provides a plug and play framework to understand the effect on performance of the system in case of any up gradations or new designs of the unit. It helps in effective planning and formulating contingency plans to address potential system failures, ensuring the continuity of operations. Furthermore, to instill confidence in system users, the duration for which the system can operate continuously with the desired level of 3 sigma reliability was estimated that turned out to be a vital input to maintenance plan. System availability and station availability was also assessed by considering scenarios of clash and non-clash to determine the overall system performance and potential bottlenecks. Overall, this paper establishes a comprehensive methodology for reliability and availability analysis of complex satellite data reception systems. The results derived from this approach facilitate effective planning contingency measures, and provide users with confidence in system performance and enables decision-makers to make informed choices about system maintenance, upgrades and replacements. It also aids in identifying critical units and assessing system availability in various scenarios and helps in minimizing downtime and optimizing resource allocation.

Keywords: exponential distribution, reliability modeling, reliability block diagram, satellite data reception system, system availability, weibull analysis

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Authors: Henar Martín-Sanz García, Eleni Chatzi, Eugen Brühwiler

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Located on the shore of Geneva Lake, in Switzerland, the Chillon Viaducts are two parallel structures consisted of post-tensioned concrete box girders, with a total length of 2 kilometers and 100m spans. Built in 1969, the bridges currently accommodate a traffic load of 50.000 vehicles per day, thereby holding a key role both in terms of historic value as well as socio-economic significance. Although several improvements have been carried out in the past two decades, recent inspections demonstrate an Alkali-Aggregate reaction in the concrete deck and piers reducing the concrete strength. In order to prevent further expansion of this issue, a layer of 40 mm of Ultra High Performance Fiber Reinforced cement-based Composite (UHPFRC) (incorporating rebars) was casted over the slabs, acting as a waterproof membrane and providing significant increase in resistance of the bridge structure by composite UHPFRC – RC composite action in particular of the deck slab. After completing the rehabilitation works, a Structural Monitoring campaign was installed on the deck slab in one representative span, based on accelerometers, strain gauges, thermal and humidity sensors. This campaign seeks to reveal information on the behavior of UHPFRC-concrete composite systems, such as increase in stiffness, fatigue strength, durability and long-term performance. Consequently, the structural monitoring is expected to last for at least three years. A first insight of the analyzed results from the initial months of measurements is presented herein, along with future improvements or necessary changes on the deployment.

Keywords: composite materials, rehabilitation, structural health monitoring, UHPFRC

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Authors: Luis C. Parra

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The significant wave height prediction is an issue of great interest in the field of coastal activities because of the non-linear behavior of the wave height and its complexity of prediction. This study aims to present a machine learning model to forecast the significant wave height of the oceanographic wave measuring buoys anchored at Mooloolaba of the Queensland Government Data. Modeling was performed by a multilayer perceptron neural network-genetic algorithm (GA-MLP), considering Relu(x) as the activation function of the MLPNN. The GA is in charge of optimized the MLPNN hyperparameters (learning rate, hidden layers, neurons, and activation functions) and wrapper feature selection for the window width size. Results are assessed using Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The GAMLPNN algorithm was performed with a population size of thirty individuals for eight generations for the prediction optimization of 5 steps forward, obtaining a performance evaluation of 0.00104 MSE, 0.03222 RMSE, 0.02338 MAE, and 0.71163% of MAPE. The results of the analysis suggest that the MLPNNGA model is effective in predicting significant wave height in a one-step forecast with distant time windows, presenting 0.00014 MSE, 0.01180 RMSE, 0.00912 MAE, and 0.52500% of MAPE with 0.99940 of correlation factor. The GA-MLP algorithm was compared with the ARIMA forecasting model, presenting better performance criteria in all performance criteria, validating the potential of this algorithm.

Keywords: significant wave height, machine learning optimization, multilayer perceptron neural networks, evolutionary algorithms

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Authors: Sasitharan Ambicapathy, J. Vignesh, P. Sivaraj, Godfrey Derek Sams, K. Sabarinath, V. R. Sanal Kumar

Abstract:

The external flow simulation of a flying car at take off phase is a daunting task owing to the fact that the prediction of the transient unsteady flow features during its deployment phase is very complex. In this paper 3D numerical simulations of external flow of Ferrari F430 proposed flying car with different NACA 9618 rectangular wings have been carried. Additionally, the aerodynamics characteristics have been generated for optimizing its geometry for achieving the minimum take off velocity with better overall performance in both road and air. The three-dimensional standard k-omega turbulence model has been used for capturing the intrinsic flow physics during the take off phase. In the numerical study, a fully implicit finite volume scheme of the compressible, Reynolds-Averaged, Navier-Stokes equations is employed. Through the detailed parametric analytical studies we have conjectured that Ferrari F430 flying car facilitated with high wings having three different deployment histories during the take off phase is the best choice for accomplishing its better performance for the commercial applications.

Keywords: aerodynamics of flying car, air taxi, negative lift, roadable airplane

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Authors: Hanna Mamo Ergando

Abstract:

Climate change is already having a severe influence on agriculture, affecting crop yields, the nutritional content of main grains, and livestock productivity. Significant adaptation investments will be necessary to sustain existing yields and enhance production and food quality to fulfill demand. Climate-smart agriculture (CSA) provides numerous potentials in this regard, combining a focus on enhancing agricultural output and incomes while also strengthening resilience and responding to climate change. To improve agriculture production and productivity, the Ethiopian government has adopted and implemented a series of strategies, including the recent agricultural cluster farming that is practiced as an effort to change, improve, and transform subsistence farming to modern, productive, market-oriented, and climate-smart approach through farmers production cluster. Besides, greater attention and focus have been given to wheat production and productivity by the government, and wheat is the major crop grown in cluster farming. Therefore, the objective of this assessment was to examine various opportunities and challenges farmers face in a cluster farming system. A qualitative research approach was used to generate primary and secondary data. Respondents were chosen using the purposeful sampling technique. Accordingly, experts from the Federal Ministry of Agriculture, the Ethiopian Agricultural Transformation Institute, the Ethiopian Agricultural Research Institute, and the Ethiopian Environment Protection Authority were interviewed. The assessment result revealed that farming in clusters is an economically viable technique for sustaining small, resource-limited, and socially disadvantaged farmers' agricultural businesses. The method assists farmers in consolidating their products and delivering them in bulk to save on transportation costs while increasing income. Smallholders' negotiating power has improved as a result of cluster membership, as has knowledge and information spillover. The key challenges, on the other hand, were identified as a lack of timely provision of modern inputs, insufficient access to credit services, conflict of interest in crop selection, and a lack of output market for agro-processing firms. Furthermore, farmers in the cluster farming approach grow wheat year after year without crop rotation or diversification techniques. Mono-cropping has disadvantages because it raises the likelihood of disease and insect outbreaks. This practice may result in long-term consequences, including soil degradation, reduced biodiversity, and economic risk for farmers. Therefore, the government must devote more resources to addressing the issue of environmental sustainability. Farmers' access to complementary services that promote production and marketing efficiencies through infrastructure and institutional services has to be improved. In general, the assessment begins with some hint that leads to a deeper study into the efficiency of the strategy implementation, upholding existing policy, and scaling up good practices in a sustainable and environmentally viable manner.

Keywords: cluster farming, smallholder farmers, wheat, challenges, opportunities

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Authors: Zaki Ahmad Khan, Jamshed Siddiqui, Abdus Samad

Abstract:

The Scheduling and mapping of tasks on a set of processors is considered as a critical problem in parallel and distributed computing system. This paper deals with the problem of dynamic scheduling on a special type of multiprocessor architecture known as Linear Crossed Cube (LCQ) network. This proposed multiprocessor is a hybrid network which combines the features of both linear type of architectures as well as cube based architectures. Two standard dynamic scheduling schemes namely Minimum Distance Scheduling (MDS) and Two Round Scheduling (TRS) schemes are implemented on the LCQ network. Parallel tasks are mapped and the imbalance of load is evaluated on different set of processors in LCQ network. The simulations results are evaluated and effort is made by means of through analysis of the results to obtain the best solution for the given network in term of load imbalance left and execution time. The other performance matrices like speedup and efficiency are also evaluated with the given dynamic algorithms.

Keywords: dynamic algorithm, load imbalance, mapping, task scheduling

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Authors: Pavan K. Rallabandi, Kailash C. Patidar

Abstract:

In this paper, we present an optimization technique or a learning algorithm using the hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov models (HMMs). In order to improve the sequence or pattern recognition/ classification performance by applying a hybrid/neural symbolic approach, a gradient descent learning algorithm is developed using the Real Time Recurrent Learning of Recurrent Neural Network for processing the knowledge represented in trained Hidden Markov Models. The developed hybrid algorithm is implemented on automata theory as a sample test beds and the performance of the designed algorithm is demonstrated and evaluated on learning the deterministic finite state automata.

Keywords: hybrid systems, hidden markov models, recurrent neural networks, deterministic finite state automata

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Authors: Emanuele Bonamente, Andrea Aquino

Abstract:

This study presents an innovative design for geothermal heat pumps with the goal of maximizing the system efficiency (COP - Coefficient of Performance), reducing the soil use (e.g. length/depth of geothermal boreholes) and initial investment costs. Based on experimental data obtained from a two-year monitoring of a working prototype implemented for a commercial building in the city of Perugia, Italy, an upgrade of the system is proposed and the performance is evaluated via CFD simulations. The prototype was designed to include a thermal heat storage (i.e. water), positioned between the boreholes and the heat pump, acting as a flywheel. Results from the monitoring campaign show that the system is still capable of providing the required heating and cooling energy with a reduced geothermal installation (approx. 30% of the standard length). In this paper, an optimization of the system is proposed, re-designing the heat storage to include phase change materials (PCMs). Two stacks of PCMs, characterized by melting temperatures equal to those needed to maximize the system COP for heating and cooling, are disposed within the storage. During the working cycle, the latent heat of the PCMs is used to heat (cool) the water used by the heat pump while the boreholes independently cool (heat) the storage. The new storage is approximately 10 times smaller and can be easily placed close to the heat pump in the technical room. First, a validation of the CFD simulation of the storage is performed against experimental data. The simulation is then used to test possible alternatives of the original design and it is finally exploited to evaluate the PCM-storage performance for two different configurations (i.e. single- and double-loop systems).

Keywords: geothermal heat pump, phase change materials (PCM), energy storage, renewable energies

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Authors: hadi kargar, bahador shabani

Abstract:

In this article, 16 cylinder motor combustion CFD modeling with a diameter of 165 mm and 195 mm along the way to help the FIRE software to optimize its function to work. A three-dimensional model of the processes that formed inside the cylinder made that involves mixing the fuel and air, ignition and spraying. In this three-dimensional model, all chemical species, density of air fuel spraying and spray with full profile intended to detailed results from mixing the fuel and air, igniting the ignition advance, spray, and mixed media in different times and get fit by moving the piston. Optimal selection of the model for the shape of the piston and spraying fuel specifications (including the management of spraying, the number of azhneh hole, start time of spraying and spraying angle) to achieve the best fuel consumption and minimal pollution. The spray hole 6 and 7 in three different configurations with five spraying and gives the best geometry and various performances in the simulation. 6 hole spray angle, finally spraying 72.5 degrees and two forms of spraying a better performance in comparison with other items of their own.

Keywords: spray, FIRE, CFD, optimize, diesel engine

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Authors: Alaa H. J. Al-Rkaby, A. Chegenizadeh, H. R. Nikraz

Abstract:

The technique of reinforcing soil is an efficient, reliable and cost-effective alternative way for improving the performance of soil in civil engineering applications. Despite the anisotropic states of stresses induced within soil elements by many geotechnical structures such as footings, highways and offshore, most of the previous studies have been carried out under isotropic conditions. The anisotropic stress state in term of the inclined principal stress and the inequality of the intermediate and minor principal stresses cannot be investigated using conventional devices. Therefore, the advanced hollow cylinder apparatus, used in this work, provides a great opportunity to simulate such anisotropic stress states. To date, very little consideration has been given to how the direction of principal stress α and intermediate principal stress ratio b can affect the performance of the reinforced sand. This study presented that the anisotropic conditions of α and b resulted in significant variations in the deviator stress and volumetric strain of sand reinforced with geosynthetics. Anisotropic effect has been decreased by adding clay content.

Keywords: anisotropy, reinforced sand, direction of principal stress, intermediate principal stress ratio

Procedia PDF Downloads 199