Search results for: minimum root mean square (RMS) error matching algorithm

Authors: Khaled Ben Oualid Medani, Samir Sayah

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The Optimal Reactive Power Dispatch (ORPD) problem has been solved and analysed usually in the normal conditions. However, network collapses appear in contingency conditions. In this paper, ORPD under several contingencies is presented using the proposed method WOA. To ensure viability of the power system in contingency conditions, several critical cases are simulated in order to prevent and prepare the power system to face such situations. The results obtained are carried out in IEEE 30 bus test system for the solution of ORPD problem in which control of bus voltages, tap position of transformers and reactive power sources are involved. Moreover, another method, namely, Particle Swarm Optimization with Time Varying Acceleration Coefficient (PSO-TVAC) has been compared with the proposed technique. Simulation results indicate that the proposed WOA gives remarkable solution in terms of effectiveness in case of outages.

Keywords: optimal reactive power dispatch, power system analysis, real power loss minimization, contingency condition, metaheuristic technique, whale optimization algorithm

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Authors: Padmini S. Navalgund, Manasi Naik, Ujwala Patil

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Road hump is a semi-cylindrical elevation on the road made across specific locations of the road. The vehicle needs to maneuver the hump by reducing the speed to avoid car damage and pass over the road hump safely. Road Humps on road surfaces, if identified in advance, help to maintain the security and stability of vehicles, especially in adverse visibility conditions, viz. night scenarios. We have proposed a deep learning architecture adaptation by implementing the MISH activation function and developing a new classification loss function called "Effective Focal Loss" for Indian road humps detection in adverse light scenarios. We captured images comprising of marked and unmarked road humps from two different types of cameras across South India to build a heterogeneous dataset. A heterogeneous dataset enabled the algorithm to train and improve the accuracy of detection. The images were pre-processed, annotated for two classes viz, marked hump and unmarked hump. The dataset from these images was used to train the single-stage object detection algorithm. We utilised an algorithm to synthetically generate reduced visible road humps scenarios. We observed that our proposed framework effectively detected the marked and unmarked hump in the images in clear and ad-verse light environments. This architectural adaptation sets up an option for early detection of Indian road humps in reduced visibility conditions, thereby enhancing the autonomous driving technology to handle a wider range of real-world scenarios.

Keywords: Indian road hump, reduced visibility condition, low light condition, adverse light condition, marked hump, unmarked hump, YOLOv9

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Authors: Sudeepti Dayal, Neelesh Gupta

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Image abbreviation is utilized for reducing the size of a file without demeaning the quality of the image to an objectionable level. The depletion in file size permits more images to be deposited in a given number of spaces. It also minimizes the time necessary for images to be transferred. Storage of medical images is a most researched area in the current scenario. To store a medical image, there are two parameters on which the image is divided, regions of interest and non-regions of interest. The best way to store an image is to compress it in such a way that no important information is lost. Compression can be done in two ways, namely lossy, and lossless compression. Under that, several compression algorithms are applied. In the paper, two algorithms are used which are, discrete cosine transform, applied to non-region of interest (lossy), and discrete wavelet transform, applied to regions of interest (lossless). The paper introduces SPIHT (set partitioning hierarchical tree) algorithm which is applied onto the wavelet transform to obtain good compression ratio from which an image can be stored efficiently.

Keywords: Compression ratio, DWT, SPIHT, DCT

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Authors: Muhammet Dursun Kaya, Hasan Asil

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In recent years, different methods have been proposed to optimize question processing in database. Although different methods have been proposed to optimize the query, but the problem which exists here is that most of these methods destroy the query execution plan after executing the query. This research attempts to solve the above problem by using a combination of methods of communicating with the database (the present questions in the programming code and using store procedures) and making query processing adaptive in database, and proposing a new approach for optimization of query processing by introducing the idea of dynamic store procedures. This research creates dynamic store procedures in the database according to the proposed algorithm. This method has been tested on applied software and results shows a significant improvement in reducing the query processing time and also reducing the workload of DBMS. Other advantages of this algorithm include: making the programming environment a single environment, eliminating the parametric limitations of the stored procedures in the database, making the stored procedures in the database dynamic, etc.

Keywords: relational database, agent, query processing, adaptable, communication with the database

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Authors: Waad Al Saleemi, Badriya Al Adawi, Zaaima Al Jabri, Sahim Al Ghafri, Jalila Al Hadhramia

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Objectives: Using standard lab methods, a positive blood culture requires a minimum of two days (two occasions of overnight incubation) to obtain a final identification (ID) and antimicrobial susceptibility results (AST) report. In this study, we aimed to evaluate the accuracy and precision of identification and antimicrobial susceptibility testing of an alternative method (direct method) that will reduce the turnaround time by 24 hours. This method involves the direct inoculation of positive blood culture broths into the Phoenix system using serum separation tubes (SST). Method: This prospective study included monomicrobial-positive blood cultures obtained from January 2022 to May 2023 in SQUH. Blood cultures containing a mixture of organisms, fungi, or anaerobic organisms were excluded from this study. The result of the new “direct method” under study was compared with the current “standard method” used in the lab. The accuracy and precision were evaluated for the ID and AST using Clinical and Laboratory Standards Institute (CLSI) recommendations. The categorical agreement, essential agreement, and the rates of very major errors (VME), major errors (ME), and minor errors (MIE) for both gram-negative and gram-positive bacteria were calculated. Passing criteria were set according to CLSI. Result: The results of ID and AST were available for a total of 158 isolates. Of 77 isolates of gram-negative bacteria, 71 (92%) were correctly identified at the species level. Of 70 isolates of gram-positive bacteria, 47(67%) isolates were correctly identified. For gram-negative bacteria, the essential agreement of the direct method was ≥92% when compared to the standard method, while the categorical agreement was ≥91% for all tested antibiotics. The precision of ID and AST were noted to be 100% for all tested isolates. For gram-positive bacteria, the essential agreement was >93%, while the categorical agreement was >92% for all tested antibiotics except moxifloxacin. Many antibiotics were noted to have an unacceptable higher rate of very major errors including penicillin, cotrimoxazole, clindamycin, ciprofloxacin, and moxifloxacin. However, no error was observed in the results of vancomycin, linezolid, and daptomycin. Conclusion: The direct method of ID and AST for positive blood cultures using SST is reliable for gram negative bacteria. It will significantly decrease the turnaround time and will facilitate antimicrobial stewardship.

Keywords: bloodstream infection, oman, direct ast, blood culture, rapid identification, antimicrobial susceptibility, phoenix, direct inoculation

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Authors: Georges Ghazi, Marc-Henry Devillers, Ruxandra M. Botez

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Aircraft flight trajectory optimization has been identified to be a promising solution for reducing both airline costs and the aviation net carbon footprint. Nowadays, this role has been mainly attributed to the flight management system. This system is an onboard multi-purpose computer responsible for providing the crew members with the optimized flight plan from a destination to the next. To accomplish this function, the flight management system uses a variety of look-up tables to compute the optimal speed and altitude for each flight regime instantly. Because the cruise is the longest segment of a typical flight, the proposed algorithm is focused on minimizing fuel consumption for this flight phase. In this paper, a complete methodology to estimate the aircraft performance and subsequently compute the optimal speed in cruise is presented. Results showed that the obtained performance database was accurate enough to predict the flight costs associated with the cruise phase.

Keywords: Cessna Citation X, cruise speed optimization, flight cost, cost index, and golden section search

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Authors: Komol Phaisarn, Anuphan Suttimarn, Vitchanan Keawtong, Kittisak Thongyoun, Chaiyos Jamsawang

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This article is intended to analyze insurance information which contains information on the customer decision when purchasing life insurance pay package. The data were analyzed in order to present new customers with Life Insurance Perfect Pay package to meet new customers’ needs as much as possible. The basic data of insurance pay package were collect to get data mining; thus, reducing the scattering of information. The data were then classified in order to get decision model or decision tree using Algorithm C4.5 (J-48). In the classification, WEKA tools are used to form the model and testing datasets are used to test the decision tree for the accurate decision. The validation of this model in classifying showed that the accurate prediction was 68.43% while 31.25% were errors. The same set of data were then tested with other models, i.e. Naive Bayes and Zero R. The results showed that J-48 method could predict more accurately. So, the researcher applied the decision tree in writing the program used to introduce the product to new customers to persuade customers’ decision making in purchasing the insurance package that meets the new customers’ needs as much as possible.

Keywords: decision tree, data mining, customers, life insurance pay package

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Authors: Mohamed Gaith, Zaid Haddadin, Abdulah Wahbe, Mahmoud Hamam, Mahmoud Qunees, Mohammad Al Khatib, Mohammad Bsaileh, Abd Al-Aziz Jaber, Ahmad Aqra’a

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The crack is one of the most common types of failure in pipelines that convey fluid, and early detection of the crack may assist to avoid the piping system from experiencing catastrophic damage, which would otherwise be fatal. The influence of flow velocity and the presence of a crack on the performance of a tapered simply supported pipe containing moving fluid is explored using the finite element approach in this study. ANSYS software is used to simulate the pipe as Bernoulli's beam theory. In this paper, the fluctuation of natural frequencies and matching mode shapes for various scenarios owing to changes in fluid speed and the presence of damage is discussed in detail.

Keywords: damage detection, finite element, tapered pipe, vibration characteristics

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Authors: Kevin Giraldo, Juan A. Gallego, Uriel Zapata, Fanny L. Casado

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Compliant mechanisms are designed to deform in a controlled manner in response to external forces, utilizing the flexibility of their components to store potential elastic energy during deformation, gradually releasing it upon returning to its original form. This article explores the design of a knee orthosis intended to assist users during stand-up motion. The orthosis makes use of a compliant mechanism to balance the user’s weight, thereby minimizing the strain on leg muscles during standup motion. The primary function of the compliant mechanism is to store and exchange potential energy, so when coupled with the gravitational potential of the user, the total potential energy variation is minimized. The design process for the semi-rigid knee orthosis involved material selection and the development of a numerical model for the compliant mechanism seen as a spring. Geometric properties are obtained through the numerical modeling of the spring once the desired stiffness and safety factor values have been attained. Subsequently, a 3D finite element analysis was conducted. The study demonstrates a strong correlation between the maximum stress in the mathematical model (250.22 MPa) and the simulation (239.8 MPa), with a 4.16% error. Both analyses safety factors: 1.02 for the mathematical approach and 1.1 for the simulation, with a consistent 7.84% margin of error. The spring’s stiffness, calculated at 90.82 Nm/rad analytically and 85.71 Nm/rad in the simulation, exhibits a 5.62% difference. These results suggest significant potential for the proposed device in assisting patients with knee orthopedic restrictions, contributing to ongoing efforts in advancing the understanding and treatment of knee osteoarthritis.

Keywords: biomechanics, complaint mechanisms, gonarthrosis, orthoses

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Authors: Tabassum Riaz

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This study provides a complete examination of the stock prices behavior in the Karachi stock exchange. It examines that whether Karachi stock exchange can be described as mean reversion or not. For this purpose daily, weekly and monthly index data from Karachi stock exchange ranging from period July 1, 1997 to July 2, 2011 was taken. After employing the Multiple variance ratio and unit root tests it is concluded that stock market follow mean reversion behavior and hence have reverting trend which opens the door for the active invest management. Thus technical analysis may be help to identify the potential areas for value creation.

Keywords: mean reversion, random walk, technical analysis, Karachi stock exchange

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Authors: Vahid Anari, Mina Bakhshi

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Visual search and identification of immunohistochemically stained tissue of meningioma was performed manually in pathologic laboratories to detect and diagnose the cancers type of meningioma. This task is very tedious and time-consuming. Moreover, because of cell's complex nature, it still remains a challenging task to segment cells from its background and analyze them automatically. In this paper, we develop and test a computerized scheme that can automatically identify cells in microscopic images of meningioma and classify them into positive (proliferative) and negative (normal) cells. Dataset including 150 images are used to test the scheme. The scheme uses Fuzzy C-means algorithm as a color clustering method based on perceptually uniform hue, saturation, value (HSV) color space. Since the cells are distinguishable by the human eye, the accuracy and stability of the algorithm are quantitatively compared through application to a wide variety of real images.

Keywords: positive cell, color segmentation, HSV color space, immunohistochemistry, meningioma, thresholding, fuzzy c-means

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Authors: Muhammad Naveed, Sohail Yousaf, Zahir Ahmad Zahir, Birgit Mitter, Angela Sessitsch

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Most bacterial endophytes originate from the soil and enter plants via the roots followed by further spread through the inner tissues. The mechanisms allowing bacteria to colonize plants endophytically are still poorly understood for most bacterial and plant species. Specific bacterial functions are required for plant colonization, but also the plant itself is a determining factor as bacterial ability to establish endophytic populations is very often dependent on the plant genotype (cultivar) and inoculums density. The effect of inoculum density (107, 108, 109 CFU mL-1) of Burkholderia phytofirmans strain PsJN was evaluated on growth and endophytic colonization of different maize and potato cultivars under axenic and natural soil conditions. PsJN inoculation significantly increased maize seedling growth and tuber yield of potato at all inoculum density compared to uninoculated control. Under axenic condition, PsJN inoculation (108 CFU mL-1) significantly improved the germination, root/shoot length and biomass up to 62, 115, 98 and 135% of maize seedling compared to uninoculated control. In case of potato, PsJN inoculation (109 CFU mL-1) showed maximum response and significantly increased root/shoot biomass and tuber yield under natural soil condition. We confirmed that PsJN is able to colonize the rhizosphere, roots and shoots of maize and potato cultivars. The endophytic colonization increased linearly with increasing inoculum density (within a range of 8 x 104 – 3 x 107 CFU mL-1) and were highest for maize (Morignon) and potato (Romina) as compared to other cultivars. Efficient colonization of cv. Morignon and Romina by strain PsJN indicates the specific cultivar colonizing capacity of the bacteria. The findings of the study indicate the non-significant relationship between colonization and plant growth promotion in maize under axenic conditions. However, the inoculum level (109 CFU mL-1) that promoted colonization of rhizosphere and plant interior (endophytic) also best promoted growth and tuber yield of potato under natural soil conditions.

Keywords: crop genotype, inoculum density, Burkholderia phytofirmans PsJN, colonization, growth, potato

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Authors: Ahmad Idris, Armaya`u Suleiman Labo, Ado Yusuf Abdulfatah, Murtala Umar

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Super pave is the short form of Superior Performing Asphalt Pavement and represents a basis for specifying component materials, asphalt mixture design and analysis, and pavement performance prediction. This new technology is the result of long research projects conducted by the strategic Highway Research program (SHRP) of the Federal Highway Administration. This research was aimed at examining the suitability of Aggregates found in Kano for used in super pave design method. Aggregates samples were collected from different sources in Kano Nigeria and their Engineering properties, as they relate to the SUPERPAVE design requirements were determined. The average result of Coarse Aggregate Angularity in Kano was found to be 87% and 86% of one fractured face and two or more fractured faces respectively with a standard of 80% and 85% respectively. Fine Aggregate Angularity average result was found to be 47% with a requirement of 45% minimum. A flat and elongated particle which was found to be 10% has a maximum criterion of 10%. Sand equivalent was found to be 51% with the criteria of 45% minimum. Strength tests were also carried out, and the results reflect the requirements of the standards. The tests include Impact value test, Aggregate crushing value and Aggregate Abrasion tests and the results are 27.5%, 26.7% and 13% respectively with a maximum criteria of 30%. Specific gravity was also carried out and the result was found to have an average value of 2.52 with a criterion of 2.6 to 2.9 and Water absorption was found to be 1.41% with maximum criteria of 0.6%. From the study, the result of the tests indicated that the aggregates properties have met the requirements of Super pave design method based on the specifications of ASTMD 5821, ASTM D 4791, AASHTO T176, AASHTO T33 and BS815.

Keywords: aggregates, construction, road design, super pave

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Authors: Ho Yeon Park, Kyoung-Jae Kim

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The purpose of this study is to investigate whether friendship in social media can be an important factor in recommender system through social scientific analysis of friendship in popular social media such as Facebook and Twitter. For this purpose, this study analyzes data on friendship in real social media using component analysis and clique analysis among sub-group analysis in social network analysis. In this study, we propose an algorithm to reflect the results of sub-group analysis on the recommender system. The key to this algorithm is to ensure that recommendations from users in friendships are more likely to be reflected in recommendations from users. As a result of this study, outcomes of various subgroup analyzes were derived, and it was confirmed that the results were different from the results of the existing recommender system. Therefore, it is considered that the results of the subgroup analysis affect the recommendation performance of the system. Future research will attempt to generalize the results of the research through further analysis of various social data.

Keywords: sub-group analysis, social media, social network analysis, recommender systems

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Authors: Debit Datta, Jeffrey J. Coleman, Scott A. Enebak, Lori G. Eckhardt

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Pinus taeda, commonly known as loblolly pine, is a crucial timber species native to the southeastern USA. An emerging problem has been encountered for the past few years, which is better to be known as loblolly pine needle defoliation (LPND), which is threatening the ecological health of southeastern forests and economic vitality of the region’s timber industry. Currently, more than 1000 hectares of loblolly plantations in Alabama are affected with similar symptoms and have created concern among southeast landowners and forest managers. However, it is still uncertain whether LPND results from one or the combination of several fungal pathogens. Therefore, the objectives of the study were to identify and characterize the fungi associated with LPND in the southeastern USA and document the damage being done to loblolly pine as a result of repeated defoliation. Identification of fungi was confirmed using classical morphological methods (microscopic examination of the infected needles), conventional and species-specific priming (SSPP) PCR, and ITS sequencing. To date, 17 species of fungi, either cultured from pine needles or formed fruiting bodies on pine needles, were identified based on morphology and genetic sequence data. Among them, brown-spot pathogen Lecanostica acicola has been frequently recovered from pine needles in both spring and summer. Moreover, Ophistomatoid fungi such as Leptographium procerum, L. terebrantis are associated with pine decline have also been recovered from root samples of the infected stands. Trees have been increasingly and repeatedly chlorotic and defoliated from 2019 to 2020. Based on morphological observations and molecular data, emerging loblolly pine needle defoliation is due in larger part to the brown-spot pathogen L. acoicola followed by pine decline pathogens L. procerum and L. terebrantis. Root pathogens were suspected to emerge later, and their cumulative effects contribute to the widespread mortality of the trees. It is more likely that longer wet spring and warmer temperatures are favorable to disease development and may be important in the disease ecology of LPND. Therefore, the outbreak of the disease is assumed to be expanded over a large geographical area in a changing climatic condition.

Keywords: brown-spot fungi, emerging disease, defoliation, loblolly pine

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Authors: Mahacine Amrani

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A simple and rapid method for the determination of alcohol in gasoline-alcohol mixtures using density measurements is described. The method can determine a minimum of 1% of alcohol by volume. The precision of the method is ± 3%.The method is more useful for field test in the quality assessment of alcohol blended fuels.

Keywords: gasoline-alcohol, mixture, alcohol determination, density, measurement, Morocco

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Authors: Yu-Chun Lin

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This study examines whether the adoption of clawback provisions increases the demand for audit service. We use abnormal audit fees to proxy for the demand for audit service. Because firms’ voluntary adoption of the clawback provisions is endogenously determined, this study controls for this bias using the propensity-score matching technique. Based on 1,247 U.S. firms that voluntarily adopt clawback provisions during 2003-2013 and a matched sample, the empirical results show that clawback provisions adoption is associated with abnormal audit fees, especially by firms with higher likelihood of misstatements. When firm executives are overconfident, abnormal audit fees increase subsequent to clawback provisions adoption. Since regulators require listed firms to adopt recoupment policy after 2015 in U.S., the evidence about higher demand for audit service might provide political implications for mandatory clawback provisions.

Keywords: clawback provisions, audit service, audit fees, overconfidence

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Authors: R. Rama Kishore, Sunesh Malik

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Development in the usage of internet for different purposes in recent times creates great threat for the copy right protection of the digital images. Digital watermarking is the best way to rescue from the said problem. This paper presents detailed review of the different watermarking techniques, latest trends in the field and categorized like spatial and transform domain, blind and non-blind methods, visible and non visible techniques etc. It also discusses the different optimization techniques used in the field of watermarking in order to improve the robustness and imperceptibility of the method. Different measures are discussed to evaluate the performance of the watermarking algorithm. At the end, this paper proposes a watermarking algorithm using (k.n) shares of halftone visual cryptography (HVC) instead of (2, 2) share cryptography. (k,n) shares visual cryptography improves the security of the watermark. As halftone is a method of reprographic, it helps in improving the visual quality of watermark image. The proposed method uses fractional transformation to improve the robustness of the copyright protection of the method.

Keywords: digital watermarking, fractional transform, halftone, visual cryptography

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Authors: E. Chávez-Vargas, M. de la L. Olvera-Amador, A. Jimenez-Gonzalez, A. Maldonado

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Undoped and fluorine doped zinc oxide (ZnO) thin films were deposited on sodocalcic glass substrates by the ultrasonic chemical spray technique. As the main goal is the manufacturing of transparent electrodes, the effects of both the solution composition and the substrate temperature on both the electrical and optical properties of ZnO thin films were studied. As a matter of fact, the effect of fluorine concentration ([F]/[F+Zn] at. %), solvent composition (acetic acid, water, methanol ratios) and ageing time, regarding solution composition, were varied. In addition, the substrate temperature and the deposition time, regarding the chemical spray technique, were also varied. Structural studies confirm the deposition of polycrystalline, hexagonal, wurtzite type, ZnO. The results show that the increase of ([F]/[F+Zn] at. %) ratio in the solution, decreases the sheet resistance, RS, of the ZnO:F films, reaching a minimum, in the order of 1.6 Ωcm, at 60 at. %; further increase in the ([F]/[F+Zn]) ratio increases the RS of the films. The same trend occurs with the variation in substrate temperature, as a minimum RS of ZnO:F thin films was encountered when deposited at TS= 450 °C. ZnO:F thin films deposited with aged solution show a significant decrease in the RS in the order of 100 ΩS. The transmittance of the films was also favorable affected by the solvent ratio and, more significantly, by the ageing of the solution. The whole evaluation of optical and electrical characteristics of the ZnO:F thin films deposited under different conditions, was done under Haacke’s figure of Merit in order to have a clear and quantitative trend as transparent conductors application.

Keywords: zinc oxide, ZnO:F, TCO, Haacke’s figure of Merit

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Authors: Arpita Mondal, Aurobinda Routray, Sreeraj Puravankara, Rajashree Biswas

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The most difficult part of designing a battery management system (BMS) is battery modeling. A good battery model can capture the dynamics which helps in energy management, by accurate model-based state estimation algorithms. So far the most suitable and fruitful model is the equivalent circuit model (ECM). However, in real-time applications, the model parameters are time-varying, changes with current, temperature, state of charge (SOC), and aging of the battery and this make a great impact on the performance of the model. Therefore, to increase the equivalent circuit model performance, the parameter estimation has been carried out in the frequency domain. The battery is a very complex system, which is associated with various chemical reactions and heat generation. Therefore, it’s very difficult to select the optimal model structure. As we know, if the model order is increased, the model accuracy will be improved automatically. However, the higher order model will face the tendency of over-parameterization and unfavorable prediction capability, while the model complexity will increase enormously. In the time domain, it becomes difficult to solve higher order differential equations as the model order increases. This problem can be resolved by frequency domain analysis, where the overall computational problems due to ill-conditioning reduce. In the frequency domain, several dominating frequencies can be found in the input as well as output data. The selective frequency domain estimation has been carried out, first by estimating the frequencies of the input and output by subspace decomposition, then by choosing the specific bands from the most dominating to the least, while carrying out the least-square, recursive least square and Kalman Filter based parameter estimation. In this paper, a second order battery model consisting of three resistors, two capacitors, and one SOC controlled voltage source has been chosen. For model identification and validation hybrid pulse power characterization (HPPC) tests have been carried out on a 2.6 Ah LiFePO₄ battery.

Keywords: equivalent circuit model, frequency estimation, parameter estimation, subspace decomposition

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Authors: Aprajeeta Jha, Punyadarshini P. Tripathy

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Drying technologies for various food processing operations shares an inevitable linkage with energy, cost and environmental sustainability. Hence, solar drying of food grains has become imperative choice to combat duo challenges of meeting high energy demand for drying and to address climate change scenario. But performance and reliability of solar dryers depend hugely on sunshine period, climatic conditions, therefore, offer a limited control over drying conditions and have lower efficiencies. Solar drying technology, supported by Photovoltaic (PV) power plant and hybrid type solar air collector can potentially overpower the disadvantages of solar dryers. For development of such robust hybrid dryers; to ensure quality and shelf-life of paddy grains the optimization of process parameter becomes extremely critical. Investigation of the moisture distribution profile within the grains becomes necessary in order to avoid over drying or under drying of food grains in hybrid solar dryer. Computational simulations based on finite element modeling can serve as potential tool in providing a better insight of moisture migration during drying process. Hence, present work aims at optimizing the process parameters and to develop a 3-dimensional (3D) finite element model (FEM) for predicting moisture profile in paddy during solar drying. COMSOL Multiphysics was employed to develop a 3D finite element model for predicting moisture profile. Furthermore, optimization of process parameters (power level, air velocity and moisture content) was done using response surface methodology in design expert software. 3D finite element model (FEM) for predicting moisture migration in single kernel for every time step has been developed and validated with experimental data. The mean absolute error (MAE), mean relative error (MRE) and standard error (SE) were found to be 0.003, 0.0531 and 0.0007, respectively, indicating close agreement of model with experimental results. Furthermore, optimized process parameters for drying paddy were found to be 700 W, 2.75 m/s at 13% (wb) with optimum temperature, milling yield and drying time of 42˚C, 62%, 86 min respectively, having desirability of 0.905. Above optimized conditions can be successfully used to dry paddy in PV integrated solar dryer in order to attain maximum uniformity, quality and yield of product. PV-integrated hybrid solar dryers can be employed as potential and cutting edge drying technology alternative for sustainable energy and food security.

Keywords: finite element modeling, moisture migration, paddy grain, process optimization, PV integrated hybrid solar dryer

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Authors: Walelgn Dilnesa Cherie, Fasikaw Atanaw Zimale, Bekalu W. Asres

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The most catastrophic condition for agricultural production is a drought event, which is also one of the most hydro-metrological-related hazards. According to the combined susceptibility of plants to meteorological and hydrological conditions, agricultural drought is defined as the magnitude, severity, and duration of a drought that affects crop production. The accurate and timely assessment of agricultural drought can lead to the development of risk management strategies, appropriate proactive mechanisms for the protection of farmers, and the improvement of food security. The evaluation of agricultural drought in the East Gojjam zone was the primary subject of this study. To identify the agricultural drought, soil moisture anomalies, soil moisture deficit indices, and Normalized Difference Vegetation Indices (NDVI) are used. The measured welting point, field capacity, and soil moisture were utilized to validate the soil water deficit indices computed from the satellite data. The soil moisture and soil water deficit indices in 2013 in all woredas were minimum; this makes vegetation stress also in all woredas. The soil moisture content decreased in 2013/2014/2019, and 2021 in Dejen, 2014, and 2019 in Awobel Woreda. The max/ min values of NDVI in 2013 are minimum; it dominantly shows vegetation stress and an observed agricultural drought that happened in all woredas. The validation process of satellite and in-situ soil moisture and soil water deficit indices shows a good agreement with a value of R²=0.87 and 0.56, respectively. The study area becomes drought detected region, so government officials, policymakers, and environmentalists pay attention to the protection of drought effects.

Keywords: NDVI, agricultural drought, SWDI, soil moisture

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Authors: Lu Yang, Meiyang Song, Xiang Yu, Wenhao Zhou, Chuntao Feng

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To address the problem that the FM continuous-wave radar (FMCW) extracts human vital sign signals which are susceptible to noise interference and low reconstruction accuracy, a new detection scheme for the sign signals is proposed. Firstly, an improved complete ensemble empirical modal decomposition with adaptive noise (ICEEMDAN) algorithm is applied to decompose the radar-extracted thoracic signals to obtain several intrinsic modal functions (IMF) with different spatial scales, and then the IMF components are optimized by a BP neural network improved by immune genetic algorithm (IGA). The simulation results show that this scheme can effectively separate the noise and accurately extract the respiratory and heartbeat signals and improve the reconstruction accuracy and signal-to-noise ratio of the sign signals.

Keywords: frequency modulated continuous wave radar, ICEEMDAN, BP neural network, vital signs signal

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Authors: Boguslaw Schreyer

Abstract:

During the wheeled robot’s braking process, the extra dynamic vertical forces act on all wheels: left, right, front or rear. Those forces are directed downward on the front wheels while directed upward on the rear wheels. In order to maximize the deceleration, therefore, minimize the braking time and braking distance, we need to calculate a correct torque distribution: the front braking torque should be increased, and rear torque should be decreased. At the same time, we need to provide better transversal stability. In a simple case of all adhesion coefficients being the same under all wheels, the torque distribution may secure the optimal (maximal) control of the robot braking process, securing the minimum braking distance and a minimum braking time. At the same time, the transversal stability is relatively good. At any time, we control the transversal acceleration. In the case of the transversal movement, we stop the braking process and re-apply braking torque after a defined period of time. If we correctly calculate the value of the torques, we may secure the traction coefficient under the front and rear wheels close to its maximum. Also, in order to provide an optimum braking control, we need to calculate the timing of the braking torque application and the timing of its release. The braking torques should be released shortly after the wheels passed a maximum traction coefficient (while a wheels’ slip increases) and applied again after the wheels pass a maximum of traction coefficient (while the slip decreases). The correct braking torque distribution secures the front and rear wheels, passing this maximum at the same time. It guarantees an optimum deceleration control, therefore, minimum braking time. In order to calculate a correct torque distribution, a control unit should receive the input signals of a rear torque value (which changes independently), the robot’s deceleration, and values of the vertical front and rear forces. In order to calculate the timing of torque application and torque release, more signals are needed: speed of the robot: angular speed, and angular deceleration of the wheels. In case of different adhesion coefficients under the left and right wheels, but the same under each pair of wheels- the same under right wheels and the same under left wheels, the Select-Low (SL) and select high (SH) methods are applied. The SL method is suggested if transversal stability is more important than braking efficiency. Often in the case of the robot, more important is braking efficiency; therefore, the SH method is applied with some control of the transversal stability. In the case that all adhesion coefficients are different under all wheels, the front-rear torque distribution is maintained as in all previous cases. However, the timing of the braking torque application and release is controlled by the rear wheels’ lowest adhesion coefficient. The Lagrange equations have been used to describe robot dynamics. Matlab has been used in order to simulate the process of wheeled robot braking, and in conclusion, the braking methods have been selected.

Keywords: wheeled robots, braking, traction coefficient, asymmetric

Procedia PDF Downloads 165

Authors: Donghwan Lee, Gichun Cha, Jooyoung Park, Junkyeong Kim, Seunghee Park

Abstract:

3D shape models of the existing structure are required for many purposes such as safety and operation management. The traditional 3D modeling methods are based on manual or semi-automatic reconstruction from close-range images. It occasions great expense and time consuming. The Terrestrial Laser Scanner (TLS) is a common survey technique to measure quickly and accurately a 3D shape model. This TLS is used to a construction site and cultural heritage management. However there are many limits to process a TLS point cloud, because the raw point cloud is massive volume data. So the capability of carrying out useful analyses is also limited with unstructured 3-D point. Thus, segmentation becomes an essential step whenever grouping of points with common attributes is required. In this paper, members segmentation algorithm was presented to separate a raw point cloud which includes only 3D coordinates. This paper presents a clustering approach based on a fuzzy method for this objective. The Fuzzy C-Means (FCM) is reviewed and used in combination with a similarity-driven cluster merging method. It is applied to the point cloud acquired with Lecia Scan Station C10/C5 at the test bed. The test-bed was a bridge which connects between 1st and 2nd engineering building in Sungkyunkwan University in Korea. It is about 32m long and 2m wide. This bridge was used as pedestrian between two buildings. The 3D point cloud of the test-bed was constructed by a measurement of the TLS. This data was divided by segmentation algorithm for each member. Experimental analyses of the results from the proposed unsupervised segmentation process are shown to be promising. It can be processed to manage configuration each member, because of the segmentation process of point cloud.

Keywords: fuzzy c-means (FCM), point cloud, segmentation, terrestrial laser scanner (TLS)

Procedia PDF Downloads 234

Authors: R. Akbari, M. A. Ehyaei, R. Shahi Shavvon

Abstract:

Nowadays, solar energy is used for energy purposes such as the use of thermal energy for domestic, industrial and power applications, as well as the conversion of the sunlight into electricity by photovoltaic cells. In this study, the thermodynamic simulation of the solar Rankin cycle with phase change material (paraffin) was first studied. Then energy and exergy analyses were performed. For optimization, a single and multi-objective genetic optimization algorithm to maximize thermal and exergy efficiency was used. The parameters discussed in this paper included the effects of input pressure on turbines, input mass flow to turbines, the surface of converters and collector angles on thermal and exergy efficiency. In the organic Rankin cycle, where solar energy is used as input energy, the fluid selection is considered as a necessary factor to achieve reliable and efficient operation. Therefore, silicon oil is selected for a high-temperature cycle and water for a low-temperature cycle as an operating fluid. The results showed that increasing the mass flow to turbines 1 and 2 would increase thermal efficiency, while it reduces and increases the exergy efficiency in turbines 1 and 2, respectively. Increasing the inlet pressure to the turbine 1 decreases the thermal and exergy efficiency, and increasing the inlet pressure to the turbine 2 increases the thermal efficiency and exergy efficiency. Also, increasing the angle of the collector increased thermal efficiency and exergy. The thermal efficiency of the system was 22.3% which improves to 33.2 and 27.2% in single-objective and multi-objective optimization, respectively. Also, the exergy efficiency of the system was 1.33% which has been improved to 1.719 and 1.529% in single-objective and multi-objective optimization, respectively. These results showed that the thermal and exergy efficiency in a single-objective optimization is greater than the multi-objective optimization.

Keywords: exergy analysis, genetic algorithm, rankine cycle, single and multi-objective function

Procedia PDF Downloads 147

Authors: Navab Karimi, Tohid Alizadeh

Abstract:

An intelligent vision-based system was designed to measure the quality and purity of raisins. A machine vision setup was utilized to capture the images of bulk raisins in ranges of 5-50% mixed pure-impure berries. The textural features of bulk raisins were extracted using Grey-level Histograms, Co-occurrence Matrix, and Local Binary Pattern (a total of 108 features). Genetic Algorithm and neural network regression were used for selecting and ranking the best features (21 features). As a result, the GLCM features set was found to have the highest accuracy (92.4%) among the other sets. Followingly, multiple feature combinations of the previous stage were fed into the second regression (linear regression) to increase accuracy, wherein a combination of 16 features was found to be the optimum. Finally, a Support Vector Machine (SVM) classifier was used to differentiate the mixtures, producing the best efficiency and accuracy of 96.2% and 97.35%, respectively.

Keywords: sun-dried organic raisin, genetic algorithm, feature extraction, ann regression, linear regression, support vector machine, south azerbaijan.

Procedia PDF Downloads 73

Authors: Cristy Stagnar, Nina Hubig, Diana Ivankovic

Abstract:

The epigenome is a compelling candidate for mediating long-term responses to environmental effects modifying disease risk. The main goal of this research is to develop a machine learning-based DNA methylation score, which will be valuable in delineating the unique contribution of paternal epigenetic modifications to the germline impacting childhood health outcomes. It will also be a useful tool in validating self-reports of nonsmoking and in adjusting epigenome-wide DNA methylation association studies for this early-life exposure. Using secondary data from two population-based methylation profiling studies, our DNA methylation score is based on CpG DNA methylation measurements from cord blood gathered from children whose fathers smoked pre- and peri-conceptually. Each child’s mother and father fell into one of three class labels in the accompanying questionnaires -never smoker, former smoker, or current smoker. By applying different machine learning algorithms to the accessible resource for integrated epigenomic studies (ARIES) sub-study of the Avon longitudinal study of parents and children (ALSPAC) data set, which we used for training and testing of our model, the best-performing algorithm for classifying the father smoker and mother never smoker was selected based on Cohen’s κ. Error in the model was identified and optimized. The final DNA methylation score was further tested and validated in an independent data set. This resulted in a linear combination of methylation values of selected probes via a logistic link function that accurately classified each group and contributed the most towards classification. The result is a unique, robust DNA methylation score which combines information on DNA methylation and early life exposure of offspring to paternal smoking during pregnancy and which may be used to examine the paternal contribution to offspring health outcomes.

Keywords: epigenome, health outcomes, paternal preconception environmental exposures, supervised machine learning

Procedia PDF Downloads 185

Authors: A. Senthil Kumar, V. Murali Bhaskaran

Abstract:

In the information technology ground, people are using various tools and software for their official use and personal reasons. Nowadays, people are worrying to choose data accessing and extraction tools at the time of buying and selling their products. In addition, worry about various quality factors such as price, durability, color, size, and availability of the product. The main purpose of the research study is to find solutions to these unsolved existing problems. The proposed algorithm is a Multidirectional Rank Prediction (MDRP) decision making algorithm in order to take an effective strategic decision at all the levels of data extraction, uses a real time textile dataset and analyzes the results. Finally, the results are obtained and compared with the existing measurement methods such as PCC, SLCF, and VSS. The result accuracy is higher than the existing rank prediction methods.

Keywords: Knowledge Discovery in Database (KDD), Multidirectional Rank Prediction (MDRP), Pearson’s Correlation Coefficient (PCC), VSS (Vector Space Similarity)

Procedia PDF Downloads 286

Authors: Sudheer Dhume, T. Prasad

Abstract:

There are distinct signs of Business Education losing its sheen. It is more so in developing countries. One of the reasons is the value addition at the end of 2 year MBA program is not matching with the requirements of present times and expectations of the students. In this backdrop, Pedagogy Innovation has become prerequisite for making our MBA programs relevant and useful. This paper is the description and analysis of innovative Action Learning pedagogical approach adopted by a group of faculty members at NITIE Mumbai. It not only promotes multidisciplinary research but also enhances integration of the functional areas skillsets in the students. The paper discusses the theoretical bases of this pedagogy and evaluates the effectiveness of it vis-à-vis conventional pedagogical tools. The evaluation research using Bloom’s taxonomy framework showed that this blended method of Business Education is much superior as compared to conventional pedagogy.

Keywords: action learning, blooms taxonomy, business education, innovation, pedagogy

Procedia PDF Downloads 270