Search results for: edge detection method

Authors: A. Chatel, I. S. Torreguitart, T. Verstraete

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The paper deals with a single point optimization of the LS89 turbine using an adjoint optimization and defining the design variables within a CAD system. The advantage of including the CAD model in the design system is that higher level constraints can be imposed on the shape, allowing the optimized model or component to be manufactured. However, CAD-based approaches restrict the design space compared to node-based approaches where every node is free to move. In order to preserve a rich design space, we develop a methodology to refine the CAD model during the optimization and to create the best parameterization to use at each time. This study presents a methodology to progressively refine the design space, which combines parametric effectiveness with a differential evolutionary algorithm in order to create an optimal parameterization. In this manuscript, we show that by doing the parameterization at the CAD level, we can impose higher level constraints on the shape, such as the axial chord length, the trailing edge radius and G2 geometric continuity between the suction side and pressure side at the leading edge. Additionally, the adjoint sensitivities are filtered out and only smooth shapes are produced during the optimization process. The use of algorithmic differentiation for the CAD kernel and grid generator allows computing the grid sensitivities to machine accuracy and avoid the limited arithmetic precision and the truncation error of finite differences. Then, the parametric effectiveness is computed to rate the ability of a set of CAD design parameters to produce the design shape change dictated by the adjoint sensitivities. During the optimization process, the design space is progressively enlarged using the knot insertion algorithm which allows introducing new control points whilst preserving the initial shape. The position of the inserted knots is generally assumed. However, this assumption can hinder the creation of better parameterizations that would allow producing more localized shape changes where the adjoint sensitivities dictate. To address this, we propose using a differential evolutionary algorithm to maximize the parametric effectiveness by optimizing the location of the inserted knots. This allows the optimizer to gradually explore larger design spaces and to use an optimal CAD-based parameterization during the course of the optimization. The method is tested on the LS89 turbine cascade and large aerodynamic improvements in the entropy generation are achieved whilst keeping the exit flow angle fixed. The trailing edge and axial chord length, which are kept fixed as manufacturing constraints. The optimization results show that the multilevel optimizations were more efficient than the single level optimization, even though they used the same number of design variables at the end of the multilevel optimizations. Furthermore, the multilevel optimization where the parameterization is created using the optimal knot positions results in a more efficient strategy to reach a better optimum than the multilevel optimization where the position of the knots is arbitrarily assumed.

Keywords: adjoint, CAD, knots, multilevel, optimization, parametric effectiveness

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Authors: Ihn Sook Jeong, Eun Joo Lee, Jae Hyung Kim, Gun Ho Kim, Young Jun Hwang

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This study investigated intravenous (IV) infiltration using bioelectrical impedance for 27 hospitalized patients in a long-term care hospital. Impedance parameters showed significant differences before and after infiltration as follows. First, the resistance (R) after infiltration significantly decreased compared to the initial resistance. This indicates that the IV solution flowing from the vein due to infiltration accumulates in the extracellular fluid (ECF). Second, the relative resistance at 50 kHz was 0.94 ± 0.07 in 9 subjects without infiltration and was 0.75 ± 0.12 in 18 subjects with infiltration. Third, the magnitude of the reactance (Xc) decreased after infiltration. This is because IV solution and blood components released from the vein tend to aggregate in the cell membrane (and acts analogously to the linear/parallel circuit), thereby increasing the capacitance (Cm) of the cell membrane and reducing the magnitude of reactance. Finally, the data points plotted in the R-Xc graph were distributed on the upper right before infiltration but on the lower left after infiltration. This indicates that the infiltration caused accumulation of fluid or blood components in the epidermal and subcutaneous tissues, resulting in reduced resistance and reactance, thereby lowering integrity of the cell membrane. Our findings suggest that bioelectrical impedance is an effective method for detection of infiltration in a noninvasive and quantitative manner.

Keywords: intravenous infiltration, impedance, parameters, resistance, reactance

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Authors: Candra Irawan, David Yudianto, Ahsanu Nadiyya, Dewi Anna Br Sitepu, Hanafi, Erna Styani

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Jelly drink was produced from a combination of both natural and synthetic materials, such as acesulfame potassium (acesulfame-K) as synthetic sweetener material. Acesulfame-K content in jelly drink could be determined by High-Performance Liquid Chromatography (HPLC), but this method needed validation due to having a change on the reagent addition step which skips the carrez addition and comparison of mix mobile phase (potassium dihydrogen phosphate and acetonitrile) with ratio from 75:25 to 90:10 to be more efficient and cheap. This study was conducted to evaluate the performance of determination method for acesulfame-K content in the jelly drink by HPLC. The method referred to Deutsches Institut fur Normung European Standard International Organization for Standardization (DIN EN ISO):12856 (1999) about Foodstuffs, Determination of acesulfame-K, aspartame and saccharin. The result of the correlation coefficient value (r) on the linearity test was 0.9987 at concentration range 5-100 mg/L. Detection limit value was 0.9153 ppm, while the quantitation limit value was 1.1932 ppm. The recovery (%) value on accuracy test for sample concentration by spiking 100 mg/L was 102-105%. Relative Standard Deviation (RSD) value for precision and homogenization tests were 2.815% and 4.978%, respectively. Meanwhile, the comparative and stability tests were tstat (0.136) < ttable (2.101) and |µ1-µ2| (1.502) ≤ 0.3×CV Horwitz. Obstinacy test value was tstat < ttable. It can be concluded that the HPLC  method for the determination of acesulfame-K in jelly drink product by HPLC has been valid and can be used for analysis with good performance.

Keywords: acesulfame-K, jelly drink, HPLC, validation

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Authors: Slimane Ouhmad, Abdellah Halimi

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In this work, we have applied neural networks method MLP type to a database from an array of six sensors for the detection of three toxic gases. As the choice of the number of hidden layers and the weight values has a great influence on the convergence of the learning algorithm, we proposed, in this article, a mathematical formulation to determine the optimal number of hidden layers and good weight values based on the method of back propagation of errors. The results of this modeling have improved discrimination of these gases on the one hand, and optimize the computation time on the other hand, the comparison to other results achieved in this case.

Keywords: MLP Neural Network, back-propagation, number of neurons in the hidden layer, identification, computing time

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Authors: Elmahdi Khalil, Saad Enniari, Mostapha Zbakh

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Cloud computing promises to increase innovation and the velocity with witch applications are deployed, all while helping any enterprise meet most IT service needs at a lower total cost of ownership and higher return investment. As the march of cloud continues, it brings both new opportunities and new security challenges. To take advantages of those opportunities while minimizing risks, we think that Intrusion Detection Systems (IDS) integrated in the cloud is one of the best existing solutions nowadays in the field. The concept of intrusion detection was known since past and was first proposed by a well-known researcher named Anderson in 1980's. Since that time IDS's are evolving. Although, several efforts has been made in the area of Intrusion Detection systems for cloud computing environment, many attacks still prevail. Therefore, the work presented in this paper proposes a multi criteria analysis and a comparative study between several IDS architectures designated to work in a cloud computing environments. To achieve this objective, in the first place we will search in the state of the art of several consistent IDS architectures designed to work in a cloud environment. Whereas, in a second step we will establish the criteria that will be useful for the evaluation of architectures. Later, using the approach of multi criteria decision analysis Mac Beth (Measuring Attractiveness by a Categorical Based Evaluation Technique we will evaluate the criteria and assign to each one the appropriate weight according to their importance in the field of IDS architectures in cloud computing. The last step is to evaluate architectures against the criteria and collecting results of the model constructed in the previous steps.

Keywords: cloud computing, cloud security, intrusion detection/prevention system, multi-criteria decision analysis

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Authors: Matias Micheletto, Rodrigo Santos, Sergio F. Ochoa

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In developing countries, the most roads in rural areas are dirt road. They require frequent maintenance since are affected by erosive events, such as rain or wind, and the transit of heavy-weight trucks and machinery. Early detection of damages on the road condition is a key aspect, since it allows to reduce the main-tenance time and cost, and also the limitations for other vehicles to travel through. Most proposals that help address this problem require the explicit participation of drivers, a permanent internet connection, or important instrumentation in vehicles or roads. These constraints limit the suitability of these proposals when applied into developing regions, like in Latin America. This paper proposes an alternative method, based on unattended crowdsensing, to determine the quality of dirt roads in rural areas. This method involves the use of a mobile application that complements the road condition surveys carried out by organizations in charge of the road network maintenance, giving them early warnings about road areas that could be requiring maintenance. Drivers can also take advantage of the early warnings while they move through these roads. The method was evaluated using information from a public dataset. Although they are preliminary, the results indicate the proposal is potentially suitable to provide awareness about dirt roads condition to drivers, transportation authority and road maintenance companies.

Keywords: dirt roads automatic quality assessment, collaborative system, unattended crowdsensing method, roads quality awareness provision

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Authors: Namita Mittal, Himakshi Shekhawat, Ankit Vidyarthi

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In medical research study, doctors and radiologists face lot of complexities in analysing the brain tumors in Magnetic Resonance (MR) images. Brain tumor detection is difficult due to amorphous tumor shape and overlapping of similar tissues in nearby region. So, radiologists require one such clinically viable solution which helps in automatic segmentation of tumor inside brain MR image. Initially, segmentation methods were used to detect tumor, by dividing the image into segments but causes loss of information. In this paper, a hybrid method is proposed which detect Region of Interest (ROI) on the basis of difference in intensity values and texture values of tumor region using nearby tissues with Gradient Vector Flow (GVF) technique in the identification of ROI. Proposed approach uses both intensity and texture values for identification of abnormal section of the brain MR images. Experimental results show that proposed method outperforms GVF method without any loss of information.

Keywords: brain tumor, GVF, intensity, MR images, segmentation, texture

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Authors: Sandy Bao, Yankang Bao

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The understanding of information that is being processed in the brain, especially in the lateral geniculate nucleus (LGN), has been proven challenging for modern neuroscience and for researchers with a focus on how neurons process signals and images. In this paper, we are proposing a method to image process different colors within different layers of LGN, that is, green information in layers 4 & 6 and red & blue in layers 3 & 5 based on the surface dimension of layers. We take into consideration the images in LGN and visual cortex, and that the edge detected information from the visual cortex needs to be considered in order to return back to the layers of LGN, along with the image in LGN to form the new image, which will provide an improved image that is clearer, sharper, and making it easier to identify objects in the image. Matrix Laboratory (MATLAB) simulation is performed, and results show that the clarity of the output image has significant improvement.

Keywords: lateral geniculate nucleus, matrix laboratory, neuroscience, visual cortex

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Authors: A. Boveiri Monji, H. Yousefnia, S. Zolghadri, B. Salimi

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The main goal of this paper was to make use of green reagents as a substitute of perilous synthetic reagents and organic solvents for spectrophotometric determination of hafnium(IV). The extracts taken from six different kinds of tree leaves including Acer negundo, Ficus carica, Cerasus avium, Chimonanthus, Salix babylonica and Pinus brutia, were applied as green reagents for the experiments. In 6-M hydrochloric acid, hafnium reacted with the reagent to form a yellow product and showed maximum absorbance at 421 nm. Among tree leaves, Chimonanthus showed satisfactory results with a molar absorptivity value of 0.61 × 10⁴ l mol^-1 cm^-1 and the method was linear in the 0.3-9 µg mL ^-1 concentration range. The detection limit value was 0.064 µg mL^-1. The proposed method was simple, low cost, clean, and selective.

Keywords: hafnium, spectrophotometric determination, synthetic reagents, tree leaves

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Authors: M. Naeimi, H. Aghazadeh, E. Afjei, A. Siadatan

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In this paper, a novel view of air gap magnetic field analysis of synchronous reluctance motor and permanent magnet assisted synchronous reluctance motor under static eccentricity to provide the precise fault diagnosis based on three-dimensional finite element method is presented. Analytical nature of this method makes it possible to simulate reliable and precise model by considering the end effects and axial fringing effects. The results of the three-dimensional finite element analysis of synchronous reluctance motor and permanent magnet synchronous reluctance motor such as flux linkage, flux density, and compression both of SynRM and PM-SynRM for various eccentric motor conditions are obtained and analyzed. These results present useful information regarding to the detection of static eccentricity.

Keywords: synchronous reluctance motor (SynRM), permanent magnet assisted synchronous reluctance motor (PMaSynRM), finite element method, static eccentricity, fault analysis

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Authors: Indu Verma, Santanu Kumar Pal

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Interactions between DNA and adsorbed Poly (L-lysine) (PLL) on liquid crystal (LC) droplets were investigated using polarizing optical microcopy (POM) and epi-fluorescence microscopy. Earlier, we demonstrated that adsorption of PLL to the LC/aqueous interface resulted in homeotropic orientation of the LC and thus exhibited a radial configuration of the LC confined within the droplets. Subsequent adsorption of DNA (single stranded DNA/double stranded DNA) at PLL coated LC droplets was found to trigger a LC reorientation within the droplets leading to pre-radial/bipolar configuration of those droplets. To our surprise, subsequent exposure of complementary ssDNA (c-ssDNA) to ssDNA/ adsorbed PLL modified LC droplets did not cause the LC reorientation. This is likely due to the formation of polyplexes (DNA-PLL complex) as confirmed by fluorescence microscopy and atomic force microscopy. In addition, dsDNA adsorbed PLL droplets have been found to be effectively used to displace (controlled release) propidium iodide (a model drug) encapsulated within dsDNA over time. These observations suggest the potential for a label free droplet based LC detection system that can respond to DNA and may provide a simple method to develop DNA-based drug nano-carriers.

Keywords: DNA biosensor, drug delivery, interfaces, liquid crystal droplets

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Authors: Gautam, Arjun, V. R. Sharma

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In the civil construction steel and concrete plays a different role but the same purposes dealing with the design of structures that support or resist loads. Concrete has been used in construction since long time from now. Being brittle and weak in tension, concrete is always reinforced with steel bars for the purposes in beams and columns etc. The paper deals with idea of special designed 3D materials which we named as “BUM629” to be placed/anchored in the structural member and mixed with concrete later on, so as to resist the developments of cracks due to shear failure , buckling,tension and compressive load in concrete. It had cutting edge technology through Draft, Analysis and Design the “BUM629”. The results show that “BUM629” has the great results in Mechanical application. Its material properties are design according to the need of structure; we apply the material such as Mild Steel and Magnesium Alloy. “BUM629” are divided into two parts one is applied at the middle section of concrete member where bending movements are maximum and the second part is laying parallel to vertical bars near clear cover, so we design this material and apply in Reinforcement of Civil Structures. “BUM629” is analysis and design for use in the mega structures like skyscrapers, dams and bridges.

Keywords: BUM629, magnesium alloy, cutting edge technology, mechanical application, draft, analysis and design, mega structures

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Authors: Ifedapo Francis Awolowo

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The unrelenting series of embarrassing audit failures should stimulate a paradigm shift in accounting. And in this age of information revolution, there is need for a constant improvement on the products or services one offers to the market in order to be relevant. This study explores the perceptions of external auditors, forensic accountants and accounting academics on whether a paradigm shift to forensic accounting can reduce financial statement frauds. Through Neo-empiricism/inductive analytical approach, findings reveal that a paradigm shift to forensic accounting might be the right step in the right direction in order to increase the chances of fraud prevention and detection in the financial statement. This research has implication on accounting education on the need to incorporate forensic accounting into present day accounting curriculum. Accounting professional bodies, accounting standard setters and accounting firms all have roles to play in incorporating forensic accounting education into accounting curriculum. Particularly, there is need to alter the ISA 240 to make the prevention and detection of frauds the responsibilities of bot those charged with the management and governance of companies and statutory auditors.

Keywords: financial statement fraud, forensic accounting, fraud prevention and detection, auditing, audit expectation gap, corporate governance

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Authors: Sylwia Baluta, Joanna Cabaj, Karol Malecha

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The evolution of biosensors was driven by the need for faster and more versatile analytical methods for application in important areas including clinical, diagnostics, food analysis or environmental monitoring, with minimum sample pretreatment. Rapid and sensitive neurotransmitters detection is extremely important in modern medicine. These compounds mainly occur in the brain and central nervous system of mammals. Any changes in the neurotransmitters concentration may lead to many diseases, such as Parkinson’s or schizophrenia. Classical techniques of chemical analysis, despite many advantages, do not permit to obtain immediate results or automatization of measurements.

Keywords: adrenaline, biosensor, dopamine, laccase, tyrosinase

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Authors: Wu Di, Yeo Khoon Seng, Lim Tee Tai

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The typical insects employ a flapping-wing mode of flight. The numerical simulations on free flight of a model fruit fly (Re=143) including hovering and are presented in this paper. Unsteady aerodynamics around a flapping insect is studied by solving the three-dimensional Newtonian dynamics of the flyer coupled with Navier-Stokes equations. A hybrid-grid scheme (Generalized Finite Difference Method) that combines great geometry flexibility and accuracy of moving boundary definition is employed for obtaining flow dynamics. The results show good points of agreement and consistency with the outcomes and analyses of other researchers, which validate the computational model and demonstrate the feasibility of this computational approach on analyzing fluid phenomena in insect flight. The present modeling approach also offers a promising route of investigation that could complement as well as overcome some of the limitations of physical experiments in the study of free flight aerodynamics of insects. The results are potentially useful for the design of biomimetic flapping-wing flyers.

Keywords: free hovering flight, flapping wings, fruit fly, insect aerodynamics, leading edge vortex (LEV), computational fluid dynamics (CFD), Navier-Stokes equations (N-S), fluid structure interaction (FSI), generalized finite-difference method (GFD)

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Authors: Ali Raza, Shafiq Ur Rehman Khan, Raja Sher Afgun Usmani, Asif Raza, Basit Umair

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Detection of Fake news and missing information is gaining popularity, especially after the advancement in social media and online news platforms. Social media platforms are the main and speediest source of fake news propagation, whereas online news websites contribute to fake news dissipation. In this study, we propose a framework to detect fake news using the temporal features of text and consider user feedback to identify whether the news is fake or not. In recent studies, the temporal features in text documents gain valuable consideration from Natural Language Processing and user feedback and only try to classify the textual data as fake or true. This research article indicates the impact of recurring and non-recurring events on fake and true news. We use two models BERT and Bi-LSTM to investigate, and it is concluded from BERT we get better results and 70% of true news are recurring and rest of 30% are non-recurring.

Keywords: natural language processing, fake news detection, machine learning, Bi-LSTM

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Authors: Maomao Cao

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Objective: To test the performance of ctDNA methylation for the detection of liver cancer. Methods: A total of 1233 individuals have been recruited in 2017. 15 male and 15 female samples (including 10 cases of liver cancer) were randomly selected in the present study. CfDNA was extracted by MagPure Circulating DNA Maxi Kit. The concentration of cfDNA was obtained by Qubit™ dsDNA HS Assay Kit. A pre-constructed predictive model was used to analyze methylation data and to give a predictive score for each cfDNA sample. Individuals with a predictive score greater than or equal to 80 were classified as having liver cancer. CT tests were considered the gold standard. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the diagnosis of liver cancer were calculated. Results: 9 patients were diagnosed with liver cancer according to the prediction model (with high sensitivity and threshold of 80 points), with scores of 99.2, 91.9, 96.6, 92.4, 91.3, 92.5, 96.8, 91.1, and 92.2, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value of ctDNA methylation for the diagnosis of liver cancer were 0.70, 0.90, 0.78, and 0.86, respectively. Conclusions: ctDNA methylation could be an acceptable diagnostic modality for the detection of liver cancer.

Keywords: liver cancer, ctDNA methylation, detection, diagnostic performance

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Authors: Jamal R. Elbergali

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Multiple Input Multiple Output (MIMO) systems are wireless systems with multiple antenna elements at both ends of the link. Wireless communication systems demand high data rate and spectral efficiency with increased reliability. MIMO systems have been popular techniques to achieve these goals because increased data rate is possible through spatial multiplexing scheme and diversity. Spatial Multiplexing (SM) is used to achieve higher possible throughput than diversity. In this paper, we propose a Zero-Forcing (ZF) detection using a combination of Ordered Successive Interference Cancellation (OSIC) and Zero Forcing using Interference Cancellation (ZF-IC). The proposed method used an OSIC based on Signal to Noise Ratio (SNR) ordering to get the estimation of last symbol (x ̃_(N_T )), then the estimated last symbol is considered to be an input to the ZF-IC. We analyze the Bit Error Rate (BER) performance of the proposed MIMO system over Rayleigh Fading Channel, using Binary Phase Shift Keying (BPSK) modulation scheme. The results show better performance than the previous methods.

Keywords: SNR, BER, BPSK, MIMO, modulation, zero forcing (ZF), OSIC, ZF-IC, spatial multiplexing (SM)

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Authors: V. Sai Geethika, Sai Snehitha Yadavalli, Swati Ghosh Acharyya

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This study involves a single element and simultaneous electrochemical detection of heavy metal ions through square wave anodic stripping voltammetry. A glassy carbon electrode was used to detect and quantify heavy metals such as As(III), Hg(II), Cr(VI) ions in water. Under optimized conditions, peak separation was obtained by varying concentrations, scan rates, and temperatures. As (III), Hg (II), Cr (III) were simultaneously detected with GCE. Several analytical methods, such as inductively coupled plasma mass spectroscopy (ICP-MS), atomic absorption spectroscopy (AAS), were used previously to detect heavy metal ions, which are authentic but are not good enough for online monitoring due to the bulkiness of the equipment. The study provides a good alternative that is simple, more efficient, and low-cost, involving a portable potentiostat. Heavy metals having different oxidation states can be detected by anodic stripping voltammetry. This method can be easily integrated with electronics. Square wave Anodic stripping voltammetry is used with a potential range of -2.5 V – 2.5 V for single ion detection by a three-electrode cell consisting of silver/silver chloride(Ag/AgCl) as reference and platinum (Pt) counter and glassy carbon (GCE) working electrodes. All three ions are optimized by varying the parameters like concentration, scan rate, pH, temperature, and all these optimized parameters were used for studying the effects of simultaneous detection. The procedure involves preparing an electrolyte using deionized water, cleaning the surface of GCE, depositing the ions by applying the redox potentials obtained from cyclic voltammetry (CV), and then detecting by applying oxidizing potential, i.e., stripping voltage. So this includes ASV techniques such as open-circuit voltage (OCV), chronoamperometry (CA), and square wave voltammetry (SWV). Firstly, the concentration of the ions varied from 50 ppb to 5000 ppb, and an optimum concentration was determined where the three ions were detected. A concentration of 400 ppb was used while varying the temperatures in the range of 25°C – 45°C. Optimum peak intensity was obtained at a temperature of 30°C with a low scan rate of 0.005 V-s⁻¹. All the parameters were optimized, and several effects have been noticed while three ions As(II), Cr(III), Hg(II) were detected alone and simultaneously.

Keywords: Arsenic(III), Chromium(III), glassy carbon electrode, Mercury (II), square wave anodic stripping voltammetry

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Authors: Lokeswara Rao P., Naga Venkata Rakesh N., V. Anantha Subramanian

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It is important to estimate, predict, and avoid the dynamic instability of high speed planing crafts. It is known that design parameters like relative location of center of gravity with respect to the dynamic lift centre and length to beam ratio of the craft have influence on the tendency to porpoise. This paper analyzes the hydrodynamic performance on the basis of the semi-empirical Savitsky method and also estimates the same by numerical simulations based on Reynolds Averaged Navier Stokes (RANS) equations using a commercial code namely, STAR- CCM+. The paper examines through the same numerical simulation considering dynamic equilibrium, the changing running trim, which results in porpoising. Some interesting results emerge from the study and this leads to early detection of the instability.

Keywords: CFD, planing hull, porpoising, Savitsky method

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Authors: Riaan Kleyn

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Mass wine production companies across the globe are provided with grapes from winegrowers that predominantly utilize mechanical harvesting machines to harvest wine grapes. Mechanical harvesting accelerates the rate at which grapes are harvested, allowing grapes to be delivered faster to meet the demands of wine cellars. The disadvantage of the mechanical harvesting method is the inclusion of material-other-than-grapes (MOG) in the harvested wine grape loads arriving at the cellar which degrades the quality of wine that can be produced. Currently, wine cellars do not have a method to determine the amount of MOG present within wine grape loads. This paper seeks to find an optimal computer vision method capable of detecting the amount of MOG within a wine grape load. A MOG detection method will encourage winegrowers to deliver MOG-free wine grape loads to avoid penalties which will indirectly enhance the quality of the wine to be produced. Traditional image segmentation methods were compared to deep learning segmentation methods based on images of wine grape loads that were captured at a wine cellar. The Mask R-CNN model with a ResNet-50 convolutional neural network backbone emerged as the optimal method for this study to determine the amount of MOG in an image of a wine grape load. Furthermore, a statistical analysis was conducted to determine how the MOG on the surface of a grape load relates to the mass of MOG within the corresponding grape load.

Keywords: computer vision, wine grapes, machine learning, machine harvested grapes

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Authors: Ju Peng Poh, Jun Yu Charles Lee, Jonathan Chew Hoe Khoo

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The emergence of digital twin technology, a digital replica of physical world, has improved the real-time access to data from sensors about the performance of buildings. This digital transformation has opened up many opportunities to improve the management of the building by using the data collected to help monitor consumption patterns and energy leakages. One example is the integration of predictive models for anomaly detection. In this paper, we use the GAM (Generalised Additive Model) for the anomaly detection of Air Handling Units (AHU) power consumption pattern. There is ample research work on the use of GAM for the prediction of power consumption at the office building and nation-wide level. However, there is limited illustration of its anomaly detection capabilities, prescriptive analytics case study, and its integration with the latest development of digital twin technology. In this paper, we applied the general GAM modelling framework on the historical data of the AHU power consumption and cooling load of the building between Jan 2018 to Aug 2019 from an education campus in Singapore to train prediction models that, in turn, yield predicted values and ranges. The historical data are seamlessly extracted from the digital twin for modelling purposes. We enhanced the utility of the GAM model by using it to power a real-time anomaly detection system based on the forward predicted ranges. The magnitude of deviation from the upper and lower bounds of the uncertainty intervals is used to inform and identify anomalous data points, all based on historical data, without explicit intervention from domain experts. Notwithstanding, the domain expert fits in through an optional feedback loop through which iterative data cleansing is performed. After an anomalously high or low level of power consumption detected, a set of rule-based conditions are evaluated in real-time to help determine the next course of action for the facilities manager. The performance of GAM is then compared with other approaches to evaluate its effectiveness. Lastly, we discuss the successfully deployment of this approach for the detection of anomalous power consumption pattern and illustrated with real-world use cases.

Keywords: anomaly detection, digital twin, generalised additive model, GAM, power consumption, supervised learning

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Authors: Simin Khodabakhshi, Hossein Rassi

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Hyperlipidemia refers to any of several acquired or genetic disorders that result in a high level of lipids circulating in the blood. Helicobacter pylori infection is a contributing factor in the progression of hyperlipidemia with serum lipid changes. The aim of this study was to detect of Helicobacter pylori by PCR and serological methods in patients with hyperlipidemia. In this case-control study, 174 patients with hyperlipidemia and 174 healthy controls were studied. Also, demographics, physical and biochemical parameters were performed in all samples. The DNA extracted from blood specimens was amplified by H pylori cagA specific primers. The results show that H. pylori cagA positivity was detected in 79% of the hyperlipidemia and in 56% of the control group by ELISA test and 49% of the hyperlipidemia and in 24% of the control group by PCR test. Prevalence of H. pylori infection was significantly higher in hyperlipidemia as compared to controls. In addition, patients with hyperlipidemia had significantly higher values for triglyceride, total cholesterol, LDL-C, waist to hip ratio, body mass index, diastolic and systolic blood pressure and lower levels of HDL-C than control participants (all p < 0.0001). Our result detected the ELISA was a rapid and cost-effective detection and considering the high prevalence of cytotoxigenic H. pylori strains, cag A is suggested as a promising target for PCR and ELISA tests for detection of infection with toxigenic strains. In general, it can be concluded that molecular analysis of H. pylori cagA and clinical parameters are important in early detection of hyperlipidemia and atherosclerosis with H. pylori infection by PCR and ELISA tests.

Keywords: Helicobacter pylori, hyperlipidemia, PCR, ELISA

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Authors: Po-Chun Wang, Yan-Ru Lai, Sophia I. C. Lin, Alvin W. Y. Su

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The Generative Theory of Tonal Music (GTTM) provides systematic approaches to recognizing local boundaries of music. The rules have been implemented in some automated melody segmentation algorithms. Besides, there are also deep learning methods with GTTM features applied to boundary detection tasks. However, these studies might face constraints such as a lack of or inconsistent label data. The GTTM database is currently the most widely used GTTM database, which includes manually labeled GTTM rules and local boundaries. Even so, we found some problems with these labels. They are sometimes discrepancies with GTTM rules. In addition, since it is labeled at different times by multiple musicians, they are not within the same scope in some cases. Therefore, in this paper, we examine this database with musicians from the aspect of classical music and relabel the scores. The relabeled database - GTTM Database v2.0 - will be released for academic research usage. Despite the experimental and statistical results showing that the relabeled database is more consistent, the improvement in boundary detection is not substantial. It seems that we need more clues than GTTM rules for boundary detection in the future.

Keywords: dataset, GTTM, local boundary, neural network

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Authors: Sofia G. Meirinho, Luis G. Dias, António M. Peres, Lígia R. Rodrigues

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The emerging development of electrochemical aptasen sors has enabled the easy and fast detection of protein biomarkers in standard and real samples. Biomarkers are produced by body organs or tumours and provide a measure of antigens on cell surfaces. When detected in high amounts in blood, they can be suggestive of tumour activity. These biomarkers are more often used to evaluate treatment effects or to assess the potential for metastatic disease in patients with established disease. Osteopontin (OPN) is a protein found in all body fluids and constitutes a possible biomarker because its overexpression has been related with breast cancer evolution and metastasis. Currently, biomarkers are commonly used for the development of diagnostic methods, allowing the detection of the disease in its initial stages. A previously described RNA aptamer was used in the current work to develop a simple and sensitive electrochemical aptasensor with high affinity for human OPN. The RNA aptamer was biotinylated and immobilized on a gold electrode by avidin-biotin interaction. The electrochemical signal generated from the aptamer–target molecule interaction was monitored electrochemically using cyclic voltammetry in the presence of [Fe (CN) 6]−3/− as a redox probe. The signal observed showed a current decrease due to the binding of OPN. The preliminary results showed that this aptasensor enables the detection of OPN in standard solutions, showing good selectivity towards the target in the presence of others interfering proteins such as bovine OPN and bovine serum albumin. The results gathered in the current work suggest that the proposed electrochemical aptasensor is a simple and sensitive detection tool for human OPN and so, may have future applications in cancer disease monitoring.

Keywords: osteopontin, aptamer, aptasensor, screen-printed electrode, cyclic voltammetry

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Authors: Farzad Razavi, Behrooz Taheri, Mohammad Parpaei, Mehdi Mohammadi Ghalesefidi, Siamak Zarei

Abstract:

As one of the major problems in protecting large-dimension power systems, power swing and its effect on distance have caused a lot of damages to energy transfer systems in many parts of the world. Therefore, power swing has gained attentions of many researchers, which has led to invention of different methods for power swing detection. Power swing detection algorithm is highly important in distance relay, but protection relays should have general requirements such as correct fault detection, response rate, and minimization of disturbances in a power system. To ensure meeting the requirements, protection relays need different tests during development, setup, maintenance, configuration, and troubleshooting steps. This paper covers power swing scheme of the modern numerical relay protection, 7sa522 to address the effect of the different fault types on the function of the power swing blocking. In this study, it was shown that the different fault types during power swing cause different time for unblocking distance relay.

Keywords: power swing, distance relay, power system protection, relay test, transient in power system

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Authors: Djamil Guettiche, Ahmed Mekki, Tighilt Fatma-Zohra, Rachid Mahmoud

Abstract:

The aim of this study was to synthesize and characterize conducting polymer composites of polypyrrole and graphene, including pristine and surface-treated graphene (PPy/GO, PPy/rGO, and PPy/rGO-ArCOOH), for use as sensitive elements in a homemade chemiresistive module for on-line detection of nitrogen oxides vapors. The chemiresistive module was prepared, characterized, and evaluated for performance. Structural and morphological characterizations of the composite were carried out using FTIR, Raman spectroscopy, and XRD analyses. After exposure to NO and NO₂ gases in both static and dynamic modes, the sensitivity, selectivity, limit of detection, and response time of the sensor were determined at ambient temperature. The resulting sensor showed high sensitivity, selectivity, and reversibility, with a low limit of detection of 1 ppm. A composite of polypyrrole and graphene functionalized with aryl 4-carboxy benzene diazonium salt was synthesized and characterized using FTIR, scanning electron microscopy, transmission electron microscopy, UV-visible, and X-ray diffraction. The PPy-rGOArCOOH composite exhibited a good electrical resistance response to NO₂ at room temperature and showed enhanced NO₂-sensing properties compared to PPy-rGO thin films. The selectivity and stability of the NO₂ sensor based on the PPy/rGO-ArCOOH nanocomposite were also investigated.

Keywords: conducting polymers, surface treated graphene, diazonium salt, polypyrrole, Nitrogen oxide sensing

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Authors: Muhammad Ali Sarwar, Muhammad Farooq, Nayab Hassan, Hammad Hassan

Abstract:

Pakistan is highly recognized for its agriculture and is well known for producing substantial amounts of wheat, cotton, and sugarcane. However, some factors contribute to a decline in crop quality and a reduction in overall output. One of the main factors contributing to this decline is the presence of weed and its late detection. This process of detection is manual and demands a detailed inspection to be done by the farmer itself. But by the time detection of weed, the farmer will be able to save its cost and can increase the overall production. The focus of this research is to identify and classify the four main types of weeds (Small-Flowered Cranesbill, Chick Weed, Prickly Acacia, and Black-Grass) that are prevalent in our region’s major crops. In this work, we implemented three different deep learning techniques: YOLO-v5, Inception-v3, and Deep CNN on the same Dataset, and have concluded that deep convolutions neural network performed better with an accuracy of 97.45% for such classification. In relative to the state of the art, our proposed approach yields 2% better results. We devised the architecture in an efficient way such that it can be used in real-time.

Keywords: deep convolution networks, Yolo, machine learning, agriculture

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Authors: Mohd Ruzlin Mohd Mokhtar

Abstract:

At present, most operators who are working on projects for utilities such as power, water, oil, gas, telecommunication and sewerage are using technologies e.g. Total station, Global Positioning System (GPS), Electromagnetic Locator (EML) and Ground Penetrating Radar (GPR) to perform underground utility mapping. With the increase in popularity of Horizontal Directional Drilling (HDD) method among the local authorities and asset owners, most of newly installed underground utilities need to use the HDD method. HDD method is seen as simple and create not much disturbance to the public and traffic. Thus, it was the preferred utilities installation method in most of areas especially in urban areas. HDDs were installed much deeper than exiting utilities (some reports saying that HDD is averaging 5 meter in depth). However, this impacts the accuracy or ability of existing underground utility mapping technologies. In most of Malaysia underground soil condition, those technologies were limited to maximum of 3 meter depth. Thus, those utilities which were installed much deeper than 3 meter depth could not be detected by using existing detection tools. The accuracy and reliability of existing underground utility mapping technologies or work procedure were in doubt. Thus, a mitigation action plan is required. While installing new utility using Horizontal Directional Drilling (HDD) method, a more accurate underground utility mapping can be achieved by using Gyro OMU compared to existing practice using e.g. EML and GPR. Gyro OMU is a method to accurately identify the location of HDD thus this mapping can be used or referred to avoid those cost of breakdown due to future HDD works which can be caused by inaccurate underground utility mapping.

Keywords: Gyro Orientation Measurement Unit (Gyro OMU), Horizontal Directional Drilling (HDD), Ground Penetrating Radar (GPR), Electromagnetic Locator (EML)

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Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

Abstract:

Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by density-based time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., mean value, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one class classifier (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, a new anomaly detector strategy is proposed, namely one class classifier neural network two (OCCNN2), which exploit the classification capability of standard classifiers in an anomaly detection problem, finding the standard class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimation. The coarse estimation uses classics OCC techniques, while the fine estimation is performed through a feedforward neural network (NN) trained that exploits the boundaries estimated in the coarse step. The detection algorithms vare then compared with known methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and auto-associative neural network (ANN). In many cases, the proposed solution increases the performance with respect to the standard OCC algorithms in terms of F1 score and accuracy. In particular, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 96% with the proposed method.

Keywords: anomaly detection, frequencies selection, modal analysis, neural network, sensor network, structural health monitoring, vibration measurement

Procedia PDF Downloads 120