Search results for: top load washing machine

Authors: Mohammad M. Khan, Pankaj Agarwal

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The present investigation deals with the microstructures, mechanical and detailed wear characteristics of in situ TiC particles reinforced zinc aluminum-based metal matrix composites. The composites have been synthesized by liquid metallurgy route using vortex technique. The composite was found to be harder than the matrix alloy due to high hardness of the dispersoid particles therein. The former was also lower in ultimate tensile strength and ductility as compared to the matrix alloy. This could be explained to be due to the use of coarser size dispersoid and larger interparticle spacing. Reasonably uniform distribution of the dispersoid phase in the alloy matrix and good interfacial bonding between the dispersoid and matrix was observed. The composite exhibited predominantly brittle mode of fracture with microcracking in the dispersoid phase indicating effective easy transfer of load from matrix to the dispersoid particles. To study the wear behavior of the samples three different types of tests were performed namely: (i) sliding wear tests using a pin on disc machine under dry condition, (ii) high stress (two-body) abrasive wear tests using different combinations of abrasive media and specimen surfaces under the conditions of varying abrasive size, traversal distance and load, and (iii) low-stress (three-body) abrasion tests using a rubber wheel abrasion tester at various loads and traversal distances using different abrasive media. In sliding wear test, significantly lower wear rates were observed in the case of base alloy over that of the composites. This has been attributed to the poor room temperature strength as a result of increased microcracking tendency of the composite over the matrix alloy. Wear surfaces of the composite revealed the presence of fragmented dispersoid particles and microcracking whereas the wear surface of matrix alloy was observed to be smooth with shallow grooves. During high-stress abrasion, the presence of the reinforcement offered increased resistance to the destructive action of the abrasive particles. Microcracking tendency was also enhanced because of the reinforcement in the matrix. The negative effect of the microcracking tendency was predominant by the abrasion resistance of the dispersoid. As a result, the composite attained improved wear resistance than the matrix alloy. The wear rate increased with load and abrasive size due to a larger depth of cut made by the abrasive medium. The wear surfaces revealed fine grooves, and damaged reinforcement particles while subsurface regions revealed limited plastic deformation and microcracking and fracturing of the dispersoid phase. During low-stress abrasion, the composite experienced significantly less wear rate than the matrix alloy irrespective of the test conditions. This could be explained to be due to wear resistance offered by the hard dispersoid phase thereby protecting the softer matrix against the destructive action of the abrasive medium. Abraded surfaces of the composite showed protrusion of dispersoid phase. The subsurface regions of the composites exhibited decohesion of the dispersoid phase along with its microcracking and limited plastic deformation in the vicinity of the abraded surfaces.

Keywords: abrasive wear, liquid metallurgy, metal martix composite, SEM

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Authors: Claus N. Holm, Oliver F. Grooss, Robert A. Alphinas

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This research strives to predict the remaining product life cycle of a song on radio after it has been played for one or two months. The best results were achieved using a k-d tree to calculate the most similar songs to the test songs and use a Random Forest model to forecast radio plays. An 82.78% and 83.44% accuracy is achieved for the two time periods, respectively. This explorative research leads to over 4500 test metrics to find the best combination of models and pre-processing techniques. Other algorithms tested are KNN, MLP and CNN. The features only consist of daily radio plays and use no musical features.

Keywords: hit song science, product life cycle, machine learning, radio

Procedia PDF Downloads 153

Authors: Nakarin Prempri

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Single-phase induction motors are widely used in industry. Most manufacturing processes use capacitor-run single-phase induction motors to drive mechanical loads. The selection of a suitable motor for driving is important. The optimum operating range of the motor can help the motor operate efficiently. Thus, this paper presents an operating range analysis of capacitor-run single-phase induction motors and a determination of suitability between motor and mechanical loads. an observational study found that the optimum operating range of the motor can be used to determine the suitability between the motor and the mechanical load. Such considerations ensure that the motor uses no more current than necessary and operates efficiently.

Keywords: single phase induction motor, operating range, torque curve, efficiency curve

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Authors: Rohit Vashistha, Amit Kumar Gupta, G. P. Ravishankar, Mahesh P. Padwale

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Accurate thrust measurement is required for aircraft during takeoff and after ski-jump. In a developmental aircraft, takeoff from ship is extremely critical and thrust produced by the engine should be known to the pilot before takeoff so that if thrust produced is not sufficient then take-off can be aborted and accident can be avoided. After ski-jump, thrust produced by engine is required because the horizontal speed of aircraft is less than the normal takeoff speed. Engine should be able to produce enough thrust to provide nominal horizontal takeoff speed to the airframe within prescribed time limit. The contemporary low bypass gas turbine engines generally have three mounts where the two side mounts transfer the engine thrust to the airframe. The third mount only takes the weight component. It does not take any thrust component. In the present method of thrust estimation, the strain gauging of the two side mounts is carried out. The strain produced at various power settings is used to estimate the thrust produced by the engine. The quarter Wheatstone bridge is used to acquire the strain data. The engine mount assembly is subjected to Universal Test Machine for determination of equivalent elasticity of assembly. This elasticity value is used in the analytical approach for estimation of engine thrust. The estimated thrust is compared with the test bed load cell thrust data. The experimental strain data is also compared with strain data obtained from FEM analysis. Experimental setup: The strain gauge is mounted on the tapered portion of the engine mount sleeve. Two strain gauges are mounted on diametrically opposite locations. Both of the strain gauges on the sleeve were in the horizontal plane. In this way, these strain gauges were not taking any strain due to the weight of the engine (except negligible strain due to material's poison's ratio) or the hoop's stress. Only the third mount strain gauge will show strain when engine is not running i.e. strain due to weight of engine. When engine starts running, all the load will be taken by the side mounts. The strain gauge on the forward side of the sleeve was showing a compressive strain and the strain gauge on the rear side of the sleeve shows a tensile strain. Results and conclusion: the analytical calculation shows that the hoop stresses dominate the bending stress. The estimated thrust by strain gauge shows good accuracy at higher power setting as compared to lower power setting. The accuracy of estimated thrust at max power setting is 99.7% whereas at lower power setting is 78%.

Keywords: engine mounts, finite elements analysis, strain gauge, stress

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Authors: Thohidul Islam, Md. Hamidul Haque, Robin Kumar Biswas

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Floods are one of the deadliest natural disasters which are very complex to model; however, machine learning is opening the door for more reliable and accurate flood prediction. In this research, a multilayer perceptron neural network (MLP) is developed to model the rainfall-water level relation, in a subtropical monsoon climatic region of the Bangladesh-India border. Our experiments show promising empirical results to forecast the water level for 1 day lead time. Our best performing MLP model achieves 98.7% coefficient of determination with lower model complexity which surpasses previously reported results on similar forecasting problems.

Keywords: flood forecasting, machine learning, multilayer perceptron network, regression

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Authors: Lakdar Sadi-Haddad

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The past few years study of very high speed electrical drives have seen a resurgence of interest. An inventory of the number of scientific papers and patents dealing with the subject makes it relevant. In fact democratization of magnetic bearing technology is at the origin of recent developments in high speed applications. These machines have as main advantage a much higher power density than the state of the art. Nevertheless particular attention should be paid to the design of the inverter as well as control and command. Surface mounted permanent magnet synchronous machine is the most appropriate technology to address high speed issues. However, it has the drawback of using a carbon sleeve to contain magnets that could tear because of the centrifugal forces generated in rotor periphery. Carbon fiber is well known for its mechanical properties but it has poor heat conduction. It results in a very bad evacuation of eddy current losses induce in the magnets by time and space stator harmonics. The three-phase inverter is the main harmonic source causing eddy currents in the magnets. In high speed applications such harmonics are harmful because on the one hand the characteristic impedance is very low and on the other hand the ratio between the switching frequency and that of the fundamental is much lower than that of the state of the art. To minimize the impact of these harmonics a first lever is to use strategy of modulation producing low harmonic distortion while the second is to introduce a sinus filter between the inverter and the machine to smooth voltage and current waveforms applied to the machine. Nevertheless, in very high speed machine the interaction of the processes mentioned above may introduce particular harmonics that can irreversibly damage the system: harmonics at the resonant frequency, harmonics at the shaft mode frequency, subharmonics etc. Some studies address these issues but treat these phenomena with separate solutions (specific strategy of modulation, active damping methods ...). The purpose of this paper is to present a complete new active harmonic compensation algorithm based on an improvement of the standard vector control as a global solution to all these issues. This presentation will be based on a complete theoretical analysis of the processes leading to the generation of such undesired harmonics. Then a state of the art of available solutions will be provided before developing the content of a new active harmonic compensation algorithm. The study will be completed by a validation study using simulations and practical case on a high speed machine.

Keywords: active harmonic compensation, eddy current losses, high speed machine

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Authors: J. Lu, H. Li, F. Cole

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This paper proposes a high-frequency rotary transformer (HFRT) for a separately excited synchronous machine (SESM) used in a flywheel energy storage system. The SESM can eliminate and reduce rare earth permanent magnet (REPM) usage and provide a better performance in renewable energy systems. However, the major drawback of such SESM is the necessity of brushes and slip rings to supply the field current, which increases the maintenance cost and operation reliability. To overcome these problems, an HFRT integrated with SiC semiconductor devices can replace brushes and slip rings in the SESM. The proposed HFRT features a high-frequency magnetic ferrite for both the stationary part as the transformer primary and the rotating part as the transformer secondary, as well as an air gap, allowing safe operation at high rotational speeds. Hence, this rotary transformer can enable the adoption of a wound rotor synchronous machine (WRSM). The HFRT, working at over 100kHz operating frequency, exhibits excellent performance of power efficiency and significant size reduction. The experimental validations to support the theoretical findings have been provided.

Keywords: brushes and slip rings, flywheel energy storage, high frequency rotary transformer, separately excited synchronous machine

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Authors: Yang Zheng, Wei Sun

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This paper describes a new approach which can be used to interpret the experimental creep deformation data obtained from miniaturized thin plate bending specimen test to the corresponding uniaxial data based on an inversed application of the reference stress method. The geometry of the thin plate is fully defined by the span of the support, l, the width, b, and the thickness, d. Firstly, analytical solutions for the steady-state, load-line creep deformation rate of the thin plates for a Norton’s power law under plane stress (b → 0) and plane strain (b → ∞) conditions were obtained, from which it can be seen that the load-line deformation rate of the thin plate under plane-stress conditions is much higher than that under the plane-strain conditions. Since analytical solution is not available for the plates with random b-values, finite element (FE) analyses are used to obtain the solutions. Based on the FE results obtained for various b/l ratios and creep exponent, n, as well as the analytical solutions under plane stress and plane strain conditions, an approximate, numerical solutions for the deformation rate are obtained by curve fitting. Using these solutions, a reference stress method is utilised to establish the conversion relationships between the applied load and the equivalent uniaxial stress and between the creep deformations of thin plate and the equivalent uniaxial creep strains. Finally, the accuracy of the empirical solution was assessed by using a set of “theoretical” experimental data.

Keywords: bending, creep, thin plate, materials engineering

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Authors: Soheila Sadeghi

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In construction projects, predicting project performance metrics accurately is essential for effective management and successful delivery. However, conventional methods often depend on fixed baseline plans, disregarding the evolving nature of project progress and external influences. To address this issue, we introduce a distinct approach based on machine learning to forecast key performance indicators, such as cost variance and earned value, for each Work Breakdown Structure (WBS) category within an urban road reconstruction project. Our proposed model leverages time series forecasting techniques, namely Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, to predict future performance by analyzing historical data and project progress. Additionally, the model incorporates external factors, including weather patterns and resource availability, as features to improve forecast accuracy. By harnessing the predictive capabilities of machine learning, our performance forecasting model enables project managers to proactively identify potential deviations from the baseline plan and take timely corrective measures. To validate the effectiveness of the proposed approach, we conduct a case study on an urban road reconstruction project, comparing the model's predictions with actual project performance data. The outcomes of this research contribute to the advancement of project management practices in the construction industry by providing a data-driven solution for enhancing project performance monitoring and control.

Keywords: project performance forecasting, machine learning, time series forecasting, cost variance, schedule variance, earned value management

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Authors: Mirjana Petronijević, Sanja Panić, Aleksandra Cvetanović, Branko Kordić, Nenad Grba

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Enzyme peroxidases are biological catalysts and play a major role in phenolic wastewater treatments and other environmental applications. The most studied species from the peroxidases family is horseradish peroxidase (HRP). In environmental processes, HRP could be used in its free or immobilized form. Enzyme immobilization onto solid support is performed to improve the enzyme properties, prolong its lifespan and operational stability and allow its reuse in industrial applications. One of the enzyme supports of a newer generation is magnetic particles (MPs). Fe₃O₄ MPs are the most widely pursued immobilization of enzymes owing to their remarkable advantages of biocompatibility and non-toxicity. Also, MPs can be easily separated and recovered from the water by applying an external magnetic field. On the other hand, metals and metal oxides are not suitable for the covalent binding of enzymes, so it is necessary to perform their surface modification. Fe₃O₄ MPs functionalization could be performed during the process of their synthesis if it takes place in the presence of plant extracts. Extracts of plant material, such as wild plants, herbs, even waste materials of the food and agricultural industry (bark, shell, leaves, peel), are rich in various bioactive components such as polyphenols, flavonoids, sugars, etc. When the synthesis of magnetite is performed in the presence of plant extracts, bioactive components are incorporated into the surface of the magnetite, thereby affecting its functionalization. In this paper, the suitability of bio-magnetite as solid support for covalent immobilization of HRP across glutaraldehyde was examined. The activity of immobilized HRP at different pH values (4-9) and temperatures (20-80°C) and reusability were examined. Bio-MP was synthesized by co-precipitation method from Fe(II) and Fe(III) sulfate salts in the presence of water extract of the Allium cepa peel. The water extract showed 81% of antiradical potential (according to DPPH assay), which is connected with the high content of polyphenols. According to the FTIR analysis, the bio-magnetite contains oxygen functional groups (-OH, -COOH, C=O) suitable for binding to glutaraldehyde, after which the enzyme is covalently immobilized. The immobilized enzyme showed high activity at ambient temperature and pH 7 (30 U/g) and retained ≥ 80% of its activity at a wide range of pH (5-8) and temperature (20-50°C). The HRP immobilized onto bio-MPs showed remarkable stability towards temperature and pH variations compared to the free enzyme form. On the other hand, immobilized HRP showed low reusability after the first washing cycle enzyme retains 50% of its activity, while after the third washing cycle retains only 22%.

Keywords: bio-magnetite, enzyme immobilization, water extracts, environmental protection

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Authors: Rida Kanwal, Wang Yuhui, Song Weiguo

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Wildfires are one of the most prominent threats to ecosystems, human health, and economic activities, with wind acting as a critical driving factor. This study combines machine learning (ML) and remote sensing (RS) to assess the effects of wind on wildfires in Chongqing Province from August 16-23, 2022. Landsat 8 satellite images were used to estimate the difference normalized burn ratio (dNBR), representing prefire and postfire vegetation conditions. Wind data was analyzed through geographic information system (GIS) mapping. Correlation analysis between wind speed and fire radiative power (FRP) revealed a significant relationship. An autoregressive integrated moving average (ARIMA) model was developed for wind forecasting, and linear regression was applied to determine the effect of wind speed on FRP. The results identified high wind speed as a key factor contributing to the surge in FRP. Wind-rose plots showed winds blowing to the northwest (NW), aligning with the wildfire spread. This model was further validated with data from other provinces across China. This study integrated ML, RS, and GIS to analyze wildfire behavior, providing effective strategies for prediction and management.

Keywords: wildfires, machine learning, remote sensing, wind speed, GIS, wildfire behavior

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Authors: O. D. Eniowo

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“Rewashing” as it is commonly called, involves the process of scooping out and washing chunks of mud from a closed alluvial gold mine site with the purpose of extracting any leftover gold deposits in the site. It is usually carried out by illegal miners who infiltrate closed mine sites with the goal of scavenging for any leftover gold deposits. Expectedly, the practice gives little or no regard for environmental protection. This paper examines the process of “rewashing” in a mining community in Nigeria. It then discusses the looming danger it portends for health, safety, and the environment. The study draws lessons from these occurrences to examine and discuss fit-for-purpose mine closure plans that could be adopted by gold mines in Nigeria and other sub-Saharan African countries.

Keywords: mine planning, mine closure, illegal mining, artisanal mining, environmental sustainability

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Authors: Hamid Chaikhy, Mouna Essaadi, Aziz El Afia

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This paper presents an experimental implementation of a new direct torque control strategy of induction machine called twelve sectors direct torque control strategy (12_DTC) using DSPACE TMS 320F2812.The aim of this work is to give an experimental performance analysis of 12_DTC in term of torque, currents distortions and stator flux, to validate simulation results obtained in previous works.

Keywords: 12_DTC, DSPACE TMS 320F2812 torque, stator flux, currents distortions, experimental performance analysis

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Authors: Hesham Hamdy Abdulmohsen, Ahmed Shawky Abdel Aziz, Mona Fawzy Aldaghma

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Helical piles have gained significant attention as efficient alternatives for deep foundations due to their rapid installation process and dual functionality in compression and tension. They experience various combinations of axial and lateral loads. While extensive research has explored helical pile behavior under individual axial or lateral loads, the effects of combined axial compression and lateral loads still need further study. This paper compares experimental and numerical (PLAXIS-3D) results for vertical helical-pile groups under combined loads. The study aims to clarify the impact of key factors, including helix location and lateral load direction, on the lateral capacity of helical-pile groups and, consequently, their overall efficiency. The study concludes that the lateral capacity of the helical-pile group significantly depends on the helix location within the pile shaft length. Optimal lateral performance occurs when helices are positioned at a depth ratio of H/L = 0.4. Furthermore, rectangular plan distribution groups exhibit greater lateral capacity when subjected to lateral loads aligned with their long axis. The presence of vertical compression loading enhances the lateral capacity of the group, with the specific enhancement depending on the value of the vertical compression load, lateral load direction, and helix location.

Keywords: experimental, numerical model, lateral loading, group efficiency, helical piles

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Authors: Ju-Young Choi, Ki-Il Song, Kyoung-Yul Kim

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During Tunnel Boring Machine (TBM) tunnel excavation, backfill grout should be injected after the installation of segment lining to ensure the stability of the tunnel and to minimize ground deformation. If grouting is not sufficient to fill the gap between the segments and rock mass, hydraulic pressures occur in the void, which can negatively influence the stability of the tunnel. Recently the tendency to use TBM tunnelling method to replace the drill and blast(NATM) method is increasing. However, there are only a few studies of evaluation of backfill grout. This study evaluates the TBM tunnel backfill state using Impact-Echo(IE). 3-layers, segment-grout-rock mass, are simulated by FLAC 2D, FDM-based software. The signals obtained from numerical analysis and IE test are analyzed by Short-Time Fourier Transform(STFT) in time domain, frequency domain, and time-frequency domain. The result of this study can be used to evaluate the quality of backfill grouting in tail void.

Keywords: tunnel boring machine, backfill grout, impact-echo method, time-frequency domain analysis, finite difference method

Procedia PDF Downloads 264

Authors: Damiaa Saad Khudor

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The paper describes an investigation for the thermal design of a fluidized bed cooler and prediction of heat transfer rate among the media categories. It is devoted to the thermal design of such equipment and their application in the industrial fields. It outlines the strategy for the fluidization heat transfer mode and its implementation in industry. The thermal design for fluidized bed cooler is used to furnish a complete design for a fluidized bed cooler of Sodium Bicarbonate. The total thermal load distribution between the air-solid and water-solid along the cooler is calculated according to the thermal equilibrium. The step by step technique was used to accomplish the thermal design of the fluidized bed cooler. It predicts the load, air, solid and water temperature along the trough. The thermal design for fluidized bed cooler revealed to the installation of a heat exchanger consists of (65) horizontal tubes with (33.4) mm diameter and (4) m length inside the bed trough.

Keywords: fluidization, powder technology, thermal design, heat exchangers

Procedia PDF Downloads 511

Authors: Jaimala Ghambir, Tilak Thakur, Puneet Chawla

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As per the statistical data, the Doubly-fed Induction Generator (DFIG) based wind turbine with variable speed and variable pitch control is the most common wind turbine in the growing wind market. This machine is usually used on the grid connected wind energy conversion system to satisfy grid code requirements such as grid stability, fault ride through (FRT), power quality improvement, grid synchronization and power control etc. Though the requirements are not fulfilled directly by the machine, the control strategy is used in both the stator as well as rotor side along with power electronic converters to fulfil the requirements stated above. To satisfy the grid code requirements of wind turbine, usually grid side converter is playing a major role. So in order to improve the operation capacity of wind turbine under critical situation, the intensive study of both machine side converter control and grid side converter control is necessary In this paper DFIG is modeled using power components as variables and the performance of the DFIG system is analysed under grid voltage fluctuations. The voltage fluctuations are made by lowering and raising the voltage values in the utility grid intentionally for the purpose of simulation keeping in view of different grid disturbances.

Keywords: DFIG, dynamic modeling, DPC, sag, swell, voltage fluctuations, FRT

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Authors: Oluwaponmile D. Alao

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In this paper, two models have been developed to ascertain the best network needed for diagnosis of breast cancer. Breast cancer has been a disease that required the attention of the medical practitioner. Experience has shown that misdiagnose of the disease has been a major challenge in the medical field. Therefore, designing a system with adequate performance for will help in making diagnosis of the disease faster and accurate. In this paper, two models: backpropagation neural network and support vector machine has been developed. The performance obtained is also compared with other previously obtained algorithms to ascertain the best algorithms.

Keywords: breast cancer, data mining, neural network, support vector machine

Procedia PDF Downloads 345

Authors: Mohamed Dhouibi, Bogdan Stirbu, Chabotier André, Marc Pirlot

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Internal barrel wear is a major factor affecting the performance of small caliber guns in their different life phases. Wear analysis is, therefore, a very important process for understanding how wear occurs, where it takes place, and how it spreads with the aim on improving the accuracy and effectiveness of small caliber weapons. This paper discusses the measurement and analysis of combustion chamber wear for a small-caliber gun using a Coordinate Measuring Machine (CMM). Initially, two different NATO small caliber guns: 5.56x45mm and 7.62x51mm, are considered. A Micura Zeiss Coordinate Measuring Machine (CMM) equipped with the VAST XTR gold high-end sensor is used to measure the inner profile of the two guns every 300-shot cycle. The CMM parameters, such us (i) the measuring force, (ii) the measured points, (iii) the time of masking, and (iv) the scanning velocity, are investigated. In order to ensure minimum measurement error, a statistical analysis is adopted to select the reliable CMM parameters combination. Next, two measurement strategies are developed to capture the shape and the volume of each gun chamber. Thus, a task-specific measurement uncertainty (TSMU) analysis is carried out for each measurement plan. Different approaches of TSMU evaluation have been proposed in the literature. This paper discusses two different techniques. The first is the substitution method described in ISO 15530 part 3. This approach is based on the use of calibrated workpieces with similar shape and size as the measured part. The second is the Monte Carlo simulation method presented in ISO 15530 part 4. Uncertainty evaluation software (UES), also known as the Virtual Coordinate Measuring Machine (VCMM), is utilized in this technique to perform a point-by-point simulation of the measurements. To conclude, a comparison between both approaches is performed. Finally, the results of the measurements are verified through calibrated gauges of several dimensions specially designed for the two barrels. On this basis, an experimental database is developed for further analysis aiming to quantify the relationship between the volume of wear and the muzzle velocity of small caliber guns.

Keywords: coordinate measuring machine, measurement uncertainty, erosion and wear volume, small caliber guns

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Authors: Mohamed Arbi Khlifi, Badr M. Alshammari

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This paper discusses the general methods to saturation in the steady-state, two axis (d & q) frame models of synchronous machines. In particular, the important role of the magnetic coupling between the d-q axes (cross-magnetizing phenomenon), is demonstrated. For that purpose, distinct methods of saturation modeling of dumper synchronous machine with cross-saturation are identified, and detailed models synthesis in d-q axes. A number of models are given in the final developed form. The procedure and the novel models are verified by a critical application to prove the validity of the method and the equivalence between all developed models is reported. Advantages of some of the models over the existing ones and their applicability are discussed.

Keywords: cross-magnetizing, models synthesis, synchronous machine, saturated modeling, state-space vectors

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Authors: Johan Loeckx, Joeri Bultheel

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Recently, the application of deep learning to music has gained popularity. Its true potential, however, has been largely unexplored. In this paper, a new idea for representing the dynamic behavior of music is proposed. A ”forward” conditional RBM takes into account not only preceding but also future samples during training. Though this may sound controversial at first sight, it will be shown that it makes sense from a musical and neuro-cognitive perspective. The model is applied to reconstruct music based upon the first notes and to improvise in the musical style of a composer. Different to expectations, reconstruction accuracy with respect to a regular CRBM with the same order, was not significantly improved. More research is needed to test the performance on unseen data.

Keywords: deep learning, restricted boltzmann machine, music generation, conditional restricted boltzmann machine (CRBM)

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Authors: Nameer A. Alwash, Dunia A. Abd AlRadha, Arshed M. Aljanaby

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This study present an experimental investigation of composite behavior for hybrid reinforced concrete slab on girder from locale material in Iraq, ordinary concrete, NC, in slab and reactive powder concrete in girder ,RPC, with steel fibers of different types(straight, hook, and mix between its), tested as simply supported span subjected under two point loading, also study effects on overall behavior such as the ultimate load, crack width and deflection. The result shows that the most suitable for production girder from RPC by using 2% micro straight steel fiber, in terms of ultimate strength and min crack width. Also the results shows that using RPC in girder of composite section increased ultimate load by 79% when compared with same section made of NC, and increased the shear strength which erased the effect of changing reinforcement in shear, and using RPC in girder and epoxy (in shear transfer between composite section) (meaning no stirrups) equivalent presence of shear reinforcement by 90% when compared with same section using Φ8@100 as shear reinforcement. And the result shows that changing the cross section girder shape of the composite section to inverted T, with same section area, increased the ultimate load by 5% when compared with same section of rectangular shape girder.

Keywords: reactive powder concrete, RPC, hybrid concrete, composite section, RC girder, RC slab, shear connecters, inverted T section, shear reinforcment, shear span over effective depth

Procedia PDF Downloads 359

Authors: Damtew Samson Zerihun

Abstract:

This paper primarily describes the concept of a hierarchical safety control (HSC) in discrete manufacturing to up-hold productivity with human intervention and machine failures using a systematic approach, through increasing the system availability and using additional knowledge on machines so as to improve the human machine collaboration (HMC). It also highlights the implemented PLC safety algorithm, in applying this generic concept to a concrete pro-duction line using a lab demonstrator called FATIE (Factory Automation Test and Integration Environment). Furthermore, the paper describes a model and provide a systematic representation of human-machine collabora-tion in discrete manufacturing and to this end, the Hierarchical Safety Control concept is proposed. This offers a ge-neric description of human-machine collaboration based on Finite State Machines (FSM) that can be applied to vari-ous discrete manufacturing lines instead of using ad-hoc solutions for each line. With its reusability, flexibility, and extendibility, the Hierarchical Safety Control scheme allows upholding productivity while maintaining safety with reduced engineering effort compared to existing solutions. The approach to the solution begins with a successful partitioning of different zones around the Integrated Manufacturing System (IMS), which are defined by operator tasks and the risk assessment, used to describe the location of the human operator and thus to identify the related po-tential hazards and trigger the corresponding safety functions to mitigate it. This includes selective reduced speed zones and stop zones, and in addition with the hierarchical safety control scheme and advanced safety functions such as safe standstill and safe reduced speed are used to achieve the main goals in improving the safe Human Ma-chine Collaboration and increasing the productivity. In a sample scenarios, It is shown that an increase of productivity in the order of 2.5% is already possible with a hi-erarchical safety control, which consequently under a given assumptions, a total sum of 213 € could be saved for each intervention, compared to a protective stop reaction. Thereby the loss is reduced by 22.8%, if occasional haz-ard can be refined in a hierarchical way. Furthermore, production downtime due to temporary unavailability of safety devices can be avoided with safety failover that can save millions per year. Moreover, the paper highlights the proof of the development, implementation and application of the concept on the lab demonstrator (FATIE), where it is realized on the new safety PLCs, Drive Units, HMI as well as Safety devices in addition to the main components of the IMS.

Keywords: discrete automation, hierarchical safety controller, human machine collaboration, programmable logical controller

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Authors: S. Vasundra, D. Venkatesh

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Wireless networks have gone through an extraordinary growth in the past few years, and will keep on playing a crucial role in future data communication. The present wireless networks aim to make communication possible anywhere and anytime. With the converging of mobile and wireless communications with Internet services, the boundary between mobile personal telecommunications and wireless computer networks is disappearing. Wireless networks of the next generation need the support of all the advances on new architectures, standards, and protocols. Since an ad hoc network may consist of a large number of mobile hosts, this imposes a significant challenge on the design of an effective and efficient routing protocol that can work well in an environment with frequent topological changes. This paper proposes improved ant colony optimization (IACO) technique. It also maintains load balancing in wireless networks. The simulation results show that the proposed IACO performs better than existing routing techniques.

Keywords: wireless networks, ant colony optimization, load balancing, architecture

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Authors: Kasra Majbouri Yazdi, Adel Majbouri Yazdi, Saeid Khodayi, Jingyu Hou, Wanlei Zhou, Saeed Saedy

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Fake news and false information are big challenges of all types of media, especially social media. There is a lot of false information, fake likes, views and duplicated accounts as big social networks such as Facebook and Twitter admitted. Most information appearing on social media is doubtful and in some cases misleading. They need to be detected as soon as possible to avoid a negative impact on society. The dimensions of the fake news datasets are growing rapidly, so to obtain a better result of detecting false information with less computation time and complexity, the dimensions need to be reduced. One of the best techniques of reducing data size is using feature selection method. The aim of this technique is to choose a feature subset from the original set to improve the classification performance. In this paper, a feature selection method is proposed with the integration of K-means clustering and Support Vector Machine (SVM) approaches which work in four steps. First, the similarities between all features are calculated. Then, features are divided into several clusters. Next, the final feature set is selected from all clusters, and finally, fake news is classified based on the final feature subset using the SVM method. The proposed method was evaluated by comparing its performance with other state-of-the-art methods on several specific benchmark datasets and the outcome showed a better classification of false information for our work. The detection performance was improved in two aspects. On the one hand, the detection runtime process decreased, and on the other hand, the classification accuracy increased because of the elimination of redundant features and the reduction of datasets dimensions.

Keywords: clustering, fake news detection, feature selection, machine learning, social media, support vector machine

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Authors: Jacky Liu

Abstract:

This paper develops a team sports outcome prediction system with potential for wide-ranging applications across various disciplines. Despite significant advancements in predictive analytics, existing studies in sports outcome predictions possess considerable limitations, including insufficient feature engineering and underutilization of advanced machine learning techniques, among others. To address these issues, we extend the Sports Cross Industry Standard Process for Data Mining (SRP-CRISP-DM) framework and propose a unique, comprehensive predictive system, using National Basketball Association (NBA) data as an example to test this extended framework. Our approach follows a holistic methodology in feature engineering, employing both Time Series and Non-Time Series Data, as well as conducting Explanatory Data Analysis and Feature Selection. Furthermore, we contribute to the discourse on target variable choice in team sports outcome prediction, asserting that point spread prediction yields higher profits as opposed to game-winner predictions. Using machine learning algorithms, particularly XGBoost, results in a significant improvement in predictive accuracy of team sports outcomes. Applied to point spread betting strategies, it offers an astounding annual return of approximately 900% on an initial investment of $100. Our findings not only contribute to academic literature, but have critical practical implications for sports betting. Our study advances the understanding of team sports outcome prediction a burgeoning are in complex system predictions and pave the way for potential profitability and more informed decision making in sports betting markets.

Keywords: machine learning, team sports, game outcome prediction, sports betting, profits simulation

Procedia PDF Downloads 101

Authors: Jian Zheng, Yoshiyasu Takahashi, Yuichi Kobayashi, Tatsuhiro Sato

Abstract:

In the study, we present a conceptual framework for developing a scheduling system that can generate self-explanatory and easy-understanding schedules. To this end, a user interface is conceived to help planners record factors that are considered crucial in scheduling, as well as internal and external sources relating to such factors. A hybrid approach combining machine learning and constraint programming is developed to generate schedules and the corresponding factors, and accordingly display them on the user interface. Effects of the proposed system on scheduling are discussed, and it is expected that scheduling efficiency and system understandability will be improved, compared with previous scheduling systems.

Keywords: constraint programming, factors considered in scheduling, machine learning, scheduling system

Procedia PDF Downloads 322

Authors: Miguel Contreras, David Long, Will Bachman

Abstract:

Background: Microscopy plays a central role in cell and developmental biology. In particular, fluorescence microscopy can be used to visualize specific cellular components and subsequently quantify their morphology through development of virtual-cell models for study of effects of mechanical forces on cells. However, there are challenges with these imaging experiments, which can make it difficult to quantify cell morphology: inconsistent results, time-consuming and potentially costly protocols, and limitation on number of labels due to spectral overlap. To address these challenges, the objective of this project is to develop an automatic computational machine learning pipeline to predict cellular components morphology for virtual-cell generation based on fluorescence cell membrane confocal z-stacks. Methods: Registered confocal z-stacks of nuclei and cell membrane of endothelial cells, consisting of 20 images each, were obtained from fluorescence confocal microscopy and normalized through software pipeline for each image to have a mean pixel intensity value of 0.5. An open source machine learning algorithm, originally developed to predict fluorescence labels on unlabeled transmitted light microscopy cell images, was trained using this set of normalized z-stacks on a single CPU machine. Through transfer learning, the algorithm used knowledge acquired from its previous training sessions to learn the new task. Once trained, the algorithm was used to predict morphology of nuclei using normalized cell membrane fluorescence images as input. Predictions were compared to the ground truth fluorescence nuclei images. Results: After one week of training, using one cell membrane z-stack (20 images) and corresponding nuclei label, results showed qualitatively good predictions on training set. The algorithm was able to accurately predict nuclei locations as well as shape when fed only fluorescence membrane images. Similar training sessions with improved membrane image quality, including clear lining and shape of the membrane, clearly showing the boundaries of each cell, proportionally improved nuclei predictions, reducing errors relative to ground truth. Discussion: These results show the potential of pre-trained machine learning algorithms to predict cell morphology using relatively small amounts of data and training time, eliminating the need of using multiple labels in immunofluorescence experiments. With further training, the algorithm is expected to predict different labels (e.g., focal-adhesion sites, cytoskeleton), which can be added to the automatic machine learning pipeline for direct input into Principal Component Analysis (PCA) for generation of virtual-cell mechanical models.

Keywords: cell morphology prediction, computational machine learning, fluorescence microscopy, virtual-cell models

Procedia PDF Downloads 203

Authors: Preston Nabors, Nasseh Tabrizi

Abstract:

Portable Document Format (PDF) files have gained significant popularity for sharing and distributing documents due to their universal compatibility. However, the widespread use of PDF files has made them attractive targets for cybercriminals, who exploit vulnerabilities to deliver malware and compromise the security of end-user systems. This paper reviews notable contributions in PDF malware detection, including static, dynamic, signature-based, and hybrid analysis. It presents a comprehensive examination of PDF malware detection techniques, focusing on the emerging threat of adversarial sampling and the need for robust defense mechanisms. The paper highlights the vulnerability of machine learning classifiers to evasion attacks. It explores adversarial sampling techniques in PDF malware detection to produce mimicry and reverse mimicry evasion attacks, which aim to bypass detection systems. Improvements for future research are identified, including accessible methods, applying adversarial sampling techniques to malicious payloads, evaluating other models, evaluating the importance of features to malware, implementing adversarial defense techniques, and conducting comprehensive examination across various scenarios. By addressing these opportunities, researchers can enhance PDF malware detection and develop more resilient defense mechanisms against adversarial attacks.

Keywords: adversarial attacks, adversarial defense, adversarial machine learning, intrusion detection, PDF malware, malware detection, malware detection evasion

Procedia PDF Downloads 37

Authors: Basel Halak, Christian Hall, Syed Abdul Father, Nelson Chow Wai Kit, Ruwaydah Widaad Raymode

Abstract:

The attack surface on computing devices is becoming very sophisticated, driven by the sheer increase of interconnected devices, reaching 50B in 2025, which makes it easier for adversaries to have direct access and perform well-known physical attacks. The impact of increased security vulnerability of electronic systems is exacerbated for devices that are part of the critical infrastructure or those used in military applications, where the likelihood of being targeted is very high. This continuously evolving landscape of security threats calls for a new generation of defense methods that are equally effective and adaptive. This paper proposes an intelligent defense mechanism to protect from physical tampering, it consists of a tamper detection system enhanced with machine learning capabilities, which allows it to recognize normal operating conditions, classify known physical attacks and identify new types of malicious behaviors. A prototype of the proposed system has been implemented, and its functionality has been successfully verified for two types of normal operating conditions and further four forms of physical attacks. In addition, a systematic threat modeling analysis and security validation was carried out, which indicated the proposed solution provides better protection against including information leakage, loss of data, and disruption of operation.

Keywords: anti-tamper, hardware, machine learning, physical security, embedded devices, ioT

Procedia PDF Downloads 151