Search results for: motor for washing machine

Authors: Efrain Rodriguez, Sergio Pertuz, Cristhian Riano

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Tool-path generation is an essential step in the FFF (Fused Filament Fabrication)-based Additive Manufacturing (AM) process planning. In the manufacture of a mechanical part by using additive processes, high resource consumption and prolonged production times are inherent drawbacks of these processes mainly due to non-optimized tool-path generation. In this work, we propose a heuristic-search intelligent algorithm-based approach for optimized tool-path generation for FFF-based AM. The main benefit of this approach is a significant reduction of travels without material deposition when the AM machine performs moves without any extrusion. The optimization method used reduces the number of travels without extrusion in comparison with commercial software as Slic3r or Cura Engine, which means a reduction of production time.

Keywords: additive manufacturing, tool-path optimization, fused filament fabrication, process planning

Procedia PDF Downloads 416

Authors: Tomasz Sapiński, Dorota Kamińska, Adam Pelikant, Egils Avots, Cagri Ozcinar, Gholamreza Anbarjafari

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People express emotions through different modalities. Integration of verbal and non-verbal communication channels creates a system in which the message is easier to understand. Expanding the focus to several expression forms can facilitate research on emotion recognition as well as human-machine interaction. In this article, the authors present a Polish emotional database composed of three modalities: facial expressions, body movement and gestures, and speech. The corpora contains recordings registered in studio conditions, acted out by 16 professional actors (8 male and 8 female). The data is labeled with six basic emotions categories, according to Ekman’s emotion categories. To check the quality of performance, all recordings are evaluated by experts and volunteers. The database is available to academic community and might be useful in the study on audio-visual emotion recognition.

Keywords: body movement, emotion recognition, emotional corpus, facial expressions, gestures, multimodal database, speech

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Authors: Milad Vahidi, Mahmod R. Sahebi, Mehrnoosh Omati, Reza Mohammadi

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Forest management organizations need information to perform their work effectively. Remote sensing is an effective method to acquire information from the Earth. Two datasets of remote sensing images were used to classify forested regions. Firstly, all of extractable features from hyperspectral and PolSAR images were extracted. The optical features were spectral indexes related to the chemical, water contents, structural indexes, effective bands and absorption features. Also, PolSAR features were the original data, target decomposition components, and SAR discriminators features. Secondly, the particle swarm optimization (PSO) and the genetic algorithms (GA) were applied to select optimization features. Furthermore, the support vector machine (SVM) classifier was used to classify the image. The results showed that the combination of PSO and SVM had higher overall accuracy than the other cases. This combination provided overall accuracy about 90.56%. The effective features were the spectral index, the bands in shortwave infrared (SWIR) and the visible ranges and certain PolSAR features.

Keywords: hyperspectral, PolSAR, feature selection, SVM

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Authors: J. Yang, M. Monnot, T. Eljaddi, L. Simonian, L. Ercolei, P. Moulin

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The application of membrane technology to wastewater treatment has expanded rapidly under increasing stringent legislation and environmental protection requirements. At the same time, the water resource is becoming precious, and water reuse has gained popularity. Particularly, ultrafiltration (UF) is a very promising technology for water reuse as it can retain organic matters, suspended solids, colloids, and microorganisms. Nevertheless, few studies dealing with operating optimization of UF as a tertiary treatment for water reuse on a semi-industrial scale appear in the literature. Therefore, this study aims to explore the permeate water quality and to optimize operating parameters (maximizing productivity and minimizing irreversible fouling) through the operation of a UF pilot plant under real conditions. The fully automatic semi-industrial UF pilot plant with periodic classic backwashes (CB) and air backwashes (AB) was set up to filtrate the secondary effluent of an urban wastewater treatment plant (WWTP) in France. In this plant, the secondary treatment consists of a conventional activated sludge process followed by a sedimentation tank. The UF process was thus defined as a tertiary treatment and was operated under constant flux. It is important to note that a combination of CB and chlorinated AB was used for better fouling management. The 200 kDa hollow fiber membrane was used in the UF module, with an initial permeability (for WWTP outlet water) of 600 L·m-2·h⁻¹·bar⁻¹ and a total filtration surface of 9 m². Fifteen filtration conditions with different fluxes, filtration times, and air backwash frequencies were operated for more than 40 hours of each to observe their hydraulic filtration performances. Through comparison, the best sustainable condition was flux at 60 L·h⁻¹·m⁻², filtration time at 60 min, and backwash frequency of 1 AB every 3 CBs. The optimized condition stands out from the others with > 92% water recovery rates, better irreversible fouling control, stable permeability variation, efficient backwash reversibility (80% for CB and 150% for AB), and no chemical washing occurrence in 40h’s filtration. For all tested conditions, the permeate water quality met the water reuse guidelines of the World Health Organization (WHO), French standards, and the regulation of the European Parliament adopted in May 2020, setting minimum requirements for water reuse in agriculture. In permeate: the total suspended solids, biochemical oxygen demand, and turbidity were decreased to < 2 mg·L-1, ≤ 10 mg·L⁻¹, < 0.5 NTU respectively; the Escherichia coli and Enterococci were > 5 log removal reduction, the other required microorganisms’ analysis were below the detection limits. Additionally, because of the COVID-19 pandemic, coronavirus SARS-CoV-2 was measured in raw wastewater of WWTP, UF feed, and UF permeate in November 2020. As a result, the raw wastewater was tested positive above the detection limit but below the quantification limit. Interestingly, the UF feed and UF permeate were tested negative to SARS-CoV-2 by these PCR assays. In summary, this work confirms the great interest in UF as intensified tertiary treatment for water reuse and gives operational indications for future industrial-scale production of reclaimed water.

Keywords: semi-industrial UF pilot plant, water reuse, fouling management, coronavirus

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Authors: Han Kexi, Lv Xuewei, Song Bing

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This paper focuses on the preparation of pelletizing with the Panzhihua ilmenite concentrate to satisfy the requirement of smelting titania slag. The effects of the moisture content, mixing time of raw materials, pressure of pellet, roller rotating speed of roller, drying temperature and time on the pelletizing yield and compressive strength were investigated. The experimental results show that the moister content was controlled at 2.0%~2.5%, mixing time at 20 min, the pressure of the ball forming machine at 13~15 mpa, the pelletizing yield can reach up 85%. When the roller rotating speed is 6~8 r/min while the drying temperature and time respectively is 350 ℃ and 40~60 min, the compressive strength of pelletizing more than 1500 N. The preparation of pelletizing can meet the requirement of smelting titania slag.

Keywords: Panzhihua fine ilmenite concentrate, pelletizing, pelletizing yield, compressive strength, drying

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Authors: Mustahsan Mir, Ahmed Hassanin, Mohammed A. Al-Saleh

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The layout optimization of building blocks of unequal areas has applications in many disciplines including VLSI floorplanning, macrocell placement, unequal-area facilities layout optimization, and plant or machine layout design. A number of heuristics and some analytical and hybrid techniques have been published to solve this problem. This paper presents an efficient high-quality building-block layout design technique especially suited for solving large-size problems. The higher efficiency and improved quality of optimized solutions are made possible by introducing the concept of Promising Infant Clusters in a constructive placement procedure. The results presented in the paper demonstrate the improved performance of the presented technique for benchmark problems in comparison with published heuristic, analytic, and hybrid techniques.

Keywords: block layout problem, building-block layout design, CAD, optimization, search techniques

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Authors: Pingping Lin, Xudong Luo, Yifan Fan

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Sentiment analysis is a very active research topic. Every day, Facebook, Twitter, Weibo, and other social media, as well as significant e-commerce websites, generate a massive amount of comments, which can be used to analyse peoples opinions or emotions. The existing methods for sentiment analysis are based mainly on sentiment dictionaries, machine learning, and deep learning. The first two kinds of methods rely on heavily sentiment dictionaries or large amounts of labelled data. The third one overcomes these two problems. So, in this paper, we focus on the third one. Specifically, we survey various sentiment analysis methods based on convolutional neural network, recurrent neural network, long short-term memory, deep neural network, deep belief network, and memory network. We compare their futures, advantages, and disadvantages. Also, we point out the main problems of these methods, which may be worthy of careful studies in the future. Finally, we also examine the application of deep learning in multimodal sentiment analysis and aspect-level sentiment analysis.

Keywords: document analysis, deep learning, multimodal sentiment analysis, natural language processing

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Authors: José M. Carmona, Diana Puigserver, Jofre Herrero

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Chlorinated solvents belong to the group of nonaqueous phase liquids (DNAPL) and have been involved in many contamination episodes. They are carcinogenic and recalcitrant pollutants that may be found in granular aquifers as: i) pools accumulated on low hydraulic conductivity layers; ii) immobile residual phase retained at the pore-scale by capillary forces; iii) dissolved phase in groundwater; iv) sorbed by particulate organic matter; and v) stored into the matrix of low hydraulic conductivity layers where they penetrated by molecular diffusion. The transition zone between granular aquifers and basal aquitards constitute the lowermost part of the aquifer and presents numerous fine-grained interbedded layers that give rise to significant textural contrasts. These layers condition the transport and fate of contaminants and lead to differences from the rest of the aquifer, given that: i) hydraulic conductivity of these layers is lower; ii) DNAPL tends to accumulate on them; iii) groundwater flow is slower in the transition zone and consequently pool dissolution is much slower; iv) sorbed concentrations are higher in the fine-grained layers because of their higher content in organic matter; v) a significant mass of pollutant penetrates into the matrix of these layers; and vi) this contaminant mass back-diffuses after remediation and the aquifer becomes contaminated again. Thus, contamination sources of chlorinated solvents are extremely more recalcitrant in transition zones, which has far-reaching implications for the environment. The aim of this study is to analyze the spatial and temporal differences in the evolution of biogeochemical processes in the transition zone and in the rest of the aquifer. For this, an unconfined aquifer with a transition zone in the lower part was selected at Vilafant (NE Spain). This aquifer was contaminated by perchloroethylene (PCE) in the 80’s. Distribution of PCE and other chloroethenes in groundwater and porewater was analyzed in: a) conventional piezometers along the plume and in two multilevel wells at the source of contamination; and b) porewater of fine grained materials from cores recovered when drilled the two multilevel wells. Currently, the highest concentrations continue to be recorded in the source area in the transition zone. By contrast, the lowest concentrations in this area correspond to the central part of the aquifer, where flow velocities are higher and a greater washing of the residual phase initially retained has occurred. The major findings of the study were: i) PCE metabolites were detected in the transition zone, where conditions were more reducing than in the rest of the aquifer; ii) however, reductive dechlorination was partial since only the formation of cis-dicholoroethylene (DCE) was reached; iii) In the central part of the aquifer, where conditions were predominantly oxidizing, the presence of nitrate significantly hindered the reductive declination of PCE. The remediation strategies to be implemented should be directed to enhance dissolution of the source, especially in the transition zone, where it is more recalcitrant. For example, by combining chemical and bioremediation methods, already tested at the laboratory scale with groundwater and sediments of this site.

Keywords: chlorinated solvents, chloroethenes, DNAPL, partial reductive dechlorination, PCE, transition zone to basal aquitard

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Authors: Emmanuel Yussuf, John Muthama, John Ng'ang'A

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There has been increasing noise levels all over the world in the last decade. Many factors contribute to this increase, which is causing health related effects to humans. Developing countries are not left out of the whole picture as they are still growing and advancing their development. Motor vehicles are increasing on urban roads; there is an increase in infrastructure due to the rising population, increasing number of industries to provide goods and so many other activities. All this activities lead to the high noise levels in cities. This study was conducted in Nairobi’s Central Business District (CBD) with the main objective of simulating noise levels in order to understand the noise exposed to the people within the urban area, in relation to weather parameters namely temperature, rainfall and wind field. The study was achieved using the Neighbourhood Proximity Model and Time Series Analysis, with data obtained from proxies/remotely-sensed from satellites, in order to establish the levels of noise exposed to which people of Nairobi CBD are exposed to. The findings showed that there is an increase in temperature (0.1°C per year) and a decrease in precipitation (40 mm per year), which in comparison to the noise levels in the area, are increasing. The study also found out that noise levels exposed to people in Nairobi CBD were roughly between 61 and 63 decibels and has been increasing, a level which is high and likely to cause adverse physical and psychological effects on the human body in which air temperature, precipitation and wind contribute so much in the spread of noise. As a noise reduction measure, the use of sound proof materials in buildings close to busy roads, implementation of strict laws to most emitting sources as well as further research on the study was recommended. The data used for this study ranged from the year 2000 to 2015, rainfall being in millimeters (mm), temperature in degrees Celsius (°C) and the urban form characteristics being in meters (m).

Keywords: simulation, noise exposure, weather, proxy

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Authors: Vaishnavi Shivde, Shrishti Sinha, Trapti Mishra

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Traffic light detection from a moving vehicle is an important technology both for driver safety assistance functions as well as for autonomous driving in the city. This paper proposed a deep-learning-based traffic light recognition method that consists of a pixel-wise image segmentation technique and a fully convolutional network i.e., UNET architecture. This paper has used a method for detecting the position and recognizing the state of the traffic lights in video sequences is presented and evaluated using Traffic Light Dataset which contains masked traffic light image data. The first stage is the detection, which is accomplished through image processing (image segmentation) techniques such as image cropping, color transformation, segmentation of possible traffic lights. The second stage is the recognition, which means identifying the color of the traffic light or knowing the state of traffic light which is achieved by using a Convolutional Neural Network (UNET architecture).

Keywords: traffic light detection, image segmentation, machine learning, classification, convolutional neural networks

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Authors: Mohammad A. Khasawneh

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The design of high pressure water jet based polishing equipment and its fabrication conducted in this study is reported herein, together with some preliminary test results for assessing its applicability for HMA surface polishing. This study also provides preliminary findings concerning the test variables, such as the rotational speed, the water jet pressure, the abrasive agent used, and the impact angel that were experimentally investigated in this study. The preliminary findings based on four trial tests (two on large slab specimens and two on small size gyratory compacted specimens), however, indicate that both friction and texture values tend to increase with the polishing durations for two combinations of pressure and rotation speed of the rotary deck. It seems that the more polishing action the specimen is subjected to; the aggregate edges are created such that the surface texture values are increased with the accompanied increase in friction values. It may be of interest (but which is outside the scope of this study) to investigate if the similar trend exist for HMA prepared with aggregate source that is sand and gravel.

Keywords: high-pressure, water jet, friction, texture, polishing, statistical analysis

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Authors: M. S. Kaiser, S. Reaz Ahmed

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Chip formation characteristics are investigated during surface finishing of high density polyethylene (HDPE) samples using a shaper machine. Both the cutting speed and depth of cut are varied continually to enable observations under various machining conditions. The generated chips are analyzed in terms of their shape, size, and deformation. Their physical appearances are also observed using digital camera and optical microscope. The investigation shows that continuous chips are obtained for all the cutting conditions. It is observed that cutting speed is more influential than depth of cut to cause dimensional changes of chips. Chips curl radius is also found to increase gradually with the increase of cutting speed. The length of continuous chips remains always smaller than the job length, and the corresponding discrepancies are found to be more prominent at lower cutting speed. Microstructures of the chips reveal that cracks are formed at higher cutting speeds and depth of cuts, which is not that significant at low depth of cut.

Keywords: HDPE, surface-finishing, chip formation, deformation, roughness

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Authors: Frederik Calsius, Mirela Popa, Alexia Briassouli

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In the field of machine learning, it is common practice to use benchmark datasets to prove the working of a method. The domain of action recognition in videos often uses datasets like Kinet-ics, Something-Something, UCF-101 and HMDB-51 to report results. Considering the properties of the datasets, there are no datasets that focus solely on very short clips (2 to 3 seconds), and on highly-similar fine-grained actions within one specific domain. This paper researches how current state-of-the-art action recognition methods perform on a dataset that consists of highly similar, fine-grained actions. To do so, a dataset of skateboarding tricks was created. The performed analysis highlights both benefits and limitations of state-of-the-art methods, while proposing future research directions in the activity recognition domain. The conducted research shows that the best results are obtained by fusing RGB data with OpenPose data for the Temporal Shift Module.

Keywords: activity recognition, fused deep representations, fine-grained dataset, temporal modeling

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Authors: Zdeněk Veselý, Milan Honner, Jiří Mach

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The aim of the performed work is to establish the 2D and 3D model of direct unsteady task of sample heat treatment by moving source employing computer model on the basis of finite element method. The complex boundary condition on heat loaded sample surface is the essential feature of the task. Computer model describes heat treatment of the sample during heat source movement over the sample surface. It is started from the 2D task of sample cross section as a basic model. Possibilities of extension from 2D to 3D task are discussed. The effect of the addition of third model dimension on the temperature distribution in the sample is showed. Comparison of various model parameters on the sample temperatures is observed. Influence of heat source motion on the depth of material heat treatment is shown for several velocities of the movement. Presented computer model is prepared for the utilization in laser treatment of machine parts.

Keywords: computer simulation, unsteady model, heat treatment, complex boundary condition, moving heat source

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Authors: B. Chandra

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In Association Rule Mining, much attention has been paid for developing algorithms for large (frequent/closed/maximal) itemsets but very little attention has been paid to improve the performance of rule generation algorithms. Rule generation is an important part of Association Rule Mining. In this paper, a novel approach named NARG (Association Rule using Antecedent Support) has been proposed for rule generation that uses memory resident data structure named FCET (Frequent Closed Enumeration Tree) to find frequent/closed itemsets. In addition, the computational speed of NARG is enhanced by giving importance to the rules that have lower antecedent support. Comparative performance evaluation of NARG with fast association rule mining algorithm for rule generation has been done on synthetic datasets and real life datasets (taken from UCI Machine Learning Repository). Performance analysis shows that NARG is computationally faster in comparison to the existing algorithms for rule generation.

Keywords: knowledge discovery, association rule mining, antecedent support, rule generation

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Authors: Ghada S. Moussa

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With the increase in population along with economic prosperity, an enormous increase in the number and types of vehicles on the roads occurred. This fact brings a growing need for efficiently yet effectively classifying vehicles into their corresponding categories, which play a crucial role in many areas of infrastructure planning and traffic management. This paper presents two vehicle-type classification approaches; 1) geometric-based and 2) appearance-based. The two classification approaches are used for two tasks: multi-class and intra-class vehicle classifications. For the evaluation purpose of the proposed classification approaches’ performance and the identification of the most effective yet efficient one, 10-fold cross-validation technique is used with a large dataset. The proposed approaches are distinguishable from previous research on vehicle classification in which: i) they consider both geometric and appearance attributes of vehicles, and ii) they perform remarkably well in both multi-class and intra-class vehicle classification. Experimental results exhibit promising potentials implementations of the proposed vehicle classification approaches into real-world applications.

Keywords: appearance attributes, geometric attributes, support vector machine, vehicle classification

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Authors: Samira Alvandi

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In the context of the circular economy, production planning aims to eliminate waste and emissions and maximize resource efficiency. Historically production planning is challenged through arrays of uncertainty and complexity arising from the interdependence and variability of products, processes, and systems. Manufacturers worldwide are facing new challenges in tackling various environmental issues such as climate change, resource depletion, and land degradation. In managing the inherited complexity and uncertainty and yet maintaining profitability, the manufacturing sector is in need of a holistic framework that supports energy efficiency and carbon emission reduction schemes. The proposed framework addresses the current challenges and integrates simulation modeling with optimization for finding optimal machine-job allocation to maximize throughput and total energy consumption while minimizing lead time. The aluminium refinery facility in western Sydney, Australia, is used as an exemplar to validate the proposed framework.

Keywords: smart production planning, simulation-optimisation, energy aware capacity planning, energy intensive industries

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Authors: Julian Y. Raheema, Michael R. Hess, Raymond C. Provost, Mark Bilinski

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The world is rapidly moving towards advancing and utilizing artificial intelligence and autonomous robotics. The ground-breaking Boston Dynamics quadruped robot, SPOT, was designed for industrial and commercial tasks requiring limited autonomous navigation. Out of the box, SPOT has route memorization and playback – it can repeat a path that it has been manually piloted through, but it cannot autonomously navigate an area that has not been previously explored. The presented SPOT payload package is built on ROS framework to support autonomous navigation and mapping of an unexplored environment. The package is fully integrated with SPOT to take advantage of motor controls and collision avoidance that comes natively with the robot. The payload runs all computations onboard, takes advantage of visual odometry SLAM and uses an Intel RealSense depth camera and Velodyne LiDAR sensor to generate 2D and 3D maps while in autonomous navigation mode. These maps are fused into the navigation stack to generate a costmap to enable the robot to safely navigate the environment without causing damage to the surroundings or the robot. The operator defines the operational zone and start location and then sends the explore command to have SPOT explore, generate 2D and 3D maps of the environment and return to the start location to await the operator's next command. The benefit of the presented package is that it is much lighter weight and less expensive than previous approaches and, importantly, operates in GPS-denied scenarios, which is ideal for indoor mapping. There are numerous applications that are hazardous to humans for SPOT enhanced with the autonomy payload, including disaster response, nuclear inspection, mine inspection, and so on. Other less extreme uses cases include autonomous 3D and 2D scanning of facilities for inspection, engineering and construction purposes.

Keywords: autonomous, SLAM, quadruped, mapping, exploring, ROS, robotics, navigation

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Authors: Lei Zhang, Tingting Xu, Jun He, Zhenyu Yan

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Email marketing is one of the most important segments of online marketing. It has been proved to be the most effective way to acquire and retain customers. The email content is vital to customers. Different customers may have different familiarity with a product, so a successful marketing strategy must personalize email content based on individual customers’ product affinity. In this study, we build our personalized email marketing strategy with three types of emails: nurture, promotion, and conversion. Each type of email has a different influence on customers. We investigate this difference by analyzing customers’ open rates, click rates and opt-out rates. Feature importance from response models is also analyzed. The goal of the marketing strategy is to improve the click rate on conversion-type emails. To build the personalized strategy, we formulate the problem as a reinforcement learning problem and adopt a Q-learning algorithm with variations. The simulation results show that our model-based strategy outperforms the current marketer’s strategy.

Keywords: email marketing, email content, reinforcement learning, machine learning, Q-learning

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Authors: Ali Asheibi

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The rapid increase in the size of databases required to store power quality monitoring data has demanded new techniques for analysing and understanding the data. One suggested technique to assist in analysis is data mining. Preparing raw data to be ready for data mining exploration take up most of the effort and time spent in the whole data mining process. Clustering is an important technique in data mining and machine learning in which underlying and meaningful groups of data are discovered. Large amounts of harmonic data have been collected from an actual harmonic monitoring system in a distribution system in Australia for three years. This amount of acquired data makes it difficult to identify operational events that significantly impact the harmonics generated on the system. In this paper, harmonic data preparation processes to better understanding of the data have been presented. Underlying classes in this data has then been identified using clustering technique based on the Minimum Message Length (MML) method. The underlying operational information contained within the clusters can be rapidly visualised by the engineers. The C5.0 algorithm was used for classification and interpretation of the generated clusters.

Keywords: data mining, harmonic data, clustering, classification

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Authors: Mohammad Reza Koushki Ardestani, Saeed Daneshmand, Mohammad Heydari Vini

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In the present study, AA7075/SiO2 composites have been fabricated via liquid metallurgy process. Using the degassing process, the wet ability of the molten aluminum alloys increased which improved the bonding between aluminum matrix and reinforcement (SiO2) particles. AA7075 alloy and SiO2 particles were taken as the base matrix and reinforcements, respectively. Then, contents of 2.5 and 5 wt. % of SiO2 subdivisions were added into the AA7075 matrix. To improve wettability and distribution, reinforcement particles were pre-heated to a temperature of 550°C for each composite sample. A uniform distribution of SiO2 particles was observed through the matrix alloy in the microstructural study. A hardened EN32 steel disc as the counter face was used to evaluate the wear rate pin-on-disc, a wear testing machine containing. The results showed that the wear rate of the AA/SiO2 composites was lesser than that of the monolithic AA7075 samples. Finally, The SEM worn surfaces of samples were investigated.

Keywords: Al7075, SiO₂, wear, composites, stir casting

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Authors: Mohamed Mokhtar, Mostafa F. Shaaban

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Among the challenges facing solar photovoltaic (PV) systems in the United Arab Emirates (UAE), dust accumulation on solar panels is considered the most severe problem that faces the growth of solar power plants. The accumulation of dust on the solar panels significantly degrades output from these panels. Hence, solar PV panels have to be cleaned manually or using costly automated cleaning methods. This paper focuses on initiating cleaning actions when required to reduce maintenance costs. The cleaning actions are triggered only when the dust level exceeds a threshold value. The amount of dust accumulated on the PV panels is estimated using an artificial neural network (ANN). Experiments are conducted to collect the required data, which are used in the training of the ANN model. Then, this ANN model will be fed by the output power from solar panels, ambient temperature, and solar irradiance, and thus, it will be able to estimate the amount of dust accumulated on solar panels at these conditions. The model was tested on different case studies to confirm the accuracy of the developed model.

Keywords: machine learning, dust, PV panels, renewable energy

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Authors: Petr Homola, Roman Růžek

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Laser beam micro-drilling (LBMD) is one of the most important non-contact machining processes of materials that are difficult to machine by means oeqf conventional machining methods used in various industries. The paper is focused on LBMD knock-down effect on Ti-6Al-4V (Grade 5) titanium alloy sheets properties. Two various process configurations were verified with a focus on laser damages in back-structure parts affected by the process. The effects of the LBMD on the material properties were assessed by means of tensile and fatigue tests and fracture surface analyses. Fatigue limit of LBMD configurations reached a significantly lower value between 15% and 30% of the static strength as compared to the reference raw material with 58% value. The farther back-structure configuration gives a two-fold fatigue life as compared to the closer LBMD configuration at a given stress applied.

Keywords: fatigue, fracture surface, laser beam micro-drilling, titanium alloy

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Authors: Mimoza Canga, Vito Malagnino, Giulia Malagnino, Irene Malagnino

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Objective: The objective of this study was to compare the effectiveness of Mtwo and RaCe rotary instruments, in cleaning and shaping root canals curvature. Material and Method: The present study was conducted on 160 simulated canals in resin blocks, with an angle curvature 15°-30°. These 160 simulated canals were divided into two groups, where each group consisted of 80 blocks. Each group was divided into two subgroups (n=40 canals each). The simulated canals subgroups were prepared with Mtwo and RaCe rotary nickel-titanium instruments. The root canals were measured at four different points of reference, starting at 13 mm from the orifice. In the first group, the canals were prepared using Mtwo rotary system (VDW, Munich, Germany). The Mtwo files used were: 10/0.04, 15/0.05, 20/0.06, and 25/0.06. These instruments entered in the full length of the canal. Each file was rotated in the canal until it reached the apical point. In the second group, the canals were prepared using RaCe instruments (La Chaux-De-Fonds, Switzerland), performing the crown down technique, using the torque electric control motor (VDWCO, Munich, Germany), with 600 RPM and 2n/cm as follow: ≠40/0.10, ≠35/0.08, ≠30/0.06, ≠25/0.04, ≠25/0.02. The data were recorded using SPSS version 23 software (Microsoft, IL, USA). Data analysis was done using ANOVA test. Results: The results obtained by using the Mtwo rotary instruments, showed that these instruments were able to clean and shape in the right-to-left motion curved canals, at different levels, without any deviation, and in perfect symmetry, with a P-value=0.000. The data showed that the greater the depth of the root canal, the greater the deviations of the RaCe rotary instruments. These deviations occurred in three levels, which are: S2(P=0.004), S3( P=0.007), S4(P=0.009). The Mtwo files can go deeper and create a greater angle in S4 level (21°-28°), compared to RaCe instruments with an angle equal to 19°-24°. Conclusion: The present study noted a clinically significant difference between Mtwo rotary instruments and RaCe rotary files used for the canal preparation and indicated that Mtwo instruments are a better choice for the curved canals.

Keywords: canal curvature, canal preparation, Mtwo, RaCe, resin blocks

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Authors: Amarendar Reddy Addula

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Definition of Artificial Intelligence: Artificial intelligence is the simulation of mortal intelligence processes by machines, especially computer systems. Explicit operations of AI comprise expert systems, natural language processing, and speech recognition, and machine vision. Artificial Intelligence (AI) is an original medium for digital business, according to a new report by Gartner. The last 10 times represent an advance period in AI’s development, prodded by the confluence of factors, including the rise of big data, advancements in cipher structure, new machine literacy ways, the materialization of pall computing, and the vibrant open- source ecosystem. Influence of AI to a broader set of use cases and druggies and its gaining fashionability because it improves AI’s versatility, effectiveness, and rigidity. Edge AI will enable digital moments by employing AI for real- time analytics closer to data sources. Gartner predicts that by 2025, further than 50 of all data analysis by deep neural networks will do at the edge, over from lower than 10 in 2021. Responsible AI is a marquee term for making suitable business and ethical choices when espousing AI. It requires considering business and societal value, threat, trust, translucency, fairness, bias mitigation, explainability, responsibility, safety, sequestration, and nonsupervisory compliance. Responsible AI is ever more significant amidst growing nonsupervisory oversight, consumer prospects, and rising sustainability pretensions. Generative AI is the use of AI to induce new vestiges and produce innovative products. To date, generative AI sweats have concentrated on creating media content similar as photorealistic images of people and effects, but it can also be used for law generation, creating synthetic irregular data, and designing medicinals and accoutrements with specific parcels. AI is the subject of a wide- ranging debate in which there's a growing concern about its ethical and legal aspects. Constantly, the two are varied and nonplussed despite being different issues and areas of knowledge. The ethical debate raises two main problems the first, abstract, relates to the idea and content of ethics; the alternate, functional, and concerns its relationship with the law. Both set up models of social geste, but they're different in compass and nature. The juridical analysis is grounded on anon-formalistic scientific methodology. This means that it's essential to consider the nature and characteristics of the AI as a primary step to the description of its legal paradigm. In this regard, there are two main issues the relationship between artificial and mortal intelligence and the question of the unitary or different nature of the AI. From that theoretical and practical base, the study of the legal system is carried out by examining its foundations, the governance model, and the nonsupervisory bases. According to this analysis, throughout the work and in the conclusions, International Law is linked as the top legal frame for the regulation of AI.

Keywords: artificial intelligence, ethics & human rights issues, laws, international laws

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Authors: Felix Olajide Ibikunle

Abstract:

Introductory Statement: Sustainable participation of teenagers in sport requires deliberate and concerted plan and managerial policy rooted in the “philosophy of catch them young”. At risk, teenagers need proper integration into societal aspiration: This direction will go a long way to streamline them into the security breach and attractive nuisance free lifestyles. Basic Methodology: The population consists of children within 13-19 years old. A proportionate sampling size technique of 60% was adopted to select seven zones out of 11 geo-political zones in the Ibadan metropolis. Qualitative information and interview were used to collect needed information. Majority of the teenagers were out of school, street hawkers, motor pack, touts, and unserious vocation apprentices. These groups have the potentials of security breaches in the metropolis and beyond. Five hundred and thirty-four (534) respondents were used for the study. They were drawn from Ojoo, Akingbile, and Moniya axis = 72, Agbowo, Ajibode, and Apete axis = 74; Akobo, Basorun, and Idi-ape axis 79; Wofun, Monatan, and Iyana-Church axis = 78; Molete, Oke-ado and Oke-Bola axis = 75; Beere, Odinjo, Elekuro axis = 77; Eleyele, Ologuneru, and Alesinloye axis = 79. Major Findings: Multiple regression was used to analyze the independent variables and percentage. The respondents average age was 15.6 years old, and with 100% male. The instrument(questionnaire) used yielded; sport preference (r = 0.72); intervention (r = 0.68) and the sustainable participation (r = 0.70).The relative contributions of sport preference on participation of at risk teenagers was (F-ratio = 1.067); Intervention contribution of sport on participation of at risk teenagers = produced (F-ratio of 12.095) was significant while sustainable participation of at risk teenager produced (F-ratio = 1.062) was significant. Closing Statement: The respondents’ sport preference stimulated their participation in sport. The intervention exposed at risk-teenagers to coaching, which activated their interest and participation in sport. While sustainable participation contributed positively to evolve at risk teenagers participation in their preferred sport.

Keywords: sport, preference, intervention, teenagers, sustainable, participation and risk teenagers

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Authors: Eva Savy, Anthony Ruiz

Abstract:

In extrusion blow molding, there is great variability in product quality due to the sensitivity of the machine settings. These variations lead to unnecessary rejects and loss of time. Yet production control is a major challenge for companies in this sector to remain competitive within their market. Current quality control methods only apply to finished products (vision control, leak test...). It has been shown that material melt temperature, blowing pressure, and ambient temperature have a significant impact on the variability of product quality. Since blowing is a key step in the process, we have studied this parameter in this paper. The objective is to determine if airflow analysis allows the identification of quality problems before the full completion of the manufacturing process. We conducted tests to determine if it was possible to identify a leakage defect and an obstructed defect, two common defects on products. The results showed that it was possible to identify a leakage defect by airflow analysis.

Keywords: extrusion blow molding, signal, sensor, defects, detection

Procedia PDF Downloads 106

Authors: Lakshmidevi Sreeramareddy, Komalpreet Kaur, Nane Pothier

Abstract:

For security purposes, it is important to detect passwords entered by unauthorized users. With traditional alphanumeric passwords, if the content of a password is acquired and correctly entered by an intruder, it is impossible to differentiate the password entered by the intruder from those entered by the authorized user because the password entries contain precisely the same character set. However, no two entries for the gesture-based passwords, even those entered by the person who created the password, will be identical. There are always variations between entries, such as the shape and length of each stroke, the location of each stroke, and the speed of drawing. It is possible that passwords entered by the unauthorized user contain higher levels of variations when compared with those entered by the authorized user (the creator). The difference in the levels of variations may provide cues to detect unauthorized entries. To test this hypothesis, we designed an empirical study, collected and analyzed the data with the help of machine-learning algorithms. The results of the study are significant.

Keywords: authentication, gesture-based passwords, shoulder-surfing attacks, usability

Procedia PDF Downloads 116

Authors: Ghada S. Moussa

Abstract:

Vehicle classification is an important task in traffic surveillance and intelligent transportation systems. Classification of vehicle images is facing several problems such as: high intra-class vehicle variations, occlusion, shadow, illumination. These problems and others must be considered to develop a reliable vehicle classification system. In this study, a reliable multi-type vehicle classification system based on Bag-of-Words (BoW) paradigm is developed. Our proposed system used and compared four well-known classifiers; Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-Nearest Neighbour (KNN), and Decision Tree to classify vehicles into four categories: motorcycles, small, medium and large. Experiments on a large dataset show that our approach is efficient and reliable in classifying vehicles with accuracy of 95.7%. The SVM outperforms other classification algorithms in terms of both accuracy and robustness alongside considerable reduction in execution time. The innovativeness of developed system is it can serve as a framework for many vehicle classification systems.

Keywords: vehicle classification, bag-of-words technique, SVM classifier, LDA classifier, KNN classifier, decision tree classifier, SIFT algorithm

Procedia PDF Downloads 327

Authors: Joo Min Kim, Dongyoun Shin

Abstract:

The project Future Station (FS) seek for a deeper understanding of metro station. The main idea of the project is enhancing the underground environment by combining new architectural design with IoT technology. This research shows the understanding of the metro environment giving references regarding traditional design approaches and IoT combined space design. Based on the analysis, this research presents design alternatives in two metro stations those are chosen for a testbed. It also presents how the FS platform giving a response to travelers and deliver the benefit to metro operators. In conclusion, the project describes methods to build future metro service and platform that understand traveler’s intentions and giving appropriate services back for enhancing travel experience. It basically used contemporary technology such as smart sensing grid, big data analysis, smart building, and machine learning technology.

Keywords: future station, digital lifestyle experience, sustainable metro, smart metro, smart city

Procedia PDF Downloads 274