Search results for: multi-source image geometric process

Authors: H. Golchoobian, S. Saedodin, M. H. Taheri, A. Sarafraz

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In this paper, natural convection in an attic is numerically investigated. The geometry of the problem is considered to be a triangular enclosure. ANSYS Fluent software is used for modeling and numerical solution. This study is for steady state. Four right-angled triangles with height to base ratios of 2, 1, 0.5 and 0.25 are considered. The behavior of various parameters related to its performance, including temperature distribution and velocity vectors are evaluated, and graphs for the Nusselt number have been drawn. Also, in this study, the effect of geometric shape of enclosure with different height-to-base ratios has been evaluated for three types of boundary conditions of winter, summer day and one another state. It can be concluded that as the bottom side temperature and ratio of base to height of the enclosure increases, the convective effects become more prominent and circulation happened.

Keywords: enclosure, natural convection, numerical solution, Nusselt number, triangular

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Authors: John Knight, Fuchun Li, Yan Xu

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Based on a new method of the nonparametric estimator of the drift function, we propose a consistent test for the parametric specification of the drift function in the short rate diffusion process using observations from a panel of yields. The test statistic is shown to follow an asymptotic normal distribution under the null hypothesis that the parametric drift function is correctly specified, and converges to infinity under the alternative. Taking the daily 7-day European rates as a proxy of the short rate, we use our test to examine whether the drift of the short rate diffusion process is linear or nonlinear, which is an unresolved important issue in the short rate modeling literature. The testing results indicate that none of the drift functions in this literature adequately captures the dynamics of the drift, but nonlinear specification performs better than the linear specification.

Keywords: diffusion process, nonparametric estimation, derivative security price, drift function and volatility function

Procedia PDF Downloads 358

Authors: Yong-Jun Jeon, Hyung-Pil Park, Min-Jae Song, Baeg-Soon Cha

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Following the recent trend of having increased demand in producing lighter-weight car bodies for improvement of automobile safety and gas mileage, there is a forming method that makes use of hot-stamping technique, which satisfies all conditions mentioned above. Hot-stamping is a forming technique with advantages of excellent formability, good dimensional precision and others since it is a process in which steel plates are heated up to temperatures of at least approximately 900°C after which forming is conducted in die at room temperature followed by rapid cooling. In addition, it has characteristics of allowing for improvement in material strength through achievement of quenching effect by having simultaneous forming and rapid cooling of material of high temperatures. However, there is insufficient information on the changes in material strength according to changes in material temperature with regards to material heating method and forming process in hot-stamping. Accordingly, this study aims to design and press die for T-type product of the scale models of the center pillar and to understand the changes in material strength in relation to changes in forming methods of hot-stamping process. Thus in order to understand the changes in material strength due to quenching effect among the hot-stamping process, material strength and material forming precision were to be studied while varying the forming and forming method when forming. For test methods, material strength was observed by using boron steel that has boron additives, which was heated up to 950°C, after which it was transferred to a die and was cooled down to material temperature of 400°C followed by air cooling process. During the forming and cooling process here, experiment was conducted with forming parameters of 2 holding rates and 3 flange heating rates wherein changing appearance in material strength according to changes forming method were observed by verifying forming strength and forming precision for each of the conditions.

Keywords: hot-stamping, formability, quenching, forming, press die, forming methods

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Authors: Bruno Di Pace, Roxanne H. Padley

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Aims: The recent increase in remote video interaction has increased the number of requests for teleconsultations with plastic surgeons in private practice (70% in the UK and 64% in the USA). This study investigated the motivations for such an increase and the underlying psychological impact on patients. Method: An anonymous web-based poll of 8 questions was designed and distributed to patients seeking cosmetic surgery through social networks in both Italy and the UK. The questions gathered responses regarding 1. Reasons for pursuing cosmetic surgery; 2. The effects of delays caused by the SARS-COV-2 pandemic; 3. The effects on mood; 4. The influence of video conferencing on body-image perception. Results: 85 respondents completed the online poll. Overall, 68% of respondents stated that seeing themselves more frequently online had influenced their decision to seek cosmetic surgery. The types of surgeries indicated were predominantly to the upper body and face (82%). Delays and access to surgeons during the pandemic were perceived as negatively impacting patients' moods (95%). Body-image perception and self-esteem were lower than in the pre-pandemic, particularly during lockdown (72%). Patients were more inclined to undergo cosmetic surgery during the pandemic, both due to the wish to improve their “lockdown face” for video conferencing (77%) and also due to the benefits of home recovery while in smart working (58%). Conclusions: Overall, findings suggest that video conferencing has led to a significant increase in requests for cosmetic surgery and the so-called “Zoom Boom” effect.

Keywords: cosmetic surgery, remote communication, telehealth, zoom boom

Procedia PDF Downloads 174

Authors: Somar Boubou, Tatsuo Narikiyo, Michihiro Kawanishi

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In this work, we address the problem of 3D object recognition with depth sensors such as Kinect or Structure sensor. Compared with traditional approaches based on local descriptors, which depends on local information around the object key points, we propose a global features based descriptor. Proposed descriptor, which we name as Differential Histogram of Normal Vectors (DHONV), is designed particularly to capture the surface geometric characteristics of the 3D objects represented by depth images. We describe the 3D surface of an object in each frame using a 2D spatial histogram capturing the normalized distribution of differential angles of the surface normal vectors. The object recognition experiments on the benchmark RGB-D object dataset and a self-collected dataset show that our proposed descriptor outperforms two others descriptors based on spin-images and histogram of normal vectors with linear-SVM classifier.

Keywords: vision in control, robotics, histogram, differential histogram of normal vectors

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Authors: L. A. Toro, N. Prieto, J. J. Vargas

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There are many control charts for monitoring mean and variance. Among these, the X y R, X y S, S2 Hotteling and Shewhart control charts, for mentioning some, are widely used for monitoring mean a variance in industrial processes. In particular, the Shewhart charts are based on the information about the process contained in the current observation only and ignore any information given by the entire sequence of points. Moreover, that the Shewhart chart is a control chart without memory. Consequently, Shewhart control charts are found to be less sensitive in detecting smaller shifts, particularly smaller than 1.5 times of the standard deviation. These kind of small shifts are important in many industrial applications. In this study and effective alternative to Shewhart control chart was implemented. In case of univariate process an Exponentially Moving Average (EWMA) control chart was developed and Multivariate Exponentially Moving Average (MEWMA) control chart in case of multivariate process. Both of these charts were based on memory and perform better that Shewhart chart while detecting smaller shifts. In these charts, information the past sample is cumulated up the current sample and then the decision about the process control is taken. The mentioned characteristic of EWMA and MEWMA charts, are of the paramount importance when it is necessary to control industrial process, because it is possible to correct or predict problems in the processes before they come to a dangerous limit.

Keywords: control charts, multivariate exponentially moving average (MEWMA), exponentially moving average (EWMA), industrial control process

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Authors: G. Krishna Mohana Rao, P. Ravi Kumar

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Milling is the process of removing unwanted material with suitable tool. Even though the milling process is having wider application, the vibration of machine tool and work piece during the process produces chatter on the products. Various methods of preventing the chatter have been incorporated into machine tool systems. Damper is cut into equal number of parts. Each part is called as finger. Multiple fingers were inserted in the hollow portion of the shank to reduce tool vibrations. In the present work, nonlinear static and dynamic analysis of the damper inserted end milling cutter used to reduce the chatter was done. A comparison is made for the milling cutter with multiple dampers. Surface roughness was determined by machining with multiple finger inserted milling cutters.

Keywords: damping inserts, end milling, vibrations, nonlinear dynamic analysis, number of fingers

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Authors: Sajjad Esmaeili, Robert Krause, Lukas Gerlich, Alireza Mohammadian Kia, Benjamin Uhlig

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This investigation aims to develop the feasible and qualitative process parameters for the thin films fabrication into ultra-large through-silicon-vias (TSVs) as vertical interconnections. The focus of the study is on TSV metallization and its challenges employing new materials for the purpose of rapid signal propagation in the microsystems technology. Cobalt metal-organic chemical vapor deposition (Co-MOCVD) process enables manufacturing an adhesive and excellent conformal ultra-thin film all the way through TSVs in comparison with the conventional non-conformal physical vapor deposition (PVD) process of copper (Cu) seed layer. Therefore, this process provides a Cu seed-free layer which is capable of direct Cu electrochemical deposition (Cu-ECD) on top of it. The main challenge of this metallization module is to achieve the proper alternative seed layer with less roughness, sheet resistance and granular organic contamination (e.g. carbon) which intensify the Co corrosion under the influence of Cu electrolyte.

Keywords: Cobalt MOCVD, direct Cu electrochemical deposition (ECD), metallization technology, through-silicon-via (TSV)

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Authors: Navajyoti Panda, R. S. Pawar

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In this study, process parameters like punch angle, die opening, grain direction, and pre-bend condition of the strip for deep draw of high strength low alloy steel HSLA 420 are investigated. The finite element method (FEM) in association with the Taguchi and the analysis of variance (ANOVA) techniques are carried out to investigate the degree of importance of process parameters in V-bending process for HSLA 420&ST12 grade material. From results, it is observed that punch angle had a major influence on the spring-back. Die opening also showed very significant role on spring back. On the other hand, it is revealed that grain direction had the least impact on spring back; however, if strip from flat sheet is taken, then it is less prone to spring back as compared to the strip from sheet metal coil. HyperForm software is used for FEM simulation and experiments are designed using Taguchi method. Percentage contribution of the parameters is obtained through the ANOVA techniques.

Keywords: bending, spring-back, v-bending, FEM, Taguchi, HSLA 420 and St12 materials, HyperForm, profile projector

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Authors: Sachin Kamble, Shradha Gawankar

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This paper presents the findings of successful implementation of Business Process Reengineering (BPR) of cement dispatch activities in a cement manufacturing plant located in India. Simulation model was developed for the purpose of identifying and analyzing the areas for improvement. The company was facing a problem of low throughput rate and subsequent forced stoppages of the plant leading to a high production loss of 15000MT per month. It was found from the study that the present systems and procedures related to the in-plant logistics plant required significant changes. The major recommendations included process improvement at the entry gate, reducing the cycle time at the security gate and installation of an additional weigh bridge. This paper demonstrates how BPR can be implemented for improving the in-plant logistics process. Various recommendations helped the plant to increase its throughput by 14%.

Keywords: in-plant logistics, cement logistics, simulation modelling, business process re-engineering, supply chain management

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Authors: Fadi Eleiwi, Taous Meriem Laleg-Kirati

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This paper presents a complete dynamic modeling of a membrane distillation process. The model contains two consistent dynamic models. A 2D advection-diffusion equation for modeling the whole process and a modified heat equation for modeling the membrane itself. The complete model describes the temperature diffusion phenomenon across the feed, membrane, permeate containers and boundary layers of the membrane. It gives an online and complete temperature profile for each point in the domain. It explains heat conduction and convection mechanisms that take place inside the process in terms of mathematical parameters, and justify process behavior during transient and steady state phases. The process is monitored for any sudden change in the performance at any instance of time. In addition, it assists maintaining production rates as desired, and gives recommendations during membrane fabrication stages. System performance and parameters can be optimized and controlled using this complete dynamic model. Evolution of membrane boundary temperature with time, vapor mass transfer along the process, and temperature difference between membrane boundary layers are depicted and included. Simulations were performed over the complete model with real membrane specifications. The plots show consistency between 2D advection-diffusion model and the expected behavior of the systems as well as literature. Evolution of heat inside the membrane starting from transient response till reaching steady state response for fixed and varying times is illustrated.

Keywords: membrane distillation, dynamical modeling, advection-diffusion equation, thermal equilibrium, heat equation

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Authors: Manjinder Bajwa, Mahipal Singh, Manish Nagpal

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This research paper aims to propose a novel approach using ANOVA technique for the strategic investigation of process parameters and their effects on the mechanical properties of Aluminium alloy cast. The two process parameters considered here were permeability of sand and pouring temperature of aluminium alloy. ANOVA has been employed for the first time to determine the effects of these selected parameters on the impact strength of alloy. The experimental results show that this proposed technique has great potential for analyzing sand casting process. Using this approach we have determined the treatment mean square, response mean square and mean square of error as 8.54, 8.255 and 0.435 respectively. The research concluded that at the 5% level of significance, permeability of sand is the more significant parameter influencing the impact strength of cast alloy.

Keywords: aluminium alloy, pouring temperature, permeability of sand, impact strength, ANOVA

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Authors: Diana Katherine Gonzalez Galindo, David Rolando Suarez Mora

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With this study, we used data mining as a new alternative of the solution to evaluate the comments of the customers in order to find a pattern that helps us to determine some behaviors to reduce the deactivation of the partners of the LEVEL app. In one of the greatest business created in the last times, the partners are being affected due to an internal process that compensates the customer for a bad experience, but these comments could be false towards the driver, that’s why we made an investigation to collect information to restructure this process, many partners have been disassociated due to this internal process and many of them refuse the comments given by the customer. The main methodology used in this case study is the observation, we recollect information in real time what gave us the opportunity to see the most common issues to get the most accurate solution. With this new process helped by data mining, we could get a prediction based on the behaviors of the customer and some basic data recollected such as the age, the gender, and others; this could help us in future to improve another process. This investigation gives more opportunities to the partner to keep his account active even if the customer writes a message through the app. The term is trying to avoid a recession of drivers in the future offering improving in the processes, at the same time we are in search of stablishing a strategy which benefits both the app’s managers and the associated driver.

Keywords: agent, driver, deactivation, rider

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Authors: Adrià Arbués-Sangüesa, Coloma Ballester, Gloria Haro

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Tracking sports players is a widely challenging scenario, specially in single-feed videos recorded in tight courts, where cluttering and occlusions cannot be avoided. This paper presents an analysis of several geometric and semantic visual features to detect and track basketball players. An ablation study is carried out and then used to remark that a robust tracker can be built with Deep Learning features, without the need of extracting contextual ones, such as proximity or color similarity, nor applying camera stabilization techniques. The presented tracker consists of: (1) a detection step, which uses a pretrained deep learning model to estimate the players pose, followed by (2) a tracking step, which leverages pose and semantic information from the output of a convolutional layer in a VGG network. Its performance is analyzed in terms of MOTA over a basketball dataset with more than 10k instances.

Keywords: basketball, deep learning, feature extraction, single-camera, tracking

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Authors: Robson da C. Santos, Carlos Henrique de A. S. P. Coutinho, Lucas Moreira Dias, Gerson Gomes Cunha

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The article refers to the development of an augmented reality 3D display, through the control of servo motors and projection of image with aid of video projector on the model. Augmented Reality is a branch that explores multiple approaches to increase real-world view by viewing additional information along with the real scene. The article presents the broad use of electrical, electronic, mechanical and industrial automation for geospatial visualizations, applications in mathematical models with the visualization of functions and 3D surface graphics and volumetric rendering that are currently seen in 2D layers. Application as a 3D display for representation and visualization of Digital Terrain Model (DTM) and Digital Surface Models (DSM), where it can be applied in the identification of canyons in the marine area of the Campos Basin, Rio de Janeiro, Brazil. The same can execute visualization of regions subject to landslides, as in Serra do Mar - Agra dos Reis and Serranas cities both in the State of Rio de Janeiro. From the foregoing, loss of human life and leakage of oil from pipelines buried in these regions may be anticipated in advance. The physical design consists of a table consisting of a 9 x 16 matrix of servo motors, totalizing 144 servos, a mesh is used on the servo motors for visualization of the models projected by a retro projector. Each model for by an image pre-processing, is sent to a server to be converted and viewed from a software developed in C # Programming Language.

Keywords: visualization, 3D models, servo motors, C# programming language

Procedia PDF Downloads 334

Authors: Rongbo Shen, Jianhua Yao, Kezhou Yan, Kuan Tian, Cheng Jiang, Ke Zhou

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This study proposes a generative adversarial networks (GAN) framework to perform synthetic sampling in feature space, i.e., feature augmentation, to address the class imbalance problem in medical image analysis. A feature extraction network is first trained to convert images into feature space. Then the GAN framework incorporates adversarial learning to train a feature generator for the minority class through playing a minimax game with a discriminator. The feature generator then generates features for minority class from arbitrary latent distributions to balance the data between the majority class and the minority class. Additionally, a data cleaning technique, i.e., Tomek link, is employed to clean up undesirable conflicting features introduced from the feature augmentation and thus establish well-defined class clusters for the training. The experiment section evaluates the proposed method on two medical image analysis tasks, i.e., mass classification on mammogram and cancer metastasis classification on histopathological images. Experimental results suggest that the proposed method obtains superior or comparable performance over the state-of-the-art counterparts. Compared to all counterparts, our proposed method improves more than 1.5 percentage of accuracy.

Keywords: class imbalance, synthetic sampling, feature augmentation, generative adversarial networks, data cleaning

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Authors: Halimi Mohamed Taher, Kordoghli Bassem, Ben Hassen Mohamed, Sakli Faouzi

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Textile and clothing manufacturing industry is based largely on subcontracting system. Choosing the right subcontractor became a strategic decision that can affect the financial position of the company and even his market position. Subcontracting firms in Tunisia are lead to define an appropriate selection process which takes into account several quantitative and qualitative criteria. In this study, a methodology is proposed that includes a Fuzzy Analytic Hierarchy Process (AHP) in order to incorporate the ambiguities and uncertainties in qualitative decision. Best subcontractors for two Tunisian firms are determined based on model results.

Keywords: AHP, subcontractor, multicriteria, selection

Procedia PDF Downloads 682

Authors: Shaira L. Kee, Michael Aaron G. Sy, Myles Joshua T. Tan, Hezerul Abdul Karim, Nouar AlDahoul

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Skin diseases are considered the fourth most common disease, with melanoma and non-melanoma skin cancer as the most common type of cancer in Caucasians. The alarming increase in Skin Cancer cases shows an urgent need for further research to improve diagnostic methods, as early diagnosis can significantly improve the 5-year survival rate. Machine Learning algorithms for image pattern analysis in diagnosing skin lesions can dramatically increase the accuracy rate of detection and decrease possible human errors. Several studies have shown the diagnostic performance of computer algorithms outperformed dermatologists. However, existing methods still need improvements to reduce diagnostic errors and generate efficient and accurate results. Our paper proposes an ensemble method to classify dermoscopic images into benign and malignant skin lesions. The experiments were conducted using the International Skin Imaging Collaboration (ISIC) image samples. The dataset contains 3,297 dermoscopic images with benign and malignant categories. The results show improvement in performance with an accuracy of 88% and an F1 score of 87%, outperforming other existing models such as support vector machine (SVM), Residual network (ResNet50), EfficientNetB0, EfficientNetB4, and VGG16.

Keywords: deep learning - VGG16 - efficientNet - CNN – ensemble – dermoscopic images - melanoma

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Authors: Sky Chou, Joseph C. Chen

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This paper focuses on using Six Sigma methodologies to improve the surface roughness of a manufactured part produced by the CNC milling machine. It presents a case study where the surface roughness of milled aluminum is required to reduce or eliminate defects and to improve the process capability index C_p and C_pk for a CNC milling process. The six sigma methodology, DMAIC (design, measure, analyze, improve, and control) approach, was applied in this study to improve the process, reduce defects, and ultimately reduce costs. The Taguchi-based six sigma approach was applied to identify the optimized processing parameters that led to the targeted surface roughness specified by our customer. A L₉ orthogonal array was applied in the Taguchi experimental design, with four controllable factors and one non-controllable/noise factor. The four controllable factors identified consist of feed rate, depth of cut, spindle speed, and surface roughness. The noise factor is the difference between the old cutting tool and the new cutting tool. The confirmation run with the optimal parameters confirmed that the new parameter settings are correct. The new settings also improved the process capability index. The purpose of this study is that the Taguchi–based six sigma approach can be efficiently used to phase out defects and improve the process capability index of the CNC milling process.

Keywords: CNC machining, six sigma, surface roughness, Taguchi methodology

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Authors: Abel S. Huaynacho, Yoni D. Huaynacho

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The improvement of procedure test ASTM D1556, plays an important role in the developing of testing in field to obtain a higher quality of data QA/QC. The traditional process takes a considerable amount of time for only one test. Even making various testing are tasks repeating and it takes a long time to obtain better results. Moreover, if the adequate tools the help these testing are not properly managed, the improvement in the development for various testing could be stooped. This paper presents an optimized process for various testing ASTM D1556 which uses an initial standard process to another one the uses a simpler and improved management tools.

Keywords: cone sand test, density bulk, ASTM D1556, QA/QC

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Authors: Krishna Mohan Bathula, Fatou Bintou Loucoubar, FNU Kaleemunnisa, Christelle Scharff, Mark Anthony De Castro

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Voice recognition algorithms such as automatic speech recognition and text-to-speech systems with African languages can play an important role in bridging the digital divide of Artificial Intelligence in Africa, contributing to the establishment of a fully inclusive information society. This paper proposes a Deep Learning model that can classify the user responses as inputs for an interactive voice response system. A dataset with Wolof language words ‘yes’ and ‘no’ is collected as audio recordings. A two stage Data Augmentation approach is adopted for enhancing the dataset size required by the deep neural network. Data preprocessing and feature engineering with Mel-Frequency Cepstral Coefficients are implemented. Convolutional Neural Networks (CNNs) have proven to be very powerful in image classification and are promising for audio processing when sounds are transformed into spectra. For performing voice response classification, the recordings are transformed into sound frequency feature spectra and then applied image classification methodology using a deep CNN model. The inference model of this trained and reusable Wolof voice response recognition system can be integrated with many applications associated with both web and mobile platforms.

Keywords: automatic speech recognition, interactive voice response, voice response recognition, wolof word classification

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Authors: Musaab Riyadh, Norwati Mustapha, Dina Riyadh

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Trajectory similarity can be defined as the cost of transforming one trajectory into another based on certain similarity method. It is the core of numerous mining tasks such as clustering, classification, and indexing. Various approaches have been suggested to measure similarity based on the geometric and dynamic properties of trajectory, the overlapping between trajectory segments, and the confined area between entire trajectories. In this article, an evaluation of these approaches has been done based on computational cost, usage memory, accuracy, and the amount of data which is needed in advance to determine its suitability to stream mining applications. The evaluation results show that the stream mining applications support similarity methods which have low computational cost and memory, single scan on data, and free of mathematical complexity due to the high-speed generation of data.

Keywords: global distance measure, local distance measure, semantic trajectory, spatial dimension, stream data mining

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Authors: Pradeep Kumar Singh Chauhan, Gayatri Devi, Zamir Ahmad, Komal Chauhan, Abha Mittal

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The application of geophysical methods to study the subsurface profile for site investigation is becoming popular globally. These methods are non-destructive and provide the image of subsurface at shallow depths. Seismic refraction method is one of the most common and efficient method being used for civil engineering site investigations particularly for knowing the seismic velocity of the subsurface layers. Resistivity imaging technique is a geo-electrical method used to image the subsurface, water bearing zone, bedrock and layer thickness. Integrated approach combining seismic refraction and 2-D resistivity imaging will provide a better and reliable picture of the subsurface. These are economical and less time-consuming field survey which provide high resolution image of the subsurface. Geophysical surveys carried out in this study include seismic refraction and 2D resistivity imaging method for delineation of sub-surface strata in different parts of Srinagar, Garhwal Himalaya, India. The aim of this survey was to map the shallow subsurface in terms of geological and geophysical properties mainly P-wave velocity, resistivity, layer thickness, and lithology of the area. Both sides of the river, Alaknanda which flows through the centre of the city, have been covered by taking two profiles on each side using both methods. Seismic and electrical surveys were carried out at the same locations to complement the results of each other. The seismic refraction survey was carried out using ABEM TeraLoc 24 channel Seismograph and 2D resistivity imaging was performed using ABEM Terrameter LS equipment. The results show three distinct layers on both sides of the river up to the depth of 20 m. The subsurface is divided into three distinct layers namely, alluvium extending up to, 3 m depth, conglomerate zone lying between the depth of 3 m to 15 m, and compacted pebbles and cobbles beyond 15 m. P-wave velocity in top layer is found in the range of 400 – 600 m/s, in second layer it varies from 700 – 1100 m/s and in the third layer it is 1500 – 3300 m/s. The resistivity results also show similar pattern and were in good agreement with seismic refraction results. The results obtained in this study were validated with an available exposed river scar at one site. The study established the efficacy of geophysical methods for subsurface investigations.

Keywords: 2D resistivity imaging, P-wave velocity, seismic refraction survey, subsurface

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Authors: Feng Yin

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Clouds are inevitable contaminants in optical satellite imagery, and prevent the satellite imaging systems from acquiring clear view of the earth surface. The presence of clouds in satellite imagery bring negative influences for remote sensing land resources investigation. As a consequence, detecting the locations of clouds in satellite imagery is an essential preprocessing step, and further remove the existing clouds is crucial for the application of imagery. In this paper, a multi-temporal based satellite imagery cloud detection and removal method is proposed, which will be used for large-scale land resource investigation. The proposed method is mainly composed of four steps. First, cloud masks are generated for cloud contaminated images by single temporal cloud detection based on multiple spectral features. Then, a cloud-free reference image of target areas is synthesized by weighted averaging time-series images in which cloud pixels are ignored. Thirdly, the refined cloud detection results are acquired by multi-temporal analysis based on the reference image. Finally, detected clouds are removed via multi-temporal linear regression. The results of a case application in Hubei province indicate that the proposed multi-temporal cloud detection and removal method is effective and promising for large-scale land resource investigation.

Keywords: cloud detection, cloud remove, multi-temporal imagery, land resources investigation

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Authors: Sabri Mrayed, David Maccioni, Greg Leslie

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Seawater desalination has been accepted as one of the most effective solutions to the growing problem of a diminishing clean drinking water supply. Currently, two desalination technologies dominate the market – the thermally driven multi-stage flash distillation (MSF) and the membrane based reverse osmosis (RO). However, in recent years membrane distillation (MD) has emerged as a potential alternative to the established means of desalination. This research project intended to determine the viability of MD as an alternative process to MSF and RO for seawater desalination. Specifically the project involves conducting a thermodynamic analysis of the process based on the second law of thermodynamics to determine the efficiency of the MD. Data was obtained from experiments carried out on a laboratory rig. In order to determine exergy values required for the exergy analysis, two separate models were built in Engineering Equation Solver – the ’Minimum Separation Work Model’ and the ‘Stream Exergy Model’. The efficiency of MD process was found to be 17.3 %, and the energy consumption was determined to be 4.5 kWh to produce one cubic meter of fresh water. The results indicate MD has potential as a technique for seawater desalination compared to RO and MSF. However, it was shown that this was only the case if an alternate energy source such as green or waste energy was available to provide the thermal energy input to the process. If the process was required to power itself, it was shown to be highly inefficient and in no way thermodynamically viable as a commercial desalination process.

Keywords: desalination, exergy, membrane distillation, second law efficiency

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Authors: Basiru Amuneni

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Astronomy is one domain with a rise in data. Traditional tools for data management have been employed in the quest for knowledge discovery. However, these traditional tools become limited in the face of big. One means of maximizing knowledge discovery for big data is the use of scientific visualisation. The aim of the work is to explore the possibilities offered by emerging creative technologies of Virtual Reality (VR) systems and game engines to visualize multiwavelength datasets. Game Engines are primarily used for developing video games, however their advanced graphics could be exploited for scientific visualization which provides a means to graphically illustrate scientific data to ease human comprehension. Modern astronomy is now in the era of multiwavelength data where a single galaxy for example, is captured by the telescope several times and at different electromagnetic wavelength to have a more comprehensive picture of the physical characteristics of the galaxy. Visualising this in an immersive environment would be more intuitive and natural for an observer. This work presents a standalone VR application that accesses galaxy FITS files. The application was built using the Unity Game Engine for the graphics underpinning and the OpenXR API for the VR infrastructure. The work used a methodology known as Design Science Research (DSR) which entails the act of ‘using design as a research method or technique’. The key stages of the galaxy modelling pipeline are FITS data preparation, Galaxy Modelling, Unity 3D Visualisation and VR Display. The FITS data format cannot be read by the Unity Game Engine directly. A DLL (CSHARPFITS) which provides a native support for reading and writing FITS files was used. The Galaxy modeller uses an approach that integrates cleaned FITS image pixels into the graphics pipeline of the Unity3d game Engine. The cleaned FITS images are then input to the galaxy modeller pipeline phase, which has a pre-processing script that extracts, pixel, galaxy world position, and colour maps the FITS image pixels. The user can visualise image galaxies in different light bands, control the blend of the image with similar images from different sources or fuse images for a holistic view. The framework will allow users to build tools to realise complex workflows for public outreach and possibly scientific work with increased scalability, near real time interactivity with ease of access. The application is presented in an immersive environment and can use all commercially available headset built on the OpenXR API. The user can select galaxies in the scene, teleport to the galaxy, pan, zoom in/out, and change colour gradients of the galaxy. The findings and design lessons learnt in the implementation of different use cases will contribute to the development and design of game-based visualisation tools in immersive environment by enabling informed decisions to be made.

Keywords: astronomy, visualisation, multiwavelenght dataset, virtual reality

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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

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Authors: N. Alhazmi

Abstract:

Knowing the thermodynamics properties of hydrocarbons is vital when it comes to analyzing the related chemical reaction outcomes and understanding the reaction process, especially in terms of petrochemical industrial applications, combustions, and catalytic reactions. However, measuring the thermodynamics properties experimentally is time-consuming and costly. In this paper, Gaussian process regression (GPR) has been used to directly predict the main thermodynamic properties - standard enthalpy of formation, standard entropy, and heat capacity -for more than 360 cyclic and non-cyclic alkanes, alkenes, and alkynes. A simple workflow has been proposed that can be applied to directly predict the main properties of any hydrocarbon by knowing its descriptors and chemical structure and can be generalized to predict the main properties of any material. The model was evaluated by calculating the statistical error R², which was more than 0.9794 for all the predicted properties.

Keywords: thermodynamic, Gaussian process regression, hydrocarbons, regression, supervised learning, entropy, enthalpy, heat capacity

Procedia PDF Downloads 214

Authors: Tony Bediako Arthur, Nejat Rahmanian, Nana Gyan Sekyi

Abstract:

The possibility of producing seeded granules from fine and course powders is a major challenge as the control parameters that affect its producibility is still under investigation. The seeded granulation is a novel form of producing granules where the granule is made up of larger particles at the core, which are surrounded by fine particles. The possibility of managing granulation through course particle feed rate control makes seeded granulation in continuous granulation useful in terms of process control. Twin screw granulation is now a major process of choice for the wet continuous granulation process in the industry. It is, therefore, imperative to investigate the process control parameters that influence the formation of seeded granules in twin screw granulation. In this paper, the effect of the twin screws rotating speed on the production of seeded granules has been examined. Pictorial and quantitative analysis indicates a high number of seeded granules forming at low screw rotating speeds. It is also instructive to say that higher tensile stress occurs at the kneading section of the screws; thus, higher rotating speed courses the fines for breaking off from the seed particle.

Keywords: DEM, twin-screw, Seeded granules, Simulation

Procedia PDF Downloads 82

Authors: Kuo-Wei Hsu, Wei-Chin Lo

Abstract:

In these years, urban agriculture development has been wide spreading all over the world. The development of urban agriculture is an evolution process of highly urbanization, as well as an agricultural phenomenon closely related to the development of economy, society and culture in urban areas. It provides densely populated areas with multi-functional uses of land, impacting strategic development of both large and small towns in the area. In addition, the participation of social enterprises keeps industrial competitiveness and makes gains when facing rapid transformation of industrial structures and new patterns of lifestyles in urban areas. They create better living conditions as well as protect the environment with innovative business beliefs, which give new ways for development of urban agriculture. Also, through building up the value chain, these social enterprises are capable of creating value for urban agriculture. Most of research regarding to social enterprises currently explore the relationship between corporate responsibilities and its role play, operational mode and performance and organizational patterns. Merely some of them discuss the function of social entrepreneurship in the development of urban agriculture. Moreover, none of them have explored the value creation for development of urban agriculture processed by social enterprises, as well as how social enterprises operate to increase competitive advantages, which make it possible to achieve industrial innovation, increase corporate value and even provide services with value creation. Therefore, this research mainly reviews current business patterns and operational conditions of social enterprises. This research endowed social responsibilities, and discusses current development process of urban agriculture. This research adopts Value Chain perspective to discuss key factors for value creation with respect to the development of urban agriculture processed by social enterprises. Thereby after organization and integration this research develops the prospect of value creation referring to urban agriculture processed by social enterprises and builds the value chain for urban agriculture. In conclusion, this research explored the relationship between value chain and value creation, which relates to values of customer, enterprise, society and economy referring to the development of urban agriculture uniquely, in consideration of the participation of social enterprises, and hence built the connection between value chain and value creation in the development of urban agriculture by social enterprises. The research found, social enterprises help to enhance the connection between the enterprise value and society value, mold corporate image with social responsibility and create brand value, and therefore impact the increase of economic value.

Keywords: urban agriculture development, value chain, social enterprise, urban systems

Procedia PDF Downloads 471