Search results for: corpus-driven approach

Authors: Jimmy Eadie

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Convolutional Cross Synthesis (CCS) has emerged as a powerful technique for blending input signals to create hybrid sounds. It has significantly expanded the horizons of digital signal processing, enabling artists to explore audio effects. However, the conventional applications of CCS primarily revolve around reverberation and room simulation rather than being utilized as a creative synthesis method. In this paper, we present the design of a digital instrument called CrossSampler that harnesses a parametric approach to convolution cross-synthesis, which involves using adjustable parameters to control the blending of audio signals through convolution. These parameters allow for customization of the resulting sound, offering greater creative control and flexibility. It enables users to shape the output by manipulating factors such as duration, intensity, and spectral characteristics. This approach facilitates experimentation and exploration in sound design and opens new sonic possibilities.

Keywords: convolution, synthesis, sampling, virtual instrument

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Authors: Rida B. Arieby, Hameed N. Hameed

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In this work, tensile tests on high density polyethylene have been carried out under various constant strain rate and strain rate jump tests. The dependency of the true stress and specially the variation of volume strain have been investigated, the volume strain due to the phenomena of damage was determined in real time during the tests by an optical extensometer called Videotraction. A modified constitutive equations, including strain rate and damage effects, are proposed, such a model is based on a non-equilibrium thermodynamic approach called (DNLR). The ability of the model to predict the complex nonlinear response of this polymer is examined by comparing the model simulation with the available experimental data, which demonstrate that this model can represent the deformation behavior of the polymer reasonably well.

Keywords: strain rate jump tests, volume strain, high density polyethylene, large strain, thermodynamics approach

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Authors: Raymond Xu, Ashley Hua, Andrew Wang, Yuru Lin

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During COVID-19, the depression rate has increased dramatically. Young adults are most vulnerable to the mental health effects of the pandemic. Lower-income families have a higher ratio to be diagnosed with depression than the general population, but less access to clinics. This research aims to achieve early depression detection at low cost, large scale, and high accuracy with an interdisciplinary approach by incorporating clinical practices defined by American Psychiatric Association (APA) as well as multimodal AI framework. The proposed approach detected the nine depression symptoms with Natural Language Processing sentiment analysis and a symptom-based Lexicon uniquely designed for young adults. The experiments were conducted on the multimedia survey results from adolescents and young adults and unbiased Twitter communications. The result was further aggregated with the facial emotional cues analyzed by the Convolutional Neural Network on the multimedia survey videos. Five experiments each conducted on 10k data entries reached consistent results with an average accuracy of 88.31%, higher than the existing natural language analysis models. This approach can reach 300+ million daily active Twitter users and is highly accessible by low-income populations to promote early depression detection to raise awareness in adolescents and young adults and reveal complementary cues to assist clinical depression diagnosis.

Keywords: artificial intelligence, COVID-19, depression detection, psychiatric disorder

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Authors: Ibrahim A. Al-Sulaihi, Khalid S. Al-Gahtani, Abdullah M. Al-Sugair, Aref A. Abadel

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Achieving environmental sustainability is one of the important issues considered in many countries’ vision. Green/Sustainable building is widely used terminology for describing a friendly environmental construction. Applying sustainable practices has a significant importance in various fields, including construction field that consumes an enormous amount of resource and causes a considerable amount of waste. The need for sustainability is increased in the regions that suffering from the limitation of natural resource and extreme weather conditions such as Saudi Arabia. Since buildings designs are getting sophisticated, the need for tools, which support decision-making for sustainability issues, is increasing, especially in the design and preconstruction stages. In this context, Building Information Modeling (BIM) can aid in performing complex building performance analyses to ensure an optimized sustainable building design. Accordingly, this paper introduces a roadmap towards developing a systematic approach for presenting the sustainability of buildings using BIM. The approach includes set of main processes including; identifying the sustainability parameters that can be used for sustainability assessment in Saudi Arabia, developing sustainability assessment method that fits the special circumstances in the Kingdom, identifying the sustainability requirements and BIM functions that can be used for satisfying these requirements, and integrating these requirements with identified functions. As a result, the sustainability-BIM approach can be developed which helps designers in assessing the sustainability and exploring different design alternatives at the early stage of the construction project.

Keywords: green buildings, sustainability, BIM, rating systems, environment, Saudi Arabia

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Authors: Roshani S. Golhar, Neerajkumar S. Sathawane, Snehal Dongre

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Background and objectives: As the, cardiovascular illnesses increasing and becoming cause of mortality worldwide, killing around lot of people each year. Arrhythmia is a type of cardiac illness characterized by a change in the linearity of the heartbeat. The goal of this study is to develop novel deep learning algorithms for successfully interpreting arrhythmia using a single second segment. Because the ECG signal indicates unique electrical heart activity across time, considerable changes between time intervals are detected. Such variances, as well as the limited number of learning data available for each arrhythmia, make standard learning methods difficult, and so impede its exaggeration. Conclusions: The proposed method was able to outperform several state-of-the-art methods. Also proposed technique is an effective and convenient approach to deep learning for heartbeat interpretation, that could be probably used in real-time healthcare monitoring systems

Keywords: electrocardiogram, ECG classification, neural networks, convolutional neural networks, portable document format

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Authors: Asma Ameur, Dhafer Malouche

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Nowadays, user comments on the Internet have an important impact on hotel bookings. This confirms that the e-reputation issue can influence the likelihood of customer loyalty to a hotel. In this way, e-reputation has become a real differentiator between hotels. For this reason, we have a unique opportunity in the opinion mining field to analyze the comments. In fact, this field provides the possibility of extracting information related to the polarity of user reviews. This sentimental study (Opinion Mining) represents a new line of research for analyzing the unstructured textual data. Knowing the score of e-reputation helps the hotelier to better manage his marketing strategy. The score we then obtain is translated into the image of hotels to differentiate between them. Therefore, this present research highlights the importance of hotel satisfaction ‘scoring. To calculate the satisfaction score, the sentimental analysis can be manipulated by several techniques of machine learning. In fact, this study treats the extracted textual data by using the Artificial Neural Networks Approach (ANNs). In this context, we adopt the aforementioned technique to extract information from the comments available in the ‘Trip Advisor’ website. This actual paper details the description and the modeling of the ANNs approach for the scoring of online hotel reviews. In summary, the validation of this used method provides a significant model for hotel sentiment analysis. So, it provides the possibility to determine precisely the polarity of the hotel users reviews. The empirical results show that the ANNs are an accurate approach for sentiment analysis. The obtained results show also that this proposed approach serves to the dimensionality reduction for textual data’ clustering. Thus, this study provides researchers with a useful exploration of this technique. Finally, we outline guidelines for future research in the hotel e-reputation field as comparing the ANNs with other technique.

Keywords: clustering, consumer behavior, data mining, e-reputation, machine learning, neural network, online hotel ‘reviews, opinion mining, scoring

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Authors: Falek Kamel

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Free vibration analysis of beams, based on the assumptions of Bernoulli-Euler theory, has been extensively studied. Many research works have focused on the study of transverse vibrations under the application of different boundary conditions where different theories have been applied. The stiffness and mass matrices considered are those obtained by assembling those resulting from the use of the finite element method. The Jacobi method has been used to solve the eigenvalue problem. These well-known concepts have been applied to the study of beams with constant geometric and mechanical characteristics having one to two overhangs with variable lengths. Murphy studied, by an algebraic solution approach, a simply supported beam with two overhangs of arbitrary length, allowing for an experimental determination of the elastic modulus E. The advantage of our article is that it offers the possibility of extending this approach to many interesting problems formed by transversely vibrating beams with various end constraints.

Keywords: beam, finite element, transverse vibrations, end restreint, Bernoulli-Euler theory

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Authors: Mirjana Ruppel, Rajendra Persad, Amit Bahl, Sanja Dogramadzi, Chris Melhuish, Lyndon Smith

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Multimodal image registration is a profoundly complex task which is why deep learning has been used widely to address it in recent years. However, two main challenges remain: Firstly, the lack of ground truth data calls for an unsupervised learning approach, which leads to the second challenge of defining a feasible loss function that can compare two images of different modalities to judge their level of alignment. To avoid this issue altogether we implement a generative adversarial network consisting of two registration networks GAB, GBA and two discrimination networks DA, DB connected by spatial transformation layers. GAB learns to generate a deformation field which registers an image of the modality B to an image of the modality A. To do that, it uses the feedback of the discriminator DB which is learning to judge the quality of alignment of the registered image B. GBA and DA learn a mapping from modality A to modality B. Additionally, a cycle-consistency loss is implemented. For this, both registration networks are employed twice, therefore resulting in images ˆA, ˆB which were registered to ˜B, ˜A which were registered to the initial image pair A, B. Thus the resulting and initial images of the same modality can be easily compared. A dataset of liver CT and MRI was used to evaluate the quality of our approach and to compare it against learning and non-learning based registration algorithms. Our approach leads to dice scores of up to 0.80 ± 0.01 and is therefore comparable to and slightly more successful than algorithms like SimpleElastix and VoxelMorph.

Keywords: cycle consistency, deformable multimodal image registration, deep learning, GAN

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Authors: Wael Elsaady, S. Olutunde Oyadiji, Adel Nasser

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The non-linear flow characteristics of magnetorheological (MR) fluids in MR dampers are studied via a coupled numerical approach that incorporates a two-phase flow model. The approach couples the Finite Element (FE) modelling of the damper magnetic circuit, with the Computational Fluid Dynamics (CFD) analysis of the flow field in the damper. The two-phase flow CFD model accounts for the effect of fluid compressibility due to the presence of liquid and gas in the closed domain of the damper. The dynamic mesh model included in ANSYS/Fluent CFD solver is used to simulate the movement of the MR damper piston in order to perform the fluid excitation. The two-phase flow analysis is studied by both Volume-Of-Fluid (VOF) model and mixture model that are included in ANSYS/Fluent. The CFD models show that the hysteretic behaviour of MR dampers is due to the effect of fluid compressibility. The flow field shows the distributions of pressure, velocity, and viscosity contours. In particular, it shows the high non-Newtonian viscosity in the affected fluid regions by the magnetic field and the low Newtonian viscosity elsewhere. Moreover, the dependence of gas volume fraction on the liquid pressure inside the damper is predicted by the mixture model. The presented approach targets a better understanding of the complicated flow characteristics of viscoplastic fluids that could be applied in different applications.

Keywords: viscoplastic fluid, magnetic FE analysis, computational fluid dynamics, two-phase flow, dynamic mesh, user-defined functions

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Authors: Fernando Miguel Campos, Carlos Ferreira, João Pestana, Pedro Campos, Nils Ehrenberg, Wojciech Hydzik

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The current trend of organizations offering their workers open-office spaces and co-working offices has been primed for stimulating teamwork and collaboration. However, this is not always valid as these kinds of spaces bring other types of challenges that compromise workers productivity and creativity. We present an approach for improving creativity and productivity at the workspace by redesigning an office chair that incorporates subtle technological elements that help users focus, relax and being more productive and creative. This sheds light on how we can better design interactive furniture for such popular contexts, as we develop this new chair through a multidisciplinary approach using ergonomics, interior design, interaction design, hardware and software engineering and psychology.

Keywords: creativity, co-working, ergonomics, human-computer interaction, interaction, interactive furniture, productivity

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Authors: Aisultan Shoiynbek, Darkhan Kuanyshbay, Paulo Menezes, Akbayan Bekarystankyzy, Assylbek Mukhametzhanov, Temirlan Shoiynbek

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Speech emotion recognition (SER) has received increasing research interest in recent years. It is a common practice to utilize emotional speech collected under controlled conditions recorded by actors imitating and artificially producing emotions in front of a microphone. There are four issues related to that approach: emotions are not natural, meaning that machines are learning to recognize fake emotions; emotions are very limited in quantity and poor in variety of speaking; there is some language dependency in SER; consequently, each time researchers want to start work with SER, they need to find a good emotional database in their language. This paper proposes an approach to create an automatic tool for speech emotion extraction based on facial emotion recognition and describes the sequence of actions involved in the proposed approach. One of the first objectives in the sequence of actions is the speech detection issue. The paper provides a detailed description of the speech detection model based on a fully connected deep neural network for Kazakh and Russian. Despite the high results in speech detection for Kazakh and Russian, the described process is suitable for any language. To investigate the working capacity of the developed model, an analysis of speech detection and extraction from real tasks has been performed.

Keywords: deep neural networks, speech detection, speech emotion recognition, Mel-frequency cepstrum coefficients, collecting speech emotion corpus, collecting speech emotion dataset, Kazakh speech dataset

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Authors: Nakul Agrawal, Y. M. Puri

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TPM methodology is a proven approach to increase Overall Equipment Effectiveness (OEE) of machine. OEE is an established method to monitor and improve the effectiveness of manufacturing process. OEE is a product of equipment availability, performance efficiency and quality performance of manufacturing operations. The paper presents a project work for improving OEE of CNC-VMC in a manufacturing industry with the help of TPM tools Kaizen and Autonomous Maintenance. The aim of paper is to enhance OEE by minimizing the breakdown and re-work, increase availability, performance and quality. The calculated OEE of bottle necking machines for 4 months is lower of 53.3%. Root Cause Analysis RCA tools like fishbone diagram, Pareto chart are used for determining the reasons behind low OEE. While Tool like Why-Why analysis is use for determining the basis reasons for low OEE. Tools like Kaizen and Autonomous Maintenance are effectively implemented on CNC-VMC which eliminate the causes of breakdown and prevent from reoccurring. The result obtains from approach shows that OEE of CNC-VMC improved from 53.3% to 73.7% which saves an average sum of Rs.3, 19,000.

Keywords: OEE, TPM, Kaizen, CNC-VMC, why-why analysis, RCA

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Authors: Subarna Ferdous, Mitsuru Ikeda

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For the shrimp companies to remain relevant to its local and international consumers, they must offer new shrimp product and services. It must work actively not just to create value for the consumer, but to involve the consumer in co-creating value for shrimp product innovation in the market. In this theoretical work, we conceptualize the business concept of value co-creation in the context of shrimp products, and propose a framework of value co-creation for shrimp product innovation in shrimp industries. With guidance on value co-creation in in shrimp industry, and shrimp value chain actors mapped to the co-creation cycle, companies can use the framework to offer new shrimp product to consumer communities. Although customer co-creation is known approach in the world, it is not commonly used by the companies in Bangladesh. This paper makes an original contribution by conceptualizing co-creation and set the examples of best co-creation practices in food sector. The results of the study provide management with guidelines for successful co-creation projects with an innovation- and market-oriented approach. The framework also provides a basis for further research in this area.

Keywords: bangladesh, shrimp industry, value co-creation, shrimp product

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Authors: Jasim Saleh Said AlDairi

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The potential of applying Lean Management in Higher Education Institutions has increased significantly in last few years, leading to tremendous savings. Reviewing and updating competencies’ curriculum matrix is one of the critical and complicated processes that consume time and effort, and this has triggered searching for a scientific and sustainable approach to manage the such review. This paper presents a novel approach of implementing Lean (5S) methodology in reviewing technical competencies required for the graduates of the Military Technological College (MTC) in the Sultanate of Oman. The 5S framework has been imbedded into an action plan using the PDCA cycle. As a result, the method applied has helped in sorting out the actual required competencies, the team has identified the required (new, amended, and deleted) competencies in all of the targeted Engineering Departments, in addition, the major wastes within the overall process were identified, and the future review process was standardized and documented.

Keywords: PDCA, 5S, lean, MTC, competencies, curriculum matrix, higher education

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Authors: Md. Wasiul Islam, Md. Jamius Shams Sowrov

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The Sundarbans is the largest single tract of mangrove forest in the world. This is located in the southwest corner of Bangladesh. This is a unique ecosystem which is a great breeding and nursing ground for a great biodiversity. It supports the livelihood of about 3.5 million coastal dwellers and also protects the coastal belt and inland areas from various natural calamities. Historically, the management of the Sundarbans was controlled by the Bangladesh Forest Department following top-down approach without the involvement of local communities. Such fence and fining-based blue-print approach was not effective to protect the forest which caused Sundarbans to degrade severely in the recent past. Fifty percent of the total tree cover has been lost in the last 30 years. Therefore, local multi-stakeholder based bottom-up co-management approach was introduced at some of the parts of the Sundarbans in 2006 to improve the biodiversity status by enhancing the protection level of the forest. Various co-management organizations were introduced under co-management approach where the local community people could actively involve in various activities related to the management and welfare of the Sundarbans including the decision-making process to achieve the goal. From this backdrop, the objective of the study was to assess the performance of co-management organizations to facilitate sustainable management of the Sundarbans mangrove forests. The qualitative study followed face-to-face interview to collect data using two sets of semi-structured questionnaires. A total of 40 respondents participated in the research that was from eight villagers under two forest ranges. 32 representatives from the local communities as well as 8 official representatives involved in co-management approach were interviewed using snowball sampling technique. The study shows that the co-management approach improved governance system of the Sundarbans through active participation of the local community people and their interactions with the officials via the platform of co-management organizations. It facilitated accountability and transparency system to some extent through following some formal and informal rules and regulations. It also improved the power structure of the management process by fostering local empowerment process particularly the women. Moreover, people were able to learn from their interactions with and within the co-management organizations as well as interventions improved environmental awareness and promoted social learning. The respondents considered good governance as the most important factor for achieving the goal of sustainable management and biodiversity conservation of the Sundarbans. The success of co-management planning process also depends on the active and functional participation of different stakeholders including the local communities where co-management organizations were considered as the most functional platform. However, the governance system was also facing various challenges which resulted in barriers to the sustainable management of the Sundarbans mangrove forest. But still there were some members involved in illegal forest operations and created obstacles against sustainable management of the Sundarbans. Respondents recommended greater patronization from the government, financial and logistic incentives for alternative income generation opportunities with effective participatory monitoring and evaluation system to improve sustainable management of the Sundarbans.

Keywords: Bangladesh, co-management approach, co-management organizations, governance, Sundarbans, sustainable management

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Authors: M. S. Nikitenko, S. A. Kizilov, D. Y. Khudonogov

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The paper considers an approach to a machine vision operating system combined with using a grid of light markers. This approach is used to solve several scientific and technical problems, such as measuring the capability of an apron feeder delivering coal from a lining return port to a conveyor in the technology of mining high coal releasing to a conveyor and prototyping an autonomous vehicle obstacle detection system. Primary verification of a method of calculating bulk material volume using three-dimensional modeling and validation in laboratory conditions with relative errors calculation were carried out. A method of calculating the capability of an apron feeder based on a machine vision system and a simplifying technology of a three-dimensional modelled examined measuring area with machine vision was offered. The proposed method allows measuring the volume of rock mass moved by an apron feeder using machine vision. This approach solves the volume control issue of coal produced by a feeder while working off high coal by lava complexes with release to a conveyor with accuracy applied for practical application. The developed mathematical apparatus for measuring feeder productivity in kg/s uses only basic mathematical functions such as addition, subtraction, multiplication, and division. Thus, this fact simplifies software development, and this fact expands the variety of microcontrollers and microcomputers suitable for performing tasks of calculating feeder capability. A feature of an obstacle detection issue is to correct distortions of the laser grid, which simplifies their detection. The paper presents algorithms for video camera image processing and autonomous vehicle model control based on obstacle detection machine vision systems. A sample fragment of obstacle detection at the moment of distortion with the laser grid is demonstrated.

Keywords: machine vision, machine vision operating system, light markers, measuring capability, obstacle detection system, autonomous transport

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Authors: Mehdi Ghommem, Daniel Garcia, Nathan Collier, Victor Calo

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We develop a fast, user-friendly implementation of a potential flow solver based on the unsteady vortex lattice method (UVLM). The computational framework uses the Python programming language which has easy integration with the scripts requiring computationally-expensive operations written in Fortran. The mixed-language approach enables high performance in terms of solution time and high flexibility in terms of easiness of code adaptation to different system configurations and applications. This computational tool is intended to predict the unsteady aerodynamic behavior of multiple moving bodies (e.g., flapping wings, rotating blades, suspension bridges...) subject to an incoming air. We simulate different aerodynamic problems to validate and illustrate the usefulness and effectiveness of the developed computational tool.

Keywords: unsteady aerodynamics, numerical simulations, mixed-language approach, potential flow

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Authors: Lorico D. S. Lapitan Jr., Yihan Xu, Yuan Guo, Dejian Zhou

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The ability of ultrasensitive detection of specific genes and discrimination of single nucleotide polymorphisms is important for clinical diagnosis and biomedical research. Herein, we report the development of a new ultrasensitive approach for label-free DNA detection using magnetic nanoparticle (MNP) assisted rapid target capture/separation in combination with signal amplification using poly-enzyme tagged polymer nanobead. The sensor uses an MNP linked capture DNA and a biotin modified signal DNA to sandwich bind the target followed by ligation to provide high single-nucleotide polymorphism discrimination. Only the presence of a perfect match target DNA yields a covalent linkage between the capture and signal DNAs for subsequent conjugation of a neutravidin-modified horseradish peroxidase (HRP) enzyme through the strong biotin-nuetravidin interaction. This converts each captured DNA target into an HRP which can convert millions of copies of a non-fluorescent substrate (amplex red) to a highly fluorescent product (resorufin), for great signal amplification. The use of polymer nanobead each tagged with thousands of copies of HRPs as the signal amplifier greatly improves the signal amplification power, leading to greatly improved sensitivity. We show our biosensing approach can specifically detect an unlabeled DNA target down to 10 aM with a wide dynamic range of 5 orders of magnitude (from 0.001 fM to 100.0 fM). Furthermore, our approach has a high discrimination between a perfectly matched gene and its cancer-related single-base mismatch targets (SNPs): It can positively detect the perfect match DNA target even in the presence of 100 fold excess of co-existing SNPs. This sensing approach also works robustly in clinical relevant media (e.g. 10% human serum) and gives almost the same SNP discrimination ratio as that in clean buffers. Therefore, this ultrasensitive SNP biosensor appears to be well-suited for potential diagnostic applications of genetic diseases.

Keywords: DNA detection, polymer beads, signal amplification, single nucleotide polymorphisms

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Authors: Joe Hachem, Marianne Cuif-Sjostrand, Thierry Schuhler, Dominique Orhon, Assaad Zoughaib

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The exhaust gas recirculation, EGR, implementation on gas turbines is increasingly gaining the attention of many researchers. This emerging technology presents many advantages, such as lowering the NOx emissions and facilitating post-combustion carbon capture as the carbon dioxide concentration in the cycle increases. As interesting as this technology may seem, the gas turbine, or its thermodynamic equivalent, the Brayton cycle, shows an intrinsic efficiency decrease with increasing EGR rate. In this paper, a thermodynamic model is presented to show the cycle efficiency decrease with EGR, alternative values of design parameters of both the pressure ratio (PR) and the turbine inlet temperature (TIT) are then proposed to optimize the cycle efficiency with different EGR rates. Results show that depending on the given EGR rate, both the design PR & TIT should be increased to compensate for the deficit in efficiency.

Keywords: gas turbines, exhaust gas recirculation, design parameters optimization, thermodynamic approach

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Authors: Timur S. Zaripov, Artur K. Gilfanov, Sergei S. Sazhin, Steven M. Begg, Morgan R. Heikal

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The calculation of the concentration of inertial droplets in complex flows is encountered in the modelling of numerous engineering and environmental phenomena; for example, fuel droplets in internal combustion engines and airborne pollutant particles. The results of recent research, focused on the development of methods for calculating concentration and their implementation in the commercial CFD code, ANSYS Fluent, is presented here. The study is motivated by the investigation of the mixture preparation processes in internal combustion engines with direct injection of fuel sprays. Two methods are used in our analysis; the Fully Lagrangian method (also known as the Osiptsov method) and the Eulerian approach. The Osiptsov method predicts droplet concentrations along path lines by solving the equations for the components of the Jacobian of the Eulerian-Lagrangian transformation. This method significantly decreases the computational requirements as it does not require counting of large numbers of tracked droplets as in the case of the conventional Lagrangian approach. In the Eulerian approach the average droplet velocity is expressed as a function of the carrier phase velocity as an expansion over the droplet response time and transport equation can be solved in the Eulerian form. The advantage of the method is that droplet velocity can be found without solving additional partial differential equations for the droplet velocity field. The predictions from the two approaches were compared in the analysis of the problem of a dilute gas-droplet flow around an infinitely long, circular cylinder. The concentrations of inertial droplets, with Stokes numbers of 0.05, 0.1, 0.2, in steady-state and transient laminar flow conditions, were determined at various Reynolds numbers. In the steady-state case, flows with Reynolds numbers of 1, 10, and 100 were investigated. It has been shown that the results predicted using both methods are almost identical at small Reynolds and Stokes numbers. For larger values of these numbers (Stokes — 0.1, 0.2; Reynolds — 10, 100) the Eulerian approach predicted a wider spread in concentration in the perturbations caused by the cylinder that can be attributed to the averaged droplet velocity field. The transient droplet flow case was investigated for a Reynolds number of 200. Both methods predicted a high droplet concentration in the zones of high strain rate and low concentrations in zones of high vorticity. The maxima of droplet concentration predicted by the Osiptsov method was up to two orders of magnitude greater than that predicted by the Eulerian method; a significant variation for an approach widely used in engineering applications. Based on the results of these comparisons, the Osiptsov method has resulted in a more precise description of the local properties of the inertial droplet flow. The method has been applied to the analysis of the results of experimental observations of a liquid gasoline spray at representative fuel injection pressure conditions. The preliminary results show good qualitative agreement between the predictions of the model and experimental data.

Keywords: internal combustion engines, Eulerian approach, fully Lagrangian approach, gasoline fuel sprays, droplets and particle concentrations

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Authors: S. H. Arnaud Kanga, O. Hili, S. Dabo-Niang

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Spatial prediction is an issue appealing and attracting several fields such as agriculture, environmental sciences, ecology, econometrics, and many others. Although multiple non-parametric prediction methods exist for spatial data, those are based on the conditional expectation. This paper took a different approach by examining a non-parametric spatial predictor of the conditional quantile. The study especially observes the stationary multidimensional spatial process over a rectangular domain. Indeed, the proposed quantile is obtained by inverting the conditional distribution function. Furthermore, the proposed estimator of the conditional distribution function depends on three kernels, where one of them controls the distance between spatial locations, while the other two control the distance between observations. In addition, the almost complete convergence and the convergence in mean order q of the kernel predictor are obtained when the sample considered is alpha-mixing. Such approach of the prediction method gives the advantage of accuracy as it overcomes sensitivity to extreme and outliers values.

Keywords: conditional quantile, kernel, nonparametric, stationary

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Authors: Hichem Kmimech, Achref Jabeur Telmoudi, Lotfi Nabli

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The estimation of the initial marking minimum (MIM) is a crucial problem in labeled Petri nets. In the case of multiple choices, the search for the initial marking leads to a problem of optimization of the minimum allocation of resources with two constraints. The first concerns the firing sequence that could be legal on the initial marking with respect to the firing vector. The second deals with the total number of tokens that can be minimal. In this article, the MIM problem is solved by the meta-heuristic particle swarm optimization (PSO). The proposed approach presents the advantages of PSO to satisfy the two previous constraints and find all possible combinations of minimum initial marking with the best computing time. This method, more efficient than conventional ones, has an excellent impact on the resolution of the MIM problem. We prove through a set of definitions, lemmas, and examples, the effectiveness of our approach.

Keywords: marking, production system, labeled Petri nets, particle swarm optimization

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Authors: Rachid Ed-Dali, Khalid Elasri

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Mass media seem to provide a rich content for language acquisition. Exposure to television, the Internet, the mobile phone and other technological gadgets and devices helps enrich the student’s lexicon positively as well as negatively. The difficulties encountered by students while learning and acquiring second languages in addition to their eagerness to comprehend the content of a particular program prompt them to diversify their methods so as to achieve their targets. The present study highlights the significance of certain media channels and their involvement in language acquisition with the employment of the Natural Approach to further grasp whether students, especially secondary and high school students, learn and acquire errors through watching subtitled television programs. The chief objective is investigating the deductive and inductive relevance of certain programs beside the involvement of peripheral learning while acquiring mistakes.

Keywords: errors, mistakes, Natural Approach, peripheral learning, solecism

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Authors: T. Spiegel, J. Siegmann, C. F. Durach

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Challenges resulting from an international and dynamic business environment are increasingly being passed on from manufacturing companies to external service providers. Especially providers of complex, customer-specific industry services have to cope with continuously changing requirements. This is particularly true for contract logistics service providers. They are forced to develop efficient and highly flexible structures and strategies to meet their customer’s needs. One core element they have to focus on is the reorganization of their service development and sales process. Based on an action research approach, this study develops and tests a concept to streamline tender management for contract logistics service providers. The concept of modularized service architecture is deployed in order to derive a practice-oriented approach for the modularization of complex service portfolios and the design of customized quotes. These findings are evaluated regarding their applicability in other service sectors and practical recommendations are given.

Keywords: contract logistics, modularization, service development, tender management

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Authors: S. Ghorbani, N. I. Polushin

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In this study, an approach to identify factors affecting on surface roughness in a machining process is presented. This study is based on 81 data about surface roughness over a wide range of cutting tools (conventional, cutting tool with holes, cutting tool with composite material), workpiece materials (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron), spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). A single decision tree (SDT) analysis was done to identify factors for predicting a model of surface roughness, and the CART algorithm was employed for building and evaluating regression tree. Results show that a single decision tree is better than traditional regression models with higher rate and forecast accuracy and strong value.

Keywords: cutting condition, surface roughness, decision tree, CART algorithm

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Authors: Abdelouahab Errida, Bouchra Lotfi, Elalami Semma

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In this paper, we try to address the topic of multiple change management by adopting an engineered research methodology, conducted within a Moroccan company during its implementation of several change projects that aim at improving its supply chain management performance. Firstly, we present the key concepts related to our research, namely change management, multiproject management and supply chain management. Then, we try to assess how the change management and multi-project management are applied in this company. Finally, we try to propose an approach that will help managers in dealing with multiple change projects. This approach proposes to integrate change management, project management and multi-project management for managing change projects according to three organizational levels: executive level, project portfolio level and change project level.

Keywords: change management, multi-project management, project management, change portfolio, supply chain management,

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Authors: Rene Hellmuth, Jorg Frohnmayer

Abstract:

The requirements of the factory planning and the building concerned have changed in the last years. Factory planning has the task of designing products, plants, processes, organization, areas, and the building of a factory. Regular restructuring gains more importance in order to maintain the competitiveness of a factory. Even today, the methods and process models used in factory planning are predominantly based on the classical planning principles of Schmigalla, Aggteleky and Kettner, which, however, are not specifically designed for reorganization. In addition, they are designed for a largely static environmental situation and a manageable planning complexity as well as for medium to long-term planning cycles with a low variability of the factory. Existing approaches already regard factory planning as a continuous process that makes it possible to react quickly to adaptation requirements. However, digital factory models are not yet used as a source of information for building data. Approaches which consider building information modeling (BIM) or digital factory models in general either do not refer to factory conversions or do not yet go beyond a concept. This deficit can be further substantiated. A method for factory conversion planning using a current digital building model is lacking. A corresponding approach must take into account both the existing approaches to factory planning and the use of digital factory models in practice. A literature review will be conducted first. In it, approaches to classic factory planning and approaches to conversion planning are examined. In addition, it will be investigated which approaches already contain digital factory models. In the second step, an approach is presented how digital factory models based on building information modeling can be used as a basis for augmented reality tablet applications. This application is suitable for construction sites and provides information on the costs and time required for conversion variants. Thus a fast decision making is supported. In summary, the paper provides an overview of existing factory planning approaches and critically examines the use of digital tools. Based on this preliminary work, an approach is presented, which suggests the sensible use of digital factory models for decision support in the case of conversion variants of the factory building. The augmented reality application is designed to summarize the most important information for decision-makers during a reconstruction process.

Keywords: augmented reality, digital factory model, factory planning, restructuring

Procedia PDF Downloads 139

Authors: Alireza Mirrashid, Mohammad Khoshbin, Ali Atghaei, Hassan Shahbazi

Abstract:

In various industries, smoke and fire are two of the most important threats in the workplace. One of the common methods for detecting smoke and fire is the use of infrared thermal and smoke sensors, which cannot be used in outdoor applications. Therefore, the use of vision-based methods seems necessary. The problem of smoke and fire detection is spatiotemporal and requires spatiotemporal solutions. This paper presents a method that uses spatial features along with temporal-based features to detect smoke and fire in the scene. It consists of three main parts; the task of each part is to reduce the error of the previous part so that the final model has a robust performance. This method also uses transformer modules to increase the accuracy of the model. The results of our model show the proper performance of the proposed approach in solving the problem of smoke and fire detection and can be used to increase workplace safety.

Keywords: attention, fire detection, smoke detection, spatio-temporal

Procedia PDF Downloads 205

Authors: Abhinav Sirvaiya, Karan Gupta, Pankaj Garg

Abstract:

The global warming due to the increased atmospheric carbon dioxide (CO2) concentration is the most prominent issue of environment that the world is facing today. To solve this problem at global level, sequestration of CO2 in deep and unmineable coal seams has come out as one of the attractive alternatives to reduce concentration in atmosphere. This sequestration technology is not only going to help in storage of CO2 beneath the sub-surface but is also playing a major role in enhancing the coal bed methane recovery (ECBM) by displacing the adsorbed methane. This paper provides the answers for the need of CO2 injection in coal seams and how recovery is enhanced. We have discussed the recent development in enhancing the coal bed methane recovery and the economic scenario of the same. The effect of injection on the coal reservoir has also been discussed. Coal is a good absorber of CO2. That is why the sequestration of CO2 is emerged out to be a great approach, not only for storage purpose but also for enhancing coal bed methane recovery.

Keywords: global warming, carbon dioxide (CO2), CO2 sequestration, enhance coal bed methane (ECBM)

Procedia PDF Downloads 506

Authors: Deepika Bhalla, Raj Kumar Bansal, Hari Om Gupta

Abstract:

Dissolved Gas Analysis (DGA) has been widely used for fault diagnosis in a transformer. Artificial neural networks (ANN) have high accuracy but are regarded as black boxes that are difficult to interpret. For many problems it is desired to extract knowledge from trained neural networks (NN) so that the user can gain a better understanding of the solution arrived by the NN. This paper applies a pedagogical approach for rule extraction from function approximating neural networks (REFANN) with application to incipient fault diagnosis using the concentrations of the dissolved gases within the transformer oil, as the input to the NN. The input space is split into subregions and for each subregion there is a linear equation that is used to predict the type of fault developing within a transformer. The experiments on real data indicate that the approach used can extract simple and useful rules and give fault predictions that match the actual fault and are at times also better than those predicted by the IEC method.

Keywords: artificial neural networks, dissolved gas analysis, rules extraction, transformer

Procedia PDF Downloads 537