Search results for: multi features

Authors: Lemac Tin

Abstract:

This paper deals with the lyrical subject of the confessional poetry which is the main part of her stylistic strucuture. We concluded two types of this subject in the classical confessional poetic discourse; reflexive and authentic subject. We offer the model of their genesis, textual features and appeareance realisations. Genesis is related to the theories of deriving poetry from emotion and magic and their similar position in the primitive lyrics and lyrics of the ancient civilizations. Textual features are related to the emotive and semiotic analysis of each type. Appearance realisations of these two types are I-subject, We-subject, transvocal and objectified subject. We check this approaches on some of the poems from World literature.

Keywords: confessional poetry, confessional lyrical subject, magic, emotion, emotive analysis, semiotic analysis

Procedia PDF Downloads 271

Authors: Zoltan Theisz, Gergely Mezei

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Modern industrial automation relies on service oriented concepts of Internet of Things (IoT) device modeling in order to provide a flexible and extendable environment for service meta-repository. However, state-of-the-art meta-modeling techniques prefer design-time modeling, which results in a heavy usage of class sometimes unnecessary static subtyping. Although this approach benefits from clear-cut object-oriented design principles, it also seals the model repository for further dynamic extensions. In this paper, a dynamic multi-level modeling approach is introduced that enables dynamic subtyping through a more relaxed partial instantiation mechanism. The approach is demonstrated on a simple sensor network example.

Keywords: meta-modeling, dynamic subtyping, DMLA, industrial automation, arrowhead

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Authors: Temitope M. Ojo, Titilayo A. Benson

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This study investigated street begging and its psychosocial effect in Ibadan Metropolis, Oyo State, Nigeria. In carrying out this study, four research questions were used. The instrument used for data collection was a face-to-face and self-developed questionnaire. The results revealed there is high awareness level on the causes of street begging among the respondents, who also mentioned several factors contributing to street begging. However, respondents disagreed that lack of education is a factor contributing to street begging in Nigeria. The psycho-social effects of street begging, as identified by the respondents, are development of inferiority complex, lack of social interaction, loss of self-respect and dignity, increased mindset of poverty and loss of self-confident. Solution to street begging as identified by the respondents also includes provision of rehabilitation centers, provision of food for students in Islamic schools and monthly survival allowance. Specific policies and other legislative frameworks are needed in terms of age, sex, disability, and family-related issues, to effectively address the begging problem. Therefore, it is recommended that policy planners must adopt multi-faceted, multi-targeted, and multi-tiered approaches if they are to have any impact on the lives of street beggars in all four categories. In this regard, both preventative and responsive interventions are needed instead of rehabilitative solutions for each category of street beggars.

Keywords: beggars, begging, psycho-social effect, respondents, street begging

Procedia PDF Downloads 185

Authors: Omid Moradi, Mostafa Rajabi

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Graphene oxide (GO), reduced graphene oxide (rGO), multi-walled carbon nanotubes MWCNT), multi-walled carbon nanotube functionalized carboxyl (MWCNT-COOH), and multi-walled carbon nanotube functionalized thiol (MWCNT-SH) were used as efficient adsorbents for the rapid removal two dyes methyl orange (MO) and malachite green (MG) from the aqueous phase. The impact of several influential parameters such as initial dye concentrations, contact time, temperature, and initial solution pH was well studied and optimized. The optimize time for adsorption process of methyl orange dye on GO, rGO, MWCNT, MWCNT-COOH, and MWCNT-SH surfaces were determined at 100, 100, 60, 25, and 60 min, respectively and The optimize time for adsorption process of malachite green dye on GO, rGO, MWCNT, MWCNT-COOH, and MWCNT-SH surfaces were determined at 100, 100, 60, 15, and 60 min, respectively. The maximum removal efficiency for methyl orange dye by GO, rGO, MWCNT, MWCNT-COOH, and MWCNT-SH surfaces were occurred at optimized pH 3, 3, 6, 2, and 6 of aqueous solutions, respectively and for malachite green dye were occurred at optimized pH 3, 3, 6, 9, and 6 of aqueous solutions, respectively. The effect of temperature showed that adsorption process of malachite green dye on GO, rGO, MWCNT, and MWCNT-SH surfaces were endothermic and for adsorption process of methyl orange dye on GO, rGO, MWCNT, and MWCNT-SH surfaces were endothermic but while adsorption of methyl orange and malachite green dyes on MWCNT-COOH surface were exothermic.On increasing the initial concentration of methyl orange dye adsorption capacity on GO surface was decreased and on rGO, MWCNT, MWCNT-COOH, and MWCNT-SH surfaces were increased and with increasing the initial concentration of malachite green dye on GO, rGO, MWCNT, MWCNT-COOH, and MWCNT-SH surfaces were increased.

Keywords: adsorption, graphene oxide, reduced graphene oxide, multi-walled carbon nanotubes, methyl orange, malachite green, removal

Procedia PDF Downloads 382

Authors: S. Kherchaoui, A. Houacine

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This paper presents a facial expression recognition system. It performs identification and classification of the seven basic expressions; happy, surprise, fear, disgust, sadness, anger, and neutral states. It consists of three main parts. The first one is the detection of a face and the corresponding facial features to extract the most expressive portion of the face, followed by a normalization of the region of interest. Then calculus of curvelet coefficients is performed with dimensionality reduction through principal component analysis. The resulting coefficients are combined with two ratios; mouth ratio and face edge ratio to constitute the whole feature vector. The third step is the classification of the emotional state using the SVM method in the feature space.

Keywords: facial expression identification, curvelet coefficient, support vector machine (SVM), recognition system

Procedia PDF Downloads 232

Authors: A. Danbaba

Abstract:

Several experiments are conducted at different environments such as locations or periods (seasons) with identical treatments to each experiment purposely to study the interaction between the treatments and environments or between the treatments and periods (seasons). The commonly used designs of experiments for this purpose are randomized block design, Latin square design, balanced incomplete block design, Youden design, and one or more factor designs. The interest is to carry out a combined analysis of the data from these multi-environment experiments, instead of analyzing each experiment separately. This paper proposed combined analysis of experiments conducted via Sudoku square design of odd order with same experimental treatments.

Keywords: combined analysis, sudoku design, common treatment, multi-environment experiments

Procedia PDF Downloads 345

Authors: Hsin-Yang Wang, K. M. A. Salam, Ying-Jia Lin, Daniel Tan, Tzu-Hsuan Chou, Hung-Yu Kao

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Detecting user intent from natural language expression has a wide variety of use cases in different natural language processing applications. Recently few-shot training has a spike of usage on commercial domains. Due to the lack of significant sample features, the downstream task performance has been limited or leads to an unstable result across different domains. As a state-of-the-art method, the pre-trained BERT model gathering the sentence-level information from a large text corpus shows improvement on several NLP benchmarks. In this research, we are proposing a method to change multi-class classification tasks into binary classification tasks, then use the confidence score to rank the results. As a language model, BERT performs well on sequence data. In our experiment, we change the objective from predicting labels into finding the relations between words in sequence data. Our proposed method achieved 71.0% accuracy in the internal intent detection dataset and 63.9% accuracy in the HuffPost dataset. Acknowledgment: This work was supported by NCKU-B109-K003, which is the collaboration between National Cheng Kung University, Taiwan, and SoftBank Corp., Tokyo.

Keywords: OSML, BERT, text classification, one shot

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Authors: Angga S. Fajar, Y. Takahashi, J. Kiyono, S. Sawada

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The problem of medium span bridge bearing support in the extreme temperatures fluctuation region is deterioration in case the suppression of superstructure that sustains temperature expansion. The other hand, the behavior and the parameter of RC column accompanied with friction damping mechanism were determined successfully based on the experiment and numerical analysis. This study proposes the structural system of RC pier accompanied with multi sliding friction damping mechanism to substitute the conventional system of pier together with bearing support. In this system, the pier has monolith behavior to the superstructure with flexible small deformation to accommodate thermal expansion of the superstructure. The flexible small deformation behavior is realized by adding the gap mechanism in the multi sliding friction devices form. The important performances of this system are sufficient lateral flexibility in small deformation, sufficient elastic deformation capacity, sufficient lateral force resistance, and sufficient energy dissipation. Numerical analysis performed for this system with fiber element model. It shows that the structural system has good performance not only under small deformation due to thermal expansion of the superstructure but also under seismic load.

Keywords: RC Pier, thermal expansion, multi sliding friction device, flexible small deformation

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Authors: Galal Elkobrosy, Amr M. Abdelrazek, Bassuny M. Elsouhily, Mohamed E. Khidr

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Crank shaft length, connecting rod length, crank angle, engine rpm, cylinder bore, mass of piston and compression ratio are the inputs that can control the performance of the slider crank mechanism and then its efficiency. Several combinations of these seven inputs are used and compared. The throughput engine torque predicted by the simulation is analyzed through two different regression models, with and without interaction terms, developed according to multi-linear regression using LU decomposition to solve system of algebraic equations. These models are validated. A regression model in seven inputs including their interaction terms lowered the polynomial degree from 3^rd degree to 1^st degree and suggested valid predictions and stable explanations.

Keywords: design of experiments, regression analysis, SI engine, statistical modeling

Procedia PDF Downloads 186

Authors: Khadra Ahmed, Khaled Kheireldin

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In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies.

Keywords: planktons, diversity indices, water quality index, water ways

Procedia PDF Downloads 518

Authors: Antonin Ponsich, Eric Alfredo Rincon Garcia, Roman Anselmo Mora Gutierrez, Miguel Angel Gutierrez Andrade, Sergio Gerardo De Los Cobos Silva, Pedro Lara Velzquez

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The electoral zone design problem consists in redrawing the boundaries of legislative districts for electoral purposes in such a way that federal or state requirements are fulfilled. In Mexico, this process has been historically carried out by the National Electoral Institute (INE), by optimizing an integer nonlinear programming model, in which population equality and compactness of the designed districts are considered as two conflicting objective functions, while contiguity is included as a hard constraint. The solution technique used by the INE is a Simulated Annealing (SA) based algorithm, which handles the multi-objective nature of the problem through an aggregation function. The present work represents the first intent to apply a classical Multi-Objective Evolutionary Algorithm (MOEA), the second version of the Non-dominated Sorting Genetic Algorithm (NSGA-II), to this hard combinatorial problem. First results show that, when compared with the SA algorithm, the NSGA-II obtains promising results. The MOEA manages to produce well-distributed solutions over a wide-spread front, even though some convergence troubles for some instances constitute an issue, which should be corrected in future adaptations of MOEAs to the redistricting problem.

Keywords: multi-objective optimization, NSGA-II, redistricting, zone design problem

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Authors: Farzad Pargar

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We introduce the preventive maintenance and renewal scheduling problem for a multi-unit system over a finite and discretized time horizon. Given the latest possible time for carrying out the next maintenance and renewal projects after the previous ones and considering several common set-up costs, the introduced scheduling model tries to minimize the cost of projects by grouping them and simultaneously finding the optimal balance between doing maintenance and renewal. We present a 0-1 pure integer linear programming that determines which projects should be performed together on which location and in which period (e.g., week or month). We consider railway track as a case for our study and test the performance of the proposed model on a set of test problems. The experimental results show that the proposed approach performs well.

Keywords: maintenance, renewal, scheduling, mathematical programming model

Procedia PDF Downloads 687

Authors: Claire Xu, Welton Wang, Manasvi Pinnaka, Anqi Pan, Michael Han

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Hospitalizations account for one-third of the total health care spending in the US. Early risk detection and intervention can reduce this high cost and increase the satisfaction of both patients and physicians. Due to the lack of awareness of the potential arising risks in home environment, the opportunities for patients to seek early actions of clinical visits are dramatically reduced. This research aims to offer a highly personalized remote patients monitoring and risk assessment AI framework to identify the potentially preventable hospitalization for both acute as well as chronic diseases. A hybrid-AI framework is trained with data from clinical setting, patients surveys, as well as online databases. 20+ risk factors are analyzed ranging from activities, biometric info, demographic info, socio-economic info, hospitalization history, medication info, lifestyle info, etc. The AI model yields high performance of 87% accuracy and 88 sensitivity with 20+ features. This hybrid-AI framework is proven to be effective in identifying the potentially preventable hospitalization. Further, the high indicative features are identified by the models which guide us to a healthy lifestyle and early intervention suggestions.

Keywords: hospitalization prevention, machine learning, remote patient monitoring, risk prediction

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Authors: A. J. S. P. Nileema, S. Kulatunga , S. H. Palihawadana

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Computer aided detection (CAD) system was developed for the diagnosis of pulmonary tuberculosis using digital chest X-rays with MATLAB image processing techniques using a statistical approach. The study comprised of 200 digital chest radiographs collected from the National Hospital for Respiratory Diseases - Welisara, Sri Lanka. Pre-processing was done to remove identification details. Lung fields were segmented and then divided into four quadrants; right upper quadrant, left upper quadrant, right lower quadrant, and left lower quadrant using the image processing techniques in MATLAB. Contrast, correlation, homogeneity, energy, entropy, and maximum probability texture features were extracted using the gray level co-occurrence matrix method. Descriptive statistics and normal distribution analysis were performed using SPSS. Depending on the radiologists’ interpretation, chest radiographs were classified manually into PTB - positive (PTBP) and PTB - negative (PTBN) classes. Features with standard normal distribution were analyzed using an independent sample T-test for PTBP and PTBN chest radiographs. Among the six features tested, contrast, correlation, energy, entropy, and maximum probability features showed a statistically significant difference between the two classes at 95% confidence interval; therefore, could be used in the classification of chest radiograph for PTB diagnosis. With the resulting value ranges of the five texture features with normal distribution, a classification algorithm was then defined to recognize and classify the quadrant images; if the texture feature values of the quadrant image being tested falls within the defined region, it will be identified as a PTBP – abnormal quadrant and will be labeled as ‘Abnormal’ in red color with its border being highlighted in red color whereas if the texture feature values of the quadrant image being tested falls outside of the defined value range, it will be identified as PTBN–normal and labeled as ‘Normal’ in blue color but there will be no changes to the image outline. The developed classification algorithm has shown a high sensitivity of 92% which makes it an efficient CAD system and with a modest specificity of 70%.

Keywords: chest radiographs, computer aided detection, image processing, pulmonary tuberculosis

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Authors: Shokoufeh Monjezi Kouchak

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Cognitive radio network (CRN) is future network .Without this network wireless devices can’t work appropriately in the next decades. Today, wireless devices use static spectrum access methods and these methods don’t use spectrums optimum so we need use dynamic spectrum access methods to solve shortage spectrum challenge and CR is a great device for DSA but first of all its challenges should be solved .security is one of these challenges .In this paper we provided a survey about CR security. You can see this survey in tables 1 to 7 .After that we proposed a multi criteria authentication method in CRN. Our criteria in this method are: sensing results, following sending data rules, position of secondary users and no talk zone. Finally we compared our method with other authentication methods.

Keywords: authentication, cognitive radio, security, radio networks

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Authors: Angelica S. Valeriani, Lorenzo Biasiolo

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In recent years, the scale of the impact of climate change and its related side effects has become ever more massive and devastating. Sustainable Development Goals (SDGs), promoted by United Nations, aim to front issues related to climate change, among others. In particular, the project CROWD4SDG focuses on a bunch of SDGs since it promotes environmental activities and climate-related issues. In this context, we developed a prototype of an application, under advanced development considering web design, that focuses on SDG 13 (SDG on climate action) by providing users with useful instruments to face environmental crises and climate-related disasters. Our prototype is thought and structured for both web and mobile development. The main goal of the application, POLI4SDG, is to help users to get through emergency services. To this extent, an organized overview and classification prove to be very effective and helpful to people in need. A careful analysis of data related to environmental crises prompted us to integrate the user contribution, i.e., exploiting a core principle of Citizen Science, into the realization of a public catalog, available for consulting and organized according to typology and specific features. In addition, gender equality and opportunity features are considered in the prototype in order to allow women, often the most vulnerable category, to have direct support. The overall description of the application functionalities is detailed. Moreover, the implementation features and properties of the prototype are discussed.

Keywords: crowdsourcing, social media, SDG, climate change, natural disasters, gender equality

Procedia PDF Downloads 112

Authors: Zohreh Khojasteh-Ghamari, Takashi Irohara

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One of the efficient ways in supply chain risk management is avoiding the interruption in Supply Chain (SC) before it occurs. Although the majority of the organizations focus on their first-tier suppliers to avoid risk in the SC, studies show that in only 60 percent of the disruption cases the reason is first tier suppliers. In the 40 percent of the SC disruptions, the reason is downstream SC, which is the second tier and lower. Due to the increasing complexity and interrelation of modern supply chains, the SC elements have become difficult to trace. Moreover, studies show that there is a vital need to better understand the integration of risk and visibility, especially in the context of multiple objectives. In this study, we propose a multi-objective programming model to avoid disruption in SC. The objective of this study is evaluating the effect of downstream SCV on managing supply chain risk. We propose a multi-objective mathematical programming model with the objective functions of minimizing the total cost and maximizing the downstream supply chain visibility (SCV). The decision variable is supplier selection. We assume there are several manufacturers and several candidate suppliers. For each manufacturer, our model proposes the best suppliers with the lowest cost and maximum visibility in downstream supply chain. We examine the applicability of the model by numerical examples. We also define several scenarios for datasets and observe the tendency. The results show that minimum visibility in downstream SC is needed to have a safe SC network.

Keywords: downstream supply chain, optimization, supply chain risk, supply chain visibility

Procedia PDF Downloads 244

Authors: Tanveer Hussain, Khan Muhammad, Amin Ullah, Mi Young Lee, Sung Wook Baik

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The Big Data generated from distributed vision sensors installed on large scale in smart cities create hurdles in its efficient and beneficial exploration for browsing, retrieval, and indexing. This paper presents a three-folded framework for effective video summarization of such data and provide a compact and representative format of Big Video Data. In the first fold, the paper acquires input video data from the installed cameras and collect clues such as type and count of objects and clarity of the view from a chunk of pre-defined number of frames of each view. The decision of representative view selection for a particular interval is based on fuzzy inference system, acquiring a precise and human resembling decision, reinforced by the known clues as a part of the second fold. In the third fold, the paper forwards the selected view frames to the summary generation mechanism that is supported by a saliency-aware convolution neural network (CNN) model. The new trend of fuzzy rules for view selection followed by CNN architecture for saliency computation makes the multi-view video summarization (MVS) framework a suitable candidate for real-world practice in smart cities.

Keywords: big video data analysis, fuzzy logic, multi-view video summarization, saliency detection

Procedia PDF Downloads 188

Authors: Kristina Pflug, Markus Busch

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Being able to predict polymer properties and processing behavior based on the applied operating reaction conditions in one of the key challenges in modern polymer reaction engineering. Especially, for cost-intensive processes such as the high-pressure polymerization of low-density polyethylene (LDPE) with high safety-requirements, the need for simulation-based process optimization and product design is high. A multi-scale modelling approach was set-up and validated via a series of high-pressure mini-plant autoclave reactor experiments. The approach starts with the numerical modelling of the complex reaction network of the LDPE polymerization taking into consideration the actual reaction conditions. While this gives average product properties, the complex polymeric microstructure including random short- and long-chain branching is calculated via a hybrid Monte Carlo-approach. Finally, the processing behavior of LDPE -its melt flow behavior- is determined in dependence of the previously determined polymeric microstructure using the branch on branch algorithm for randomly branched polymer systems. All three steps of the multi-scale modelling approach can be independently validated against analytical data. A triple-detector GPC containing an IR, viscosimetry and multi-angle light scattering detector is applied. It serves to determine molecular weight distributions as well as chain-length dependent short- and long-chain branching frequencies. 13C-NMR measurements give average branching frequencies, and rheological measurements in shear and extension serve to characterize the polymeric flow behavior. The accordance of experimental and modelled results was found to be extraordinary, especially taking into consideration that the applied multi-scale modelling approach does not contain parameter fitting of the data. This validates the suggested approach and proves its universality at the same time. In the next step, the modelling approach can be applied to other reactor types, such as tubular reactors or industrial scale. Moreover, sensitivity analysis for systematically varying process conditions is easily feasible. The developed multi-scale modelling approach finally gives the opportunity to predict and design LDPE processing behavior simply based on process conditions such as feed streams and inlet temperatures and pressures.

Keywords: low-density polyethylene, multi-scale modelling, polymer properties, reaction engineering, rheology

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Authors: Rik van Leeuwen, Ger Koole

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Within hospitality, marketing departments use segmentation to create tailored strategies to ensure personalized marketing. This study provides a data-driven approach by segmenting guest profiles via hierarchical clustering based on an extensive set of features. The industry requires understandable outcomes that contribute to adaptability for marketing departments to make data-driven decisions and ultimately driving profit. A marketing department specified a business question that guides the unsupervised machine learning algorithm. Features of guests change over time; therefore, there is a probability that guests transition from one segment to another. The purpose of the study is to provide steps in the process from raw data to actionable insights, which serve as a guideline for how hospitality companies can adopt an algorithmic approach.

Keywords: hierarchical cluster analysis, hospitality, market segmentation

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Authors: Naveed Ghani

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With the arrival of higher speed communication links and mature application running over the internet, the requirement for reliable, efficient and robust network designs rising day by day. Multi-Protocol Label Switching technology (MPLS) Virtual Private Networks (VPNs) have committed to provide optimal network services. They are gaining popularity in industry day by day. Enterprise customers are moving to service providers that offer MPLS VPNs. The main reason for this shifting is the capability of MPLS VPN to provide built in security features and any-to-any connectivity. MPLS VPNs improved the network performance due to fast label switching as compare to traditional IP Forwarding but traffic classification and policing was still required on per hop basis to enhance the performance of real time traffic which is delay sensitive (particularly voice and video). QoS (Quality of service) is the most important factor to prioritize enterprise networks’ real time traffic such as voice and video. This thesis is focused on the study of QoS parameters (e.g. delay, jitter and MOS (Mean Opinion Score)) for the real time traffic over MPLS VPNs. DiffServ (Differentiated Services) QoS model will be used over MPLS VPN network to get end-to-end service quality.

Keywords: network, MPLS, VPN, DiffServ, MPLS VPN, DiffServ QoS, QoS Model, GNS2

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Authors: Shimaa Holail, Tamer Saleh, Xiongwu Xiao

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Building change detection (BCD) from satellite imagery is an essential topic in urbanization monitoring, agricultural land management, and updating geospatial databases. Recently, methods for detecting changes based on deep learning have made significant progress and impressive results. However, it has the problem of being insensitive to changes in buildings with complex spectral differences, and the features being extracted are not discriminatory enough, resulting in incomplete buildings and irregular boundaries. To overcome these problems, we propose a dual Siamese network based on the Unet model with the addition of a deep supervision strategy (DS) in this paper. This network consists of a backbone (encoder) based on ImageNet pre-training, a fusion block, and feature pyramid networks (FPN) to enhance the step-by-step information of the changing regions and obtain a more accurate BCD map. To train the proposed method, we created a new dataset (EGY-BCD) of high-resolution and multi-temporal aerial images captured over New Cairo in Egypt to detect building changes for this purpose. The experimental results showed that the proposed method is effective and performs well with the EGY-BCD dataset regarding the overall accuracy, F1-score, and mIoU, which were 91.6 %, 80.1 %, and 73.5 %, respectively.

Keywords: building change detection, deep supervision, semantic segmentation, EGY-BCD dataset

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Authors: Syed Atif Hassan, Luv Mehta, Syed Asif Hassan

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Hit song science is a field of investigation wherein machine learning techniques are applied to music tracks in order to extract such features from audio signals which can capture information that could explain the popularity of respective tracks. Record companies invest huge amounts of money into recruiting fresh talents and churning out new music each year. Gaining insight into the basis of why a song becomes popular will result in tremendous benefits for the music industry. This paper aims to extract basic musical and more advanced, acoustic features from songs while also taking into account external factors that play a role in making a particular song popular. We use a dataset derived from popular Spotify playlists divided by genre. We use ten genres (blues, classical, country, disco, hip-hop, jazz, metal, pop, reggae, rock), chosen on the basis of clear to ambiguous delineation in the typical sound of their genres. We feed these features into three different classifiers, namely, SVM with RBF kernel, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model at the end. Predicting song popularity is particularly important for the music industry as it would allow record companies to produce better content for the masses resulting in a more competitive market.

Keywords: classifier, machine learning, music tracks, popularity, prediction

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Authors: Amorn Kritsanaphan

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This paper illustrates the decentralization of cultural heritage management in local Thailand, a place similar to other middle- income developing countries characterized by rapid tourism-industrialization, weakness formal state institutions and procedures, and intensity use of the cultural heritage resources. The author conducted field research in local Thailand, principally using qualitative primary data gathering. These were combined with records reviews and content analysis of documents. The author also attended local public meetings, and social activities, and interacted casually with local residents and governments. Cultural heritage management has been supposed to improve through multi-stakeholder participation and decentralization. However, processes and outcomes are far from being straightforward and depend on a variety of contingencies and contexts involved. Multi-stakeholder and participatory approach in decentralization of the cultural heritage management in Thailand have pushed to the forefront and sharpened a number of existing problems. However, under the decentralization, the most significant contribution has been in creating real political space where various local stakeholders have become active, respond and address their concerns in various ways vis-à-vis cultural heritage problems. Improving cultural heritage sustainability and viability of local livelihoods through decentralization and participatory approach is by no means certain. However, the shift instead creates spaces potent with possibilities for a meaningful and constructive engagement between and among local state and non-state actors that can lead to synergies and positive outcomes.

Keywords: decentralization, participatory approach, cultural heritage management, multi-stakeholder approach

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Authors: Michael Gidey Gebru

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Most DEA models operate in a static environment with input and output parameters that are chosen by deterministic data. However, due to ambiguity brought on shifting market conditions, input and output data are not always precisely gathered in real-world scenarios. Fuzzy numbers can be used to address this kind of ambiguity in input and output data. Therefore, this work aims to expand crisp DEA into DEA with fuzzy environment. In this study, the input and output data are regarded as fuzzy triangular numbers. Then, the DEA model with fuzzy environment is solved using a multi-objective method to gauge the Decision Making Units’ efficiency. Finally, the developed DEA model is illustrated with an application on real data 50 educational institutions.

Keywords: efficiency, DEA, fuzzy, decision making units, higher education institutions

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Authors: Carlos A. Acosta, Amar Bhalla, Ruyan Guo

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Scraped surface heat exchangers (SSHE) use a rotor shaft assembly with scraping blades to homogenize viscous fluids during the heat transfer process. Obtaining in-situ measurements is difficult because the rotor and scraping blades spin continuously inside the mixing chamber, obstructing the instrumentation pathway. Computational fluid dynamics simulations provide useful insight into the flow behavior around the scraper blades for a variety of fluids and blade geometries. However, numerical solutions often focus on the fluid dynamics and heat transfer phenomena of rotating flow, ignoring the glass-transition temperature and freezing point depression. This research studies the multi-phase fluid dynamics and freezing point depression inside the SSHE with non-isothermal conditions in a time dependent process using an aqueous solution that contains 13.5 wt.% high fructose corn syrup and CO₂. The computational results were validated with in-situ pressure, temperature, and optical spectroscopy measurements. Results from the numerical model show good quantitatively agreement with experimental values.

Keywords: computational fluid dynamics, freezing point depression, phase-transition temperature, multi-phase flow

Procedia PDF Downloads 147

Authors: Rabi Mouhcine, Amrouch Mustapha, Mahani Zouhir, Mammass Driss

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In this paper, we present a system for offline recognition cursive Arabic handwritten text based on Hidden Markov Models (HMMs). The system is analytical without explicit segmentation used embedded training to perform and enhance the character models. Extraction features preceded by baseline estimation are statistical and geometric to integrate both the peculiarities of the text and the pixel distribution characteristics in the word image. These features are modelled using hidden Markov models and trained by embedded training. The experiments on images of the benchmark IFN/ENIT database show that the proposed system improves recognition.

Keywords: recognition, handwriting, Arabic text, HMMs, embedded training

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Authors: Mona Fawzy Aldaghma

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Helical piles are gaining popularity as a viable deep foundation alternative due to their quick installation and multipurpose use in compression and tension. These piles are commonly used as foundations for constructions such as solar panels, wind turbines and offshore platforms. These structures typically transfer various combinations of loads to their helical-pile foundations, including axial and lateral loads. Further research is needed to determine the effects of loading patterns that may act on helical piles as compounds of axial compression and lateral stresses. Multi helical piles are used to increase the efficiency of these piles. In this study, it investigate the behavior of laterally loaded helical piles with multiple helices when subjected to vertical loading conditions in both cohesive and cohesionless soils. Two models of intermediate shaft rigidity are studied with either two or three helices. Additionally, the vertical loading conditions were altered between successive and simultaneous loading. The cohesionless soil is sand with medium density and the cohesive soil is clay with medium cohesion. The study will carried out with numerical analysis using PLAXIS 3D and will be verified by an experimental tests. The numerical simulations reveal that helical piles exhibit different behavior in cohesive soil compared to cohesionless soil.

Keywords: helical piles, multi-helix, numerical modeling, PLAXIS 3D, cohesive soil, cohesionless soil, experimental

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Authors: A. N. Moulai Khatir

Abstract:

SOI-Multifin-FETs are placed to be the workhorse of the industry for the coming few generations, and thus, in a few years because their excellent transistor characteristics, ideal sub-threshold swing, low drain induced barrier lowering (DIBL) without pocket implantation, and negligible body bias dependency. The corner effect may also exist in the two lower corners; this effect is called the BOX effect, which can also occur in the direction X-Z. The electric field lines from the source and drain cross the bottom oxide and arrive in the silicon. This effect is also called DIVSB (Drain Induced Virtual Substrate Basing). The potential in the silicon film in particular near the drain is increased by the drain bias. It is similar to DIBL and result in a decrease of the threshold voltage. This work provides an understanding of the limitation of this effect by reducing the fin width for components with increased fin number.

Keywords: SOI, finFET, corner effect, dual-gate, tri-gate, BOX, multi-finFET

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Authors: Saif Ullah, Guan Zailin, Xu Xianhao, He Zongdong, Wang Baoxi

Abstract:

Assembly line balancing problem is aimed to divide the tasks among the stations in assembly lines and optimize some objectives. In assembly lines the workload on stations is different from each other due to different tasks times and the difference in workloads between stations can cause blockage or starvation in some stations in assembly lines. Buffers are used to store the semi-finished parts between the stations and can help to smooth the assembly production. The assembly line balancing and buffer sizing problem can affect the throughput of the assembly lines. Assembly line balancing and buffer sizing problems have been studied separately in literature and due to their collective contribution in throughput rate of assembly lines, balancing and buffer sizing problem are desired to study simultaneously and therefore they are considered concurrently in current research. Current research is aimed to maximize throughput, minimize total size of buffers in assembly line and minimize workload variations in assembly line simultaneously. A multi objective optimization objective is designed which can give better Pareto solutions from the Pareto front and a simple example problem is solved for assembly line balancing and buffer sizing simultaneously. Current research is significant for assembly line balancing research and it can be significant to introduce optimization approaches which can optimize current multi objective problem in future.

Keywords: assembly line balancing, buffer sizing, Pareto solutions

Procedia PDF Downloads 491