Search results for: machine vision

Authors: Gueidi Afef, Gharsellaoui Hamza, Ben Ahmed Samir

Abstract:

This paper deals with the secret of the continual progression data that new data management solutions have been emerged: The NoSQL databases. They crossed several areas like personalization, profile management, big data in real-time, content management, catalog, view of customers, mobile applications, internet of things, digital communication and fraud detection. Nowadays, these database management systems are increasing. These systems store data very well and with the trend of big data, a new challenge’s store demands new structures and methods for managing enterprise data. The new intelligent machine in the e-learning sector, thrives on more data, so smart machines can learn more and faster. The robotics are our use case to focus on our test. The implementation of NoSQL for Robotics wrestle all the data they acquire into usable form because with the ordinary type of robotics; we are facing very big limits to manage and find the exact information in real-time. Our original proposed approach was demonstrated by experimental studies and running example used as a use case.

Keywords: NoSQL databases, database management systems, robotics, big data

Procedia PDF Downloads 348

Authors: Abid Kousay

Abstract:

Appeared and developed essentially in large companies and multinationals, Talent Management (TM) in Small and Medium-Sized Enterprises (SMEs) has remained an under-explored subject till today. Although the literature on TM in the Anglo-Saxon context is developing, it remains monopolized in non-European contexts, especially in France. Therefore, this article aims to address these shortcomings through contributing to TM issues by adopting a multilevel approach holding the goal of reaching a global holistic vision of interactions between various levels while applying TM. A qualitative research study carried out within 12 SMEs in France, built on the methodological perspective of grounded theory, will be used in order to go beyond description, to generate or discover a theory or even a unified theoretical explanation. Our theoretical contributions are the results of the grounded theory, the fruit of context considerations and the dynamic of the multilevel approach. We aim firstly to determine the perception of talent and TM in SMEs. Secondly, we formalize TM in SME through the empowerment of all 3 levels in the organization (individual, collective, and organizational). And we generate a multilevel dynamic system model, highlighting the institutionalization dimension in SMEs and the managerial conviction characterized by the domination of the leader’s role. Thirdly, this first study sheds light on the importance of rigorous implementation of TM in SMEs in France by directing CEO and HR and TM managers to focus on elements that upstream TM implementation and influence the system internally. Indeed, our systematic multilevel approach policy reminds them of the importance of strategic alignment while translating TM policy into strategies and practices in SMEs.

Keywords: French context, multilevel approach, talent management, , TM system

Procedia PDF Downloads 214

Authors: Derek Brandon G. Yu, Clyde Vincent O. Pilapil, Christine F. Peña

Abstract:

College students of Cebu province have been indoors since March 2020, and a challenge encountered is the sudden shift from face to face to online learning and with the lack of empirical data on online learning on Higher Education Institutions (HEIs) in the Philippines. Sentiments on face to face and online learning will be collected from University of San Carlos (USC), School of Arts and Sciences (SAS) students regarding Mathematics in the Modern World (MMW), a General Education (GE) course. Natural Language Processing with machine learning algorithms will be used to classify the sentiments of the students. Results of the research study are the themes identified through topic modelling and the overall sentiments of the students in USC SAS

Keywords: natural language processing, online learning, sentiment analysis, topic modelling

Procedia PDF Downloads 238

Authors: Ankit Sharma, Mudit Sachan, Anurag Prakash

Abstract:

Rapid growth in data acquisition technologies through drones, have led to advances and interests in collecting high-resolution images of geological fields. Being advantageous in capturing high volume of data in short flights, a number of challenges have to overcome for efficient analysis of this data, especially while data acquisition, image interpretation and processing. We introduce a method that allows effective mapping of geological fields using photogrammetric data of surfaces, drainage area, water bodies etc, which will be captured by airborne vehicles like UAVs, we are not taking satellite images because of problems in adequate resolution, time when it is captured may be 1 yr back, availability problem, difficult to capture exact image, then night vision etc. This method includes advanced automated image interpretation technology and human data interaction to model structures and. First Geological structures will be detected from the primary photographic dataset and the equivalent three dimensional structures would then be identified by digital elevation model. We can calculate dip and its direction by using the above information. The structural map will be generated by adopting a specified methodology starting from choosing the appropriate camera, camera’s mounting system, UAVs design ( based on the area and application), Challenge in air borne systems like Errors in image orientation, payload problem, mosaicing and geo referencing and registering of different images to applying DEM. The paper shows the potential of using our method for accurate and efficient modeling of geological structures, capture particularly from remote, of inaccessible and hazardous sites.

Keywords: digital elevation model, mapping, photogrammetric data analysis, geological structures

Procedia PDF Downloads 683

Authors: Mohamed Fadzlee Sulaiman, Mohd Zabri Adil Talib, Aswami Fadillah Mohd Ariffin

Abstract:

Each individual organization has their own mechanism to build up cyber defense capability in protecting their information infrastructures from data breaches and cyber espionage. But, we can not deny the possibility of failing to detect and stop cyber attacks especially for those targeting credential information and intellectual property (IP). In this paper, we would like to share the modern approach of effective digital forensic methodology in order to identify the artifacts in tracing the trails of evidence while mitigating the infection from the target machine/s. This proposed approach will suit the digital forensic investigation to be conducted while resuming the business critical operation after mitigating the infection and minimizing the risk from the identified attack to transpire. Therefore, traditional digital forensics methodology has to be improvised to be proactive which not only focusing to discover the root caused and the threat actor but to develop the relevant mitigation plan in order to prevent from the same attack.

Keywords: digital forensic, detection, eradication, targeted attack, malware

Procedia PDF Downloads 269

Authors: Min Jindong, Wang Mingxia

Abstract:

With the widespread use of artificial intelligence in life, computer vision, especially deep convolutional neural network models, has developed rapidly. With the increase of the complexity of the real visual target detection task and the improvement of the recognition accuracy, the target detection network model is also very large. The huge deep neural network model is not conducive to deployment on edge devices with limited resources, and the timeliness of network model inference is poor. In this paper, knowledge distillation is used to compress the huge and complex deep neural network model, and the knowledge contained in the complex network model is comprehensively transferred to another lightweight network model. Different from traditional knowledge distillation methods, we propose a novel knowledge distillation that incorporates multi-faceted features, called M-KD. In this paper, when training and optimizing the deep neural network model for target detection, the knowledge of the soft target output of the teacher network in knowledge distillation, the relationship between the layers of the teacher network and the feature attention map of the hidden layer of the teacher network are transferred to the student network as all knowledge. in the model. At the same time, we also introduce an intermediate transition layer, that is, an intermediate guidance layer, between the teacher network and the student network to make up for the huge difference between the teacher network and the student network. Finally, this paper adds an exploration module to the traditional knowledge distillation teacher-student network model. The student network model not only inherits the knowledge of the teacher network but also explores some new knowledge and characteristics. Comprehensive experiments in this paper using different distillation parameter configurations across multiple datasets and convolutional neural network models demonstrate that our proposed new network model achieves substantial improvements in speed and accuracy performance.

Keywords: object detection, knowledge distillation, convolutional network, model compression

Procedia PDF Downloads 274

Authors: Muhammad Izzam Bin Mohd Khazar, Ruqayyah Binti Mohamad Ali, Nurul Atiqah Binti Yusri

Abstract:

Value-based intermediation (VBI) has been introduced by Bank Negara Malaysia (BNM) as the next strategic direction and growth driver for Islamic banking institutions. The aim of VBI is to deliver the intended outcome of Shariah through practices, conducts, and offerings that generate positive and sustainable impact to the economy, community and environment which is aligned to Maqasid Shariah in preserving the common interest of society by preventing harm and maximizing benefit. Hence, upon its implementation, VBI will experiment the current Shariah compliance treatment and revolutionise new policies and systems that can meritoriously entrench and convey the objectives of Shariah. However, discussion revolving VBI in the light of Maqasid analysis is still scarce hence further research needs to be undertaken. The idea of implementation of VBI vision into quantifiable Maqasid Shariah measurement is yet to be explored due to the nature of Maqasid that is variable. The contemporary scholars also have different views on the implementation of VBI. This paper aims to discuss on the importance of Maqasid Shariah in the current Islamic finance transactions by providing Shariah index measurement in the application of VBI. This study also intends to explore basic Shariah guidelines and parameters based on the objectives of Shariah; preservation of the five pillars (religion, life, progeny, intellect and wealth) with further elaboration on preservation of wealth under five headings: rawaj (circulation and marketability); wuduh (transparency); hifz (preservation); thabat (durability and tranquillity); and ‘adl (equity and justice). In alignment with these headings, Islamic finance can be innovated for VBI implementation, particularly in Maybank Islamic being a significant leader in the IFI market.

Keywords: Islamic Financial Institutions, Maqasid Index, Maqasid Shariah, sustainability, value-based intermediation

Procedia PDF Downloads 161

Authors: Sandra So, Mohd Roslan Ismail, Safurah Jaafar

Abstract:

With the rapid proliferation of data in healthcare has provided an opportune platform creation of Artificial Intelligence (AI). AI has brought a paradigm shift for healthcare professionals, promising improvement in delivery and quality. This study aims to determine the perception of healthcare personnel on perceived ease of use, perceived usefulness, and subjective norm toward attitude for artificial intelligence acceptance. A cross-sectional single institutional study of employees’ perception of adopting AI in the hospital was conducted. The survey was conducted using a questionnaire adapted from Technology Acceptance Model and a four-point Likert scale was used. There were 96 or 75.5% of the total population responded. This study has shown the significant relationship and the importance of ease of use, perceived usefulness, and subjective norm to the acceptance of AI. In the study results, it concluded that the determining factor to the strong acceptance of AI in their practices is mostly those respondents with the most interaction with the patients and clinical management.

Keywords: artificial intelligence, machine learning, perceived ease of use, perceived usefulness, subjective norm

Procedia PDF Downloads 222

Authors: Repaul Kanji, Rajat Agrawal

Abstract:

In this era where issues like climate change and disasters are the topics of discussion at national and international forums, it is very often that humanity questions the causative role of corporates in such events. It is beyond any doubt that rapid industrialisation and development has taken a toll in the form of climate change and even disasters, in some case. Thus, demanding to fulfill a corporate's responsibilities in the form of rescue and relief in times of disaster, rehabilitation and even mitigation and preparedness to adapt to the oncoming changes is obvious. But how can the responsibilities of the corporates be channelised to ensure all this, i.e., develop a resilient society? More than that, which factors, when emphasised upon, can lead to the holistic development of the society. To answer this query, an extensive literature review was done to identify several enablers like legislations of a nation, the role of brand and reputation, ease of doing Corporate Social Responsibility, mission and vision of an organisation, religion and culture, etc. as a tool for building disaster resilience. A questionnaire survey, interviews with experts and academicians followed by interpretive structural modelling (ISM) were used to construct a multi-hierarchy model depicting the contextual relationship among the identified enablers. The study revealed that culture and religion are the most powerful driver, which affects other enablers either directly or indirectly. Taking cognisance of the fact that an idea of separation between religion and workplace (business) resides subconsciously within the society, the study tries to interpret the outcome of the ISM through the lenses of past researches (The Integrating Box) and explores how it can be leveraged to build a resilient society.

Keywords: corporate social responsibility, interpretive structural modelling, disaster resilience and risk reduction, the integration box (TIB)

Procedia PDF Downloads 204

Authors: Pemika Ketsuwan, Pitt Supaphol, Manit Nithitanakul

Abstract:

A typical used of ethylene methyl acrylate (EMA) gasket is in the manufacture of optical lens, and often, they are deteriorated rapidly due to high temperature during the process. The objective of this project is to improve the thermal stability of the EMA copolymer gasket by preparing EMA with cellulose and silica composites. Hydroxy propyl methyl cellulose (HPMC) and Carboxy methyl cellulose (CMC) were used in preparing of EMA/cellulose composites and fumed silica (SiO2) was used in preparing EMA/silica composites with different amounts of filler (3, 5, 7, 10, 15 wt.%), using a twin screw extruder at 160 °C and the test specimens were prepared by the injection molding machine. The morphology and dispersion of fillers in the EMA matrix were investigated by field emission scanning electron microscopy (FESEM). The thermal stability of the composite was determined by thermal gravimetric analysis (TGA), and differential scanning calorimeter (DSC). Mechanical properties were evaluated by tensile testing. The developed composites were found to enhance thermal and mechanical properties when compared to that of the EMA copolymer alone.

Keywords: ethylene methyl acrylate, HPMC, Silica, Thermal stability

Procedia PDF Downloads 117

Authors: K. Doi, T. Nishimura, H. Fujimoto, Y. Hoshikawa, T. Wada

Abstract:

Japanese industrial standard (JIS) association established one JIS (JIS S 0021) regarding packaging accessible design for people with visual impairments and elderly people in 2000. Recently, tactile symbol on shampoo bottle has been known as one of package accessible design and more effectively used. However, it has been said that people with visual impairment have been not been in trouble with difficulty of identifying body soap bottle between three bottles such as body soap bottle, shampoo bottle, and conditioner bottle. Japanese low vision association asked JIS association to solve this problem. JIS association and Japan cosmetic industry association constituted one review team for solving the problem. The review team asked our research team to make a proposal regarding new tactile symbol on body soap bottle. We conducted user survey and maker survey regarding tactile symbol on body soap bottle with easily identification. Seven test tactile symbol marks were elected in our proposed tactile symbols. In this study, we evaluate easily identification of tactile symbol on body soap bottle. Six visual impaired subjects were participated in our experiment. These subjects were asked to identify body soap bottle between three bottles such as body soap bottle, shampoo bottle, and conditioner bottle. The test tactile symbol on body soap were presented in random order. The test tactile symbols were produced by use of our originally developed 3D raised equipment. From our study, test tactile symbol marks with easily identification were made a short list of our proposed tactile symbols. This knowledge will be helpful in revision of ISO 11156.

Keywords: tactile symbol, easily identification, body soap, people with visual impairments

Procedia PDF Downloads 305

Authors: Christoph Linse, Thomas Martinetz

Abstract:

Recent findings have shown that Neural Networks generalize also in over-parametrized regimes with zero training error. This is surprising, since it is completely against traditional machine learning wisdom. In our empirical study we fortify these findings in the domain of fine-grained image classification. We show that very large Convolutional Neural Networks with millions of weights do learn with only a handful of training samples and without image augmentation, explicit regularization or pretraining. We train the architectures ResNet018, ResNet101 and VGG19 on subsets of the difficult benchmark datasets Caltech101, CUB_200_2011, FGVCAircraft, Flowers102 and StanfordCars with 100 classes and more, perform a comprehensive comparative study and draw implications for the practical application of CNNs. Finally, we show that VGG19 with 140 million weights learns to distinguish airplanes and motorbikes with up to 95% accuracy using only 20 training samples per class.

Keywords: convolutional neural networks, fine-grained image classification, generalization, image recognition, over-parameterized, small data sets

Procedia PDF Downloads 85

Authors: Anita Yaghobi, Homayoun Ebrahimian

Abstract:

One of the important tools that can help orthopedic doctors for diagnose diseases is imaging scan. Imaging techniques can help physicians in see different parts of the body, including the bones, muscles, tendons, nerves, and cartilage. During CT scan, a patient must be in the same position from the start to the end of radiation treatment. Patient movements are usually monitored by the technologists through the closed circuit television (CCTV) during scan. If the patient makes a small movement, it is difficult to be noticed by them. In the present work, a simple patient movement monitoring device is fabricated to monitor the patient movement. It uses an electronic sensing device. It continuously monitors the patient’s position while the CT scan is in process. The device has been retrospectively tested on 51 patients whose movement and distance were measured. The results show that 25 patients moved 1 cm to 2.5 cm from their initial position during the CT scan. Hence, the device can potentially be used to control and monitor patient movement during CT scan and Radiography. In addition, an audible alarm situated at the control panel of the control room is provided with this device to alert the technologists. It is an inexpensive, compact device which can be used in any CT scan machine.

Keywords: CT scan, radiology, X Ray, unwanted movement

Procedia PDF Downloads 457

Authors: Abid Kousay

Abstract:

Appeared and developed essentially in large companies and multinationals, Talent Management (TM) in Small and Medium-Sized Enterprises (SMEs) has remained an under-explored subject till today. Although the literature on TM in the Anglo-Saxon context is developing, it remains monopolized in non-European contexts, especially in France. Therefore, this article aims to address these shortcomings through contributing to TM issues, by adopting a multilevel approach holding the goal of reaching a global holistic vision of interactions between various levels, while applying TM. A qualitative research study carried out within 12 SMEs in France, built on the methodological perspective of grounded theory, will be used in order to go beyond description, to generate or discover a theory or even a unified theoretical explanation. Our theoretical contributions are the results of the grounded theory, the fruit of context considerations and the dynamic of the multilevel approach. We aim firstly to determine the perception of talent and TM in SMEs. Secondly, we formalize TM in SME through the empowerment of all 3 levels in the organization (individual, collective, and organizational). And we generate a multilevel dynamic system model, highlighting the institutionalization dimension in SMEs and the managerial conviction characterized by the domination of the leader's role. Thirdly, this first study shed the light on the importance of rigorous implementation of TM in SMEs in France by directing CEO and HR and TM managers to focus on elements that upstream TM implementation and influence the system internally. Indeed, our systematic multilevel approach policy reminds them of the importance of the strategic alignment while translating TM policy into strategies and practices in SMEs.

Keywords: French context, institutionalization, talent, multilevel approach, talent management system

Procedia PDF Downloads 198

Authors: Kamyar Tolouei, Ehsan Moosavi

Abstract:

In open-pit mines, the long-term production scheduling optimization problem (LTPSOP) is a complicated problem that contains constraints, large datasets, and uncertainties. Uncertainty in the output is caused by several geological, economic, or technical factors. Due to its dimensions and NP-hard nature, it is usually difficult to find an ideal solution to the LTPSOP. The optimal schedule generally restricts the ore, metal, and waste tonnages, average grades, and cash flows of each period. Past decades have witnessed important measurements of long-term production scheduling and optimal algorithms since researchers have become highly cognizant of the issue. In fact, it is not possible to consider LTPSOP as a well-solved problem. Traditional production scheduling methods in open-pit mines apply an estimated orebody model to produce optimal schedules. The smoothing result of some geostatistical estimation procedures causes most of the mine schedules and production predictions to be unrealistic and imperfect. With the expansion of simulation procedures, the risks from grade uncertainty in ore reserves can be evaluated and organized through a set of equally probable orebody realizations. In this paper, to synthesize grade uncertainty into the strategic mine schedule, a stochastic integer programming framework is presented to LTPSOP. The objective function of the model is to maximize the net present value and minimize the risk of deviation from the production targets considering grade uncertainty simultaneously while satisfying all technical constraints and operational requirements. Instead of applying one estimated orebody model as input to optimize the production schedule, a set of equally probable orebody realizations are applied to synthesize grade uncertainty in the strategic mine schedule and to produce a more profitable and risk-based production schedule. A mixture of metaheuristic procedures and mathematical methods paves the way to achieve an appropriate solution. This paper introduced a hybrid model between the augmented Lagrangian relaxation (ALR) method and the metaheuristic algorithm, the Harris Hawks optimization (HHO), to solve the LTPSOP under grade uncertainty conditions. In this study, the HHO is experienced to update Lagrange coefficients. Besides, a machine learning method called Random Forest is applied to estimate gold grade in a mineral deposit. The Monte Carlo method is used as the simulation method with 20 realizations. The results specify that the progressive versions have been considerably developed in comparison with the traditional methods. The outcomes were also compared with the ALR-genetic algorithm and ALR-sub-gradient. To indicate the applicability of the model, a case study on an open-pit gold mining operation is implemented. The framework displays the capability to minimize risk and improvement in the expected net present value and financial profitability for LTPSOP. The framework could control geological risk more effectively than the traditional procedure considering grade uncertainty in the hybrid model framework.

Keywords: grade uncertainty, metaheuristic algorithms, open-pit mine, production scheduling optimization

Procedia PDF Downloads 100

Authors: Gaurav Kumar, Shilpa

Abstract:

Digital libraries have been established all over the world to create, maintain and to preserve the digital materials. This paper focuses on operational digital preservation systems specifically in educational organizations in India. It considers the broad range of digital objects including e-journals, technical reports, e-records, project documents, scientific data, etc. This paper describes the main objectives, process and technological issues involved in preservation of digital materials. Digital preservation refers to the various methods of keeping digital materials alive for the future. It includes everything from electronic publications on CD-ROM to Online database and collections of experimental data in digital format maintains the ability to display, retrieve and use digital collections in the face of rapidly changing technological and organizational infrastructures elements. This paper exhibits the importance and objectives of digital preservation. The necessities of preservation are hardware and software technology to interpret the digital documents and discuss various aspects of digital preservation.

Keywords: preservation, digital preservation, digital dark age, conservation, archive, repository, document, information technology, hardware, software, organization, machine readable format

Procedia PDF Downloads 450

Authors: D.Mendes, J. Henriques, P. Carvalho, T. Rocha, S. Paredes, R. Cabiddu, R. Trimer, R. Mendes, A. Borghi-Silva, L. Kaminsky, E. Ashley, R. Arena, J. Myers

Abstract:

The continued exploration of clinically relevant predictive models continues to be an important pursuit. Cardiorespiratory fitness (CRF) portends clinical vital information and as such its accurate prediction is of high importance. Therefore, the aim of the current study was to develop a data-driven model, based on computational intelligence techniques and, in particular, clustering approaches, to predict CRF. Two prediction models were implemented and compared: 1) the traditional Wasserman/Hansen Equations; and 2) an interpretable clustering approach. Data used for this analysis were from the 'FRIEND - Fitness Registry and the Importance of Exercise: The National Data Base'; in the present study a subset of 10690 apparently healthy individuals were utilized. The accuracy of the models was performed through the computation of sensitivity, specificity, and geometric mean values. The results show the superiority of the clustering approach in the accurate estimation of CRF (i.e., maximal oxygen consumption).

Keywords: cardiorespiratory fitness, data-driven models, knowledge extraction, machine learning

Procedia PDF Downloads 283

Authors: Perminder JitKaur, Santosh Satya, K. K. Pant, S. N. Naik

Abstract:

Bamboo is extensively used in construction industry. Low durability of bamboo due to fungus infestation and termites attack under storage puts certain constrains for it usage as modern structural material. Looking at many chemical formulations for bamboo treatment leading to severe harmful environment effects, research on eco-friendly preservatives for bamboo treatment has been initiated world-over. In the present studies, eco-friendly preservative for bamboo treatment has been developed. To validate its application for structural purposes, investigation of effect of treatment on compressive strength has been investigated. Neem oil(25%) integrated with copper naphthenate (0.3%) on dilution with kerosene oil impregnated into bamboo culm at 2 bar pressure, has shown weight loss of only 3.15% in soil block analysis method. The results of compressive strength analysis using The results from compressive strength analysis using HEICO Automatic Compression Testing Machine, reveal that preservative treatment has not altered the structural properties of bamboo culms. Compressive strength of control (11.72 N/mm2) and above treated samples (11.71 N/mm2) was found to be comparable.

Keywords: D. strictus, bamboo, neem oil, presure treatment, compressive strength

Procedia PDF Downloads 403

Authors: Abdullah Al-Alaian, Ahmad Al-Enzi, Hasan Al-Herz, Ahmad Bakri, Shant Tatorian, Amr Nounou

Abstract:

Kuwait is a country that has an imbalanced economy since most of its national outcome comes from the oil trade. It is so risky for a country to be dependent on a single source for income, and this increases the need to diversify its economy. In addition, according to the Public Authority for Industry, the contribution of the industrial sector to the current Gross Domestic Product (GDP) of Kuwait is low which is about 4.33%. Therefore, the development of the industrial sector can be one of the means to diversify the economy and increase the industry's contribution to the national outcome. This is in accordance with Kuwait’s vision of 2035 which aims at increasing the contribution of the industrial sector to the GDP to 12%. In order to do so, this study aims at proposing a strategic plan that will accomplish certain objectives when implemented. It is based on analyzing the industrial sectors in Kuwait taking into consideration studying the strengths, weaknesses, opportunities, and threats facing them. At the same time, it tends to gain from the experience of leading models and neighboring countries regarding the development of the industrial sector. In this study, the SWOT analysis technique will be conducted on all industrial sectors based on evaluation criteria in which it is determined whether any of them has a potential for improvement or not. In other words, it is determined whether the sectors are able to compete locally, regionally, or globally. Based on the results of the SWOT analysis, certain sectors will be chosen, assessed based on an assessment scheme, and their potentials for improvement will be aligned with the overall objectives. To ensure the achievement of the study’s objectives, an action plan will be proposed regarding recommendations for the related authorities, and for entrepreneurs. In addition, monitoring tools are going to be provided for the purpose of periodically checking the progress made in the implementation of the plan.

Keywords: industrial sector, SWOT analysis, productivity, competitiveness, GDP, Kuwait, economy

Procedia PDF Downloads 476

Authors: Xiuying Wang, Changliang Li, Bo Xu

Abstract:

A fixed-length feature vector is required for many machine learning algorithms in NLP field. Word embeddings have been very successful at learning lexical information. However, they cannot capture the compositional meaning of sentences, which prevents them from a deeper understanding of language. In this paper, we introduce a novel hierarchical tree long short-term memory (HTLSTM) model that learns vector representations for sentences of arbitrary syntactic type and length. We propose to split one sentence into three hierarchies: short phrase, long phrase and full sentence level. The HTLSTM model gives our algorithm the potential to fully consider the hierarchical information and long-term dependencies of language. We design the experiments on both English and Chinese corpus to evaluate our model on sentiment analysis task. And the results show that our model outperforms several existing state of the art approaches significantly.

Keywords: deep learning, hierarchical tree long short-term memory, sentence representation, sentiment analysis

Procedia PDF Downloads 348

Authors: Ahmad Bilal

Abstract:

Digital and convergent media can be helpful in terms of acquiring film production skills and knowledge, and it has also reduced the cost of production. Thus, allowing filmmakers greater opportunities and access to the medium of film. Both these dimensions of new and convergent media have been challenging the established cinema of Pakistan, as traditionally, it has been controlled by the authorities through censorship policies. The use of the smartphone as a movie camera, editing machine, and a transmitter can further challenge the control in a postcolonial society. To explore the impact of new and convergent media on the art of filmmaking, a film 'Sohni Dharti: An untrue story' is produced. It is shot both on a smartphone and a Digital Single Lens Reflex Camera (DSLR), with almost zero budgets. It is distributed through Vimeo from Pakistan. This process reveals how the technologies that are available today, and the increased knowledge of film production that they bring, allow a more inclusive experience of the film production and distribution. At the same time, however, it also discloses the limitations that accompany new technologies within the context of a postcolonial society. This paper will investigate the role of technology to bring filmmaking at a level of pencil and paper.

Keywords: convergent media, filmmaking, smartphone, Pakistan

Procedia PDF Downloads 273

Authors: Jalil Salimi, Mandana Asadi, Naser Fathi

Abstract:

Urmia Lake (UL) has undergone a significant decline in water levels, resulting in severe environmental, socioeconomic, and health-related challenges. This paper examines the restoration process of UL from a water governance perspective. By applying a water governance model, the study evaluates the process based on six selected principles: stakeholder engagement, transparency and accountability, effectiveness, equitable water use, adaptation capacity, and water usage efficiency. The dominance of structural and physicalist approaches to water governance has led to a weak understanding of social and environmental issues, contributing to social crises. Urgent efforts are required to address the water crisis and reform water governance in the country, making water-related issues a top national priority. The UL restoration process has achieved significant milestones, including stakeholder consensus, scientific and participatory planning, environmental vision, intergenerational justice considerations, improved institutional environment for NGOs, investments in water infrastructure, transparency promotion, environmental effectiveness, and local issue resolutions. However, challenges remain, such as power distribution imbalances, bureaucratic administration, weak conflict resolution mechanisms, financial constraints, accountability issues, limited attention to social concerns, overreliance on structural solutions, legislative shortcomings, program inflexibility, and uncertainty management weaknesses. Addressing these weaknesses and challenges is crucial for the successful restoration and sustainable governance of UL.

Keywords: evaluation, restoration process, Urmia Lake, water governance, water resource management

Procedia PDF Downloads 64

Authors: Aysegul Sarac

Abstract:

The aim of this study is to analyze the impacts of integrating agile development principles and practices, in particular to reduce project lead time in a multi-project environment. We analyze Arçelik Washing Machine R&D Center in which multiple projects are conducted by shared resources. In the first part of the study, we illustrate the current waterfall model system by using a value stream map. We define all activities starting from the first idea of the project to the customer and measure process time and lead time of projects. In the second part of the study we estimate potential improvements and select a set of these improvements to integrate agile principles. We aim to develop a future state map and analyze the impacts of integrating lean principles on project lead time. The main contribution of this study is that we analyze and integrate agile product development principles in a real multi-project system.

Keywords: agile project management, multi project system, project lead time, product development

Procedia PDF Downloads 297

Authors: Dhanuj M. Gandikota

Abstract:

Sensor-derived three-dimensional (3D) point clouds of trees are invaluable in remote sensing analysis for the accurate measurement of key structural metrics, bio-inventory values, spatial planning/visualization, and ecological modeling. Machine learning (ML) holds the potential in addressing the restrictive tradeoffs in cost, spatial coverage, resolution, and information gain that exist in current point cloud sensing methods. Terrestrial laser scanning (TLS) remains the highest fidelity source of both canopy and below-canopy structural features, but usage is limited in both coverage and cost, requiring manual deployment to map out large, forested areas. While aerial laser scanning (ALS) remains a reliable avenue of LIDAR active remote sensing, ALS is also cost-restrictive in deployment methods. Space-borne photogrammetry from high-resolution satellite constellations is an avenue of passive remote sensing with promising viability in research for the accurate construction of vegetation 3-D point clouds. It provides both the lowest comparative cost and the largest spatial coverage across remote sensing methods. However, both space-borne photogrammetry and ALS demonstrate technical limitations in the capture of valuable below-canopy point cloud data. Looking to minimize these tradeoffs, we explored a class of powerful ML algorithms called Deep Learning (DL) that show promise in recent research on 3-D point cloud reconstruction and interpolation. Our research details the efficacy of applying these DL techniques to reconstruct accurate below-canopy point clouds from space-borne and aerial remote sensing through learned patterns of tree species fractal symmetry properties and the supplementation of locally sourced bio-inventory metrics. From our dataset, consisting of tree point clouds obtained from TLS, we deconstructed the point clouds of each tree into those that would be obtained through ALS and satellite photogrammetry of varying resolutions. We fed this ALS/satellite point cloud dataset, along with the simulated local bio-inventory metrics, into the DL point cloud reconstruction architectures to generate the full 3-D tree point clouds (the truth values are denoted by the full TLS tree point clouds containing the below-canopy information). Point cloud reconstruction accuracy was validated both through the measurement of error from the original TLS point clouds as well as the error of extraction of key structural metrics, such as crown base height, diameter above root crown, and leaf/wood volume. The results of this research additionally demonstrate the supplemental performance gain of using minimum locally sourced bio-inventory metric information as an input in ML systems to reach specified accuracy thresholds of tree point cloud reconstruction. This research provides insight into methods for the rapid, cost-effective, and accurate construction of below-canopy tree 3-D point clouds, as well as the supported potential of ML and DL to learn complex, unmodeled patterns of fractal tree growth symmetry.

Keywords: deep learning, machine learning, satellite, photogrammetry, aerial laser scanning, terrestrial laser scanning, point cloud, fractal symmetry

Procedia PDF Downloads 98

Authors: Misrak Assefa

Abstract:

The Amharic language is the second most widely spoken Semitic language in the world. There are several new overloaded on the web. Searching some useful documents from the web on a specific topic, which is written in the Amharic language, is a challenging task. Hence, document categorization is required for managing and filtering important information. In the classification of Amharic text news, there is still a gap in the domain of information that needs to be launch. This study attempts to design an automatic Amharic news classification using a supervised learning mechanism on four un-touch classes. To achieve this research, 4,182 news articles were used. Naive Bayes (NB) and Decision tree (j48) algorithms were used to classify the given Amharic dataset. In this paper, k-fold cross-validation is used to estimate the accuracy of the classifier. As a result, it shows those algorithms can be applicable in Amharic news categorization. The best average accuracy result is achieved by j48 decision tree and naïve Bayes is 95.2345 %, and 94.6245 % respectively using three categories. This research indicated that a typical decision tree algorithm is more applicable to Amharic news categorization.

Keywords: text categorization, supervised machine learning, naive Bayes, decision tree

Procedia PDF Downloads 201

Authors: Tianyu Cao, KIRA (Ruizhi Zhao)

Abstract:

The purpose of our research is to study the possibility of people with physical disabilities manipulating mechanical prostheses through brain-computer interface (BCI) technology. The brain-machine interface (BCI) of the neural prosthesis records signals from neurons and uses mathematical modeling to decode them, converting desired movements into body movements. In order to improve the patient's neural control, the prosthesis is given a natural feeling. It records data from sensitive areas from the body to the prosthetic limb and encodes signals in the form of electrical stimulation to the brain. In our research, the brain-computer interface (BCI) is a bridge connecting patients’ cognition and the real world, allowing information to interact with each other. The efficient work between the two is achieved through external devices. The flow of information is controlled by BCI’s ability to record neuronal signals and decode signals, which are converted into device control. In this way, we could encode information and then send it to the brain through electrical stimulation, which has significant medical application.

Keywords: biomedical engineering, brain-computer interface, prosthesis, neural control

Procedia PDF Downloads 174

Authors: Walid M. Adel, Liang Guo-Zhu

Abstract:

To investigate the characterization of the mechanical properties of composite solid propellant (CSP) based on hydroxyl-terminated polybutadiene (HTPB) at different temperatures and strain rates, uniaxial tensile tests were conducted over a range of temperatures -60 °C to +76 °C and strain rates 0.000164 to 0.328084 s^-1 using a conventional universal testing machine. From the experimental data, it can be noted that the mechanical properties of AP/HTPB propellant are mainly dependent on the applied strain rate and the temperature condition. The stress-strain responses exhibited an initial yielding followed by the viscoelastic phase, which was strongly affected by the strain rate and temperature. It was found that the mechanical properties increased with both increasing strain rate and decreasing temperature. Based on the experimental tests, the master curves of the tensile properties are drawn using predetermined shift factor and the results were discussed. This work is a first step in preliminary investigation the nonlinear viscoelasticity behavior of CSP.

Keywords: AP/HTPB composite solid propellant, mechanical behavior, nonlinear viscoelastic, tensile test, strain rate

Procedia PDF Downloads 227

Authors: Kanokrat Makkwao, Tassanee Prueksasit

Abstract:

This study aimed to investigate PM₁₀ emitted from different limestone mines in Saraburi province, Thailand. The blasting and crushing were the main processes selected for PM₁₀ sampling. PM₁₀ was collected in two mines including, a limestone mine for cement manufacturing (mine A) and a limestone mine for construction (mine B). The IMPACT samplers were used to collect PM₁₀. At blasting, the points aligning with the upwind and downwind direction were assigned for the sampling. The ranges of PM₁₀ concentrations at mine A and B were 0.267-5.592 and 0.130-0.325 mg/m³, respectively, and the concentration at blasting from mine A was significantly higher than mine B (p < 0.05). During crushing at mine A, the PM₁₀ concentration with the range of 1.153-3.716 and 0.085-1.724 mg/m³ at crusher and piles in respectively were observed whereas the PM₁₀ concentration measured at four sampling points in mine B, including secondary crusher, tertiary crusher, screening point, and piles, were ranged 1.032-16.529, 10.957-74.057, 0.655-4.956, and 0.169-1.699 mg/m³, respectively. The emission of PM₁₀ concentration at the crushing units was different in the ranges depending on types of machine, its operation, dust collection and control system, and environmental conditions.

Keywords: PM₁₀ concentration, limestone mines, blasting, crushing

Procedia PDF Downloads 139

Authors: Rimma Dzhusupova, Jan Bosch, Helena Holmström Olsson

Abstract:

Artificial Intelligence (AI) in the Engineering, Procurement, and Construction (EPC) industry has not yet a proven track record in large-scale projects. Since AI solutions for industrial applications became available only recently, deployment experience and lessons learned are still to be built up. Nevertheless, AI has become an attractive technology for organizations looking to automate repetitive tasks to reduce manual work. Meanwhile, the current AI market has started offering various solutions and services. The contribution of this research is that we explore in detail the challenges and obstacles faced in developing and deploying AI in a large-scale project in the EPC industry based on real-life use cases performed in an EPC company. Those identified challenges are not linked to a specific technology or a company's know-how and, therefore, are universal. The findings in this paper aim to provide feedback to academia to reduce the gap between research and practice experience. They also help reveal the hidden stones when implementing AI solutions in the industry.

Keywords: artificial intelligence, machine learning, deep learning, innovation, engineering, procurement and construction industry, AI in the EPC industry

Procedia PDF Downloads 114

Authors: Piyas Palit, Urbi Pal, Jitendra Mathur, Santanu Das

Abstract:

Bearing is an important machinery part in the industry. When bearings fail to meet their expected life the consequences are increased downtime, loss of revenue and missed the delivery. This article describes the failure of a gearbox bearing in rolling contact fatigue. The investigation consists of visual observation, chemical analysis, characterization of microstructures using optical microscopes and hardness test. The present study also considers bearing life as well as the operational condition of bearings. Surface-initiated rolling contact fatigue, leading to a surface failure known as pitting, is a life-limiting failure mode in many modern machine elements, particularly rolling element bearings. Metallography analysis of crack propagation, crack morphology was also described. Indication of fatigue spalling in the ferrography test was also discussed. The analysis suggested the probable reasons for such kind of failure in operation. This type of spalling occurred due to (1) heavier external loading condition or (2) exceeds its service life.

Keywords: bearing, rolling contact fatigue, bearing life

Procedia PDF Downloads 167