Search results for: attention-based fully convolutional network

Authors: Zhi Liu, Hui Jiang, Jin Li, Kunhao Chen, Langfang Zhang

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Fast acquisition of the seismic information and accurate assessment of the earthquake disaster is the key problem for emergency rescue after a destructive earthquake. In order to meet the requirements of the earthquake emergency response and rescue for the cities and counties, a quasi real-time comprehensive evaluation system for earthquake disaster is developed. Based on monitoring data of Micro-Electro-Mechanical Systems (MEMS) strong motion network, structure database of a county area and the real-time disaster information by the mobile terminal after an earthquake, fragility analysis method and dynamic correction algorithm are synthetically obtained in the developed system. Real-time evaluation of the seismic disaster in the county region is finally realized to provide scientific basis for seismic emergency command, rescue and assistant decision.

Keywords: quasi real-time, earthquake disaster data collection, MEMS accelerometer, dynamic correction, comprehensive evaluation

Procedia PDF Downloads 203

Authors: Chengfeng Fang, Mohamed Ali Sadakkathulla, Abdul Sheikh

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Ultra-high-performance fiber reinforced concrete (UHPFRC) is a specially formulated cement-based composite characterized with an ultra-high compressive strength (f_c^’ = 240 MPa) and a low water-cement ratio (W/B= 0.2). With such material characteristics, UHPFRC is favored for the design and constructions of structures required high structural performance and slender geometries. Unlike conventional concrete, the structural performance of members manufactured with UHPFRC has not yet been fully studied, particularly, for UHPFRC columns with high slenderness. In this study, the behaviors of slender UHPFRC columns under concentric or eccentric load will be investigated both experimentally and numerically. Four slender UHPFRC columns were tested under eccentric loads with eccentricities, of 0 mm, 35 mm, 50 mm, and 85 mm, respectively, and one UHPFRC beam was tested under four-point bending. Finite element (FE) analysis was conducted with concrete damage plasticity (CDP) modulus to simulating the load-middle height or middle span deflection relationships and damage patterns of all UHPFRC members. Simulated results were compared against the experimental results and observation to gain the confidence of FE model, and this model was further extended to conduct parametric studies, which aim to investigate the effects of slenderness regarding failure modes and load-moment interaction relationships. Experimental results showed that the load bearing capacities of the slender columns reduced with an increase in eccentricity. Comparisons between load-middle height and middle span deflection relationships as well as damage patterns of all UHPFRC members obtained both experimentally and numerically demonstrated high accuracy of the FE simulations. Based on the available FE model, the following parametric study indicated that a further increase in the slenderness of column resulted in significant decreases in the load-bearing capacities, ductility index, and flexural bending capacities.

Keywords: eccentric loads, ductility index, RC column, slenderness, UHPFRC

Procedia PDF Downloads 118

Authors: Ahmed Abdulla Ashlam, Atta Badii, Frederic Stahl

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In recent years, SQL injection attacks have been identified as being prevalent against web applications. They affect network security and user data, which leads to a considerable loss of money and data every year. This paper presents the use of classification algorithms in machine learning using a method to classify the login data filtering inputs into "SQLi" or "Non-SQLi,” thus increasing the reliability and accuracy of results in terms of deciding whether an operation is an attack or a valid operation. A method Web-App auto-generated twin data structure replication. Shielding against SQLi attacks (WebAppShield) that verifies all users and prevents attackers (SQLi attacks) from entering and or accessing the database, which the machine learning module predicts as "Non-SQLi" has been developed. A special login form has been developed with a special instance of data validation; this verification process secures the web application from its early stages. The system has been tested and validated, up to 99% of SQLi attacks have been prevented.

Keywords: SQL injection, attacks, web application, accuracy, database

Procedia PDF Downloads 134

Authors: Yi-Chiang Huang, Hsu-Feng Lee, Yu-Chao Tseng, Wen-Yao Huang

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Proton exchange membranes as a key component in fuel cells have been widely studying over the past few decades. As proton exchange, membranes should have some main characteristics, such as good mechanical properties, low oxidative stability and high proton conductivity. In this work, trifluoromethyl groups had been introduced on polymer backbone and phenyl side chain which can provide densely located sulfonic acid group substitution and also promotes solubility, thermal and oxidative stability. Herein, a series of novel sulfonated aromatic hydrocarbon polyelectrolytes was synthesized by polycondensation of 4,4''''-difluoro-3,3''''- bis(trifluoromethyl)-2'',3''-bis(3-(trifluoromethyl)phenyl)-1,1':4',1'':4'',1''':4''',1''''-quinquephenyl with 2'',3''',5'',6''-tetraphenyl-[1,1':4',1'': 4'',1''':4''',1''''-quinquephenyl]-4,4''''-diol and post-sulfonated was through chlorosulfonic acid to given sulfonated polymers (SFC3-X) possessing ion exchange capacities ranging from 1.93, 1.91 and 2.53 mmol/g. ¹H NMR and FT-IR spectroscopy were applied to confirm the structure and composition of sulfonated polymers. The membranes exhibited considerably dimension stability (10-27.8% in length change; 24-56.5% in thickness change) and excellent oxidative stability (weight remain higher than 97%). The mechanical properties of membranes demonstrated good tensile strength on account of the high rigidity multi-phenylated backbone. Young's modulus were ranged 0.65-0.77GPa which is much larger than that of Nafion 211 (0.10GPa). Proton conductivities of membranes ranged from 130 to 240 mS/cm at 80 °C under fully humidified which were comparable or higher than that of Nafion 211 (150 mS/cm). The morphology of membranes was investigated by transmission electron microscopy which demonstrated a clear hydrophilic/hydrophobic phase separation with spherical ionic clusters in the size range of 5-20 nm. The SFC3-1.97 single fuel cell performance demonstrates the maximum power density at 1.08W/cm², and Nafion 211 was 1.24W/cm² as a reference in this work. The result indicated that SFC3-X are good candidates for proton exchange membranes in fuel cell applications. Fuel cell of other membranes is under testing.

Keywords: fuel cells, polyelectrolyte, proton exchange membrane, sulfonated polymers

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Authors: Mecheri Zeid Belmecheri, Maamar Ahfir, Izzet Kale

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Automatic cardiac auscultation is still a subject of research in order to establish an objective diagnosis. Recorded heart sounds as Phonocardiogram signals (PCG) can be used for automatic segmentation into components that have clinical meanings. These are the first sound, S1, the second sound, S2, and the systolic and diastolic components, respectively. In this paper, an automatic method is proposed for the robust segmentation of heart sounds. This method is based on calculating an intermediate sawtooth-shaped signal from the length variation of the recorded Phonocardiogram (PCG) signal in the time domain and, using its positive derivative function that is a binary signal in training a Recurrent Neural Network (RNN). Results obtained in the context of a large database of recorded PCGs with their simultaneously recorded ElectroCardioGrams (ECGs) from different patients in clinical settings, including normal and abnormal subjects, show a segmentation testing performance average of 76 % sensitivity and 94 % specificity.

Keywords: heart sounds, PCG segmentation, event detection, recurrent neural networks, PCG curve length

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Authors: Niloufar Yadgari

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GANs are a potent form of deep learning models that have found success in various fields. They are part of the larger group of generative techniques, which aim to produce authentic data using a probabilistic model that learns distributions from actual samples. In clinical settings, GANs have demonstrated improved abilities in capturing spatially intricate, nonlinear, and possibly subtle disease impacts in contrast to conventional generative techniques. This review critically evaluates the current research on how GANs are being used in imaging studies of different neurological conditions like Alzheimer's disease, brain tumors, aging of the brain, and multiple sclerosis. We offer a clear explanation of different GAN techniques for each use case in neuroimaging and delve into the key hurdles, unanswered queries, and potential advancements in utilizing GANs in this field. Our goal is to connect advanced deep learning techniques with neurology studies, showcasing how GANs can assist in clinical decision-making and enhance our comprehension of the structural and functional aspects of brain disorders.

Keywords: GAN, pathology, generative adversarial network, neuro imaging

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Authors: Rutuja Rajendra Ghadage

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Villages surrounding the city undergo the process of peri urbanization, which transforms their original village character. These villages undergo fast and unplanned physical growth and development. Due to the expansion of urban activities, peri-urban villages are experiencing extensive changes. Focusing on the peri-urban villages of Baramati city in Maharashtra, India, this paper assesses the nature and extent of spatial development and identifies the factors contributing to the rapid development of eleven sample Peri-urban villages. After reviewing similar studies, four indicators are selected to assess the spatial development of peri-urban villages; 1) population, 2) road network, 3) land use landcover change, and 4) built-up distribution. The spatial development of peri-urban villages of Baramati is uneven as few villages are still expanding or growing while few villages have started intensifying. The main factor for this development is the presence of industries and educational institutions. They have affected spatial development directly as well as indirectly. In the future, most of the peri-urban villages of Baramati will be in the intensification phase, so if this happens in an unplanned manner, it will create stress on services and facilities.

Keywords: factors and indicators of spatial development, peri urban villages, peri urbanization, spatial development

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Authors: Afrouz Yousefi, Mohtada Sadrzadeh

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The widespread presence of poly- and perfluoroalkyl substances (PFAS) poses a significant concern due to their ability to accumulate in living organisms and their persistence in the environment, thanks to their robust carbon-fluorine (C-F) bonds, which require substantial energy to break (485 kJ/mol). The prevalence of toxic PFAS compounds can be highly detrimental to ecosystems, wildlife, and human health. Ongoing efforts are dedicated to investigating methods for fully breaking down and eliminating PFAS from the environment. Among the various techniques employed, advanced oxidation processes have shown promise in completely breaking down emerging contaminants in wastewater. However, the drawback lies in the relatively slow reaction rates of these processes and the substantial energy input required, which currently impedes their widespread commercial adoption. We developed a hybrid process, comprising electro-Fenton as an advanced oxidation process and membrane distillation, to simultaneously degrade organic PFAS pollutants and extract pure water from the mixture. In this study, environmentally persistent perfluorooctanoic acid (PFOA), as an emerging contaminant, was used to study the effectiveness of the electro-Fenton/membrane distillation hybrid system. The PFOA degradation studies were conducted in two modes: electro-Fenton and electro-Fenton coupled with membrane distillation. High-performance liquid chromatography with ultraviolet detection (HPLC-UV), ion-chromatography (measuring fluoride ion concentration), total organic carbon (TOC) decay, mineralization current efficiency (MCE), and specific energy consumption (SEC) were evaluated for a single EF and hybrid EF-MD processes. In contrast to a single EF reaction, TOC decay improved significantly in the EF-MD process. Overall, the MCE of hybrid processes surpassed 100% while it remained under 50% for a single EF reaction. Calculations of specific energy consumption (SEC) demonstrated a substantial decrease of nearly one-third in energy usage when integrating the EF reaction with the MD process.

Keywords: water treatment, PFAS, membrane distillation, electro-Fenton, advanced oxidation

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Authors: Sultan Ali Suleiman AlMazrouei

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Purpose: The purpose of this paper is to examine the effect of communication through social networking sites (Facebook, Twitter) on employee engagement. Methodology: A quantitative survey was used to collect data from 440 employees from the Ministry of Education in Oman. SPSS software was used to analyze the data. Findings: The results revealed a positive significant relationship between communication via Facebook and employee engagement. However, communication via Twitter does not influence employee engagement significantly. Practical implications: Managers can benefit from the study by understanding the importance of communication via Facebook with employees in order to increase their engagement. They should post their views and thoughts on Facebook and encourage their employees to be members which would be reflected on their psychological side positively. That gives them a feeling of belonging to a network. Originality/value: The study enriches the human resources management literature by examining a theoretical framework about the influence of social networking sites usage on employee engagement. This is one of the few studies that focus on the relationship of social networking sites usage with employees' engagement. It is the first study in an Omani context.

Keywords: employee engagement, social networking sites, Facebook, Twitter

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Authors: S. M. Gupta, Vanita Aggarwal, Som Nath Sachdeva

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The work reported through this paper is an experimental work conducted on High Performance Concrete (HPC) with super plasticizer with the aim to develop some models suitable for prediction of compressive strength of HPC mixes. In this study, the effect of varying proportions of fly ash (0% to 50% at 10% increment) on compressive strength of high performance concrete has been evaluated. The mix designs studied were M30, M40 and M50 to compare the effect of fly ash addition on the properties of these concrete mixes. In all eighteen concrete mixes have been designed, three as conventional concretes for three grades under discussion and fifteen as HPC with fly ash with varying percentages of fly ash. The concrete mix designing has been done in accordance with Indian standard recommended guidelines i.e. IS: 10262. All the concrete mixes have been studied in terms of compressive strength at 7 days, 28 days, 90 days and 365 days. All the materials used have been kept same throughout the study to get a perfect comparison of values of results. The models for compressive strength prediction have been developed using Linear Regression method (LR), Artificial Neural Network (ANN) and Leave One Out Validation (LOOV) methods.

Keywords: high performance concrete, fly ash, concrete mixes, compressive strength, strength prediction models, linear regression, ANN

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Authors: Nattawipa Pakasri

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58S (60SiO2-36CaO-4P2O5), three-dimensionally ordered macroporous bioactive glasses (3DOM-BGs) were synthesized by the sol-gel method using dual templating methods. non-ionic surfactant Brij56 used as templates component produced mesoporous and the spherical PMMA colloidal crystals as one template component yielded either three-dimensionally ordered microporous products or shaped bioactive glass nanoparticles. The bioactive glass with aging step for 12 h at room temperature, no structure transformation occurred and the 3DOM structure was produced (Figure a) due to no shrinkage process between the aging step. After 48 h time of o 3DOM structure remained and, nanocube with ∼120 nm edge lengths and nanosphere particle with ∼50 nm was obtained (Figure c, d). PMMA packing templates have octahedral and tetrahedral holes to make 2 final shapes of 3DOM-BGs which is rounded and cubic, respectively. The ageing time change from 12h, 24h and 48h affected to the thickness of interconnecting macropores network. The wall thickness was gradually decrease after increase aging time.

Keywords: three-dimensionally ordered macroporous bioactive glasses, sol-gel method, PMMA, bioactive glass

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Authors: Smithinun Thairoongrojana

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This research is a qualitative research with three main purposes: (1) to develop the local tax map software system to be linked to the main Local Tax Map System (LTAX3000) system; (2) to design and develop a program for tax data fieldwork on wireless devices and link it to LTAX3000 database of Surat Thani Municipality; (3) to develop the human resource responsible for the fieldwork to be able to use the program and maintain the system and also to be able to work with the dynamic of technologies. In-depth interviews with the two groups of samples, the board of Surat Thani Municipality and operation staff responsible for observing and taxing fieldworks were conducted. The result of this study demonstrates the new developed fieldworks system that can be used both stand-alone usage and networking usage. The fieldworks system to collect and store the variety of taxing information within Surat Thani Municipality will be explained. Then the fieldwork operation process development and the replacement of transferring and storing the information via the network communication.

Keywords: Local tax map, software system development, wireless devices, human resource

Procedia PDF Downloads 189

Authors: Defne Uz

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Horizontal Tail of helicopter is exposed to repeated oscillatory loading generated by aerodynamic and inertial loads, and bending moments depending on operating conditions and maneuvers of the helicopter. In order to ensure that maximum stress levels do not exceed certain fatigue limit of the material and to prevent damage, a numerical analysis approach can be utilized through the Finite Element Method. Therefore, in this paper, fatigue analysis of the Horizontal Tail model is studied numerically to predict high-cycle and low-cycle fatigue life related to defined loading. The analysis estimates the stress field at stress concentration regions such as around fastener holes where the maximum principal stresses are considered for each load case. Critical element identification of the main load carrying structural components of the model with rivet holes is performed as a post-process since critical regions with high-stress values are used as an input for fatigue life calculation. Once the maximum stress is obtained at the critical element and the related mean and alternating components, it is compared with the endurance limit by applying Soderberg approach. The constant life straight line provides the limit for several combinations of mean and alternating stresses. The life calculation based on S-N (Stress-Number of Cycles) curve is also applied with fully reversed loading to determine the number of cycles corresponds to the oscillatory stress with zero means. The results determine the appropriateness of the design of the model for its fatigue strength and the number of cycles that the model can withstand for the calculated stress. The effect of correctly determining the critical rivet holes is investigated by analyzing stresses at different structural parts in the model. In the case of low life prediction, alternative design solutions are developed, and flight hours can be estimated for the fatigue safe operation of the model.

Keywords: fatigue analysis, finite element method, helicopter horizontal tail, life prediction, stress concentration

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Authors: Şule Turhan, Özlem Turan

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Permanence in the international markets for many global companies is about being known as having effective logistics which targets customer satisfaction management and lower costs. Under competitive conditions, the necessity of providing the products to customers quickly and on time for the companies which constantly aim to improve their profitability increased the strategic importance of the logistics concept. Food logistic is one of the most difficult areas in logistics. In the process from manufacturer to final consumer, quality and hygiene standards must be provided constantly. In food logistics, reliable and extensive service network has great importance and on time delivery is the target. Developing logistics industry provide the supply of foods in the country and the development of export markets more quickly and has an important role in providing added value to the country's economy. Turkey that creates a bridge between the east and the west is an attractive market for logistics companies. In this study, by examining both the place and the importance of logistics in Turkish food sector, recommendations will be made for the food industry.

Keywords: logistics, Turkish food industry, competition, food industry

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Authors: Mouhoubi Nedjima, Sassi Boudemagh Souad

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In the framework of the modernization of the city of Constantine, and in order to restore its status as a regional metropolis and introduce it into the network of cities international metropolises, a major urban project was launched: project of modernization and of metropolitanization of the city of Constantine (PMMC). Our research project focuses on the management of the project for the modernization of the city of Constantine (PMMC) focusing on the management of some aspects of the urban project whose participation, with the objective assessment of the managerial approach business. Among the cases revealing taken into account in our research work on the question of participation of actors and their organizations, the operation relating to "the urban improvement in the city of the Brothers FERRAD in the district of Zouaghi". This operation with the objective of improving the living conditions of citizens has faced several challenges and obstacles that have been in major part the factors of its failure. Through this study, we examine the management process and the mode of organization of the actors of the project as well as the level of participation of the citizen to finally propose managerial solutions to conflict situations observed.

Keywords: the urban project, the urban improvement, participation, Constantine

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Authors: Siriporn Poolsuwan, Kanyarat Bussaban

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This research aims to investigate and analyze user’s behaviour towards the utilization of the online news clipping database at Suan Sunandha Rajabhat University, Thailand. Data is gathered from 214 lecturers and 380 undergraduate students by using questionnaires. Findings show that most users knew the online news clipping service from their friends, library’s website and their teachers. The users learned how to use it by themselves and others learned by training of SSRU library. Most users used the online news clipping database one time per month at home and always used the service for general knowledge, up-to-date academic knowledge and assignment reference. Moreover, the results of using the online news clipping service problems include the users themselves, service management, service device- computer and tools – and the network, service provider, and publicity. This research would be benefit for librarians and teachers for planning and designing library services in their works and organization.

Keywords: online database, user behavior, news clipping, library services

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Authors: Su-Lin Yu

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Coalition politics has been one of feminists’ persistent concerns. Following recent feminist discussion on new modes of affiliation across difference, she will explore how the process of female subject formation depends on alliances across different cultural locations. First, she will examine how coalition politics is reformulated across difference in contemporary feminist literature. In particular, the paper will identify the particular contexts and locations in which coalition building both enables and constrains the female subject. She will attempt to explore how contemporary feminist literature highlights the possibilities and limitations for solidarity and affiliations. To understand coalition politics in contemporary feminist works, she will engage in close readings of two texts: Rebecca Walker’s Black, White and Jewish: Memoir of a Shifting Self and Danzy Senna’s Caucasia. Both Walker and Senna have articulated the complex nodes of identity that are staged by a politics of location as they refuse to be boxed into simplistic essentialist positions. Their texts are characterized by the characters’ racial ambiguity and their social and geographical mobility of life in the contemporary United States. Their experiences of living through conflictual and contradictory relationships never fully fit the boundaries of racial categorization. Each of these texts demonstrates the limits as well as the possibilities of working with diversity among and within persons and groups, thus, laying the ground for complex alliance formation. Because each of the protagonists must negotiate a set of contradictions, they will have to constantly shift their affiliations. Rather than construct a static alliance, they describe a process of moving ‘beyond boundaries,’ an embracing of multiple locations. As self-identified third wavers, Rebecca Walker and Danzy Senna have been identified and marked with the status of ‘leader’ by the feminist establishment and by mainstream U.S. media. Their texts have captured both mass popularity and critical attention in the feminist and, often, the non-feminist literary community. By analyzing these texts, she will show how contemporary American feminist literature reveals coalition politics which is fraught with complications and unintended consequences. Taken as a whole, then, these works provide an important examination not only of coalition politics of American feminism, but also a snapshot of a central debate among feminist critique of coalition politics as a whole.

Keywords: coalition politics, contemporary women’s literature, identity, female subject

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Authors: Chutikarn Sriwiboon, Suwaree Yordchim

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Benjarong handicraft product is one of the most important handicraft products from Thailand. It involves the management of traditional wisdom of arts and Thai culture. This paper drew upon data collection from local communities by using an in-depth interview technique which was conducted in Thailand during summer of 2014. The survey was structured primarily to obtain local wisdom and concerns toward their career development. This research paper was a qualitative research conducted by focus groups with a total of 51 cooperative women and occupational groups around Thailand which produced the Benjarong products. The data were significantly collected from many sources and many communities, which totaled 24,430 handicraft products, in which the 668 different patterns of Benjarong products were produced by 51 local community network groups in Thailand. The findings revealed that after applying the Philosophy of Sufficiency Economy, there was a significantly positive change in their career development and the process of knowledge management enables local community to enhance their personal development and career.

Keywords: Benjarong, career development, community, handicraft

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Authors: Kavyadevi N., Kaviya G., Gowsalya P., Janani M., Mohanraj S.

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Hevea brasiliensis, also known as the rubber tree, is one of the foremost assets of crops in the world. One of the most significant advantages of the Rubber Plant in terms of air oxygenation is its capacity to reduce the likelihood of an individual developing respiratory allergies like asthma. To construct such a system that can properly identify crop diseases and pests and then create a database of insecticides for each pest and disease, we must first give treatment for the illness that has been detected. We shall primarily examine three major leaf diseases since they are economically deficient in this article, which is Bird's eye spot, algal spot and powdery mildew. And the recommended work focuses on disease identification on rubber tree leaves. It will be accomplished by employing one of the superior algorithms. Input, Preprocessing, Image Segmentation, Extraction Feature, and Classification will be followed by the processing technique. We will use time-consuming procedures that they use to detect the sickness. As a consequence, the main ailments, underlying causes, and signs and symptoms of diseases that harm the rubber tree are covered in this study.

Keywords: image processing, python, convolution neural network (CNN), machine learning

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Authors: Mohsen Maadani, Seyed Ahmad Motamedi

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The use of wireless technology in industrial networks has gained vast attraction in recent years. In this paper, we have thoroughly analyzed the effect of contention window (CW) size on the performance of IEEE 802.11-based industrial wireless networks (IWN), from delay and reliability perspective. Results show that the default values of CWmin, CWmax, and retry limit (RL) are far from the optimum performance due to the industrial application characteristics, including short packet and noisy environment. An adaptive CW algorithm (payload-dependent) has been proposed to minimize the average delay. Finally a simple, but effective CW and RL setting has been proposed for industrial applications which outperforms the minimum-average-delay solution from maximum delay and jitter perspective, at the cost of a little higher average delay. Simulation results show an improvement of up to 20%, 25%, and 30% in average delay, maximum delay and jitter respectively.

Keywords: average delay, contention window, distributed coordination function (DCF), jitter, industrial wireless network (IWN), maximum delay, reliability, retry limit

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Authors: Provides Ng

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Platform economy is a peer-to-peer model of distributing resources facilitated by community-based digital platforms. In recent years, digital platforms are rapidly reconfiguring the public realm using hyper-personalisation techniques. This paper aims at investigating how urban planning can leapfrog into the digital age to help relieve the rising tension of the global issue of labour flow; it discusses the means to transfer techniques of hyper-personalisation into urban planning for plasticity using platform technologies. This research first denotes the limitations of the current system of urban residency, where the system maintains itself on the circulation of documents, which are data on paper. Then, this paper tabulates how some of the institutions around the world, both public and private, digitise data, and streamline communications between a network of systems and citizens using platform technologies. Subsequently, this paper proposes ways in which hyper-personalisation can be utilised to form a digital planning platform. Finally, this paper concludes by reviewing how the proposed strategy may help to open up new ways of thinking about how we affiliate ourselves with cities.

Keywords: platform urbanism, hyper-personalisation, digital inventory, urban accessibility

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Authors: Tomas Menard

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The design of control-law for engineering systems has been investigated for many decades. While many results are concerned with continuous systems with continuous output, nowadays, many controlled systems have to transmit their output measurements through network, hence making it discrete-time. But it is well known that the sampling of a system whose control-law is based on the continuous output may render the system unstable, especially when this sampling period is long compared to the system dynamics. The control design then has to be adapted in order to cope with this issue. In this paper, we consider systems which can be modeled as double integrator with uncertainty on the input since many mechanical systems can be put under such form. We present a control scheme based on an observer using only discrete time measurement and which provides continuous time estimation of the state, combined with a continuous control law, which stabilized a system with second-order dynamics even in the presence of uncertainty. It is further shown that arbitrarily long sampling periods can be dealt with properly setting the control scheme parameters.

Keywords: dynamical system, control law design, sampled output, observer design

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Authors: Mohd Zamri Osman, Mohd Aizaini Maarof, Mohd Foad Rohani

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Human skin detection recognized as the primary step in most of the applications such as face detection, illicit image filtering, hand recognition and video surveillance. The performance of any skin detection applications greatly relies on the two components: feature extraction and classification method. Skin color is the most vital information used for skin detection purpose. However, color feature alone sometimes could not handle images with having same color distribution with skin color. A color feature of pixel-based does not eliminate the skin-like color due to the intensity of skin and skin-like color fall under the same distribution. Hence, the statistical color analysis will be exploited such mean and standard deviation as an additional feature to increase the reliability of skin detector. In this paper, we studied the effectiveness of statistical color feature for human skin detection. Furthermore, the paper analyzed the integrated color and texture using eight classifiers with three color spaces of RGB, YCbCr, and HSV. The experimental results show that the integrating statistical feature using Random Forest classifier achieved a significant performance with an F1-score 0.969.

Keywords: color space, neural network, random forest, skin detection, statistical feature

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Authors: Ronald L. Kaunda

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The education system in Zambia still bears scars of colonialism in the area of policy, curriculum and implementation. This historical context resulted in the failure by the Government of the Republic of Zambia to achieve literacy goals expected among school going children. Specifically, research shows that the use of English for initial literacy and Western based teaching methods to engage learners in literacy activities at lower levels of education including preschool has exacerbated this situation. In 2014, the Government of the Republic of Zambia implemented a new curriculum that, among others things, required preschool teachers to use local and cultural materials and familiar languages for early literacy teaching from preschool to grade 4. This paper presents findings from a study that sought to establish ways in which preschool teachers use Zambian Indigenous knowledge systems and Indigenous teaching strategies to support literacy development among preschool children. The study used Indigenous research methodology for data collection and iterative feature of Constructivist Grounded Theory (CGT) in the data collection process and analysis. This study established that, as agents of education, preschool teachers represented community adult educators because of some roles which they played beyond their academic mandate. The study further found that classrooms as venues of learning were equipped with learning corners reflecting Indigenous literacy materials and Indigenous ways of learning. Additionally, the study found that learners were more responsive to literacy lessons because of the use of familiar languages and local contextualized environments that supported their own cultural ways of learning. The study recommended that if the education system in Zambia is to be fully inclusive of Indigenous knowledge systems and cultural ways of learning, the education policy and curriculum should include conscious steps on how this should be implemented at the classroom level. The study further recommended that more diverse local literacy materials and teaching aids should be produced for use in the classroom.

Keywords: agents of learning, early literacy, indigenous knowledge systems, venues of education

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Authors: Ming-Jen Huang, Chun-Fang Huang, Chiching Wei

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With the recent advance of the deep neural network, we observe new applications of NLP (natural language processing) and CV (computer vision) powered by deep neural networks for processing business documents. However, creating a real-world document processing system needs to integrate several NLP and CV tasks, rather than treating them separately. There is a need to have a unified approach for processing documents containing textual and graphical elements with rich formats, diverse layout arrangement, and distinct semantics. In this paper, a framework that fulfills this unified approach is presented. The framework includes a representation model definition for holding the information generated by various tasks and specifications defining the coordination between these tasks. The framework is a blueprint for building a system that can process documents with rich formats, styles, and multiple types of elements. The flexible and lightweight design of the framework can help build a system for diverse business scenarios, such as contract monitoring and reviewing.

Keywords: document processing, framework, formal definition, machine learning

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Authors: Jolanta Uszko, Tomasz Wloch, Aneta Pirowska, Roman Nowobilski

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Introduction: Phantom phenomena are often reported by subjects who have undergone limb amputation. Mostly, patients feel the amputated part of the limb as if it was still attached to the body. Two types of phantom phenomena: painless (phantom sensation) and painful (phantom pain) were described. Triggers of phantom sensations and phantom pain, as well as fully effective treatment, have not been clearly described yet. Purpose: To assess the influence of psychosocial factors and some clinical conditions on the occurrence of phantom phenomena in amputee athletes. Subjects: 21 men (age: 31 years, SD = 7.5 years) after lower or upper extremity amputation, who regularly performed high-intensity sports (Amp Football Team Players) were included to the study. Method and equipment: In the research, the following method and tools were used: Questionnaire [Pirowska] adapted for athletes with disabilities, Numerical Rating Scale (NRS) - for phantom pain assessment, McGill Pain Assessment Questionnaire (short version), Beck's Depression Inventory (BDI), State Trait Anxiety Inventory (STAI): X-1 and X-2, shortened version of The World Health Organization Quality of Life (WHOQOLBREFF). Results: In the study group, the lower leg amputations with traumatic etiology were predominant. Phantom sensations were present in all subjects. Half of the respondents claimed to experience phantom sensations at least once a day, paroxysmally. There was a prevalence of phantom sensations characterized as incomplete, immobile limb. Phantom pain was reported by over 85% of respondents. The nature of phantom pain was frequently described as stabbing, squeezing, shooting, pulsing, tiring. There was a significant correlation between phantom pain intensity and anxiety, quality of life, depressive tendencies, perception of phantom pain as the obstacle in daily functioning and intensity of the limb pain before amputation. Conclusions: The etiology of phantom phenomena is complex. Psychological factors seem to have a significant influence on the intensity of the phantom pain. Particular attention should be paid to patients who complain about persistent limb pain before the amputation. These are patients with an increased risk of the phantom pain of relatively high intensity.

Keywords: amputation, phantom pain, phantom sensations, adaptive sports

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Authors: Z. Gharaee

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Recognizing actions performed by others is important in our daily lives since it is necessary for communicating with others in a proper way. We perceive an action by observing the kinematics of motions involved in the performance. We use our experience and concepts to make a correct recognition of the actions. Although building the action concepts is a life-long process, which is repeated throughout life, we are very efficient in applying our learned concepts in analyzing motions and recognizing actions. Experiments on the subjects observing the actions performed by an actor show that an action is recognized after only about two hundred milliseconds of observation. In this study, hierarchical action recognition architecture is proposed by using growing grid layers. The first-layer growing grid receives the pre-processed data of consecutive 3D postures of joint positions and applies some heuristics during the growth phase to allocate areas of the map by inserting new neurons. As a result of training the first-layer growing grid, action pattern vectors are generated by connecting the elicited activations of the learned map. The ordered vector representation layer receives action pattern vectors to create time-invariant vectors of key elicited activations. Time-invariant vectors are sent to second-layer growing grid for categorization. This grid creates the clusters representing the actions. Finally, one-layer neural network developed by a delta rule labels the action categories in the last layer. System performance has been evaluated in an experiment with the publicly available MSR-Action3D dataset. There are actions performed by using different parts of human body: Hand Clap, Two Hands Wave, Side Boxing, Bend, Forward Kick, Side Kick, Jogging, Tennis Serve, Golf Swing, Pick Up and Throw. The growing grid architecture was trained by applying several random selections of generalization test data fed to the system during on average 100 epochs for each training of the first-layer growing grid and around 75 epochs for each training of the second-layer growing grid. The average generalization test accuracy is 92.6%. A comparison analysis between the performance of growing grid architecture and self-organizing map (SOM) architecture in terms of accuracy and learning speed show that the growing grid architecture is superior to the SOM architecture in action recognition task. The SOM architecture completes learning the same dataset of actions in around 150 epochs for each training of the first-layer SOM while it takes 1200 epochs for each training of the second-layer SOM and it achieves the average recognition accuracy of 90% for generalization test data. In summary, using the growing grid network preserves the fundamental features of SOMs, such as topographic organization of neurons, lateral interactions, the abilities of unsupervised learning and representing high dimensional input space in the lower dimensional maps. The architecture also benefits from an automatic size setting mechanism resulting in higher flexibility and robustness. Moreover, by utilizing growing grids the system automatically obtains a prior knowledge of input space during the growth phase and applies this information to expand the map by inserting new neurons wherever there is high representational demand.

Keywords: action recognition, growing grid, hierarchical architecture, neural networks, system performance

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Authors: Armin Nabaei, M. Omair Ahmad, M. N. S. Swamy

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Many facial expression and emotion recognition methods in the traditional approaches of using LDA, PCA, and EBGM have been proposed. In recent years deep learning models have provided a unique platform addressing by automatically extracting the features for the detection of facial expression and emotions. However, deep networks require large training datasets to extract automatic features effectively. In this work, we propose an efficient emotion detection algorithm using face images when only small datasets are available for training. We design a deep network whose feature extraction capability is enhanced by utilizing several parallel modules between the input and output of the network, each focusing on the extraction of different types of coarse features with fined grained details to break the symmetry of produced information. In fact, we leverage long range dependencies, which is one of the main drawback of CNNs. We develop this work by introducing a Dynamic Soft-Margin SoftMax.The conventional SoftMax suffers from reaching to gold labels very soon, which take the model to over-fitting. Because it’s not able to determine adequately discriminant feature vectors for some variant class labels. We reduced the risk of over-fitting by using a dynamic shape of input tensor instead of static in SoftMax layer with specifying a desired Soft- Margin. In fact, it acts as a controller to how hard the model should work to push dissimilar embedding vectors apart. For the proposed Categorical Loss, by the objective of compacting the same class labels and separating different class labels in the normalized log domain.We select penalty for those predictions with high divergence from ground-truth labels.So, we shorten correct feature vectors and enlarge false prediction tensors, it means we assign more weights for those classes with conjunction to each other (namely, “hard labels to learn”). By doing this work, we constrain the model to generate more discriminate feature vectors for variant class labels. Finally, for the proposed optimizer, our focus is on solving weak convergence of Adam optimizer for a non-convex problem. Our noteworthy optimizer is working by an alternative updating gradient procedure with an exponential weighted moving average function for faster convergence and exploiting a weight decay method to help drastically reducing the learning rate near optima to reach the dominant local minimum. We demonstrate the superiority of our proposed work by surpassing the first rank of three widely used Facial Expression Recognition datasets with 93.30% on FER-2013, and 16% improvement compare to the first rank after 10 years, reaching to 90.73% on RAF-DB, and 100% k-fold average accuracy for CK+ dataset, and shown to provide a top performance to that provided by other networks, which require much larger training datasets.

Keywords: computer vision, facial expression recognition, machine learning, algorithms, depp learning, neural networks

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Authors: Hema Vanita Kesevan

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The global spread of Covid-19 virus in early 2020 has led to major changes in many walks of life, including the education system. Traditional face to face lessons that were carried out for years has been replaced by online learning. Although online learning has been used before the pandemic, it has not been the only source of teaching and learning. This drastic change has brought significant impact to the process of teaching and learning in many classrooms around the world. Likewise, in country like Malaysia that that has been promoting online learning but has not utilize it fully due to many restrictions in terms of technology, accessibility, and online literacy, the sudden change to full online platform learning in all educational sector has definitely caused Issues in terms of its adaptation and usage. Although many studies have been conducted to explore the efficiency and impact of online learning during the pandemic, studies focusing on the same are limited in Malaysian classroom context, especially in English language classrooms. Thus, this study seeks to explore on the efficacy and effectiveness of online learning tools in ESL classroom contexts during the pandemic. The aim of this study is to understand the educator's and student's perceptions on the implementation of online learning tools in the teaching and learning process and the types of online learning tools that were used to assist the teaching and learning process during the pandemic. Particularly, this study focused to explore the types of online learning tools used in Malaysian schools and university during the online teaching and learning process and further explores how the various types of tools used impacted the students' participation in the lessons conducted. The participants of this study are secondary school students, teachers, and university students. Data will be collected in terms of survey questionnaire and interviews. The survey data intends to obtain information on the types of online learning used in ESL teaching and learning practices during the pandemic, how the various types of online tools influence students' participation during lessons. The interview data from the teachers serves to provide information about the selection of online learning tools, challenges of using it to conduct online lessons, and other arising issues. A mixed method design will be used to analysed the data obtained. The questionnaire will be analysed quantitatively using descriptive analysis meanwhile, the interview data will be analysed qualitatively.

Keywords: Covid 19, online learning tools, ESL classroom, effectiveness, efficacy

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Authors: Yan Zhang

Abstract:

Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Analytics-driven predictive maintenance is gaining increasing attention in many industries such as manufacturing, utilities, aerospace, etc., along with the emerging demand of Internet of Things (IoT) applications and the maturity of technologies that support Big Data storage and processing. This study aims to build an end-to-end analytics solution that includes both real-time machine condition monitoring and machine learning based predictive analytics capabilities. The goal is to showcase a general predictive maintenance solution architecture, which suggests how the data generated from field machines can be collected, transmitted, stored, and analyzed. We use a publicly available aircraft engine run-to-failure dataset to illustrate the streaming analytics component and the batch failure prediction component. We outline the contributions of this study from four aspects. First, we compare the predictive maintenance problems from the view of the traditional reliability centered maintenance field, and from the view of the IoT applications. When evolving to the IoT era, predictive maintenance has shifted its focus from ensuring reliable machine operations to improve production/maintenance efficiency via any maintenance related tasks. It covers a variety of topics, including but not limited to: failure prediction, fault forecasting, failure detection and diagnosis, and recommendation of maintenance actions after failure. Second, we review the state-of-art technologies that enable a machine/device to transmit data all the way through the Cloud for storage and advanced analytics. These technologies vary drastically mainly based on the power source and functionality of the devices. For example, a consumer machine such as an elevator uses completely different data transmission protocols comparing to the sensor units in an environmental sensor network. The former may transfer data into the Cloud via WiFi directly. The latter usually uses radio communication inherent the network, and the data is stored in a staging data node before it can be transmitted into the Cloud when necessary. Third, we illustrate show to formulate a machine learning problem to predict machine fault/failures. By showing a step-by-step process of data labeling, feature engineering, model construction and evaluation, we share following experiences: (1) what are the specific data quality issues that have crucial impact on predictive maintenance use cases; (2) how to train and evaluate a model when training data contains inter-dependent records. Four, we review the tools available to build such a data pipeline that digests the data and produce insights. We show the tools we use including data injection, streaming data processing, machine learning model training, and the tool that coordinates/schedules different jobs. In addition, we show the visualization tool that creates rich data visualizations for both real-time insights and prediction results. To conclude, there are two key takeaways from this study. (1) It summarizes the landscape and challenges of predictive maintenance applications. (2) It takes an example in aerospace with publicly available data to illustrate each component in the proposed data pipeline and showcases how the solution can be deployed as a live demo.

Keywords: Internet of Things, machine learning, predictive maintenance, streaming data

Procedia PDF Downloads 377