Search results for: input processing

Authors: Sinead Morrison, Ann Swillen, Therese Van Amelsvoort, Samuel Chawner, Elfi Vergaelen, Michael Owen, Marianne Van Den Bree

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22q11.2 Deletion Syndrome (22q11.2DS) is caused by the deletion of approximately 60 genes on chromosome 22 and is associated with high rates of neurodevelopmental disorders such as Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorders (ASD). The presentation of these disorders in 22q11.2DS is reported to be comparable to idiopathic forms and therefore presents a valuable model for understanding mechanisms of neurodevelopmental disorders. Cognitive deficits are thought to be a core feature of neurodevelopmental disorders, and possibly manifest in behavioural and emotional problems. There have been mixed findings in 22q11.2DS on whether the presence of ADHD or ASD is associated with greater cognitive deficits. Furthermore, the influence of developmental stage has never been taken into account. The aim was therefore to examine whether the presence of ADHD or ASD was associated with cognitive deficits in childhood and/or adolescence in 22q11.2DS. We conducted the largest study to date of this kind in 22q11.2DS. The same battery of tasks measuring processing speed, attention and spatial working memory were completed by 135 participants with 22q11.2DS. Wechsler IQ tests were completed, yielding Full Scale (FSIQ), Verbal (VIQ) and Performance IQ (PIQ). Age-standardised difference scores were produced for each participant. Developmental stages were defined as children (6-10 years) and adolescents (10-18 years). ADHD diagnosis was ascertained from a semi-structured interview with a parent. ASD status was ascertained from a questionnaire completed by a parent. Interaction and main effects of cognitive performance of those with or without a diagnosis of ADHD or ASD in childhood or adolescence were conducted with 2x2 ANOVA. Significant interactions were followed up with t-tests of simple effects. Adolescents with ASD displayed greater deficits in all measures (processing speed, p = 0.022; sustained attention, p = 0.016; working memory, p = 0.006) than adolescents without ASD; there was no difference between children with and without ASD. There were no significant differences on IQ measures. Both children and adolescents with ADHD displayed greater deficits on sustained attention (p = 0.002) than those without ADHD. There were no significant differences on any other measures for ADHD. Magnitude of cognitive deficit in individuals with 22q11.2DS varied by cognitive domain, developmental stage and presence of neurodevelopmental disorder. Adolescents with 22q11.2DS and ASD showed greater deficits on all measures, which suggests there may be a sensitive period in childhood to acquire these domains, or reflect increasing social and academic demands in adolescence. The finding of poorer sustained attention in children and adolescents with ADHD supports previous research and suggests a specific deficit which can be separated from processing speed and working memory. This research provides unique insights into the association of ASD and ADHD with cognitive deficits in a group at high genomic risk of neurodevelopmental disorders.

Keywords: 22q11.2 deletion syndrome, attention deficit hyperactivity disorder, autism spectrum disorder, cognitive development

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Authors: Bara M Yousef, Naresh B Raj, Nadiah W Arfah, Brightlin N Dhas

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Background: Executive function (EF) impairment is common in children with autism spectrum disorder (ASD). EF strategies are considered effective in improving the therapeutic outcomes of children with ASD. Aims: This study primarily aims to explore whether integrating EF strategies combined with regular occupational therapy intervention is more effective in improving daily life skills (DLS) and sensory integration/processing (SI/SP) skills than regular occupational therapy alone in children with ASD and secondarily aims to assess treatment outcomes on improving visual motor integration (VMI) skills. Procedures: A total of 92 children with ASD will be recruited and, following baseline assessments, randomly assigned to the treatment group (45-min once weekly individual occupational therapy plus EF strategies) and control group (45-min once weekly individual therapy sessions alone). Results and Outcomes: All children will be evaluated systematically by assessing SI/SP, DLS, and VMI, skills at baseline, 7 weeks, and 14 weeks of treatment. Data will be analyzed using ANCOVA and T-test. Conclusions and Implications: This single-blind, randomized controlled trial will provide empirical evidence for the effectiveness of EF strategies when combined with regular occupational therapy programs. Based on trial results, EF strategies could be recommended in multidisciplinary programs for children with ASD. Trial Registration: The trial has been registered in the clinicaltrail.gov for a registry, protocol ID: MRC-01-22-509 ClinicalTrials.gov Identifier: NCT05829577, registered 25th April 2023

Keywords: autism spectrum disorder, executive function strategies, daily life skills, sensory integration/processing, visual motor integration, occupational therapy, effectiveness

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Authors: V. Suganya, V. Balasuriya

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The functioning of Micro and Small Scale (MSS) businesses in the northern part of Sri Lanka was vulnerable due to three decades of internal conflict and the subsequent post-war economic openings has resulted new market prospects for MSS businesses. MSS businesses survive and operate with limited resources and struggle to access finance, raw material, markets, and technology. This study attempts to identify the manner in which entrepreneurial orientation puts into practice by the business operators to overcome these business challenges. Business operators in the traditional food processing sector are taken for this study as this sub-sector of the food industry is developing at a rapid pace. A review of the literature was done to recognize the concepts of entrepreneurial orientation, defining MMS businesses and the manner in which business performance is measured. Direct interview method supported by a structured questionnaire is used to collect data from 80 respondents; based on a fixed interval random sampling technique. This study reveals that more than half of the business operators have opted to commence their business ventures as a result of identifying a market opportunity. 41 per cent of the business operators are highly entrepreneurial oriented in a scale of 1 to 5. Entrepreneurial orientation shows significant relationship and strongly correlated with business performance. Pro-activeness, innovativeness and competitive aggressiveness shows a significant relationship with business performance while risk taking is negative and autonomy is not significantly related to business performance. It is evident that entrepreneurial oriented business practices contribute to better business performance even though 70 per cent prefer the ideas/views of the support agencies than the stakeholders when making business decisions. It is recommended that appropriate training should be introduced to develop entrepreneurial skills focusing to improve business networks so that new business opportunities and innovative business practices are identified.

Keywords: Micro and Small Scale (MMS) businesses, entrepreneurial orientation (EO), food processing, business operators

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Authors: Irtaza M. Syed, Kaamran Raahemifar

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This paper presents a model predictive control (MPC) of a utility interactive (UI) single phase inverter (SPI) for a photovoltaic (PV) system at residential/distribution level. The proposed model uses single-phase phase locked loop (PLL) to synchronize SPI with the grid and performs MPC control in a dq reference frame. SPI model consists of boost converter (BC), maximum power point tracking (MPPT) control, and a full bridge (FB) voltage source inverter (VSI). No PI regulators to tune and carrier and modulating waves are required to produce switching sequence. Instead, the operational model of VSI is used to synthesize sinusoidal current and track the reference. Model is validated using a three kW PV system at the input of UI-SPI in Matlab/Simulink. Implementation and results demonstrate simplicity and accuracy, as well as reliability of the model.

Keywords: phase locked loop, voltage source inverter, single phase inverter, model predictive control, Matlab/Simulink

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Authors: Teodora Vukovic, Christoph Hottiger, Noah Bubenhofer

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The development of VIAN-DH aims at bridging two linguistic approaches: conversation analysis/interactional linguistics (IL), so far a dominantly qualitative field, and computational/corpus linguistics and its quantitative and automated methods. Contemporary IL investigates the systematic organization of conversations and interactions composed of speech, gaze, gestures, and body positioning, among others. These highly integrated multimodal behaviour is analysed based on video data aimed at uncovering so called “multimodal gestalts”, patterns of linguistic and embodied conduct that reoccur in specific sequential positions employed for specific purposes. Multimodal analyses (and other disciplines using videos) are so far dependent on time and resource intensive processes of manual transcription of each component from video materials. Automating these tasks requires advanced programming skills, which is often not in the scope of IL. Moreover, the use of different tools makes the integration and analysis of different formats challenging. Consequently, IL research often deals with relatively small samples of annotated data which are suitable for qualitative analysis but not enough for making generalized empirical claims derived quantitatively. VIAN-DH aims to create a workspace where many annotation layers required for the multimodal analysis of videos can be created, processed, and correlated in one platform. VIAN-DH will provide a graphical interface that operates state-of-the-art tools for automating parts of the data processing. The integration of tools that already exist in computational linguistics and computer vision, facilitates data processing for researchers lacking programming skills, speeds up the overall research process, and enables the processing of large amounts of data. The main features to be introduced are automatic speech recognition for the transcription of language, automatic image recognition for extraction of gestures and other visual cues, as well as grammatical annotation for adding morphological and syntactic information to the verbal content. In the ongoing instance of VIAN-DH, we focus on gesture extraction (pointing gestures, in particular), making use of existing models created for sign language and adapting them for this specific purpose. In order to view and search the data, VIAN-DH will provide a unified format and enable the import of the main existing formats of annotated video data and the export to other formats used in the field, while integrating different data source formats in a way that they can be combined in research. VIAN-DH will adapt querying methods from corpus linguistics to enable parallel search of many annotation levels, combining token-level and chronological search for various types of data. VIAN-DH strives to bring crucial and potentially revolutionary innovation to the field of IL, (that can also extend to other fields using video materials). It will allow the processing of large amounts of data automatically and, the implementation of quantitative analyses, combining it with the qualitative approach. It will facilitate the investigation of correlations between linguistic patterns (lexical or grammatical) with conversational aspects (turn-taking or gestures). Users will be able to automatically transcribe and annotate visual, spoken and grammatical information from videos, and to correlate those different levels and perform queries and analyses.

Keywords: multimodal analysis, corpus linguistics, computational linguistics, image recognition, speech recognition

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Authors: Kadam Bhambri, Neena Gupta

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All optical wavelength conversion is essential in present day optical networks for transparent interoperability, contention resolution, and wavelength routing. The incorporation of all optical wavelength convertors leads to better utilization of the network resources and hence improves the efficiency of optical networks. Wavelength convertors that can work with Dispersion Managed (DM) solitons are attractive due to their superior transmission capabilities. In this paper, wavelength conversion for dispersion managed soliton signals was demonstrated at 100 Gbps through semiconductor optical amplifier and an optical filter. The wavelength conversion was achieved for a 1550 nm input signal to1555nm output signal. The output signal was measured in terms of BER, Q factor and system margin.    

Keywords: all optical wavelength conversion, dispersion managed solitons, semiconductor optical amplifier, cross gain modultation

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Authors: C. Sánchez de Rojas Candela, A. Riquelme, P. Rodrigo, M. D. Escalera-Rodríguez, B. Torres, J. Rams

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Selective laser melting is one of the most commonly used AM techniques. In it, a thin layer of metallic powder is deposited, and a laser is used to melt selected zones. The accumulation of layers, each one molten in the preselected zones, gives rise to the formation of a 3D sample with a nearly arbitrary design. To ensure that the properties of the final parts match those of the powder, all the process is carried out in an inert atmosphere, preferentially Ar, although this gas could be substituted. Ti6Al4V alloy is widely used in multiple industrial applications such as aerospace, maritime transport and biomedical, due to its properties. However, due to the demanding requirements of these applications, greater hardness and wear resistance are necessary, together with a better machining capacity, which currently limits its commercialization. To improve these properties, in this study, Selective Laser Melting (SLM) is used to manufacture Ti/TiN metal matrix composites with in-situ and ex-situ titanium nitride reinforcement where the scanning speed is modified (from 28.5 up to 65 mm/s) to study the influence of the processing parameters in SLM. A one-step method of nitriding the Ti6Al4V alloy is carried out to create in-situ TiN reinforcement in a reactive atmosphere and it is compared with ex-situ composites manufactured by previous mixture of both the titanium alloy powder and the ceramic reinforcement particles. The microstructure and mechanical properties of the different Ti/TiN composite materials have been analyzed. As a result, the existence of a similar matrix has been confirmed in in-situ and ex-situ fabrications and the growth mechanisms of the nitrides have been studied. An increase in the mechanical properties with respect to the initial alloy has been observed in both cases and related to changes in their microstructure. Specifically, a greater improvement (around 30.65%) has been identified in those manufactured by the in-situ method at low speeds although other properties such as porosity must be improved for their future industrial applicability.

Keywords: in-situ reinforcement, nitriding reaction, selective laser melting, titanium nitride

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Authors: Mahdi Shaghaghi, Amir Ghaebi, Fariba Ahmadi

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Purpose: The main purpose of the present research is to analyze the knowledge creation environment at a Iranian University (Alzahra University) as a typical University in Iran, using a combination of the i-System and Ba models. This study is necessary for understanding the determinants of knowledge creation at Alzahra University as a typical University in Iran. Methodology: To carry out the present research, which is an applied study in terms of purpose, a descriptive survey method was used. In this study, a combination of the i-System and Ba models has been used to analyze the knowledge creation environment at Alzahra University. i-System consists of 5 constructs including intervention (input), intelligence (process), involvement (process), imagination (process), and integration (output). The Ba environment has three pillars, namely the infrastructure, the agent, and the information. The integration of these two models resulted in 11 constructs which were as follows: intervention (input), infrastructure-intelligence, agent-intelligence, information-intelligence (process); infrastructure-involvement, agent-involvement, information-involvement (process); infrastructure-imagination, agent-imagination, information-imagination (process); and integration (output). These 11 constructs were incorporated into a 52-statement questionnaire and the validity and reliability of the questionnaire were examined and confirmed. The statistical population included the faculty members of Alzahra University (344 people). A total of 181 participants were selected through the stratified random sampling technique. The descriptive statistics, binomial test, regression analysis, and structural equation modeling (SEM) methods were also utilized to analyze the data. Findings: The research findings indicated that among the 11 research constructs, the levels of intervention, information-intelligence, infrastructure-involvement, and agent-imagination constructs were average and not acceptable. The levels of infrastructure-intelligence and information-imagination constructs ranged from average to low. The levels of agent-intelligence and information-involvement constructs were also completely average. The level of infrastructure-imagination construct was average to high and thus was considered acceptable. The levels of agent-involvement and integration constructs were above average and were in a highly acceptable condition. Furthermore, the regression analysis results indicated that only two constructs, viz. the information-imagination and agent-involvement constructs, positively and significantly correlate with the integration construct. The results of the structural equation modeling also revealed that the intervention, intelligence, and involvement constructs are related to the integration construct with the complete mediation of imagination. Discussion and conclusion: The present research suggests that knowledge creation at Alzahra University relatively complies with the combination of the i-System and Ba models. Unlike this model, the intervention, intelligence, and involvement constructs are not directly related to the integration construct and this seems to have three implications: 1) the information sources are not frequently used to assess and identify the research biases; 2) problem finding is probably of less concern at the end of studies and at the time of assessment and validation; 3) the involvement of others has a smaller role in the summarization, assessment, and validation of the research.

Keywords: i-System, Ba model , knowledge creation , knowledge management, knowledge creation environment, Iranian Universities

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Authors: Pulkeria Pascoe, Hawa P. Tundui, Marcia Dutra de Barcellos, Hans de Steur, Xavier Gellynck

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A majority of Micro and Small Enterprises (MSEs) experience low or no growth. Understanding their performance remains unfinished and disjointed as there is no consensus on the factors influencing it, especially in developing countries. Using a Resource-Based View (RBV) as the theoretical background, this cross-sectional study employed four regression models to examine the influence of firm-level factors (firm-specific characteristics, firm resources, manager socio-demographic characteristics, and selected management practices) on the overall performance variations among 442 Tanzanian micro and small agri-food processing firms. Study results confirmed the RBV argument that intangible resources make a larger contribution to overall performance variations among firms than that tangible resources. Firms' tangible and intangible resources explained 34.5% of overall performance variations (intangible resources explained the overall performance variability by 19.4% compared to tangible resources, which accounted for 15.1%), ranking first in explaining the overall performance variance. Firm-specific characteristics ranked second by influencing variations in overall performance by 29.0%. Selected management practices ranked third (6.3%), while the manager's socio-demographic factors were last on the list, as they influenced the overall performance variability among firms by only 5.1%. The study also found that firms that focus on proper utilization of tangible resources (financial and physical), set targets, and undertake better working capital management practices performed higher than their counterparts (low and average performers). Furthermore, accumulation and proper utilization of intangible resources (relational, organizational, and reputational), undertaking performance monitoring practices, age of the manager, and the choice of the firm location and activity were the dominant significant factors influencing the variations among average and high performers, relative to low performers. The entrepreneurial background was a significant factor influencing variations in average and low-performing firms, indicating that entrepreneurial skills are crucial to achieving average levels of performance. Firm age, size, legal status, source of start-up capital, gender, education level, and total business experience of the manager were not statistically significant variables influencing the overall performance variations among the agri-food processors under the study. The study has identified both significant and non-significant factors influencing performance variations among low, average, and high-performing micro and small agri-food processing firms in Tanzania. Therefore, results from this study will help managers, policymakers and researchers to identify areas where more attention should be placed in order to improve overall performance of MSEs in agri-food industry.

Keywords: firm-level factors, micro and small enterprises, performance, regression analysis, resource-based-view

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Authors: Nikat Parveen, M. Ananthi

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Big data is one of the emerging technology, which collects the data from various sensors and those data will be used in many fields. Data retrieval is one of the major issue where there is a need to extract the exact data as per the need. In this paper, large amount of data set is processed by using the feature selection. Feature selection helps to choose the data which are actually needed to process and execute the task. The key value is the one which helps to point out exact data available in the storage space. Here the available data is streamed and R-Center is proposed to achieve this task.

Keywords: big data, key value, feature selection, retrieval, performance

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Authors: Liu Zhiyuan, Sun Zongdi

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In this paper, the BP neural network model is established to predict the carbon trading price and carbon trading volume in Shanghai City. First of all, we find the data of carbon trading price and carbon trading volume in Shanghai City from September 30, 2015 to December 23, 2016. The carbon trading price and trading volume data were processed to get the average value of each 5, 10, 20, 30, and 60 carbon trading price and trading volume. Then, these data are used as input of BP neural network model. Finally, after the training of BP neural network, the prediction values of Shanghai carbon trading price and trading volume are obtained, and the model is tested.

Keywords: Carbon trading price, carbon trading volume, BP neural network model, Shanghai City

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Authors: Mohamed Othmani, Yassine Khlifi

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This paper presents a technique for compact three dimensional (3D) object model reconstruction using wavelet networks. It consists to transform an input surface vertices into signals,and uses wavelet network parameters for signal approximations. To prove this, we use a wavelet network architecture founded on several mother wavelet families. POLYnomials WindOwed with Gaussians (POLYWOG) wavelet families are used to maximize the probability to select the best wavelets which ensure the good generalization of the network. To achieve a better reconstruction, the network is trained several iterations to optimize the wavelet network parameters until the error criterion is small enough. Experimental results will shown that our proposed technique can effectively reconstruct an irregular 3D object models when using the optimized wavelet network parameters. We will prove that an accurateness reconstruction depends on the best choice of the mother wavelets.

Keywords: 3d object, optimization, parametrization, polywog wavelets, reconstruction, wavelet networks

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Authors: Volkan Isbuga, Joman M. Mahmood, Ali Firat Cabalar

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This study presents the site response analysis of mica-sand mixtures available in certain parts of the world including Izmir, a highly populated city and located in a seismically active region in western part of Turkey. We performed site response analyses by employing SHAKE, an equivalent linear approach, for the micaceous soil deposits consisting of layers with different amount of mica contents and thicknesses. Dynamic behavior of micaceous sands such as shear modulus reduction and damping ratio curves are input for the ground response analyses. Micaceous sands exhibit a unique dynamic response under a scenario earthquake with a magnitude of Mw=6. Results showed that higher amount of mica caused higher spectral accelerations.

Keywords: micaceous sands, site response, equivalent linear approach, SHAKE

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Authors: Noha Al Majid, Said Mazer, Moulhime El Bekkali, Catherine Algani, Mahmoud Mehdi

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A low noise figure (NF) and high linearity V-band Low Noise Amplifier (LNA) is reported in this article. The LNA compromises a three-stage cascode configuration. This LNA will be used as a part of a WPAN (Wireless Personal Area Network) receiver in the millimeter-wave band at 60 GHz. It is designed according to the MMIC technology (Monolithic Microwave Integrated Circuit) in PH 15 process from UMS foundry and uses a 0.15 μm GaAs PHEMT (Pseudomorphic High Electron Mobility Transistor). The particularity of this LNA compared to other LNAs in literature is its very low noise figure which is equal to 1 dB and its high linearity (IIP3 is about 22 dB). The LNA consumes 0.24 Watts, achieving a high gain which is about 23 dB, an input return loss better than -10 dB and an output return loss better than -8 dB.

Keywords: low noise amplifier, V-band, MMIC technology, LNA, amplifier, cascode, pseudomorphic high electron mobility transistor (PHEMT), high linearity

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Authors: Tayfun Salihoğlu

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Following the Law No: 6306 on Transformation of Disaster Risk Areas, urban transformation in Turkey found its legal basis. In the best practices all over the World, the urban transformation was shaped as part of comprehensive social programs through the discourses of renewing the economic, social and physical degraded parts of the city, producing spaces resistant to earthquakes and other possible disasters and creating a livable environment. In Turkish practice, a contradictory process is observed. In this study, it is aimed to develop a method for better understanding of the urban space in terms of disaster risks in order to constitute a basis for decisions in Kocaeli Urban Transformation Master Plan, which is being prepared by Kocaeli Metropolitan Municipality. The spatial unit used in the study is the 50x50 meter grids. In order to reflect the multidimensionality of urban transformation, three basic components that have spatial data in Kocaeli were identified. These components were named as 'Problems in Built-up Areas', 'Disaster Risks arising from Geological Conditions of the Ground and Problems of Buildings', and 'Inadequacy of Urban Services'. Each component was weighted and scored for each grid. In order to delimitate urban transformation zones Optimized Outlier Analysis (Local Moran I) in the ArcGIS 10.6.1 was conducted to test the type of distribution (clustered or scattered) and its significance on the grids by assuming the weighted total score of the grid as Input Features. As a result of this analysis, it was found that the weighted total scores were not significantly clustering at all grids in urban space. The grids which the input feature is clustered significantly were exported as the new database to use in further mappings. Total Score Map reflects the significant clusters in terms of weighted total scores of 'Problems in Built-up Areas', 'Disaster Risks arising from Geological Conditions of the Ground and Problems of Buildings' and 'Inadequacy of Urban Services'. Resulting grids with the highest scores are the most likely candidates for urban transformation in this citywide study. To categorize urban space in terms of urban transformation, Grouping Analysis in ArcGIS 10.6.1 was conducted to data that includes each component scores in significantly clustered grids. Due to Pseudo Statistics and Box Plots, 6 groups with the highest F stats were extracted. As a result of the mapping of the groups, it can be said that 6 groups can be interpreted in a more meaningful manner in relation to the urban space. The method presented in this study can be magnified due to the availability of more spatial data. By integrating with other data to be obtained during the planning process, this method can contribute to the continuation of research and decision-making processes of urban transformation master plans on a more consistent basis.

Keywords: urban transformation, GIS, disaster risk assessment, Kocaeli

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Authors: Shreya Thusoo, Karan Modi, Ankit Kumar Jha, Rajesh Kumar

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Since the evolution of computational tools and simulation software, there has been considerable increase in research on Soil Structure Interaction (SSI) to decrease the computational time and increase accuracy in the results. To aid the designer with a proper understanding of the response of structure in different soil types, the presented paper compares the deformation, shear stress, acceleration and other parameters of multi-storey building for a specific input ground motion using Response-spectrum Analysis (RSA) method. The response of all the models of different heights have been compared in different soil types. Finite Element Simulation software, ANSYS, has been used for all the computational purposes. Overall, higher response is observed with SSI, while it increases with decreasing stiffness of soil.

Keywords: soil-structure interaction, response spectrum, analysis, finite element method, multi-storey buildings

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Authors: Zeinab Syed Abdel Rehim

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Water shortage is considered as one of the huge problems the world encounter now. Water desalination is considered as one of the more suitable methods governments can use to substitute the increased need for potable water. The humidification-dehumidification process for water desalination is viewed as a promising technique for small capacity production plants. The process has several attraction features which include the use of sustainable energy sources, low technology, and low-temperature dehumidification. A pilot experimental set-up plant was constructed with the conventional HVAC components such as air blower that supplies air to an air duct inside which air preheater, steam injector and cooling coil of a small refrigeration unit are placed. The present work evaluates the characteristics of humidification-dehumidification process for water desalination as a function of air flow rate, total power input and air inlet temperature in order to study the optimum conditions required to produce distilled water.

Keywords: condensation, dehumidification, evaporation, humidification, water desalination

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Authors: Archana Rao R., Deepak P., Chayashree P. D., Darshan H. S.

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Cognitive abilities in bilinguals have been widely studied over the last few decades. Bilingualism has been found to extensively facilitate the ability to store and manipulate information in Working Memory (WM). The mechanism of WM includes primary memory, attentional control, and secondary memory, each of which makes a contribution to WM. Many researches have been done in an attempt to measure WM capabilities through both verbal (phonological) and nonverbal tasks (visuospatial). Since there is a lot of speculations regarding the relationship between WM and bilingualism, further investigation is required to understand the nature of WM in bilinguals, i.e., with respect to sequential and simultaneous bilinguals. Hence the present study aimed to highlight the verbal working memory abilities in sequential and simultaneous bilinguals with respect to the processing and recall abilities of nouns and verbs. Two groups of bilinguals aged between 18-30 years were considered for the study. Group 1 consisted of 20 (10 males and 10 females) sequential bilinguals who had acquired L1 (Kannada) before the age of 3 and had exposure to L2 (English) for a period of 8-10 years. Group 2 consisted of 20 (10 males and 10 females) simultaneous bilinguals who have acquired both L1 and L2 before the age of 3. Working memory abilities were assessed using two tasks, and a set of stimuli which was presented in gradation of complexity and the stimuli was inclusive of frequent and infrequent nouns and verbs. The tasks involved the participants to judge the correctness of the sentence and simultaneously remember the last word of each sentence and the participants are instructed to recall the words at the end of each set. The results indicated no significant difference between sequential and simultaneous bilinguals in processing the nouns and verbs, and this could be attributed to the proficiency level of the participants in L1 and the alike cognitive abilities between the groups. And recall of nouns was better compared to verbs, maybe because of the complex argument structure involved in verbs. Similarly, authors found a frequency of occurrence of nouns and verbs also had an effect on WM abilities. The difference was also found across gradation due to the load imposed on the central executive function and phonological loop.

Keywords: bilinguals, nouns, verbs, working memory

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Authors: J. D. Martínez-Vargas, C. Castro-Hoyos, G. Castellanos-Dominguez

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In this work, we propose a novel approach for measuring the stationarity level of a multichannel time-series. This measure is based on a stationarity definition over time-varying spectrum, and it is aimed to quantify the relation between local stationarity (single-channel) and global dynamic behavior (multichannel dynamics). To assess the proposed approach validity, we use a well known EEG-BCI database, that was constructed for separate between motor/imagery tasks. Thus, based on the statement that imagination of movements implies an increase on the EEG dynamics, we use as discriminant features the proposed measure computed over an estimation of the non-stationary components of input time-series. As measure of separability we use a t-student test, and the obtained results evidence that such measure is able to accurately detect the brain areas projected on the scalp where motor tasks are realized.

Keywords: stationary measure, entropy, sub-space projection, multichannel dynamics

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Authors: S. Ghorbani, N. I. Polushin

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Vibration during machining process is crucial since it affects cutting tool, machine, and workpiece leading to a tool wear, tool breakage, and an unacceptable surface roughness. This paper applies a nonparametric statistical method, single decision tree (SDT), to identify factors affecting on vibration in machining process. Workpiece material (AISI 1045 Steel, AA2024 Aluminum alloy, A48-class30 Gray Cast Iron), cutting tool (conventional, cutting tool with holes in toolholder, cutting tool filled up with epoxy-granite), tool overhang (41-65 mm), spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev) and depth of cut (0.05-0.15 mm) were used as input variables, while vibration was the output parameter. It is concluded that workpiece material is the most important parameters for natural frequency followed by cutting tool and overhang.

Keywords: cutting condition, vibration, natural frequency, decision tree, CART algorithm

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Authors: Jae-Il Jeong, Hoon-Jae Park, Jung-Woo Cho, Yang-Gon Kim, Jin-Young Park, Joo-Young Oh, Si-Geun Choi, Seock-Sam Kim, Young Tae Cho, Chan Gyu Kim, Jong-Hyoung Kim

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Bearing component has strongly required to decrease vibration and wear to achieve high durability and life time. In the industry field, bearing durability is improved by surface treatment on the bearing surface by centrifugal casting or gravity casting production method. However, this manufacturing method has caused problems such as long processing time, defect rate, and health harmful effect. To solve this problem, there is a laser cladding deposition treatment, which provides fast processing and food adhesion. Therefore, optimum conditions of white metal laser deposition should be studied to minimize bearing contact axis wear using laser cladding techniques. In this study, we deposit a soft white metal layer on SCM440, which is mainly used for shaft and bolt. On laser deposition process, the laser power and powder feed rate and laser head speed factors are controlled to find out the optimal conditions. We also measure hardness using micro Vickers, analyze FE-SEM (Field Emission Scanning Electron Microscope) and EDS (Energy Dispersive Spectroscopy) to study the mechanical properties and surface characteristics with various parameters change. Furthermore, this paper suggests the optimum condition of laser cladding deposition to apply in industrial fields. This work was supported by the Industrial Innovation Project of the Korea Evaluation Institute of Industrial Technology (KEIT) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (Research no. 10051653).

Keywords: laser deposition, bearing, white metal, mechanical properties

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Authors: Zhaojun Wang, Zongdi Sun, Zhiyuan Liu

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First of all, the carbon trading price and trading volume in Shanghai are transformed by Fourier transform, and the frequency response diagram is obtained. Then, the frequency response diagram is analyzed and the Blackman filter is designed. The Blackman filter is used to filter, and the carbon trading time domain and frequency response diagram are obtained. After wavelet analysis, the carbon trading data were processed; respectively, we got the average value for each 5 days, 10 days, 20 days, 30 days, and 60 days. Finally, the data are used as input of the Back Propagation Neural Network model for prediction.

Keywords: Shanghai carbon trading, carbon trading price, carbon trading volume, wavelet analysis, BP neural network model

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Authors: Yonggu Jang, Jisong Ryu, Woosik Lee

Abstract:

The study aims to address the limitations of existing underground facility exploration equipment in terms of exploration depth range, relative depth measurement, data processing time, and human-centered ground penetrating radar image interpretation. The study proposed the use of 3D absolute positioning technology to develop a precision underground facility exploration system. The aim of this study is to establish a precise exploration system for underground facilities based on 3D absolute positioning technology, which can accurately survey up to a depth of 5m and measure the 3D absolute location of precise underground facilities. The study developed software and hardware technologies to build the precision exploration system. The software technologies developed include absolute positioning technology, ground surface location synchronization technology of GPR exploration equipment, GPR exploration image AI interpretation technology, and integrated underground space map-based composite data processing technology. The hardware systems developed include a vehicle-type exploration system and a cart-type exploration system. The data was collected using the developed exploration system, which employs 3D absolute positioning technology. The GPR exploration images were analyzed using AI technology, and the three-dimensional location information of the explored precise underground facilities was compared to the integrated underground space map. The study successfully developed a precision underground facility exploration system based on 3D absolute positioning technology. The developed exploration system can accurately survey up to a depth of 5m and measure the 3D absolute location of precise underground facilities. The system comprises software technologies that build a 3D precise DEM, synchronize the GPR sensor's ground surface 3D location coordinates, automatically analyze and detect underground facility information in GPR exploration images and improve accuracy through comparative analysis of the three-dimensional location information, and hardware systems, including a vehicle-type exploration system and a cart-type exploration system. The study's findings and technological advancements are essential for underground safety management in Korea. The proposed precision exploration system significantly contributes to establishing precise location information of underground facility information, which is crucial for underground safety management and improves the accuracy and efficiency of exploration. The study addressed the limitations of existing equipment in exploring underground facilities, proposed 3D absolute positioning technology-based precision exploration system, developed software and hardware systems for the exploration system, and contributed to underground safety management by providing precise location information. The developed precision underground facility exploration system based on 3D absolute positioning technology has the potential to provide accurate and efficient exploration of underground facilities up to a depth of 5m. The system's technological advancements contribute to the establishment of precise location information of underground facility information, which is essential for underground safety management in Korea.

Keywords: 3D absolute positioning, AI interpretation of GPR exploration images, complex data processing, integrated underground space maps, precision exploration system for underground facilities

Procedia PDF Downloads 56

Authors: Robert Caulk

Abstract:

A generalized framework for adaptive machine learning deployments in algorithmic trading is introduced, tested, and released as open-source code. The presented software aims to test the hypothesis that recent data contains enough information to form a probabilistically favorable short-term price prediction. Further, the framework contains various adaptive machine learning techniques that are geared toward generating profit during strong trends and minimizing losses during trend changes. Results demonstrate that this adaptive machine learning approach is capable of capturing trends and generating profit. The presentation also discusses the importance of defining the parameter space associated with the dynamic training data-set and using the parameter space to identify and remove outliers from prediction data points. Meanwhile, the generalized architecture enables common users to exploit the powerful machinery while focusing on high-level feature engineering and model testing. The presentation also highlights common strengths and weaknesses associated with the presented technique and presents a broad range of well-tested starting points for feature set construction, target setting, and statistical methods for enforcing risk management and maintaining probabilistically favorable entry and exit points. The presentation also describes the end-to-end data processing tools associated with FreqAI, including automatic data fetching, data aggregation, feature engineering, safe and robust data pre-processing, outlier detection, custom machine learning and statistical tools, data post-processing, and adaptive training backtest emulation, and deployment of adaptive training in live environments. Finally, the generalized user interface is also discussed in the presentation. Feature engineering is simplified so that users can seed their feature sets with common indicator libraries (e.g. TA-lib, pandas-ta). The user also feeds data expansion parameters to fill out a large feature set for the model, which can contain as many as 10,000+ features. The presentation describes the various object-oriented programming techniques employed to make FreqAI agnostic to third-party libraries and external data sources. In other words, the back-end is constructed in such a way that users can leverage a broad range of common regression libraries (Catboost, LightGBM, Sklearn, etc) as well as common Neural Network libraries (TensorFlow, PyTorch) without worrying about the logistical complexities associated with data handling and API interactions. The presentation finishes by drawing conclusions about the most important parameters associated with a live deployment of the adaptive learning framework and provides the road map for future development in FreqAI.

Keywords: machine learning, market trend detection, open-source, adaptive learning, parameter space exploration

Procedia PDF Downloads 82

Authors: Mohammed Abu Shquier

Abstract:

This paper introduces a word segmentation method using the novel BP-LSTM-CRF architecture for processing semantic output training. The objective of web morphological analysis tools is to link a formal morpho-syntactic description to a lemma, along with morpho-syntactic information, a vocalized form, a vocalized analysis with morpho-syntactic information, and a list of paradigms. A key objective is to continuously enhance the proposed system through an inductive learning approach that considers semantic influences. The system is currently under construction and development based on data-driven learning. To evaluate the tool, an experiment on homograph analysis was conducted. The tool also encompasses the assumption of deep binary segmentation hypotheses, the arbitrary choice of trigram or n-gram continuation probabilities, language limitations, and morphology for both Modern Standard Arabic (MSA) and Dialectal Arabic (DA), which provide justification for updating this system. Most Arabic word analysis systems are based on the phonotactic morpho-syntactic analysis of a word transmitted using lexical rules, which are mainly used in MENA language technology tools, without taking into account contextual or semantic morphological implications. Therefore, it is necessary to have an automatic analysis tool taking into account the word sense and not only the morpho-syntactic category. Moreover, they are also based on statistical/stochastic models. These stochastic models, such as HMMs, have shown their effectiveness in different NLP applications: part-of-speech tagging, machine translation, speech recognition, etc. As an extension, we focus on language modeling using Recurrent Neural Network (RNN); given that morphological analysis coverage was very low in dialectal Arabic, it is significantly important to investigate deeply how the dialect data influence the accuracy of these approaches by developing dialectal morphological processing tools to show that dialectal variability can support to improve analysis.

Keywords: NLP, DL, ML, analyser, MSA, RNN, CNN

Procedia PDF Downloads 35

Authors: Nasser Mohamed Ramli, Nurul Izzati Zulkifli

Abstract:

Sump can be defined as a reservoir which contains slurry; a mixture of solid and liquid or water, in it. Sump system is an unsteady process owing to the level response. Sump level shall be monitored carefully by using a good controller to avoid overflow. The current conventional controllers would not be able to solve problems with large time delay and nonlinearities, Fuzzy Logic controller is tested to prove its ability in solving the listed problems of slurry sump. Therefore, in order to justify the effectiveness and reliability of these controllers, simulation of the sump system was created by using MATLAB and the results were compared. According to the result obtained, instead of Proportional-Integral (PI) and Proportional-Integral and Derivative (PID), Fuzzy Logic controller showed the best result by offering quick response of 0.32 s for step input and 5 s for pulse generator, by producing small Integral Absolute Error (IAE) values that are 0.66 and 0.36 respectively.

Keywords: fuzzy, sump, level, controller

Procedia PDF Downloads 236

Authors: Matheus Pessoa, Matthias Kraume

Abstract:

Scale-Down rules in process engineering help us to improve and develop Industrial scale parameters into lab scale. Several scale-down rules available in the literature like Impeller Power Number, Agitation device Power Input, Substrate Tip Speed, Reynolds Number and Cavern Development were investigated in order to stipulate the rotational speed to operate an 11 L working volume lab-scale bioreactor within industrial process parameters. Herein, xanthan gum was used as a fluid with a representative viscosity of a hypothetical biogas plant, with H/D = 1 and central agitation, fermentation broth using sewage sludge and sugar beet pulp as substrate. The results showed that the cavern development strategy was the best method for establishing a rotational speed for the bioreactor operation, while the other rules presented values out of reality for this article proposes.

Keywords: anaerobic digestion, cavern development, scale down rules, xanthan gum

Procedia PDF Downloads 486

Authors: Noha Seddik, Sherine Youssef, Mohamed Kholeif

Abstract:

The automated epileptic seizure detection research field has emerged in the recent years; this involves analyzing the Electroencephalogram (EEG) signals instead of the traditional visual inspection performed by expert neurologists. In this study, a Support Vector Machine (SVM) that uses Weighted Permutation Entropy (WPE) as the input feature is proposed for classifying normal and seizure EEG records. WPE is a modified statistical parameter of the permutation entropy (PE) that measures the complexity and irregularity of a time series. It incorporates both the mapped ordinal pattern of the time series and the information contained in the amplitude of its sample points. The proposed system utilizes the fact that entropy based measures for the EEG segments during epileptic seizure are lower than in normal EEG.

Keywords: electroencephalogram (EEG), epileptic seizure detection, weighted permutation entropy (WPE), support vector machine (SVM)

Procedia PDF Downloads 363

Authors: Mafawez Alharbi, Mahdi Jemmali

Abstract:

E-learning is a developing area especially in education. E-learning can provide several benefits to learners. An intelligent system to collect all components satisfying user preferences is so important. This research presents an approach that it capable to personalize e-information and give the user their needs following their preferences. This proposal can make some knowledge after more evaluations made by the user. In addition, it can learn from the habit from the user. Finally, we show a walk-through to prove how intelligent process work.

Keywords: artificial intelligence, architecture, e-learning, software engineering, processing

Procedia PDF Downloads 186

Authors: Shan-Jen Cheng, Te-Jen Chang, Kuang-Hsiung Tan, Shou-Ling Kuo

Abstract:

Polymer Electrolyte Membrane Fuel Cell (PEMFC) is such a time-vary nonlinear dynamic system. The traditional linear modeling approach is hard to estimate structure correctly of PEMFC system. From this reason, this paper presents a nonlinear modeling of the PEMFC using Neural Network Auto-regressive model with eXogenous inputs (NNARX) approach. The multilayer perception (MLP) network is applied to evaluate the structure of the NNARX model of PEMFC. The validity and accuracy of NNARX model are tested by one step ahead relating output voltage to input current from measured experimental of PEMFC. The results show that the obtained nonlinear NNARX model can efficiently approximate the dynamic mode of the PEMFC and model output and system measured output consistently.

Keywords: PEMFC, neural network, nonlinear modeling, NNARX

Procedia PDF Downloads 374