Search results for: adaptive computer games

Authors: Mohammad Besharatloo

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Intrusion detection is an important research topic in network security because of increasing growth in the use of computer network services. Intrusion detection is done with the aim of detecting the unauthorized use or abuse in the networks and systems by the intruders. Therefore, the intrusion detection system is an efficient tool to control the user's access through some predefined regulations. Since, the data used in intrusion detection system has high dimension, a proper representation is required to show the basis structure of this data. Therefore, it is necessary to eliminate the redundant features to create the best representation subset. In the proposed method, a hybrid model of differential evolution and firefly algorithms was employed to choose the best subset of properties. In addition, decision tree and support vector machine (SVM) are adopted to determine the quality of the selected properties. In the first, the sorted population is divided into two sub-populations. These optimization algorithms were implemented on these sub-populations, respectively. Then, these sub-populations are merged to create next repetition population. The performance evaluation of the proposed method is done based on KDD Cup99. The simulation results show that the proposed method has better performance than the other methods in this context.

Keywords: intrusion detection system, differential evolution, firefly algorithm, support vector machine, decision tree

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Authors: Samah Senbel

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Database Design is a fundamental part of the Computer Science and Information technology curricula in any school, as well as in the study of management, business administration, and data analytics. In this study, we compare the performance of two groups of students studying the same database design and implementation course at Sacred Heart University in the fall of 2018. Both courses used the same textbook and were taught by the same professor, one for seven graduate students and one for 26 undergraduate students (juniors). The undergraduate students were aged around 20 years old with little work experience, while the graduate students averaged 35 years old and all were employed in computer-related or management-related jobs. The textbook used was 'Database Systems, Design, Implementation, and Management' by Coronel and Morris, and the course was designed to follow the textbook roughly a chapter per week. The first 6 weeks covered the design aspect of a database, followed by a paper exam. The next 6 weeks covered the implementation aspect of the database using SQL followed by a lab exam. Since the undergraduate students are on a 16 week semester, we spend the last three weeks of the course covering NoSQL. This part of the course was not included in this study. After the course was over, we analyze the results of the two groups of students. An interesting discrepancy was observed: In the database design part of the course, the average grade of the graduate students was 92%, while that of the undergraduate students was 77% for the same exam. In the implementation part of the course, we observe the opposite: the average grade of the graduate students was 65% while that of the undergraduate students was 73%. The overall grades were quite similar: the graduate average was 78% and that of the undergraduates was 75%. Based on these results, we concluded that having both classes follow the same time schedule was not beneficial, and an adjustment is needed. The graduates could spend less time on design and the undergraduates would benefit from more design time. In the fall of 2019, 30 students registered for the undergraduate course and 15 students registered for the graduate course. To test our conclusion, the undergraduates spend about 67% of time (eight classes) on the design part of the course and 33% (four classes) on the implementation part, using the exact exams as the previous year. This resulted in an improvement in their average grades on the design part from 77% to 83% and also their implementation average grade from 73% to 79%. In conclusion, we recommend using two separate schedules for teaching the database design course. For undergraduate students, it is important to spend more time on the design part rather than the implementation part of the course. While for the older graduate students, we recommend spending more time on the implementation part, as it seems that is the part they struggle with, even though they have a higher understanding of the design component of databases.

Keywords: computer science education, database design, graduate and undergraduate students, pedagogy

Procedia PDF Downloads 125

Authors: K. C. Ching

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There is growing public concern over the increasing prevalence of student suicide in Hong Kong. Some identify the problem with insufficient social support, while some attribute it to the vast fluctuations in emotional experience and the hindrances to emotion-regulation, both typical of adolescence and emerging adulthood. This study is thus designed to explore the respective effect of perceived social support and emotion self-confidence, on positive emotions and negative emotions. Fifty-seven Hong Kong higher-education students (17 males, 40 females) aged between 18 and 25 (M = 21.78) responded to an online questionnaire consisted of self-reported measures of perceived social support, emotional self-confidence, positive emotions, and negative emotions. Hierarchical regression analysis revealed that emotional self-confidence positively associated with positive emotions and negatively with negative emotions, while perceived social support positively associated with positive emotions but was not related to negative emotions. Perceived social support and emotional self-confidence both predicted positive emotions, but did not interact to predict any emotional outcome. It is concluded that students’ positive and negative emotional experiences are closely related to their emotion-regulation process. But for social support, its effect is merely protective, meaning that although perceived social support generally promotes positive emotions, it alone does not suffice to alleviate students’ negative emotions. These conclusions carry profound implications to suicide prevention practices, including that most existing suicide prevention campaigns should advance from merely fostering mutual support to directly promoting adaptive coping of emotional negativity.

Keywords: emerging adulthood, emotional self-confidence, hong kong, perceived social support, suicide prevention

Procedia PDF Downloads 147

Authors: Somayeh Komeylian

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The direction of arrival (DoA) estimation is the crucial aspect of the radar technologies for detecting and dividing several signal sources. In this scenario, the antenna array output modeling involves numerous parameters including noise samples, signal waveform, signal directions, signal number, and signal to noise ratio (SNR), and thereby the methods of the DoA estimation rely heavily on the generalization characteristic for establishing a large number of the training data sets. Hence, we have analogously represented the two different optimization models of the DoA estimation; (1) the implementation of the decision directed acyclic graph (DDAG) for the multiclass least-squares support vector machine (LS-SVM), and (2) the optimization method of the deep neural network (DNN) radial basis function (RBF). We have rigorously verified that the LS-SVM DDAG algorithm is capable of accurately classifying DoAs for the three classes. However, the accuracy and robustness of the DoA estimation are still highly sensitive to technological imperfections of the antenna arrays such as non-ideal array design and manufacture, array implementation, mutual coupling effect, and background radiation and thereby the method may fail in representing high precision for the DoA estimation. Therefore, this work has a further contribution on developing the DNN-RBF model for the DoA estimation for overcoming the limitations of the non-parametric and data-driven methods in terms of array imperfection and generalization. The numerical results of implementing the DNN-RBF model have confirmed the better performance of the DoA estimation compared with the LS-SVM algorithm. Consequently, we have analogously evaluated the performance of utilizing the two aforementioned optimization methods for the DoA estimation using the concept of the mean squared error (MSE).

Keywords: DoA estimation, Adaptive antenna array, Deep Neural Network, LS-SVM optimization model, Radial basis function, and MSE

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Authors: Sachi Nandan Mohanty, Shreya Sinha, Sweeti Sah, Shweta Sharma

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The advancement of natural language processing has majorly influenced the field of medical transcript classification, providing a robust framework for enhancing the accuracy of clinical data processing. It has enormous potential to transform healthcare and improve people's livelihoods. This research focuses on improving the accuracy of medical transcript categorization using Bidirectional Encoder Representations from Transformers (BERT) and its specialized variants, including BioBERT, ClinicalBERT, SciBERT, and BlueBERT. The experimental work employs Optuna, an optimization framework, for hyperparameter tuning to identify the most effective variant, concluding that BioBERT yields the best performance. Furthermore, various optimizers, including Adam, RMSprop, and Layerwise adaptive large batch optimization (LAMB), were evaluated alongside BERT's default AdamW optimizer. The findings show that the LAMB optimizer achieves a performance that is equally good as AdamW's. Synthetic data generation techniques from Gretel were utilized to augment the dataset, expanding the original dataset from 5,000 to 10,000 rows. Subsequent evaluations demonstrated that the model maintained its performance with synthetic data, with the LAMB optimizer showing marginally better results. The enhanced dataset and optimized model configurations improved classification accuracy, showcasing the efficacy of the BioBERT variant and the LAMB optimizer. It resulted in an accuracy of up to 98.2% and 90.8% for the original and combined datasets.

Keywords: BioBERT, clinical data, healthcare AI, transformer models

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Authors: Christophe Spaas, Lies Pottel, Joke De Ceulaer, Johan Abeloos, Philippe Lamoral, Tom De Backer, Calix De Clercq

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We aimed to evaluate the accuracy of an in-house developed low-cost computer-assisted surgery (CAS) protocol for osseous free flap mandibular reconstruction. All patients who underwent primary or secondary mandibular reconstruction with a free (solely or composite) osseous flap, either a fibula free flap or iliac crest free flap, between January 2014 and December 2017 were evaluated. The low-cost protocol consisted out of a virtual surgical planning, a prebend custom reconstruction plate and an individualized free flap positioning guide. The accuracy of the protocol was evaluated through comparison of the postoperative outcome with the 3D virtual planning, based on measurement of the following parameters: intercondylar distance, mandibular angle (axial and sagittal), inner angular distance, anterior-posterior distance, length of the fibular/iliac crest segments and osteotomy angles. A statistical analysis of the obtained values was done. Virtual 3D surgical planning and cutting guide design were performed with Proplan CMF® software (Materialise, Leuven, Belgium) and IPS Gate (KLS Martin, Tuttlingen, Germany). Segmentation of the DICOM data as well as outcome analysis were done with BrainLab iPlan® Software (Brainlab AG, Feldkirchen, Germany). A cost analysis of the protocol was done. Twenty-two patients (11 fibula /11 iliac crest) were included and analyzed. Based on voxel-based registration on the cranial base, 3D virtual planning landmark parameters did not significantly differ from those measured on the actual treatment outcome (p-values >0.05). A cost evaluation of the in-house developed CAS protocol revealed a 1750 euro cost reduction in comparison with a standard CAS protocol with a patient-specific reconstruction plate. Our results indicate that an accurate transfer of the planning with our in-house developed low-cost CAS protocol is feasible at a significant lower cost.

Keywords: CAD/CAM, computer-assisted surgery, low-cost, mandibular reconstruction

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Authors: Deyu Zhou, Xiao Xue, Lizhen Cui

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With the increasing collaboration among various services and the growing complexity of user demands, there are more and more factors affecting the stable development of the cloud manufacturing service economic system (CMSE). This poses new challenges to the evolution analysis of the CMSE. Many researchers have modeled and analyzed the evolution process of CMSE from the perspectives of individual learning and internal factors influencing the system, but without considering other important characteristics of the system's individuals (such as heterogeneity, bounded rationality, etc.) and the impact of external environmental factors. Therefore, this paper proposes an integrated artificial social model for the cloud manufacturing service economic system, which considers both the characteristics of the system's individuals and the internal and external influencing factors of the system. The model consists of three parts: the Agent model, environment model, and rules model (Agent-Environment-Rules, AER): (1) the Agent model considers important features of the individuals, such as heterogeneity and bounded rationality, based on the adaptive behavior mechanisms of perception, action, and decision-making; (2) the environment model describes the activity space of the individuals (real or virtual environment); (3) the rules model, as the driving force of system evolution, describes the mechanism of the entire system's operation and evolution. Finally, this paper verifies the effectiveness of the AER model through computational and experimental results.

Keywords: cloud manufacturing service economic system (CMSE), AER model, artificial social modeling, integrated framework, computing experiment, agent-based modeling, social networks

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Authors: Mario Pérez-Won, Anais Palma-Acevedo, Luis González-Cavieres, Roberto Lemus-Mondaca, Gipsy Tabilo-Munizaga

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The Chilean abalone (Concholepas Concholepas) is a gastropod mollusk; it has a high commercial value due to the qualities of its meat, especially hardness, as a critical acceptance parameter. However, its main problem is its short shelf-life which is usually extended using traditional technologies with high energy consumption. Therefore, applying different technologies for the pre-treatment and drying process is necessary. In this research, pulsed electric field (PEF) was used as a pre-treatment for vacuum microwave drying (VMD), freeze-drying (FD), and hot-air drying (HAD). Drying conditions and characteristics were set according to previous experiments. The Drying samples were analyzed in terms of physical quality (color, texture, microstructure, and rehydration capacity), protein quality (degree of hydrolysis and computer protein efficiency ratio), and energy parameters. Regarding quality, the treatment that obtained lower harness was PEF+FD (195 N ± 10), the lowest change of color was for treatment PEF+VMD (ΔE: 17 ± 1.5), and the best rehydration capacity was for treatment PEF+VMD (1.2 h for equilibrium). For protein quality, the highest Computer-Protein Efficiency Ratio was the sample 2.0 kV/ cm of PEF (index of 4.18 ± 0.26 at the end of the digestion). Moreover, about energetic consumption, results show that VMD decreases the drying process by 97% whether PEF was used or not. Consequently, it is possible to conclude that using PEF as a pre-treatment for VMD and FD treatments has advantages that must be used following the consumer’s needs or preferences.

Keywords: chilean abalone, freeze-drying, proteins, pulsed electric fields

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Authors: Shabadini Sampath, Jinglang Feng

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With huge kinetic energy, space debris poses a major threat to astronauts’ space activities and spacecraft in orbit if a collision happens. The active removal of space debris is required in order to avoid frequent collisions that would occur. In addition, the amount of space debris will increase uncontrollably, posing a threat to the safety of the entire space system. But the safe and reliable removal of large-scale space debris has been a huge challenge to date. While capturing and deorbiting space debris, the space manipulator has to achieve high control precision. However, due to uncertainties and unknown disturbances, there is difficulty in coordinating the control of the space manipulator. To address this challenge, this paper focuses on developing a robust and intelligent control algorithm that controls joint movement and restricts it on the sliding manifold by reducing uncertainties. A neural network adaptive sliding mode controller (NNASMC) is applied with the objective of finding the control law such that the joint motions of the space manipulator follow the given trajectory. A computed torque control (CTC) is an effective motion control strategy that is used in this paper for computing space manipulator arm torque to generate the required motion. Based on the Lyapunov stability theorem, the proposed intelligent controller NNASMC and CTC guarantees the robustness and global asymptotic stability of the closed-loop control system. Finally, the controllers used in the paper are modeled and simulated using MATLAB Simulink. The results are presented to prove the effectiveness of the proposed controller approach.

Keywords: GNC, active removal of space debris, AI controllers, MatLabSimulink

Procedia PDF Downloads 135

Authors: Nasiru Medugu Idris

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This research is aimed at assessing the vulnerability of climate change to rural communities (farmers, herdsmen and fishermen) in Nigeria with the view to study the underlying causes and degree of vulnerability to climate change and examine the conflict between farmers and herdsmen as a result of climate change. This research employed the use of quantitative and qualitative means of data gathering techniques as well as physical observations. Six states (Kebbi, Adamawa, Nasarawa, Osun, Ebonyi, and Akwa Ibom) have been selected on the ground that they are key food production areas in the country and are therefore essential to continual food security in the country. So also, they also double as fishing communities in order to aid the comprehensive study of all the effects on climate on farmers and fishermen alike. Community focus group discussions were carried out in the various states for an interactive session and also to have firsthand information on their level of awareness on climate change. Climate data from the Nigerian Meteorological Agency over the past decade were collected for the purpose of analyzing trends in climate. The study observed that the level of vulnerability of rural dwellers most especially farmers, herdsmen and fishermen to climate change is very high due to their socioeconomic, ethnic and historical perspective of their trend. The study, therefore, recommends that urgent step needs to be put in place to help control natural hazards and man-made disasters and serious measures are also needed in order to minimize severe societal, economic and political crises; some of which may either escalate to violent conflicts or could be avoided by efforts of conflict resolution and prevention by the initiation of a process of de-escalation. So this study has recommended the best-fit adaptive and mitigation measures to climate change vulnerability in rural communities of Nigeria.

Keywords: adaptation, farmers, fishermen, herdsmen

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Authors: Saifullah Karimullah

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Additive manufacturing techniques are becoming more common with the latest technological advancements. It is composed to bring a revolution in the way products are designed, planned, manufactured, and distributed to end users. Fused deposition modeling (FDM) based 3D printing is one of those promising aspects that have revolutionized the prototyping processes. The purpose of this design and study project is to design a customized laboratory-scale FDM-based 3D printer from locally available sources. The primary goal is to design and fabricate the FDM-based 3D printer. After the fabrication, a tensile test specimen would be designed in Solid Works or [Creo computer-aided design (CAD)] software. A .stl file is generated of the tensile test specimen through slicing software and the G-codes are inserted via a computer for the test specimen to be printed. Different parameters were under studies like printing speed, layer thickness and infill density of the printed object. Some parameters were kept constant such as temperature, extrusion rate, raster orientation etc. Different tensile test specimens were printed for a different sets of parameters of the FDM-based 3d printer. The tensile test specimen were subjected to tensile tests using a universal testing machine (UTM). Design Expert software has been used for analyses, So Different results were obtained from the different tensile test specimens. The best, average and worst specimen were also observed under a compound microscope to investigate the layer bonding in between.

Keywords: additive manufacturing techniques, 3D printing, CAD software, UTM machine

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Authors: Sonia Anand Knowlton

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As the use of Artificial Intelligence is becoming increasingly integrated in virtually every area of life, the need and interest to logically formalize the law and judicial reasoning is growing tremendously. The study of argumentation frameworks (AFs) provides promise in this respect. AF’s provide a way of structuring human reasoning using a formal system of non-monotonic logic. P.M. Dung first introduced this framework and demonstrated that certain arguments must prevail and certain arguments must perish based on whether they are logically “attacked” by other arguments. Dung labelled the set of prevailing arguments as the “preferred extension” of the given argumentation framework. Trevor Bench-Capon’s Value-based Argumentation Frameworks extended Dung’s AF system by allowing arguments to derive their force from the promotion of “preferred” values. In VAF systems, the success of an attack from argument A to argument B (i.e., the triumph of argument A) requires that argument B does not promote a value that is preferred to argument A. There has been thorough discussion of the application of VAFs to the law within the computer science literature, mainly demonstrating that legal cases can be effectively mapped out using VAFs. This article analyses VAFs from a jurisprudential standpoint to provide a philosophical and theoretical analysis of what VAFs tell the legal community about the judicial reasoning, specifically distinguishing between legal and moral reasoning. It highlights the limitations of using VAFs to account for judicial moral reasoning in theory and in practice.

Keywords: nonmonotonic logic, legal formalization, computer science, artificial intelligence, morality

Procedia PDF Downloads 80

Authors: Nikolina Ribarić

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E-learning is most commonly defined as a set of applications and processes, such as Web-based learning, computer-based learning, virtual classrooms, and digital collaboration, that enable access to instructional content through a variety of electronic media. The main goal of an e-learning system is learning, and the way to evaluate the impact of an e-learning system is by examining whether students learn effectively with the help of that system. Testmoz is a program for online preparation of knowledge evaluation assignments. The program provides teachers with computer support during the design of assignments and evaluating them. Students can review and solve assignments and also check the correctness of their solutions. Research into the increase of motivation by the practice of providing teaching content by applying online tests prepared in the Testmoz program was carried out with students of the 8th grade of Ljubo Babić Primary School in Jastrebarsko. The students took the tests in their free time, from home, for an unlimited number of times. SPSS was used to process the data obtained by the research instruments. The results of the research showed that students preferred to practice teaching content and achieved better educational results in chemistry when they had access to online tests for repetition and practicing in relation to subject content which was checked after repetition and practicing in "the classical way" -i.e., solving assignments in a workbook or writing assignments in worksheets.

Keywords: chemistry class, e-learning, motivation, Testmoz

Procedia PDF Downloads 162

Authors: Therese Besnard

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Internationally in early childhood education (ECE), the great majority of teachers are women. Some groups believe that a greater male teacher presence in ECE would be beneficial for children, specifically for boys as it could offer a positive male model. It is a common belief that children would benefit from being exposed to both male and female models. Some believe that women are naturally better suited to offer quality care to young children comparatively to men. Some authors bring forth that after equivalent training, differences in the educational practices are purely individual and do not depend on the teacher’s gender. Others believe that a greater male presence in ECE would increase the risk of pedophilia or child abuse. The few scientific studies in this area suggest that differences could exist between male and female ECE teacher, in particular when it comes to play which is the mainstay of the ECE educational program. Male teachers describe themselves as being more playful and having a greater tendency to initiate physical and turbulent play comparatively to female teachers, who describe themselves as favoring games that are calmer and focused on social interaction. Observed directly, male teachers appear more actively engaged in play with children and propose more motor play than female teachers. Furthermore children who have both male and female teachers for one year show less behavior difficulties when compared to children with only female teachers. Despite a variety of viewpoints we don’t know if the educational practices of male ECE teachers, (emotional support, classroom organization or instructional support) are different than the educational practices of female teachers and if these practices are linked with children’s adaptation. This study compares the educational practices of 37 ECE teachers (57 % male) and analyses the link with children' social adaptation (n=221). Educational practices were assessed through observational measurements with the Classroom Assessment Scoring System (CLASS) in a natural class environment. Child social adaptation was assessed with the Social Competence and Behavior Evaluation (SCBE). Observational data reveals no differences between men's and women's scale of the CLASS. Results using Multilevel models analyses suggest that the ability to propose good classroom organization and give good instructional support are linked with better child' social adaptation, and that is always true for men and women teachers. The results are discussed on the basis of their potential impact on future educational interventions.

Keywords: child social adaptation, early childhood education, educational practices, men teacher

Procedia PDF Downloads 374

Authors: Cristian Pauna

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Trading the financial markets is a widespread activity today. A large number of investors, companies, public of private funds are buying and selling every day in order to make profit. Algorithmic trading is the prevalent method to make the trade decisions after the electronic trading release. The orders are sent almost instantly by computers using mathematical models. This paper will present a price prediction methodology based on a multi-dimensional neural network. Using the Fisher transform, the neural network will be instructed for a low-latency auto-adaptive process in order to predict the price evolution for the next period of time. The model is designed especially for algorithmic trading and uses the real-time price series. It was found that the characteristics of the Fisher function applied at the nodes scale level can generate reliable trading signals using the neural network methodology. After real time tests it was found that this method can be applied in any timeframe to trade the financial markets. The paper will also include the steps to implement the presented methodology into an automated trading system. Real trading results will be displayed and analyzed in order to qualify the model. As conclusion, the compared results will reveal that the neural network methodology applied together with the Fisher transform at the nodes level can generate a good price prediction and can build reliable trading signals for algorithmic trading.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, neural network

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Authors: Aragorn Joaquin Pineda Dela Cruz

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Road traffic congestion is continually deteriorating in Hong Kong. The largest contributing factor is the increase in vehicle fleet size, resulting in higher competition over the utilisation of road space. This study proposes a project that can process closed-circuit television images and videos to provide real-time traffic detection and prediction capabilities. Specifically, a deep-learning model involving computer vision techniques for video and image-based vehicle counting, then a separate model to detect and predict traffic congestion levels based on said data. State-of-the-art object detection models such as You Only Look Once and Faster Region-based Convolutional Neural Networks are tested and compared on closed-circuit television data from various major roads in Hong Kong. It is then used for training in long short-term memory networks to be able to predict traffic conditions in the near future, in an effort to provide more precise and quicker overviews of current and future traffic conditions relative to current solutions such as navigation apps.

Keywords: intelligent transportation system, vehicle detection, traffic analysis, deep learning, machine learning, computer vision, traffic prediction

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Authors: Liza Ireni-Saban

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The 6.6 magnitude quake event that occurred in 2003 (Bam, Iran) made it impossible for the Iranian government to handle disaster relief efforts domestically. In this extreme event, the Iranian government reached out to the international community, and this created a momentum that had to be carried out by trust-building efforts on all sides, often termed ‘Disaster Diplomacy’. Indeed, the circumstances were even more critical when one considers the increasing political and economic isolation of Iran within the international community. The potential for transformative political space to be opened by disaster has been recognized by dominant international political actors. Despite the fact that Bam 2003 post-disaster relief efforts did not catalyze any diplomatic activities on all sides, it is suggested that few international aid agencies have successfully used disaster recovery to enhance their popular legitimacy and reputation among the international community. In terms of disaster diplomacy, an actor’s reputational capital may affect his ability to build coalitions and alliances to achieve international political ends, to negotiate and build understanding and trust with foreign publics. This study suggests that the post-disaster setting may benefit from using the ecology of games framework to evaluate the role of bridging actors and mediators in facilitating collaborative governance networks. Recent developments in network theory and analysis provide means of structural embeddedness to explore how reputational capital can be built through brokerage roles of actors engaged in a disaster management network. This paper then aims to structure the relations among actors that participated in the post-disaster relief efforts in the 2003 Bam earthquake (Iran) in order to assess under which conditions actors may be strategically utilized to serve as mediating organizations for future disaster events experienced by isolated nations or nations in conflict. The results indicate the strategic use of reputational capital by the Iranian Ministry of Foreign Affairs as key broker to build a successful coordinative system for reducing disaster vulnerabilities. International aid agencies rarely played brokerage roles to coordinate peripheral actors. U.S. foreign assistance (USAID), despite coordination capacities, was prevented from serving brokerage roles in the system.

Keywords: coordination, disaster diplomacy, international aid organizations, Iran

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Authors: Maria C. Proença, Marco Aniceto, Pedro N. Santos, José C. Freitas

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A methodology based on wavelets is proposed for the automatic location and delimitation of defects in limestone plates. Natural defects include dark colored spots, crystal zones trapped in the stone, areas of abnormal contrast colors, cracks or fracture lines, and fossil patterns. Although some of these may or may not be considered as defects according to the intended use of the plate, the goal is to pair each stone with a map of defects that can be overlaid on a computer display. These layers of defects constitute a database that will allow the preliminary selection of matching tiles of a particular variety, with specific dimensions, for a requirement of N square meters, to be done on a desktop computer rather than by a two-hour search in the storage park, with human operators manipulating stone plates as large as 3 m x 2 m, weighing about one ton. Accident risks and work times are reduced, with a consequent increase in productivity. The base for the algorithm is wavelet decomposition executed in two instances of the original image, to detect both hypotheses – dark and clear defects. The existence and/or size of these defects are the gauge to classify the quality grade of the stone products. The tuning of parameters that are possible in the framework of the wavelets corresponds to different levels of accuracy in the drawing of the contours and selection of the defects size, which allows for the use of the map of defects to cut a selected stone into tiles with minimum waste, according the dimension of defects allowed.

Keywords: automatic detection, defects, fracture lines, wavelets

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Authors: Ranojoy Dutta, Adam Barker

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Multifamily residential buildings are increasingly being built with large glazed areas to provide tenants with greater daylight and outdoor views. However, traditional double-glazed window assemblies can lead to significant thermal discomfort from high radiant temperatures as well as increased cooling energy use to address solar gains. Dynamic glazing provides an effective solution by actively controlling solar transmission to maintain indoor thermal comfort, without compromising the visual connection to outdoors. This study uses thermal simulations across three Canadian cities (Toronto, Vancouver and Montreal) to verify if dynamic glazing along with operable windows and ceiling fans can maintain the indoor operative temperature of a prototype southwest facing high-rise apartment unit within the ASHRAE 55 adaptive comfort range for a majority of the year, without any mechanical cooling. Since this study proposes the use of natural ventilation for cooling and the typical building life cycle is 30-40 years, the typical weather files have been modified based on accepted global warming projections for increased air temperatures by 2050. Results for the prototype apartment confirm that thermal discomfort with dynamic glazing occurs only for less than 0.7% of the year. However, in the baseline scenario with low-E glass there are up to 7% annual hours of discomfort despite natural ventilation with operable windows and improved air movement with ceiling fans.

Keywords: electrochromic glazing, multi-family housing, passive cooling, thermal comfort, natural ventilation

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Authors: Fan Li, Chun-Hsien Chen, Li Pheng Khoo

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Operators of vessel traffic service (VTS) center provides three different types of services; namely information service, navigational assistance and traffic organization to vessels. To provide these services, operators monitor vessel traffic through computer interface and provide navigational advice based on the information integrated from multiple sources, including automatic identification system (AIS), radar system, and closed circuit television (CCTV) system. Therefore, this information is crucial in VTS operation. However, what information the VTS operator actually need to efficiently and properly offer services is unclear. The aim of this study is to investigate into information requirements for VTS operation. To achieve this aim, field observation was carried out to elicit the information requirements for VTS operation. The study revealed that the most frequent and important tasks were handling arrival vessel report, potential conflict control and abeam vessel report. Current location and vessel name were used in all tasks. Hazard cargo information was particularly required when operators handle arrival vessel report. The speed, the course, and the distance of two or several vessels were only used in potential conflict control. The information requirements identified in this study can be utilized in designing a human-computer interface that takes into consideration what and when information should be displayed, and might be further used to build the foundation of a decision support system for VTS.

Keywords: vessel traffic service, information requirements, hierarchy task analysis, field observation

Procedia PDF Downloads 252

Authors: A. El-S. Makled, M. K. Al-Tamimi

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A mathematical model to predict the performance parameters (thrusts, chamber pressures, fuel mass flow rates, mixture ratios, and regression rates during firing time) of hybrid rocket motor (HRM) is evaluated. The internal ballistic (IB) hybrid combustion model assumes that the solid fuel surface regression rate is controlled only by heat transfer (convective and radiative) from flame zone to solid fuel burning surface. A laboratory HRM is designed, manufactured, and tested for low thrust profile space missions (10-15 N) and for validating the mathematical model (computer program). The polymer material and gaseous oxidizer which are selected for this experimental work are polymethyle-methacrylate (PMMA) and polyethylene (PE) as solid fuel grain and gaseous oxygen (GO₂) as oxidizer. The variation of various operational parameters with time is determined systematically and experimentally in firing of up to 20 seconds, and an average combustion efficiency of 95% of theory is achieved, which was the goal of these experiments. The comparison between recording fire data and predicting analytical parameters shows good agreement with the error that does not exceed 4.5% during all firing time. The current mathematical (computer) code can be used as a powerful tool for HRM analytical design parameters.

Keywords: hybrid combustion, internal ballistics, hybrid rocket motor, performance parameters

Procedia PDF Downloads 316

Authors: K. T. Sun, Y. H. Tai, H. W. Yang, H. T. Lin

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This paper presented the technique of robot control by event-related potentials (ERPs) of brain waves. Based on the proposed technique, severe physical disabilities can free browse outside world. A specific component of ERPs, N2P3, was found and used to control the movement of robot and the view of camera on the designed brain-computer interface (BCI). Users only required watching the stimuli of attended button on the BCI, the evoked potentials of brain waves of the target button, N2P3, had the greatest amplitude among all control buttons. An experimental scene had been constructed that the robot required walking to a specific position and move the view of camera to see the instruction of the mission, and then completed the task. Twelve volunteers participated in this experiment, and experimental results showed that the correct rate of BCI control achieved 80% and the average of execution time was 353 seconds for completing the mission. Four main contributions included in this research: (1) find an efficient component of ERPs, N2P3, for BCI control, (2) embed robot's viewpoint image into user interface for robot control, (3) design an experimental scene and conduct the experiment, and (4) evaluate the performance of the proposed system for assessing the practicability.

Keywords: severe physical disabilities, robot control, event-related potentials (ERPs), brain-computer interface (BCI), brain waves

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Authors: Gozde Inal Kiziltepe, Ozgun Uyanik

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There is growing evidence that socioeconomic (SES)-related differences in mathematical knowledge primarily start in early childhood period. Preschoolers from low-income families are likely to perform substantially worse in mathematical knowledge than their counterparts from middle and higher income families. The differences are seen on a wide range of recognizing written numerals, counting, adding and subtracting, and comparing numerical magnitudes. Early differences in numerical knowledge have a permanent effect childrens’ mathematical knowledge in other grades. In this respect, analyzing the effect of number board game on the number knowledge of 48-60 month-old children from disadvantaged low-income families constitutes the main objective of the study. Participants were the 71 preschoolers from a childcare center which served low-income urban families. Children were randomly assigned to the number board condition or to the color board condition. The number board condition included 35 children and the color board game condition included 36 children. Both board games were 50 cm long and 30 cm high; had ‘The Great Race’ written across the top; and included 11 horizontally arranged, different colored squares of equal sizes with the leftmost square labeled ‘Start’. The numerical board had the numbers 1–10 in the rightmost 10 squares; the color board had different colors in those squares. A rabbit or a bear token were presented to children for selecting, and on each trial spun a spinner to determine whether the token would move one or two spaces. The number condition spinner had a ‘1’ half and a ‘2’ half; the color condition spinner had colors that matched the colors of the squares on the board. Children met one-on-one with an experimenter for four 15- to 20-min sessions within a 2-week period. In the first and fourth sessions, children were administered identical pretest and posttest measures of numerical knowledge. All children were presented three numerical tasks and one subtest presented in the following order: counting, numerical magnitude comparison, numerical identification and Count Objects – Circle Number Probe subtest of Early Numeracy Assessment. In addition, same numerical tasks and subtest were given as a follow-up test four weeks after the post-test administration. Findings obtained from the study; showed that there was a meaningful difference between scores of children who played a color board game in favor of children who played number board game.

Keywords: low income, numerical board game, numerical knowledge, preschool education

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Authors: Alae El Fahsi

Abstract:

This study explores the ‘Tajawob’ app's role in urban development, demonstrating how mobile applications can empower citizens and facilitate urban planning. Tajawob serves as a digital platform for community feedback, engagement, and participatory governance, addressing urban challenges through innovative tech solutions. This research synthesizes data from a variety of sources, including user feedback, engagement metrics, and interviews with city officials, to assess the app’s impact on citizen participation in urban development in Morocco. By integrating advanced data analytics and user experience design, Tajawob has bridged the communication gap between citizens and government officials, fostering a more collaborative and transparent urban planning process. The findings reveal a significant increase in civic engagement, with users actively contributing to urban management decisions, thereby enhancing the responsiveness and inclusivity of urban governance. Challenges such as digital literacy, infrastructure limitations, and privacy concerns are also discussed, providing a comprehensive overview of the obstacles and opportunities presented by mobile app-based citizen engagement platforms. The study concludes with strategic recommendations for scaling the Tajawob model to other contexts, emphasizing the importance of adaptive technology solutions in meeting the evolving needs of urban populations. This research contributes to the burgeoning field of smart city innovations, offering key insights into the role of digital tools in facilitating more democratic and participatory urban environments.

Keywords: smart cities, digital governance, urban planning, strategic design

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Authors: M. Taheri Tehrani, H. Ajorloo

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In this paper, an intelligent multi-agent framework is developed for each router in which agents have two vital functionalities, traffic shaping and buffer allocation and are positioned in the ports of the routers. With traffic shaping functionality agents shape the traffic forward by dynamic and real time allocation of the rate of generation of tokens in a Token Bucket algorithm and with buffer allocation functionality agents share their buffer capacity between each other based on their need and the conditions of the network. This dynamic and intelligent framework gives this opportunity to some ports to work better under burst and more busy conditions. These agents work intelligently based on Reinforcement Learning (RL) algorithm and will consider effective parameters in their decision process. As RL have limitation considering much parameter in its decision process due to the volume of calculations, we utilize our novel method which invokes Principle Component Analysis (PCA) on the RL and gives a high dimensional ability to this algorithm to consider as much as needed parameters in its decision process. This implementation when is compared to our previous work where traffic shaping was done without any sharing and dynamic allocation of buffer size for each port, the lower packet drop in the whole network specifically in the source routers can be seen. These methods are implemented in our previous proposed intelligent simulation environment to be able to compare better the performance metrics. The results obtained from this simulation environment show an efficient and dynamic utilization of resources in terms of bandwidth and buffer capacities pre allocated to each port.

Keywords: principal component analysis, reinforcement learning, buffer allocation, multi- agent systems

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Authors: Himanshu Sharma, Karthik Kumar, Harish Kumar

Abstract:

The major problem in crop cultivation is the occurrence of multiple crop diseases. During the growth stage, timely identification of crop diseases is paramount to ensure the high yield of crops, lower production costs, and minimize pesticide usage. In most cases, crop diseases produce observable characteristics and symptoms. The Surveyors usually diagnose crop diseases when they walk through the fields. However, surveyor inspections tend to be biased and error-prone due to the nature of the monotonous task and the subjectivity of individuals. In addition, visual inspection of each leaf or plant is costly, time-consuming, and labour-intensive. Furthermore, the plant pathologists and experts who can often identify the disease within the plant according to their symptoms in early stages are not readily available in remote regions. Therefore, this study specifically addressed early detection of leaf scald, red rot, and eyespot types of diseases within sugarcane plants. The study proposes a computer vision-based approach using a convolutional neural network (CNN) for automatic identification of crop diseases. To facilitate this, firstly, images of sugarcane diseases were taken from google without modifying the scene, background, or controlling the illumination to build the training dataset. Then, the testing dataset was developed based on the real-time collected images from the sugarcane field from India. Then, the image dataset is pre-processed for feature extraction and selection. Finally, the CNN-based Visual Geometry Group (VGG) model was deployed on the training and testing dataset to classify the images into diseased and healthy sugarcane plants and measure the model's performance using various parameters, i.e., accuracy, sensitivity, specificity, and F1-score. The promising result of the proposed model lays the groundwork for the automatic early detection of sugarcane disease. The proposed research directly sustains an increase in crop yield.

Keywords: automatic classification, computer vision, convolutional neural network, image processing, sugarcane disease, visual geometry group

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Authors: Zahra Alipour, Amirreza Moheb Afzali

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In the field of human-computer interaction (HCI), hand gestures play a crucial role in facilitating communication by expressing emotions and intentions. The precise tracking of the index finger and the estimation of joint positions are essential for developing effective gesture recognition systems. However, various challenges, such as anatomical variations, occlusions, and environmental influences, hinder optimal functionality. This study investigates the performance of the YOLOv8m model for hand detection using the EgoHands dataset, which comprises diverse hand gesture images captured in various environments. Over three training processes, the model demonstrated significant improvements in precision (from 88.8% to 96.1%) and recall (from 83.5% to 93.5%), achieving a mean average precision (mAP) of 97.3% at an IoU threshold of 0.7. We also compared YOLOv8m with MediaPipe and an integrated YOLOv8 + MediaPipe approach. The combined method outperformed the individual models, achieving an accuracy of 99% and a recall of 99%. These findings underscore the benefits of model integration in enhancing gesture recognition accuracy and localization for real-time applications. The results suggest promising avenues for future research in HCI, particularly in augmented reality and assistive technologies, where improved gesture recognition can significantly enhance user experience.

Keywords: YOLOv8, mediapipe, finger tracking, joint estimation, human-computer interaction (HCI)

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Authors: Ae Ra Han, Seoung Eun Huh, Ji Yeon Kim, Joanne Kwak-Kim, Sung Ki Lee

Abstract:

Objective: Prevalence of osteoporosis, cardiovascular disorders, and Alzheimer's disease rapidly increase after menopause. Immune activation and inflammation are suggested as an important pathogenesis of these serious diseases. Several pro-inflammatory cytokines are increased in women with surgical or natural menopause. However, the little is known about IL-17 producing T cells and Foxp3+ regulatory T (Treg) cells in post-menopause. Methods: A total of 34 postmenopausal women, who had no active cardiovascular, endocrine and infectious disorders were recruited as study group and healthy premenopausal women participated as controls. Peripheral blood mononuclear cells were isolated. Immuno-morphologic (CD3, CD4, CD8, CD19, CD56/CD16), intracellular cytokine (TNF-alpha, IFN-gamma, IL-10, IL-17), and Treg cell (Foxp3) studies were carried out using flow cytometry. The proportion of peripheral lymphocytes, including IL-17 producing and Foxp3+ Treg cells immune cell in each group were statistically analyzed. Results: The proportion of NK cells was significantly increased in menopausal women as compared to that of controls (P=.005). The ratios of TNF-alpha/IL-10 producing CD3+CD4+ T cells were increased in postmenopausal women. CD3+IL-17+ T cell level was higher in postmenopausal women and CD4+ Foxp3+ Treg cells was lower than that of controls. The ratios of CD3+IL-17+ T cell to CD3+Foxp3+ and to CD4+Foxp3+ Treg cells were significantly increased in postmenopausal women (P=.001). Conclusions: We found enhanced innate immunity and Th1- and Th17-mediated adaptive immunity in postmenopausal women. This may explain increasing prevalence of chronic inflammatory diseases after menopause. Further studies are needed to elucidate what factors contribute to this inflammatory shift in the postmenopause.

Keywords: inflammation, immune cell, menopause, Th17, regulatory T cell

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Authors: V. Santamarina Campos, M. de Miguel Molina, B. de Miguel Molina, M. Á. Carabal Montagud

Abstract:

With this contribution, we want to show a successful example of the application of the Design Thinking methodology, in the European project 'Technology transfer of Remotely Piloted Aircraft Systems (RPAS) for the creative industry'. The use of this methodology has allowed us to design and build a drone, based on the real needs of prospective users. It has demonstrated that this is a powerful tool for generating innovative ideas in the field of robotics, by focusing its effectiveness on understanding and solving real user needs. In this way, with the support of an interdisciplinary team, comprised of creatives, engineers and economists, together with the collaboration of prospective users from three European countries, a non-linear work dynamic has been created. This teamwork has generated a sense of appreciation towards the creative industries, through continuously adaptive, inventive, and playful collaboration and communication, which has facilitated the development of prototypes. These have been designed to enable filming and photography in interior spaces, within 13 sectors of European creative industries: Advertising, Architecture, Fashion, Film, Antiques and Museums, Music, Photography, Televison, Performing Arts, Publishing, Arts and Crafts, Design and Software. Furthermore, it has married the real needs of the creative industries, with what is technologically and commercially viable. As a result, a product of great value has been obtained, which offers new business opportunities for small companies across this sector.

Keywords: design thinking, design for effectiveness, methodology, active toolkit, storyboards, PAR, focus group, innovation, RPAS, indoor drone, aerial film, creative industry, end users, stakeholder

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Authors: Hussein Alahmer, Amr Ahmed

Abstract:

Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate.  This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.

Keywords: CAD system, difference of feature, fuzzy c means, lesion detection, liver segmentation

Procedia PDF Downloads 328