Search results for: Robust algorithm

Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 100

Authors: Anderson S. Fonseca, Italo F. S. Da Silva, Robert D. A. Santos, Mayara G. Da Silva, Pedro H. C. Vieira, Antonio M. S. Sobrinho, Victor H. B. Lemos, Petterson S. Diniz, Anselmo C. Paiva, Eliana M. G. Monteiro

Abstract:

In Brazil, the National Electric Energy Agency (ANEEL) establishes that electrical energy companies are responsible for measuring and billing their customers. Among these regulations, it’s defined that a company must bill your customers within 27-33 days. If a relocation or a change of period is required, the consumer must be notified in writing, in advance of a billing period. To make it easier to organize a workday’s measurements, these companies create a reading plan. These plans consist of grouping customers into reading groups, which are visited by an employee responsible for measuring consumption and billing. The creation process of a plan efficiently and optimally is a capacitated clustering problem with constraints related to homogeneity and compactness, that is, the employee’s working load and the geographical position of the consuming unit. This process is a work done manually by several experts who have experience in the geographic formation of the region, which takes a large number of days to complete the final planning, and because it’s human activity, there is no guarantee of finding the best optimization for planning. In this paper, the GBKMeans method presents a technique based on K-Means and genetic algorithms for creating a capacitated cluster that respects the constraints established in an efficient and balanced manner, that minimizes the cost of relocating consumer units and the time required for final planning creation. The results obtained by the presented method are compared with the current planning of a real city, showing an improvement of 54.71% in the standard deviation of working load and 11.97% in the compactness of the groups.

Keywords: capacitated clustering, k-means, genetic algorithm, districting problems

Procedia PDF Downloads 179

Authors: Joshua Kaufman, Felix Tan, Morgan Monroe, Ayman Abouraddy

Abstract:

Textiles and clothing have been a staple of human existence for millennia, yet the basic structure and functionality of textile fibers and yarns has remained unchanged. While color and appearance are essential characteristics of a textile, an advancement in the fabrication of yarns that allows for user-controlled dynamic changes to the color or appearance of a garment has been lacking. Touch-activated and photosensitive pigments have been used in textiles, but these technologies are passive and cannot be controlled by the user. The technology described here allows the owner to control both when and in what pattern the fabric color-change takes place. In addition, the manufacturing process is compatible with mass-producing the user-controlled, color-changing yarns. The yarn fabrication utilizes a fiber spinning system that can produce either monofilament or multifilament yarns. For products requiring a more robust fabric (backpacks, purses, upholstery, etc.), larger-diameter monofilament yarns with a coarser weave are suitable. Such yarns are produced using a thread-coater attachment to encapsulate a 38-40 AWG metal wire inside a polymer sheath impregnated with thermochromic pigment. Conversely, products such as shirts and pants requiring yarns that are more flexible and soft against the skin comprise multifilament yarns of much smaller-diameter individual fibers. Embedding a metal wire in a multifilament fiber spinning process has not been realized to date. This research has required collaboration with Hills, Inc., to design a liquid metal-injection system to be combined with fiber spinning. The new system injects molten tin into each of 19 filaments being spun simultaneously into a single yarn. The resulting yarn contains 19 filaments, each with a tin core surrounded by a polymer sheath impregnated with thermochromic pigment. The color change we demonstrate is distinct from garments containing LEDs that emit light in various colors. The pigment itself changes its optical absorption spectrum to appear a different color. The thermochromic color-change is induced by a temperature change in the inner metal wire within each filament when current is applied from a small battery pack. The temperature necessary to induce the color change is near body temperature and not noticeable by touch. The prototypes already developed either use a simple push button to activate the battery pack or are wirelessly activated via a smart-phone app over Wi-Fi. The app allows the user to choose from different activation patterns of stripes that appear in the fabric continuously. The power requirements are mitigated by a large hysteresis in the activation temperature of the pigment and the temperature at which there is full color return. This was made possible by a collaboration with Chameleon International to develop a new, customized pigment. This technology enables a never-before seen capability: user-controlled, dynamic color and pattern change in large-area woven and sewn textiles and fabrics with wide-ranging applications from clothing and accessories to furniture and fixed-installation housing and business décor. The ability to activate through Wi-Fi opens up possibilities for the textiles to be part of the ‘Internet of Things.’ Furthermore, this technology is scalable to mass-production levels for wide-scale market adoption.

Keywords: activation, appearance, color, manufacturing

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Authors: Monish Sengupta, Benjamin Heydecker, Daniel Woodland

Abstract:

New train control systems rely mainly on Automatic Train Protection (ATP) and Automatic Train Operation (ATO) dynamically to control the speed and hence performance. The ATP and the ATO form the vital element within the CBTC (Communication Based Train Control) and within the ERTMS (European Rail Traffic Management System) system architectures. Reliable and accurate measurement of train location, speed and acceleration are vital to the operation of train control systems. In the past, all CBTC and ERTMS system have deployed a balise or equivalent to correct the uncertainty element of the train location. Typically a CBTC train is allowed to miss only one balise on the track, after which the Automatic Train Protection (ATP) system applies emergency brake to halt the service. This is because the location uncertainty, which grows within the train control system, cannot tolerate missing more than one balise. Balises contribute a significant amount towards wayside maintenance and studies have shown that balises on the track also forms a constraint for future track layout change and change in speed profile.This paper investigates the causes of the location uncertainty that is currently experienced and considers whether it is possible to identify an effective filter to ascertain, in conjunction with appropriate sensors, more accurate speed, distance and location for a CBTC driven train without the need of any external balises. An appropriate sensor fusion algorithm and intelligent sensor selection methodology will be deployed to ascertain the railway location and speed measurement at its highest precision. Similar techniques are already in use in aviation, satellite, submarine and other navigation systems. Developing a model for the speed control and the use of Kalman filter is a key element in this research. This paper will summarize the research undertaken and its significant findings, highlighting the potential for introducing alternative approaches to train positioning that would enable removal of all trackside location correction balises, leading to huge reduction in maintenances and more flexibility in future track design.

Keywords: ERTMS, CBTC, ATP, ATO

Procedia PDF Downloads 396

Authors: Su Yen Chen

Abstract:

Extensive research on ChatGPT has revealed its capabilities and limitations across various clinical, educational, and research contexts, emphasizing crucial issues such as accuracy, transparency, and ethical usage. Studies applying the Technology Acceptance Model (TAM) and Uses and Gratifications Theory have deepened our understanding of the factors that drive user acceptance and satisfaction of ChatGPT for general use. These insights are particularly valuable for examining healthcare-specific behaviors, trust levels, and perceived risks. Despite these advancements, there remains a notable gap in our understanding of how general user perceptions of AI translate into its practical applications within the healthcare sector. This study focuses on examining the perceptions of ChatGPT's impact among 266 healthcare majors in Taiwan, exploring its implications for their career development and utility in clinical practice, medical education, and research. By employing a structured survey with precisely defined subscales, this research aims to probe the breadth of ChatGPT's applications within healthcare, assessing both the perceived benefits and the challenges it presents. The findings from the survey reveal that perceptions and usage of ChatGPT among healthcare majors vary significantly, influenced by factors such as its perceived utility, risk, novelty, and trustworthiness. Graduate students and those who perceive ChatGPT as more beneficial and less risky are particularly inclined to use it more frequently. This increased usage is closely linked to significant impacts on personal career development. Furthermore, ChatGPT's perceived usefulness contributes to its broader impact within the healthcare domain, suggesting that both innovation and practical utility are key drivers of acceptance and perceived effectiveness in professional healthcare settings. Trust emerges as an important factor, especially in clinical settings where the stakes are high. The trust that healthcare professionals place in ChatGPT significantly affects its integration into clinical practice and influences outcomes in medical education and research. Thus, ChatGPT's reliability and practical value are critical for its successful adoption in these areas. However, an interesting paradox arises with regard to ease of use. While making ChatGPT more user-friendly is generally seen as beneficial, it also raises concerns among users who have lower levels of trust and perceive higher risks associated with its use. This complex interplay between ease of use and safety concerns necessitates a careful balance, highlighting the need for robust security measures and clear, transparent communication about how AI systems work and their limitations. The study suggests several strategic approaches to enhance the adoption and integration of AI in healthcare. These include targeted training programs for healthcare professionals to increase familiarity with AI technologies, reduce perceived risks, and build trust. Ensuring transparency and conducting rigorous testing are also vital to foster trust and reliability. Moreover, comprehensive policy frameworks are needed to guide the implementation of AI technologies, ensuring high standards of patient safety, privacy, and ethical use. These measures are crucial for fostering broader acceptance of AI in healthcare, as the study contributes to enriching the discourse on AI's role by detailing how various factors affect its adoption and impact.

Keywords: ChatGPT, healthcare, survey study, IT adoption, behaviour, applcation, concerns

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Authors: Shanhua Hu

Abstract:

Social media as a critical propagandizing form of film, video games, and digital products has received substantial research attention, but there exists several critical barriers such as: (1) few studies exploring the internal and external connections of a product as part of the multimodal context that gives rise to readability and commercial return; (2) the lack of study of multimodal analysis in product’s official account of game publishers and its impact on users’ behaviors including purchase intention, social media engagement, and playing time; (3) no standardized ecologically-valid, game type-varying data can be used to study the complexity of official account’s postings within a time period. This proposed research helps to tackle these limitations in order to develop a model of readability study that is more ecologically valid, robust, and thorough. To accomplish this objective, this paper provides a more diverse dataset comprising different visual elements and messages collected from the official Twitter accounts of the Top 20 best-selling games of 2021. Video game companies target potential users through social media, a popular approach is to set up an official account to maintain exposure. Typically, major game publishers would create an official account on Twitter months before the game's release date to update on the game's development, announce collaborations, and reveal spoilers. Analyses of tweets from those official Twitter accounts would assist publishers and marketers in identifying how to efficiently and precisely deploy advertising to increase game sales. The purpose of this research is to determine how official game accounts use Twitter to attract new customers, specifically which types of messages are most effective at increasing sales. The dataset includes the number of days until the actual release date on Twitter posts, the readability of the post (Flesch Reading Ease Score, FRES), the number of emojis used, the number of hashtags, the number of followers of the mentioned users, the categorization of the posts (i.e., spoilers, collaborations, promotions), and the number of video views. The timeline of Twitter postings from official accounts will be compared to the history of pre-orders and sales figures to determine the potential impact of social media posts. This study aims to determine how the above-mentioned characteristics of official accounts' Twitter postings influence the sales of the game and to examine the possible causes of this influence. The outcome will provide researchers with a list of potential aspects that could influence people's judgments in limited-word posts. With the increased average online time, users would adapt more quickly than before in online information exchange and readings, such as the word to use sentence length, and the use of emojis or hashtags. The study on the promotion of official game accounts will not only enable publishers to create more effective promotion techniques in the future but also provide ideas for future research on the influence of social media posts with a limited number of words on consumers' purchasing decisions. Future research can focus on more specific linguistic aspects, such as precise word choice in advertising.

Keywords: engagement, official account, promotion, twitter, video game

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Authors: Luis Amador Jimenez, Md. Shohel Amin

Abstract:

Pavement performance models are based on projections of observed traffic loads, which makes uncertain to study funding strategies in the long run if history does not repeat. Neural networks can be used to estimate deterioration rates but the learning rate and momentum have not been properly investigated, in addition, economic evolvement could change traffic flows. This study addresses both issues through a case study for roads of Montreal that simulates traffic for a period of 50 years and deals with the measurement error of the pavement deterioration model. Travel demand models are applied to simulate annual average daily traffic (AADT) every 5 years. Accumulated equivalent single axle loads (ESALs) are calculated from the predicted AADT and locally observed truck distributions combined with truck factors. A back propagation Neural Network (BPN) method with a Generalized Delta Rule (GDR) learning algorithm is applied to estimate pavement deterioration models capable of overcoming measurement errors. Linear programming of lifecycle optimization is applied to identify M&R strategies that ensure good pavement condition while minimizing the budget. It was found that CAD 150 million is the minimum annual budget to good condition for arterial and local roads in Montreal. Montreal drivers prefer the use of public transportation for work and education purposes. Vehicle traffic is expected to double within 50 years, ESALS are expected to double the number of ESALs every 15 years. Roads in the island of Montreal need to undergo a stabilization period for about 25 years, a steady state seems to be reached after.

Keywords: pavement management system, traffic simulation, backpropagation neural network, performance modeling, measurement errors, linear programming, lifecycle optimization

Procedia PDF Downloads 440

Authors: Daria Zabolotnaya, Svetlana Degtyareva, Veronika Kanestri, Danila Konnov

Abstract:

An adequate choice of the initial antiretroviral treatment determines the treatment efficacy. In the clinical guidelines in Russia non-nucleoside reverse transcriptase inhibitors (NNRTIs) are still considered to be an option for first-line treatment while pretreatment drug resistance (PDR) testing is not routinely performed. We conducted a cohort retrospective study in HIV-positive treatment naïve patients of the H-clinic (Moscow, Russia) who performed PDR testing from July 2017 to November 2021. All the information was obtained from the medical records anonymously. We analyzed the mutations in reverse transcriptase and protease genes. RT-sequences were obtained by AmpliSens HIV-Resist-Seq kit. Drug resistance was defined using the HIVdb Program v. 8.9-1. PDR was estimated using the Stanford algorithm. Descriptive statistics were performed in Excel (Microsoft Office, 2019). A total of 261 HIV-1 infected patients were enrolled in the study including 197 (75.5%) male and 64 (24.5%) female. The mean age was 34.6±8.3 years. The median CD4 count – 521 cells/µl (IQR 367-687 cells/µl). Data on risk factors of HIV-infection were scarce. The total quantity of strains containing mutations in the reverse transcriptase gene was 75 (28.7%). From these 5 (1.9%) mutations were associated with PDR to nucleoside reverse transcriptase inhibitors (NRTIs) and 30 (11.5%) – with PDR to NNRTIs. The number of strains with mutations in protease gene was 43 (16.5%), from these only 3 (1.1%) mutations were associated with resistance to protease inhibitors. For NNRTIs the most prevalent PDR mutations were E138A, V106I. Most of the HIV variants exhibited a single PDR mutation, 2 were found in 3 samples. Most of HIV variants with PDR mutation displayed a single drug class resistance mutation. 2/37 (5.4%) strains had both NRTIs and NNRTIs mutations. There were no strains identified with PDR mutations to all three drug classes. Though earlier data demonstrated a lower level of PDR in HIV treatment naïve population in Russia and our cohort can be not fully representative as it is taken from the private clinic, it reflects the trend of increasing PDR especially to NNRTIs. Therefore, we consider either pretreatment testing or giving the priority to other drugs as first-line treatment necessary.

Keywords: HIV, resistance, mutations, treatment

Procedia PDF Downloads 75

Authors: Rashima Mahajan, Dipali Bansal, Shweta Singh

Abstract:

Real time non-invasive Brain Computer Interfaces have a significant progressive role in restoring or maintaining a quality life for medically challenged people. This manuscript provides a comprehensive review of emerging research in the field of cognitive/affective computing in context of human neural responses. The perspectives of different emotion assessment modalities like face expressions, speech, text, gestures, and human physiological responses have also been discussed. Focus has been paid to explore the ability of EEG (Electroencephalogram) signals to portray thoughts, feelings, and unspoken words. An automated workflow-based protocol to design an EEG-based real time Brain Computer Interface system for analysis and classification of human emotions elicited by external audio/visual stimuli has been proposed. The front end hardware includes a cost effective and portable Emotive EEG Neuroheadset unit, a personal computer and a set of external stimulators. Primary signal analysis and processing of real time acquired EEG shall be performed using MATLAB based advanced brain mapping toolbox EEGLab/BCILab. This shall be followed by the development of MATLAB based self-defined algorithm to capture and characterize temporal and spectral variations in EEG under emotional stimulations. The extracted hybrid feature set shall be used to classify emotional states using artificial intelligence tools like Artificial Neural Network. The final system would result in an inexpensive, portable and more intuitive Brain Computer Interface in real time scenario to control prosthetic devices by translating different brain states into operative control signals.

Keywords: brain computer interface, electroencephalogram, EEGLab, BCILab, emotive, emotions, interval features, spectral features, artificial neural network, control applications

Procedia PDF Downloads 301

Authors: Keith Schofield, Garry R. Prentice

Abstract:

The perceptions of the work an academic does, and the environment in which they do it, contributes to the professional identity of that academic. In turn, this has implications for the level of involvement they have in their job, their satisfaction, and their work product. This research explores academic identities in British and Irish institutions and considers the complex interplay between identity, practice, and participation. Theoretical assumptions made in this paper assert that meaningful work has positive effects on work pride, organisational commitment, organisational citizenship, and motivation; when employees participate enthusiastically they are likely to be more engaged, more successful, and more satisfied. Further examination is given to the context in which this participation happens; the nature of institutional process, management, and relationships with colleagues, team members, and students is considered. The present study follows a mixed-methods approach to explore work satisfaction constructs in a number of academic contexts in the UK and Ireland. The quantitative component of this research (Convenience Sample: 155 academics, and support/ administrative staff; 36.1% male, 63.9% female; 60.8% academic staff, 39.2% support/ administration staff; across a number of universities in the UK and Ireland) was based on an established emotional labour model and was tested across gender groups, job roles, and years of service. This was complimented by qualitative semi-structured interviews (Purposive Sample: 10 academics, and 5 support/ administrative staff across the same universities in the UK and Ireland) to examine various themes including values within academia, work conditions, professional development, and transmission of knowledge to students. Experiences from both academic and support perspectives were sought in order to gain a holistic view of academia and to provide an opportunity to explore the dynamic of the academic/administrator relationship within the broader institutional context. The quantitative emotional labour model, tested via a path analysis, provided a robust description of the relationships within the data. The significant relationships found within the quantitative emotional labour model included a link between non-expression of true feelings resulting in emotional labourious work and lower levels of intrinsic motivation and higher levels of extrinsic motivation. Higher levels of intrinsic motivation also linked positively to work pride. These findings were further explored in the qualitative elements of the research where themes emerged including the disconnection between faculty management and staff, personal fulfilment and the friction between the identities of teacher, researcher/ practitioner and administrator. The implications of the research findings from this study are combined and discussed in relation to possible identity-related and emotional labour management-related interventions. Further, suggestions are made to institutions concerning the application of these findings including the development of academic practices, with specific reference to the duality of identity required to service the combined teacher/ researcher role. Broader considerations of the paper include how individuals and institutions may engage with the changing nature of students-as-consumers as well as a recommendation to centralise personal fulfillment through the development of professional academic identities.

Keywords: academic work, emotional labour, identity friction, mixed methods

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Authors: Pedro Llanos, Diego García

Abstract:

Spaceflight is considered the last frontier in terms of science, technology, and engineering. But it is also the next frontier in terms of human physiology and performance. After more than 200,000 years humans have evolved under earth’s gravity and atmospheric conditions, spaceflight poses environmental stresses for which human physiology is not adapted. Hypoxia, accelerations, and radiation are among such stressors, our research involves suborbital flights aiming to develop effective countermeasures in order to assure sustainable human space presence. The physiologic baseline of spaceflight participants is subject to great variability driven by age, gender, fitness, and metabolic reserve. The objective of the present study is to characterize different physiologic variables in a population of STEM practitioners during an aerobatic flight. Cardiovascular and pulmonary responses were determined in Science Astronaut Candidates (SACs) during unusual attitude aerobatic flight indoctrination. Physiologic data recordings from 20 subjects participating in high-G flight training were analyzed. These recordings were registered by wearable sensor-vest that monitored electrocardiographic tracings (ECGs), signs of dysrhythmias or other electric disturbances during all the flight. The same cardiovascular parameters were also collected approximately 10 min pre-flight, during each high-G/unusual attitude maneuver and 10 min after the flights. The ratio (pre-flight/in-flight/post-flight) of the cardiovascular responses was calculated for comparison of inter-individual differences. The resulting tracings depicting the cardiovascular responses of the subjects were compared against the G-loads (Gs) during the aerobatic flights to analyze cardiovascular variability aspects and fluid/pressure shifts due to the high Gs. In-flight ECG revealed cardiac variability patterns associated with rapid Gs onset in terms of reduced heart rate (HR) and some scattered dysrhythmic patterns (15% premature ventricular contractions-type) that were considered as triggered physiological responses to high-G/unusual attitude training and some were considered as instrument artifact. Variation events were observed in subjects during the +Gz and –Gz maneuvers and these may be due to preload and afterload, sudden shift. Our data reveal that aerobatic flight influenced the breathing rate of the subject, due in part by the various levels of energy expenditure due to the increased use of muscle work during these aerobatic maneuvers. Noteworthy was the high heterogeneity in the different physiological responses among a relatively small group of SACs exposed to similar aerobatic flights with similar Gs exposures. The cardiovascular responses clearly demonstrated that SACs were subjected to significant flight stress. Routine ECG monitoring during high-G/unusual attitude flight training is recommended to capture pathology underlying dangerous dysrhythmias in suborbital flight safety. More research is currently being conducted to further facilitate the development of robust medical screening, medical risk assessment approaches, and suborbital flight training in the context of the evolving commercial human suborbital spaceflight industry. A more mature and integrative medical assessment method is required to understand the physiology state and response variability among highly diverse populations of prospective suborbital flight participants.

Keywords: g force, aerobatic maneuvers, suborbital flight, hypoxia, commercial astronauts

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Authors: Young-Tae Lee, Hee-Chang Lim

Abstract:

This paper presents the performance characteristics of Darrieus-type vertical axis wind turbine (VAWT) with NACA airfoil blades. The performance of Darrieus-type VAWT can be characterized by torque and power. There are various parameters affecting the performance such as chord length, helical angle, pitch angle and rotor diameter. To estimate the optimum shape of Darrieustype wind turbine in accordance with various design parameters, we examined aerodynamic characteristics and separated flow occurring in the vicinity of blade, interaction between flow and blade, and torque and power characteristics derived from it. For flow analysis, flow variations were investigated based on the unsteady RANS (Reynolds-averaged Navier-Stokes) equation. Sliding mesh algorithm was employed in order to consider rotational effect of blade. To obtain more realistic results we conducted experiment and numerical analysis at the same time for three-dimensional shape. In addition, several parameters (chord length, rotor diameter, pitch angle, and helical angle) were considered to find out optimum shape design and characteristics of interaction with ambient flow. Since the NACA airfoil used in this study showed significant changes in magnitude of lift and drag depending on an angle of attack, the rotor with low drag, long cord length and short diameter shows high power coefficient in low tip speed ratio (TSR) range. On the contrary, in high TSR range, drag becomes high. Hence, the short-chord and long-diameter rotor produces high power coefficient. When a pitch angle at which airfoil directs toward inside equals to -2° and helical angle equals to 0°, Darrieus-type VAWT generates maximum power.

Keywords: darrieus wind turbine, VAWT, NACA airfoil, performance

Procedia PDF Downloads 351

Authors: K. R. Roopesh Bharatwaj, Avinash Maharana, Favour Tobi Aborisade, Roger Young

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Presently, there are few cases of complete automation of drones and its allied intelligence capabilities. In essence, the potential of the drone has not yet been fully utilized. This paper presents feasible methods to build an intelligent drone with smart capabilities such as self-driving, and obstacle avoidance. It does this through advanced Reinforcement Learning Techniques and performs object detection using latest advanced algorithms, which are capable of processing light weight models with fast training in real time instances. For the scope of this paper, after researching on the various algorithms and comparing them, we finally implemented the Deep-Q-Networks (DQN) algorithm in the AirSim Simulator. In future works, we plan to implement further advanced self-driving and object detection algorithms, we also plan to implement voice-based speech recognition for the entire drone operation which would provide an option of speech communication between users (People) and the drone in the time of unavoidable circumstances. Thus, making drones an interactive intelligent Robotic Voice Enabled Service Assistant. This proposed drone has a wide scope of usability and is applicable in scenarios such as Disaster management, Air Transport of essentials, Agriculture, Manufacturing, Monitoring people movements in public area, and Defense. Also discussed, is the entire drone communication based on the satellite broadband Internet technology for faster computation and seamless communication service for uninterrupted network during disasters and remote location operations. This paper will explain the feasible algorithms required to go about achieving this goal and is more of a reference paper for future researchers going down this path.

Keywords: convolution neural network, natural language processing, obstacle avoidance, satellite broadband technology, self-driving

Procedia PDF Downloads 226

Authors: Claudia Trillo

Abstract:

Regional innovation eco-ecosystems are composed of a variety of interconnected urban innovation eco-systems, mutually reinforcing each other and making the whole territorial system successful. Combining principles drawn from the new economic growth theory and from the socio-constructivist approach to the economic growth, with the new geography of innovation emerging from the networked nature of innovation districts, this paper explores the spatial configuration of urban innovation districts, with the aim of unveiling replicable spatial patterns and transferable portfolios of urban policies. While some authors suggest that cities should be considered ideal natural clusters, supporting cross-fertilization and innovation thanks to the physical setting they provide to the construction of collective knowledge, still a considerable distance persists between regional development strategies and urban policies. Moreover, while public and private policies supporting entrepreneurship normally consider innovation as the cornerstone of any action aimed at uplifting the competitiveness and economic success of a certain area, a growing body of literature suggests that innovation is non-neutral, hence, it should be constantly assessed against equity and social inclusion. This paper draws from a robust qualitative empirical dataset gathered through 4-years research conducted in Boston to provide readers with an evidence-based set of recommendations drawn from the lessons learned through the investigation of the chosen innovation districts in the Boston area. The evaluative framework used for assessing the overall performance of the chosen case studies stems from the Habitat III Sustainable Development Goals rationale. The concept of inclusive growth has been considered essential to assess the social innovation domain in each of the chosen cases. The key success factors for the development of the Boston innovation ecosystem can be generalized as follows: 1) a quadruple helix model embedded in the physical structure of the two cities (Boston and Cambridge), in which anchor Higher Education (HE) institutions continuously nurture the Entrepreneurial Environment. 2) an entrepreneurial approach emerging from the local governments, eliciting risk-taking and bottom-up civic participation in tackling key issues in the city. 3) a networking structure of some intermediary actors supporting entrepreneurial collaboration, cross-fertilization and co-creation, which collaborate at multiple-scales thus enabling positive spillovers from the stronger to the weaker contexts. 4) awareness of the socio-economic value of the built environment as enabler of cognitive networks allowing activation of the collective intelligence. 5) creation of civic-led spaces enabling grassroot collaboration and cooperation. Evidence shows that there is not a single magic recipe for the successful implementation of place-based and social innovation-driven strategies. On the contrary, the variety of place-grounded combinations of micro and macro initiatives, embedded in the social and spatial fine grain of places and encompassing a diversity of actors, can create the conditions enabling places to thrive and local economic activities to grow in a sustainable way.

Keywords: innovation-driven sustainable Eco-systems , place-based sustainable urban development, sustainable innovation districts, social innovation, urban policie

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Authors: Ana Londral, Burcu Demiray, Marcus Cheetham

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Speech recording is a methodology used in many different studies related to cognitive and behaviour research. Modern advances in digital equipment brought the possibility of continuously recording hours of speech in naturalistic environments and building rich sets of sound files. Speech analysis can then extract from these files multiple features for different scopes of research in Language and Communication. However, tools for analysing a large set of sound files and automatically extract relevant features from these files are often inaccessible to researchers that are not familiar with programming languages. Manual analysis is a common alternative, with a high time and efficiency cost. In the analysis of long sound files, the first step is the voice segmentation, i.e. to detect and label segments containing speech. We present a comprehensive methodology aiming to support researchers on voice segmentation, as the first step for data analysis of a big set of sound files. Praat, an open source software, is suggested as a tool to run a voice detection algorithm, label segments and files and extract other quantitative features on a structure of folders containing a large number of sound files. We present the validation of our methodology with a set of 5000 sound files that were collected in the daily life of a group of voluntary participants with age over 65. A smartphone device was used to collect sound using the Electronically Activated Recorder (EAR): an app programmed to record 30-second sound samples that were randomly distributed throughout the day. Results demonstrated that automatic segmentation and labelling of files containing speech segments was 74% faster when compared to a manual analysis performed with two independent coders. Furthermore, the methodology presented allows manual adjustments of voiced segments with visualisation of the sound signal and the automatic extraction of quantitative information on speech. In conclusion, we propose a comprehensive methodology for voice segmentation, to be used by researchers that have to work with large sets of sound files and are not familiar with programming tools.

Keywords: automatic speech analysis, behavior analysis, naturalistic environments, voice segmentation

Procedia PDF Downloads 267

Authors: Ling Liyun

Abstract:

In the field of contemporary architecture, complex forms of architectural works continue to emerge in the world, along with some new terminology emerged: digital architecture, parametric design, algorithm generation, building information modeling, CNC construction and so on. Architects gradually mastered the new skills of mathematical logic in the form of exploration, virtual simulation, and the entire design and coordination in the construction process. Digital construction technology has a greater degree in controlling construction, and ensure its accuracy, creating a series of new construction techniques. As a result, the use of digital technology is an improvement and expansion of the practice of digital architecture design revolution. We worked by reading and analyzing information about the digital architecture development process, a large number of cases, as well as architectural design and construction as a whole process. Thus current developments were introduced and discussed in our paper, such as architectural discourse, design theory, digital design models and techniques, material selecting, as well as artificial intelligence space design. Our paper also pays attention to the representative three cases of digital design and construction experiment at great length in detail to expound high-informatization, high-reliability intelligence, and high-technique in constructing a humane space to cope with the rapid development of urbanization. We concluded that the opportunities and challenges of the shift existed in architectural paradigms, such as the cooperation methods, theories, models, technologies and techniques which were currently employed in digital design research and digital praxis. We also find out that the innovative use of space can gradually change the way people learn, talk, and control information. The past two decades, digital technology radically breaks the technology constraints of industrial technical products, digests the publicity on a particular architectural style (era doctrine). People should not adapt to the machine, but in turn, it’s better to make the machine work for users.

Keywords: artificial intelligence, collaboration, digital architecture, digital design theory, material selection, space construction

Procedia PDF Downloads 119

Authors: Antonios Paraskevas, Michael Madas

Abstract:

For a university to maintain its international competitiveness in education, it is essential to recruit qualitative academic staff as it constitutes its most valuable asset. This selection demonstrates a significant role in achieving strategic objectives, particularly by emphasizing a firm commitment to the exceptional student experience and innovative teaching and learning practices of high quality. In this vein, the appropriate selection of academic staff establishes a very important factor of competitiveness, efficiency and reputation of an academic institute. Within this framework, our work demonstrates a comprehensive methodological concept that emphasizes the multi-criteria nature of the problem and how decision-makers could utilize our approach in order to proceed to the appropriate judgment. The conceptual framework introduced in this paper is built upon a hybrid neutrosophic method based on the Neutrosophic Analytical Hierarchy Process (N-AHP), which uses the theory of neutrosophy sets and is considered suitable in terms of a significant degree of ambiguity and indeterminacy observed in the decision-making process. To this end, our framework extends the N-AHP by incorporating the Neutrosophic Delphi Method (N-DM). By applying the N-DM, we can take into consideration the importance of each decision-maker and their preferences per evaluation criterion. To the best of our knowledge, the proposed model is the first which applies the Neutrosophic Delphi Method in the selection of academic staff. As a case study, it was decided to use our method for a real problem of academic personnel selection, having as the main goal to enhance the algorithm proposed in previous scholars’ work, and thus taking care of the inherent ineffectiveness which becomes apparent in traditional multi-criteria decision-making methods when dealing with situations alike. As a further result, we prove that our method demonstrates greater applicability and reliability when compared to other decision models.

Keywords: multi-criteria decision making methods, analytical hierarchy process, delphi method, personnel recruitment, neutrosophic set theory

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Authors: Ghufran Ahmed, Muhammad Osama Siddiqui, Shahbaz Siddiqui, Rauf Ahmad Shams Malick, Faisal Khan, Mubashir Khan

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In this paper, a machine learning-based approach for early prediction of diseases in cows is proposed. Different ML algos are applied to extract useful patterns from the available dataset. Technology has changed today’s world in every aspect of life. Similarly, advanced technologies have been developed in livestock and dairy farming to monitor dairy cows in various aspects. Dairy cattle monitoring is crucial as it plays a significant role in milk production around the globe. Moreover, it has become necessary for farmers to adopt the latest early prediction technologies as the food demand is increasing with population growth. This highlight the importance of state-ofthe-art technologies in analyzing how important technology is in analyzing dairy cows’ activities. It is not easy to predict the activities of a large number of cows on the farm, so, the system has made it very convenient for the farmers., as it provides all the solutions under one roof. The cattle industry’s productivity is boosted as the early diagnosis of any disease on a cattle farm is detected and hence it is treated early. It is done on behalf of the machine learning output received. The learning models are already set which interpret the data collected in a centralized system. Basically, we will run different algorithms on behalf of the data set received to analyze milk quality, and track cows’ health, location, and safety. This deep learning algorithm draws patterns from the data, which makes it easier for farmers to study any animal’s behavioral changes. With the emergence of machine learning algorithms and the Internet of Things, accurate tracking of animals is possible as the rate of error is minimized. As a result, milk productivity is increased. IoT with ML capability has given a new phase to the cattle farming industry by increasing the yield in the most cost-effective and time-saving manner.

Keywords: IoT, machine learning, health care, dairy cows

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Authors: Amèdédjihundé H. J. Hounnou, Frédéric Dubas, François-Xavier Fifatin, Didier Chamagne, Antoine Vianou

Abstract:

This paper presents the techno-economic evaluation of run-of-river small-hydropower plants. In this regard, a multi-objective optimization procedure is proposed for the optimal sizing of the hydropower plants, and NSGAII is employed as the optimization algorithm. Annual generated energy and investment cost are considered as the objective functions, and number of generator units (n) and nominal turbine flow rate (Q_T) constitute the decision variables. Site of Yeripao in Benin is considered as the case study. We have categorized the river of this site using its environmental characteristics: gross head, and first quartile, median, third quartile and mean of flow. Effects of each decision variable on the objective functions are analysed. The results gave Pareto Front which represents the trade-offs between annual energy generation and the investment cost of hydropower plants, as well as the recommended optimal solutions. We noted that with the increase of the annual energy generation, the investment cost rises. Thus, maximizing energy generation is contradictory with minimizing the investment cost. Moreover, we have noted that the solutions of Pareto Front are grouped according to the number of generator units (n). The results also illustrate that the costs per kWh are grouped according to the n and rise with the increase of the nominal turbine flow rate. The lowest investment costs per kWh are obtained for n equal to one and are between 0.065 and 0.180 €/kWh. Following the values of n (equal to 1, 2, 3 or 4), the investment cost and investment cost per kWh increase almost linearly with increasing the nominal turbine flowrate while annual generated. Energy increases logarithmically with increasing of the nominal turbine flowrate. This study made for the Yeripao river can be applied to other rivers with their own characteristics.

Keywords: hydropower plant, investment cost, multi-objective optimization, number of generator units

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Authors: Wajeeha Zahid, Shafquat Rozi, Farhan Raza, Masood Kadir

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Background: Dental caries affects the overall health and well-being of children. Findings from various international studies regarding the association of dental caries with asthma are inconsistent. With the increasing burden of caries and childhood asthma, it becomes imperative for an underdeveloped country like Pakistan where resources are limited to identify whether there is a relationship between the two. This study aims to identify an association between dental caries and asthma. Methods: A cross-sectional study was conducted on 544 children aged 12-15 years recruited from five private schools in Karachi. Information on asthma was collected through the International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire. The questionnaire addressed questions regarding child’s demographics, physician diagnoses of asthma, type of medication administered, family history of asthma and allergies, dietary habits and oral hygiene behavior. Dental caries was assessed using DMFT Index (Decayed, Missing, Filled teeth) index The data was analyzed using Cox proportional Hazard algorithm and crude and adjusted prevalence ratios with 95% CI were reported. Results: This study comprises of 306 (56.3%) boys and 238 (43.8%) girls. The mean age of children was 13.2 ± (0.05) years. The total number of children with carious teeth (DMFT > 0) were 166/544 (30.5%), and the decayed component contributed largely (22.8%) to the DMFT score. The prevalence of physician’s diagnosed asthma was 13%. This study identified almost 7% asthmatic children using the internationally validated International Study of Asthma and Allergies in Childhood (ISAAC) tool and 8 children with childhood asthma were identified by parent interviews. Overall prevalence of asthma was 109/544 (20%). The prevalence of caries in asthmatic children was 28.4% as compared to 31% among non-asthmatic children. The adjusted prevalence ratio of dental caries in asthmatic children was 0.8 (95% CI 0.59-1.29). After adjusting for carious food intake, age, oral hygiene index and dentist visit, the association between asthma and dental caries turned out to be non-significant. Conclusion: There was no association between asthma and dental caries among children who participated in this study.

Keywords: asthma, caries, children, school-based

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Authors: Ibrahim Khan, Waqas Khalid

Abstract:

The Healthcare sector is known to consume a higher proportion of total energy consumption in the HVAC market owing to an excessive cooling and heating requirement in maintaining human thermal comfort in indoor conditions, catering to patients undergoing treatment in hospital wards, rooms, and intensive care units. The indoor thermal comfort conditions in selected hospitals of Islamabad, Pakistan, were measured on a real-time basis with the collection of first-hand experimental data using calibrated sensors measuring Ambient Temperature, Wet Bulb Globe Temperature, Relative Humidity, Air Velocity, Light Intensity and CO2 levels. The Experimental data recorded was analyzed in conjunction with the Thermal Comfort Questionnaire Surveys, where the participants, including patients, doctors, nurses, and hospital staff, were assessed based on their thermal sensation, acceptability, preference, and comfort responses. The Recorded Dataset, including experimental and survey-based responses, was further analyzed in the development of a correlation between operative temperature, operative relative humidity, and other measured operative parameters with the predicted mean vote and adaptive predicted mean vote, with the adaptive temperature and adaptive relative humidity estimated using the seasonal data set gathered for both summer – hot and dry, and hot and humid as well as winter – cold and dry, and cold and humid climate conditions. The Machine Learning Logistic Regression Algorithm was incorporated to train the operative experimental data parameters and develop a correlation between patient sensations and the thermal environmental parameters for which a new ML-based adaptive thermal comfort model was proposed and developed in our study. Finally, the accuracy of our model was determined using the K-fold cross-validation.

Keywords: predicted mean vote, thermal comfort, energy management, logistic regression, machine learning

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Authors: Yaolin Tian, Shanxiong Chen, Fujia Zhao, Xiaoyu Lin, Hailing Xiong

Abstract:

Ancient books are significant culture inheritors and their background textures convey the potential history information. However, multi-style texture recovery of ancient books has received little attention. Restricted by insufficient ancient textures and complex handling process, the generation of ancient textures confronts with new challenges. For instance, training without sufficient data usually brings about overfitting or mode collapse, so some of the outputs are prone to be fake. Recently, image generation and style transfer based on deep learning are widely applied in computer vision. Breakthroughs within the field make it possible to conduct research upon multi-style texture recovery of ancient books. Under the circumstances, we proposed a network of layout analysis and image fusion system. Firstly, we trained models by using Deep Convolution Generative against Networks (DCGAN) to synthesize multi-style ancient textures; then, we analyzed layouts based on the Position Rearrangement (PR) algorithm that we proposed to adjust the layout structure of foreground content; at last, we realized our goal by fusing rearranged foreground texts and generated background. In experiments, diversified samples such as ancient Yi, Jurchen, Seal were selected as our training sets. Then, the performances of different fine-turning models were gradually improved by adjusting DCGAN model in parameters as well as structures. In order to evaluate the results scientifically, cross entropy loss function and Fréchet Inception Distance (FID) are selected to be our assessment criteria. Eventually, we got model M8 with lowest FID score. Compared with DCGAN model proposed by Radford at el., the FID score of M8 improved by 19.26%, enhancing the quality of the synthetic images profoundly.

Keywords: deep learning, image fusion, image generation, layout analysis

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Authors: S. Gokul Prassad, S. Aakash, K. Malar Mohan

Abstract:

In the process to cope with the challenges faced by the automobile industry in providing ride comfort, the electronics and control systems play a vital role. The control systems in an automobile monitor various parameters, controls the performances of the systems, thereby providing better handling characteristics. The automobile suspension system is one of the main systems that ensure the safety, stability and comfort of the passengers. The system is solely responsible for the isolation of the entire automobile from harmful road vibrations. Thus, integration of the control systems in the automobile suspension system would enhance its performance. The diverse road conditions of India demand the need of an efficient suspension system which can provide optimum ride comfort in all road conditions. For any passenger vehicle, the design of the suspension system plays a very important role in assuring the ride comfort and handling characteristics. In recent years, the air suspension system is preferred over the conventional suspension systems to ensure ride comfort. In this article, the ride comfort of the adaptive suspension system is compared with that of the passive suspension system. The schema is created in MATLAB/Simulink environment. The system is controlled by a proportional integral differential controller. Tuning of the controller was done with the Particle Swarm Optimization (PSO) algorithm, since it suited the problem best. Ziegler-Nichols and Modified Ziegler-Nichols tuning methods were also tried and compared. Both the static responses and dynamic responses of the systems were calculated. Various random road profiles as per ISO 8608 standard are modelled in the MATLAB environment and their responses plotted. Open-loop and closed loop responses of the random roads, various bumps and pot holes are also plotted. The simulation results of the proposed design are compared with the available passive suspension system. The obtained results show that the proposed adaptive suspension system is efficient in controlling the maximum over shoot and the settling time of the system is reduced enormously.

Keywords: automobile suspension, MATLAB, control system, PID, PSO

Procedia PDF Downloads 278

Authors: Antonios Paraskevas, Michael Madas

Abstract:

For a university to maintain its international competitiveness in education, it is essential to recruit qualitative academic staff as it constitutes its most valuable asset. This selection demonstrates a significant role in achieving strategic objectives, particularly by emphasizing a firm commitment to exceptional student experience and innovative teaching and learning practices of high quality. In this vein, the appropriate selection of academic staff establishes a very important factor of competitiveness, efficiency and reputation of an academic institute. Within this framework, our work demonstrates a comprehensive methodological concept that emphasizes on the multi-criteria nature of the problem and on how decision makers could utilize our approach in order to proceed to the appropriate judgment. The conceptual framework introduced in this paper is built upon a hybrid neutrosophic method based on the Neutrosophic Analytical Hierarchy Process (N-AHP), which uses the theory of neutrosophy sets and is considered suitable in terms of significant degree of ambiguity and indeterminacy observed in decision-making process. To this end, our framework extends the N-AHP by incorporating the Neutrosophic Delphi Method (N-DM). By applying the N-DM, we can take into consideration the importance of each decision-maker and their preferences per evaluation criterion. To the best of our knowledge, the proposed model is the first which applies Neutrosophic Delphi Method in the selection of academic staff. As a case study, it was decided to use our method to a real problem of academic personnel selection, having as main goal to enhance the algorithm proposed in previous scholars’ work, and thus taking care of the inherit ineffectiveness which becomes apparent in traditional multi-criteria decision-making methods when dealing with situations alike. As a further result, we prove that our method demonstrates greater applicability and reliability when compared to other decision models.

Keywords: analytical hierarchy process, delphi method, multi-criteria decision maiking method, neutrosophic set theory, personnel recruitment

Procedia PDF Downloads 167

Authors: Mst Shapna Akter, Hossain Shahriar

Abstract:

One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. Every year, more and more software flaws are found, either internally in proprietary code or revealed publicly. These flaws are highly likely exploited and lead to system compromise, data leakage, or denial of service. C and C++ open-source code are now available in order to create a largescale, machine-learning system for function-level vulnerability identification. We assembled a sizable dataset of millions of opensource functions that point to potential exploits. We developed an efficient and scalable vulnerability detection method based on deep neural network models that learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the dependency. Moreover, we keep the semantic and syntactic information using state-of-the-art word embedding algorithms such as glove and fastText. The embedded vectors are subsequently fed into deep learning networks such as LSTM, BilSTM, LSTM-Autoencoder, word2vec, BERT, and GPT-2 to classify the possible vulnerabilities. Furthermore, we proposed a neural network model which can overcome issues associated with traditional neural networks. Evaluation metrics such as f1 score, precision, recall, accuracy, and total execution time have been used to measure the performance. We made a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information. We found that all of the deep learning models provide comparatively higher accuracy when we use semantic and syntactic information as the features but require higher execution time as the word embedding the algorithm puts on a bit of complexity to the overall system.

Keywords: cyber security, vulnerability detection, neural networks, feature extraction

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Authors: Talal Alsulaiman, Khaldoun Khashanah

Abstract:

In this paper, we provided a literature survey on the artificial stock problem (ASM). The paper began by exploring the complexity of the stock market and the needs for ASM. ASM aims to investigate the link between individual behaviors (micro level) and financial market dynamics (macro level). The variety of patterns at the macro level is a function of the AFM complexity. The financial market system is a complex system where the relationship between the micro and macro level cannot be captured analytically. Computational approaches, such as simulation, are expected to comprehend this connection. Agent-based simulation is a simulation technique commonly used to build AFMs. The paper proceeds by discussing the components of the ASM. We consider the roles of behavioral finance (BF) alongside the traditionally risk-averse assumption in the construction of agent's attributes. Also, the influence of social networks in the developing of agents’ interactions is addressed. Network topologies such as a small world, distance-based, and scale-free networks may be utilized to outline economic collaborations. In addition, the primary methods for developing agents learning and adaptive abilities have been summarized. These incorporated approach such as Genetic Algorithm, Genetic Programming, Artificial neural network and Reinforcement Learning. In addition, the most common statistical properties (the stylized facts) of stock that are used for calibration and validation of ASM are discussed. Besides, we have reviewed the major related previous studies and categorize the utilized approaches as a part of these studies. Finally, research directions and potential research questions are argued. The research directions of ASM may focus on the macro level by analyzing the market dynamic or on the micro level by investigating the wealth distributions of the agents.

Keywords: artificial stock markets, market dynamics, bounded rationality, agent based simulation, learning, interaction, social networks

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Authors: M. Uysal, M. Yilmaz, I. Tiryakioğlu

Abstract:

Digital Terrain Model (DTM) is a digital numerical representation of the Earth's surface. DTMs have been applied to a diverse field of tasks, such as urban planning, military, glacier mapping, disaster management. In the expression of the Earth' surface as a mathematical model, an infinite number of point measurements are needed. Because of the impossibility of this case, the points at regular intervals are measured to characterize the Earth's surface and DTM of the Earth is generated. Hitherto, the classical measurement techniques and photogrammetry method have widespread use in the construction of DTM. At present, RADAR, LiDAR, and stereo satellite images are also used for the construction of DTM. In recent years, especially because of its superiorities, Airborne Light Detection and Ranging (LiDAR) has an increased use in DTM applications. A 3D point cloud is created with LiDAR technology by obtaining numerous point data. However recently, by the development in image mapping methods, the use of unmanned aerial vehicles (UAV) for photogrammetric data acquisition has increased DTM generation from image-based point cloud. The accuracy of the DTM depends on various factors such as data collection method, the distribution of elevation points, the point density, properties of the surface and interpolation methods. In this study, the random data reduction method is compared for DTMs generated from image based point cloud data. The original image based point cloud data set (100%) is reduced to a series of subsets by using random algorithm, representing the 75, 50, 25 and 5% of the original image based point cloud data set. Over the ANS campus of Afyon Kocatepe University as the test area, DTM constructed from the original image based point cloud data set is compared with DTMs interpolated from reduced data sets by Kriging interpolation method. The results show that the random data reduction method can be used to reduce the image based point cloud datasets to 50% density level while still maintaining the quality of DTM.

Keywords: DTM, Unmanned Aerial Vehicle (UAV), uniform, random, kriging

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Authors: Jin-Siang Shaw, Patricia Moya Caceres, Sheng-Xiang Xu

Abstract:

This paper aims to integrate the particle swarm optimization (PSO) method with the adaptive fuzzy sliding mode controller (AFSMC) to achieve vibration attenuation in a piezoelectric actuator subject to base excitation. The piezoelectric actuator is a complicated system made of ferroelectric materials and its performance can be affected by nonlinear hysteresis loop and unknown system parameters and external disturbances. In this study, an adaptive fuzzy sliding mode controller is proposed for the vibration control of the system, because the fuzzy sliding mode controller is designed to tackle the unknown parameters and external disturbance of the system, and the adaptive algorithm is aimed for fine-tuning this controller for error converging purpose. Particle swarm optimization method is used in order to find the optimal controller parameters for the piezoelectric actuator. PSO starts with a population of random possible solutions, called particles. The particles move through the search space with dynamically adjusted speed and direction that change according to their historical behavior, allowing the values of the particles to quickly converge towards the best solutions for the proposed problem. In this paper, an initial set of controller parameters is applied to the piezoelectric actuator which is subject to resonant base excitation with large amplitude vibration. The resulting vibration suppression is about 50%. Then PSO is applied to search for an optimal controller in the neighborhood of this initial controller. The performance of the optimal fuzzy sliding mode controller found by PSO indeed improves up to 97.8% vibration attenuation. Finally, adaptive version of fuzzy sliding mode controller is adopted for further improving vibration suppression. Simulation result verifies the performance of the adaptive controller with 99.98% vibration reduction. Namely the vibration of the piezoelectric actuator subject to resonant base excitation can be completely annihilated using this PSO based adaptive fuzzy sliding mode controller.

Keywords: adaptive fuzzy sliding mode controller, particle swarm optimization, piezoelectric actuator, vibration suppression

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Authors: Pablo Chico De Guzman, Cesar Sanchez

Abstract:

Microservice architectures are increasingly in distributed cloud applications due to the advantages on the software composition, development speed, release cycle frequency and the business logic time to market. On the other hand, these architectures also introduce some challenges on the testing and release phases of applications. Container technology solves some of these issues by providing reproducible environments, easy of software distribution and isolation of processes. However, there are other issues that remain unsolved in current container technology when dealing with multiple machines, such as networking for multi-host communication, service discovery, load balancing or data persistency (even though some of these challenges are already solved by traditional cloud vendors in a very mature and widespread manner). Container cluster management tools, such as Kubernetes, Mesos or Docker Swarm, attempt to solve these problems by introducing a new control layer where the unit of deployment is the container (or the pod — a set of strongly related containers that must be deployed on the same machine). These tools are complex to configure and manage and they do not follow a pure immutable infrastructure approach since servers are reused between deployments. Indeed, these tools introduce dependencies at execution time for solving networking or service discovery problems. If an error on the control layer occurs, which would affect running applications, specific expertise is required to perform ad-hoc troubleshooting. As a consequence, it is not surprising that container cluster support is becoming a source of revenue for consulting services. This paper presents i2kit, a deployment tool based on the immutable infrastructure pattern, where the virtual machine is the unit of deployment. The input for i2kit is a declarative definition of a set of microservices, where each microservice is defined as a pod of containers. Microservices are built into machine images using linuxkit —- a tool for creating minimal linux distributions specialized in running containers. These machine images are then deployed to one or more virtual machines, which are exposed through a cloud vendor load balancer. Finally, the load balancer endpoint is set into other microservices using an environment variable, providing service discovery. The toolkit i2kit reuses the best ideas from container technology to solve problems like reproducible environments, process isolation, and software distribution, and at the same time relies on mature, proven cloud vendor technology for networking, load balancing and persistency. The result is a more robust system with no learning curve for troubleshooting running applications. We have implemented an open source prototype that transforms i2kit definitions into AWS cloud formation templates, where each microservice AMI (Amazon Machine Image) is created on the fly using linuxkit. Even though container cluster management tools have more flexibility for resource allocation optimization, we defend that adding a new control layer implies more important disadvantages. Resource allocation is greatly improved by using linuxkit, which introduces a very small footprint (around 35MB). Also, the system is more secure since linuxkit installs the minimum set of dependencies to run containers. The toolkit i2kit is currently under development at the IMDEA Software Institute.

Keywords: container, deployment, immutable infrastructure, microservice

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Authors: Sachoiba Inkah

Abstract:

The Northeast region has not been a major factor in India’s foreign policy calculation since independence. Instead, the region was ignored and marginalized even to the extent of using force and repressive Acts such as AFSPA(Armed Forces Special Powers Act) to suppress the voices of both states and non-state actors. The liberalization of the economy in the 90s in the wake of globalization gave India a new outlook and the Look East Policy (LEP) was a paradigm shift in India’s engagement with the Southeast Asian nations as it seeks to explore the benefits of the ASEAN. The reorienting of India’s foreign policy to ‘Neighborhood First” is attributed to the present political dispensation, which is further widened to include ‘Extended Neighborhood.’ As a result, the Northeastern states have become key players in India’s participation in regional groupings such as SAARC, BIMSTEC, and BCIM. The need for external balancing, diplomacy and development has reset India’s foreign policy priorities as the Northeast states lie in the confluence of South Asia, Southeast and East Asia, and a stakeholder in Act East Policy. The paper will explore the role of Northeastern states in the framework of Indian foreign policy as it shares international boundaries with China, Bhutan, Bangladesh, and Myanmar and most importantly, study the case of Nagas who are spread across Manipur, Nagaland, and Arunachal Pradesh bordering Myanmar. The Indo-Myanmar border is an area of conflict and various illegal activities such as arms trafficking, illegal migrants, drug, and human trafficking are still being carried out and in order to address this issue, both India and Myanmar need to take into consideration the various communities living across the border. And conflict and insurgency should not be a yardstick to curtailed development of infrastructures such as roads, health facilities, transport, and communication in the contested region. The realities, perceptions, and contentions of the Northeastern states and the different communities living in the border areas need a wider discourse as the region the potential to drive India’s diplomatic relations with its neighbors and extended neighborhood. The methods employed are analytical and more of a descriptive analysis on India’s foreign policy framework with a focus on Nagas in Myanmar, drawing from both primary and secondary sources. Primary sources include official documents, data, and statistics released by various governmental agencies, parliamentary debates, political speeches, press releases, treaties and agreements, historical biographies and organizational policy papers, protocols and procedures of government conferences, regional organization study reports etc. The paper concludes that the recent proactive engagement between India and Myanmar on trade, defense, economic, and infrastructure development are positive signs cementing bilateral ties, but there is not much room for the people-to-people connect, especially for people living in the borderland. The Freedom of Movement Regime that is in place is limited and there is more scope for improvement as people in the borderland looks towards trade and commerce to not only uplift the border economy but also act as a catalyst for robust engagement between the two countries, albeit with more infrastructure such as road, healthcare, education, a tourist hotspot, trade centers, mobile connectivity, etc.

Keywords: foreign policy, infrastructure development, insurgency, people to people connect

Procedia PDF Downloads 182