Search results for: emotional intelligent

Authors: Y. Alrubyli

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Unmanned Arial Vehicles (UAVs), and more specifically, quadcopters need to be stable during their flights. Altitude stability is usually achieved by using a PID controller that is built into the flight controller software. Furthermore, the PID controller has gains that need to be tuned to reach optimal altitude stabilization during the quadcopter’s flight. For that, control system engineers need to tune those gains by using extensive modeling of the environment, which might change from one environment and condition to another. As quadcopters penetrate more sectors, from the military to the consumer sectors, they have been put into complex and challenging environments more than ever before. Hence, intelligent self-stabilizing quadcopters are needed to maneuver through those complex environments and situations. Here we show that by using online reinforcement learning with minimal background knowledge, the altitude stability of the quadcopter can be achieved using a model-free approach. We found that by using background knowledge instead of letting the online reinforcement learning algorithm wander for a while to tune the PID gains, altitude stabilization can be achieved faster. In addition, using this approach will accelerate development by avoiding extensive simulations before applying the PID gains to the real-world quadcopter. Our results demonstrate the possibility of using the trial and error approach of reinforcement learning combined with background knowledge to achieve faster quadcopter altitude stabilization in different environments and conditions.

Keywords: reinforcement learning, Q-leanring, online learning, PID tuning, unmanned aerial vehicle, quadcopter

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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: S. Sharan Sundar, Nithish B. Moudhgalya, Nidhi Bhandari, Vineeth Vijayaraghavan

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Automatic Short Answer Grading is one of the prime applications of artificial intelligence in education. Several approaches involving the utilization of selective handcrafted features, graphical matching techniques, concept identification and mapping, complex deep frameworks, sentence embeddings, etc. have been explored over the years. However, keeping in mind the real-world application of the task, these solutions present a slight overhead in terms of computations and resources in achieving high performances. In this work, a simple and effective solution making use of elemental features based on statistical, linguistic properties, and word-based similarity measures in conjunction with tree-based classifiers and regressors is proposed. The results for classification tasks show improvements ranging from 1%-30%, while the regression task shows a stark improvement of 35%. The authors attribute these improvements to the addition of multiple similarity scores to provide ensemble of scoring criteria to the models. The authors also believe the work could reinstate that classical natural language processing techniques and simple machine learning models can be used to achieve high results for short answer grading.

Keywords: artificial intelligence, intelligent systems, natural language processing, text mining

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Authors: Malgorzata Wojcik

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Groups are a powerful and significant part of human development. They serve as major emergent microsocial structures in children’s and youth’s ecological system. During middle and secondary school, peer groups become a particularly salient influence. While they promote a range of prosocial and positive emotional and behavioral attributes, they can also elicit negative or antisocial attributes, effectively “bringing out the worst” in some individuals. The grounded theory approach was employed to guide data collection and analysis, as it allows for a deeper understanding of the group processes and students’ perspectives on complex intragroup relations. Students’ perspectives on bullying cases were investigated by observing daily interactions among those involved and interviewing 47 students. The results complement theories of labeling in bullying by showing that all students self-label themselves and find it difficult to break patterns of behaviors related to bullying, such as supporting the bully or not defending the victim. In terms of the practical implications, the findings indicate that it could be beneficial to use non-punitive, restorative anti-bullying interventions that implement peer influence to transform bullying relations by removing behavioral labels.

Keywords: bullying, peer group, victimization, class reputation

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Authors: Jennifer Loar, Jason Montefalcon, Kissy Mae Alimpangog, Miriam Bongo

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The demanding, professional roles that air traffic controllers (ATC) play in air transport operation provided the main motivation of this paper. As the controllers’ workload stress becomes more complex due to various stressors, the challenge to overcome these in the pursuit of improving the efficiency of controllers and safety level of aircrafts has been relevant. Therefore, in order to determine the main stressors and surface the best alternative, two widely-known multi-criteria decision-making (MCDM) methods, DANP and PROMETHEE II, are applied. The proposed method is demonstrated in a case study at Mactan Civil Aviation Authority of the Philippines (CAAP). The results showed that the main stressors are high air traffic volume, extraneous traffic, unforeseen events, limitations and reliability of equipment, noise/distracter, micro climate, bad posture, relations with supervisors and colleagues, private life conditions/relationships, and emotional conditions. In the outranking of alternatives, compartmentalization is believed to be the most preferred alternative to overcome controllers’ workload stress. This implies that compartmentalization can best be applied to reduce controller workload stress.

Keywords: air traffic controller, DANP, MCDM, PROMETHEE II, workload stress

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Authors: Tryfon Mavropalias, Anastasia Alevriadou

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Co-teaching is a relatively recent model of providing teaching services to students with disabilities in Greece. According to recent studies, it seems that the largest number of students who take part in the Greek co-teaching programme are children with Autistic Spectrum Disorders (ASD). The aim of the suggested study is to investigate the effectiveness and usefulness of co-teaching to students with ASD as well as skills students with ASD develop during co-teaching in primary education classes. To conduct the research, quantitative method of research was used, with the means of research being a questionnaire including open and close type questions. The sample of this research consists of 142 primary school co-teachers from all over Northern Greece (71 general education teachers and 71 special education teachers). Given the results, it was concluded that co-teachers believe that including and educating children with Autistic Spectrum Disorders in the general class benefits those who autism is measured from the middle to the upper end of the spectrum. Additionally, children develop social skills first, followed by emotional and cognitive skills. Ultimately, educators declared that they are prepared only to a limited degree to effectively support students with Autistic Spectrum Disorders in general classes.

Keywords: Autistic spectrum disorders, co-teaching, co-teachers, co-taught class

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Authors: Mehravar Javid, James Sexton, S. Taridashti, Joseph Dorer

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Objective: Intimacy is among the most important factors in marital relationships and includes different aspects. Communication skills can enable couples to promote their intimacy. This experimental study was conducted to measure the effectiveness of communication skills using Transactional Analysis (TA) on various dimensions of marital intimacy. Method: The participants in this study were female teachers. Analysis of covariance was recruited in the experimental group (n =15) and control group (n =15) with pre-test and post-test. Random assignment was applied. The experimental group received the Transactional Analysis training program for 9 sessions of 2 hours each week. The instrument was the Marital Intimacy Questionnaire, with 87 items and 9 subscales. Result: The findings suggest that training in Transactional Analysis significantly increased the total score of intimacy except spiritual intimacy on the post-test. Discussion: According to the obtained data, it is concluded that communication skills using Transactional Analysis (TA) training could increase intimacy and improve marital relationships. The study highlights the differential effects on emotional, rational, sexual, and psychological intimacy compared to physical, social/recreational, and relational intimacy over a 9-week period.

Keywords: communication skills, intimacy, marital relationships, transactional analysis

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Authors: Fei Gao, Xinyang Dong, Yisu Ge, Shufang Lu, Libo Weng

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Vehicle color recognition plays an important role in traffic accident investigation. However, due to the influence of illumination, weather, and noise, vehicle color recognition still faces challenges. In this paper, a vehicle color classification network based on spatial cluster loss and channel attention mechanism (SCNet) is proposed for vehicle color recognition. A channel attention module is applied to extract the features of vehicle color representative regions and reduce the weight of nonrepresentative color regions in the channel. The proposed loss function, called spatial clustering loss (SC-loss), consists of two channel-specific components, such as a concentration component and a diversity component. The concentration component forces all feature channels belonging to the same class to be concentrated through the channel cluster. The diversity components impose additional constraints on the channels through the mean distance coefficient, making them mutually exclusive in spatial dimensions. In the comparison experiments, the proposed method can achieve state-of-the-art performance on the public datasets, VCD, and VeRi, which are 96.1% and 96.2%, respectively. In addition, the ablation experiment further proves that SC-loss can effectively improve the accuracy of vehicle color recognition.

Keywords: feature extraction, convolutional neural networks, intelligent transportation, vehicle color recognition

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Authors: Robert McGrath, Zara Qureshi

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A innovative leadership philosophy that is proposed herein is distinctly more humane than most leadership approaches Concerned Leadership. The central idea to this approach is to consider the whole person that comes to work; their professional skills and talents, as well as any personal, emotional challenges that could be affecting productivity and effectiveness at work. This paper explores Concerned Leadership as an integration of the two conceptual models areas examined in this paper –(1) leaderful organizations and practices, as well as (2) organizational culture, and defines leadership in the context of Mental Health and Wellness in the workplace. Leaderful organizations calls for organizations to implement leaderful practice. Leaderful practice is when leadership responsibility and decision-making is shared across all team members and levels, versus only delegated to top management as commonly seen. A healthy culture thrives off key aspects such as acceptance, employee pride, equal opportunity, and strong company leadership. Concerned Leadership is characterized by five main components: Self-Concern, Leaderful Practice, Human Touch, Belonging, and Compassion. As scholars and practitioners conceptualize leadership in practice, the present model seeks to uphold the dignity of each organizational member, thereby having the potential to transform workplaces and support all members.

Keywords: leadership, mental health, reflective practice, organizational culture

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Authors: Rohit Rutka

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The aim of the present paper is to reveal the importance of music as an indispensable aspect in education of art, with regard to every single culture which serves as indisputable support to sustainable development in India. Indian system of education is one of the oldest systems of the world. Both secular and sacred education was handed over systematically by formalizing the system of education. We have found significant growth in the system of education in our country since ancient times. It is a veritable avenue which enables societies to transmit music and musical skills from one generation to the upcoming ones. The research is based on a comprehensive literature review on the impact of music to sustainable development. This paper contextualized that music education is imperative to Sustainable Development, to the adult. It is a vital force of self-expression, communication and empowerment economically, in growing children, involvement in music education will promote their creative ability, thereby contribute to the full development of intellectual capacities, apt emotional development that gives the right values and feelings to various events and happenings, music helps to develop skills, innate and instinctive talent in human being and recommend that the informal music teaching should be incorporated into school system so as to transmit and preserve the cultural music and that the study of music should be made compulsory at all levels of the Indian educational system.

Keywords: sustainable development, music education, culture, music as a pillar to sustainable development

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Authors: An-Hsu Chen

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The study intends to explore how the athletic trainings (12 hours per day, four days per week) have impacts on athlete burnout and their leisure participations. The connection between athlete burnout and leisure participation of collegiate athletes is also discussed. Athlete burnout and leisure participation questionnaire were administrated and 186 valid responses were collected. The data were analyzed with descriptive statistics, t-test, one-way ANOVA, Pearson product-moment correlation coefficient. Results suggest that athlete burnout among collegiate athletes with different specialties are significant distinct. Participants who train more days per week are more likely to participate in entertainment activities while those who have higher training hours per day tend to avoid knowledge-based activities. The research also finds there is a significant positive correlation between athlete burnout and leisure participation of collegiate athletes while sport devaluation is negatively correlated with sport activities in leisure participation. Hence, adjust and well-arrange training quality and quantity may help to avoid over-trainings. Away trainings, uploading training volumes, and group leisure activities are suggested to be arranged properly to allow athletes cope with the burnout and stress caused by long-term trainings and periodical competitions.

Keywords: emotional and physical exhaustion, leisure activities, sport devaluation, training hours

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Authors: Ronahlee Asuncion

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The nature of their work and employment relationship make domestic workers easy prey to abuse, maltreatment, and exploitation. Considering their plight, this research was conceptualized and examined the: a) level of awareness of Filipino domestic workers on occupational safety and health (OSH); b) their issues/problems/concerns on OSH; c) their intervention strategies at work to address OSH related issues/problems/concerns; d) issues/problems/concerns of government, employers, and non-government organizations with regard to implementation of OSH to Filipino domestic workers; e) the role of government, employers and non-government organizations to help Filipino domestic workers address OSH related issues/problems/concerns; and f) the necessary policy amendments/initiatives/programs to address OSH related issues/problems/concerns of Filipino domestic workers. The study conducted a survey using non-probability sampling, two focus group discussions, two group interviews, and fourteen face-to-face interviews. These were further supplemented with an email correspondence to a key informant based in another country. Books, journals, magazines, and relevant websites further substantiated and enriched data of the research. Findings of the study point to the fact that domestic workers have low level of awareness on OSH because of poor information drive, fragmented implementation of the Domestic Workers Act, inactive campaign at the barangay level, weakened advocacy for domestic workers, absence of law on OSH for domestic workers, and generally low safety culture in the country among others. Filipino domestic workers suffer from insufficient rest, long hours of work, heavy workload, occupational stress, poor accommodation, insufficient hours of sleep, deprivation of day off, accidents and injuries such as cuts, burns, slipping, stumbling, electrical grounding, and fire, verbal, physical and sexual abuses, lack of medical assistance, none provision of personal protective equipment (PPE), absence of knowledge on the proper way of lifting, working at heights, and insufficient food provision. They also suffer from psychological problems because of separation from one’s family, limited mobility in the household where they work, injuries and accidents from using advanced home appliances and taking care of pets, low self-esteem, ergonomic problems, the need to adjust to all household members who have various needs and demands, inability to voice their complaints, drudgery of work, and emotional stress. With regard to illness or health problems, they commonly experience leg pains, back pains, and headaches. In the absence of intervention programs like those offered in the formal employment set up, domestic workers resort to praying, turn to family, relatives and friends for social and emotional support, connect with them through social media like Facebook which also serve as a means of entertainment to them, talk to their employer, and just try to be optimistic about their situation. Promoting OSH for domestic workers is very challenging and complicated because of interrelated factors such as cultural, knowledge, attitudinal, relational, social, resource, economic, political, institutional and legal problems. This complexity necessitates using a holistic and integrated approach as this is not a problem requiring simple solutions. With this recognition comes the full understanding that its success involves the action and cooperation of all duty bearers in attaining decent work for domestic workers.

Keywords: decent work, Filipino domestic workers, occupational safety and health, working conditions

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Authors: Zhang Shuqi, Liu Dan

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For the problems in intelligent network anomaly traffic detection models, such as low detection accuracy caused by the lack of training samples, poor effect with small sample attack detection, a classification model enhancement method, F-ACGAN(Flow Auxiliary Classifier Generative Adversarial Network) which introduces generative adversarial network and adversarial training, is proposed to solve these problems. Generating adversarial data with category labels could enhance the training effect and improve classification accuracy and model robustness. FACGAN consists of three steps: feature preprocess, which includes data type conversion, dimensionality reduction and normalization, etc.; A generative adversarial network model with feature learning ability is designed, and the sample generation effect of the model is improved through adversarial iterations between generator and discriminator. The adversarial disturbance factor of the gradient direction of the classification model is added to improve the diversity and antagonism of generated data and to promote the model to learn from adversarial classification features. The experiment of constructing a classification model with the UNSW-NB15 dataset shows that with the enhancement of FACGAN on the basic model, the classification accuracy has improved by 8.09%, and the score of F1 has improved by 6.94%.

Keywords: data imbalance, GAN, ACGAN, anomaly detection, adversarial training, data augmentation

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Authors: Weiming Hu, Xu Li, Xiaonan Li, Zhong Xu, Li Yuan, Xuan Dong

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Maneuver decision-making plays a critical role in high-performance intelligent driving. This paper proposes a risk assessment-based decision-making network (RADMN) to address the problem of driving strategy for the commercial vehicle. RADMN integrates two networks, aiming at identifying the risk degree of collision and rollover and providing decisions to ensure the effectiveness and reliability of driving strategy. In the risk assessment module, risk degrees of the backward collision, forward collision and rollover are quantified for hazard recognition. In the decision module, a deep reinforcement learning based on multi-objective optimization (DRL-MOO) algorithm is designed, which comprehensively considers the risk degree and motion states of each traffic participant. To evaluate the performance of the proposed framework, Prescan/Simulink joint simulation was conducted in highway scenarios. Experimental results validate the effectiveness and reliability of the proposed RADMN. The output driving strategy can guarantee the safety and provide key technical support for the realization of autonomous driving of commercial vehicles.

Keywords: decision-making strategy, risk assessment, multi-objective optimization, commercial vehicle

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Authors: Anna Claudia Pellicelli, Silvia Procacci

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The study aims to demonstrate how different locations, where the same product is unveiled and tested, can provide a different result in terms of perception by the same kind of people. The experiment was done in occasion of the presentation of a new bike. A group of dealers has been invited in Lloret de Mar, two persons from the headquarter were present to run the presentation, together with an outsourced trainer. Half day dedicated to the theoretical presentation and half day to the test of the new bike on the road, including the test of its direct competitors. The same presentation, organized in the same way, has been delivered in Italy, in 4 locations often used to run business meetings with dealers. In the end of all days of the presentation, dealers had to fill a questionnaire regarding the evaluation of the different bikes tested. The result of the questionnaire showed how the group invited in Spain rated much higher the new bike compared with the dealers testing the bike in locations already known and close to their home. So, in terms of business strategy, it is important to take into account how the location and the way of presenting any product or service can have a favourable impact on the people we want to convince. The next step of the experiment will be to cross check the sales of that bike with the dealers and measure if there is a relation between the top sellers and the one that appreciated the bike the most, in Spain. It would mean that they were able to transfer to customers the same good feelings and impressions they had in Spain.

Keywords: product presentation, locations, emotional effect, business strategy

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Authors: Dimitrios Ntardas, Alexandros Tzanetos, Georgios Dounias

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High Voltage Substations (HVS) are the intermediate step between production of power and successfully transmitting it to clients, making them one of the most important checkpoints in power grids. Nowadays - renewable resources and consequently distributed generation are growing fast, the construction of HVS is of high importance both in terms of quality and time completion so that new energy producers can quickly and safely intergrade in power grids. The resources needed, such as machines and workers, should be carefully allocated so that the construction of a HVS is completed on time, with the lowest possible cost (e.g. not spending additional cost that were not taken into consideration, because of project delays), but in the highest quality. In addition, there are milestones and several checkpoints to be precisely achieved during construction to ensure the cost and timeline control and to ensure that the percentage of governmental funding will be granted. The management of such a demanding project is a NP-hard problem that consists of prerequisite constraints and resource limits for each task of the project. In this work, a hybrid meta-heuristic method is implemented to solve this problem. Meta-heuristics have been proven to be quite useful when dealing with high-dimensional constraint optimization problems. Hybridization of them results in boost of their performance.

Keywords: hybrid meta-heuristic methods, substation construction, resource allocation, time-cost efficiency

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Authors: Su Xiaohan, Jin Chicheng, Liu Yijing, Burra Venkata Durga Kumar

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Energy-aware scheduling in real-time systems aims to minimize energy consumption, but issues related to resource reservation and timing constraints remain challenges. This study focuses on analyzing two scheduling algorithms, Fair-Share Scheduling (FFS) and Priority-Driven Preemptive Scheduling (PDPS), for solving these issues and energy-aware scheduling in real-time systems. Based on research on both algorithms and the processes of solving two problems, it can be found that Fair-Share Scheduling ensures fair allocation of resources but needs to improve with an imbalanced system load, and Priority-Driven Preemptive Scheduling prioritizes tasks based on criticality to meet timing constraints through preemption but relies heavily on task prioritization and may not be energy efficient. Therefore, improvements to both algorithms with energy-aware features will be proposed. Future work should focus on developing hybrid scheduling techniques that minimize energy consumption through intelligent task prioritization, resource allocation, and meeting time constraints.

Keywords: energy-aware scheduling, fair-share scheduling, priority-driven preemptive scheduling, real-time systems, optimization, resource reservation, timing constraints

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Authors: Phan Nghiem Vu, Le Tuan Vu, Ta Quang Tai

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The optimal construction design used during the execution of a construction project is a key factor in determining high productivity and customer satisfaction, however, this management process sometimes is carried out without care and the systematic method that it deserves, bringing negative consequences. This study proposes a knowledge management (KM) approach that will enable the intelligent use of experienced and acknowledged engineers to improve the management of construction design works for a project. Then a knowledge-based application to support this decision-making process is proposed and described. To define and design the system for the application, semi-structured interviews were conducted within five construction consulting organizations with the purpose of studying the way that the method’ optimizing process is implemented in practice and the knowledge supported with it. A system of an optimizing construction design works (OCDW) based on knowledge was developed then validated with construction experts. The OCDW was liked as a valuable tool for construction design works’ optimization, by supporting organizations to generate a corporate memory on this issue, reducing the reliance on individual knowledge and also the subjectivity of the decision-making process. The benefits are described as provided by the performance support system, reducing costs and time, improving product design quality, satisfying customer requirements, expanding the brand organization.

Keywords: optimizing construction design work, construction consultant organization, knowledge management, knowledge-based application

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Authors: Hui Zhang, Sherif Beskhyroun

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Structural Health monitoring systems (SHM) are commonly used to identify and assess structural damage. In terms of damage detection, SHM needs to periodically collect data from sensors placed in the structure as damage-sensitive features. This includes abnormal changes caused by the strain field and abnormal symptoms of the structure, such as damage and deterioration. Currently, deploying sensors on a large scale in a building structure is a challenge. In this study, a highly stretchable strain sensors are used in this study to collect data sets of strain generated on the surface of full-size reinforced concrete (RC) frames under extreme cyclic load application. This sensing sheet can be switched freely between the test bending strain and the axial strain to achieve two different configurations. On this basis, the deep neural network prediction model of the frame beam and frame column is established. The training results show that the method can accurately predict the strain value and has good generalization ability. The two deep neural network prediction models will also be deployed in the SHM system in the future as part of the intelligent strain sensor system.

Keywords: strain sensing sheets, deep neural networks, strain measurement, SHM system, RC frames

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Authors: Khalid Ali, Manar Jammal

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In order to meet the stringent latency and reliability requirements of the upcoming 5G networks, Open Radio Access Networks (O-RAN) have been proposed. The virtualization of O-RAN has allowed it to be treated as a Network Function Virtualization (NFV) architecture, while its components are considered Virtualized Network Functions (VNFs). Hence, intelligent Machine Learning (ML) based solutions can be utilized to apply different resource management and allocation techniques on O-RAN. However, intelligently allocating resources for O-RAN VNFs can prove challenging due to the dynamicity of traffic in mobile networks. Network providers need to dynamically scale the allocated resources in response to the incoming traffic. Elastically allocating resources can provide a higher level of flexibility in the network in addition to reducing the OPerational EXpenditure (OPEX) and increasing the resources utilization. Most of the existing elastic solutions are reactive in nature, despite the fact that proactive approaches are more agile since they scale instances ahead of time by predicting the incoming traffic. In this work, we propose and evaluate traffic forecasting models based on the ML algorithm. The algorithms aim at predicting future O-RAN traffic by using previous traffic data. Detailed analysis of the traffic data was carried out to validate the quality and applicability of the traffic dataset. Hence, two ML models were proposed and evaluated based on their prediction capabilities.

Keywords: O-RAN, traffic forecasting, NFV, ARIMA, LSTM, elasticity

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Authors: Shimony Agrawal

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In this continually evolving world, change is the most imperative component of our identity. The term alteration alludes to degree by which an individual adapts to inward strains, needs, clashes and can bring coordination between his internal requests and those forced by the external world. Adjustment is a way of managing various demands of life. . Entering school is a defining moment for school first year recruits in their adulthood. The progress from school to school can be rationally and in addition physically troubling. Students deal with a unique amount of stressors when they enter college. Introductory months of school are loaded with apprehension and attempting to fit in the new condition. Colleges and schools should ensure their understudies are balanced in the new condition by giving help at whatever point vital.. The main objective of the study was a comparative analysis of adjustment level with respect to overall adjustment level, gender and living environment. This research has been conducted using Adjustment Inventory for College Students (AICS). The total population is comprised of 240 college-going students. The data majority of the population scored poorly on Emotional Adjustment. Also, female students faced more adjustment problems as compared to male students. However, no significant change was noticed in living environment of the students.

Keywords: adjustment, college students, freshmen year, senior year

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Authors: Suhadiyana Hanapi, Alhassan Salami Tijani, W. A. N Wan Mohamed

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In this paper, a prototype PEM fuel cell vehicle integrated with a 1 kW air-blowing proton exchange membrane fuel cell (PEMFC) stack as a main power sources has been developed for a lightweight cruising vehicle. The test vehicle is equipped with a PEM fuel cell system that provides electric power to a brushed DC motor. This vehicle was designed to compete with industrial lightweight vehicle with the target of consuming least amount of energy and high performance. Individual variations in driving style have a significant impact on vehicle energy efficiency and it is well established from the literature. The primary aim of this study was to assesses the power and fuel consumption of a hydrogen fuel cell vehicle operating at three difference driving technique (i.e. 25 km/h constant speed, 22-28 km/h speed range, 20-30 km/h speed range). The goal is to develop the best driving strategy to maximize performance and minimize fuel consumption for the vehicle system. The relationship between power demand and hydrogen consumption has also been discussed. All the techniques can be evaluated and compared on broadly similar terms. Automatic intelligent controller for driving prototype fuel cell vehicle on different obstacle while maintaining all systems at maximum efficiency was used. The result showed that 25 km/h constant speed was identified for optimal driving with less fuel consumption.

Keywords: prototype fuel cell electric vehicles, energy efficient, control/driving technique, fuel economy

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Authors: Layla Mujahed

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COVID-19 is an exam for all the city’s systems. It shows many gaps in the systems such as healthcare, economic, social, and environment. This pandemic is paving for a new era, an era of technology and it has changed people’s lives, such as physical, and emotional changes, and converting communication into digitalized. The effect of COVID-19 has covered all urban city parts. COVID-19 will not be the last pandemic our cities will face. For that, more researches focus on enhancing the quality of the urban environment. This pandemic encourages a rethinking of the environment’s role, especially in cities. Cities are trying to provide the best suitable strategies and regulations to prevent the spread of COVID-19, and an example of that is Amman city. Amman has a high increment in the number of COVID-19 infected people, while it has controlled the situation for months. For that, this paper studies the relation between COVID-19 and urban environmental studies cases about cities around the world, and learns from their models to face COVID-19. In Amman, people’s behavior has changed towards public transportation and public green spaces. N­ew governmental regulations focus on increasing people’s mental awareness, supporting local businesses, and enhancing neighborhood planning that can help Amman to face any future pandemics.

Keywords: COVID-19, urban environment, urban planning, urban resilience

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Authors: Kriangkrai Vathanalaoha

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Misery generates fear and anxiety in readers through its intense plot associated with the unpredictable emotional states of the nurse, Annie Wilkes. At the same time, she mentally and physically abuses the novelist victim, Paul Sheldon. The suspense is not only at the story level, where the violent expressions are used but also at the discourse level, where the linguistic structures may intentionally cause the reader to view language as disturbing performative. This performativity could be reflected through linguistic choices where the writer triggers a new imaginative world through experiential metafunction and schema disruption. This study explores striking excerpts from the fiction through mind style and transitivity analysis to demonstrate how the horrific experience contrasts when the protagonist and the antagonist converse extensively. The results reveal that stylistic deviation can be found at the syntactic levels, where the intensity of emotions can be apparent when the protagonist is verbally abused. In addition, transitivity can flesh out how the protagonist is expressed chiefly through the internalized process, whereas the antagonist is eminent with the externalized process. The findings suggest that the application of cognitive stylistics, such as mind style and transitivity analysis, could contribute to the mental representation of horrific reality.

Keywords: horror, mind style, misery, stylistics, transitivity

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Authors: Ashish Kumar, Munesh Raghuraj Varma, Nisarg Joshi, Gujjula Vishwa Teja, Srikanth Sambi, Arpit Awasthi

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Autonomous driving vehicles are revolutionizing the transportation system of the 21st century. This has been possible due to intensive research put into making a robust, reliable, and intelligent program that can perceive and understand its environment and make decisions based on the understanding. It is a very data-intensive task with data coming from multiple sensors and the amount of data directly reflects on the performance of the system. Researchers have to design the preprocessing pipeline for different datasets with different sensor orientations and alignments before the dataset can be fed to the model. This paper proposes a solution that provides a method to unify all the data from different sources into a uniform format using the intrinsic and extrinsic parameters of the sensor used to capture the data allowing the same pipeline to use data from multiple sources at a time. This also means easy adoption of new datasets or In-house generated datasets. The solution also automates the complete deep learning pipeline from preprocessing to post-processing for various tasks allowing researchers to design multiple custom end-to-end pipelines. Thus, the solution takes care of the input and output data handling, saving the time and effort spent on it and allowing more time for model improvement.

Keywords: augmentation, autonomous driving, camera, custom end-to-end pipeline, data unification, lidar, post-processing, preprocessing

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Authors: Khalil Ibrahim, Ahmed Ramadan, Mohamed Fanni, Yo Kobayashi, Ahmed Abo-Ismail, Masakatus G. Fujie

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This paper deals with the design process and the dynamic control simulation of a new type of 4-DOFs parallel mechanism that can be used as an endoscopic surgical manipulator. The proposed mechanism, 2-PUU_2-PUS, is designed based on the screw theory and the parallel virtual chain type synthesis method. Based on the structure analysis of the 4-DOF parallel mechanism, the inverse position equation is studied using the inverse analysis theory of kinematics. The design and the stress analysis of the mechanism are investigated using SolidWorks software. The virtual prototype of the parallel mechanism is constructed, and the dynamic simulation is performed using ADAMS TM software. The system model utilizing PID and PI controllers has been built using MATLAB software. A more realistic simulation in accordance with a given bending angle and point to point control is implemented by the use of both ADAMS/MATLAB software. The simulation results showed that this control method has solved the coordinate control for the 4-DOF parallel manipulator so that each output is feedback to the four driving rods. From the results, the tracking performance is achieved. Other control techniques, such as intelligent ones, are recommended to improve the tracking performance and reduce the numerical truncation error.

Keywords: parallel mechanisms, medical robotics, tracjectory control, virtual chain type synthesis method

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Authors: V. Santamarina Campos, M. de Miguel Molina, S. Kröner, B. de Miguel Molina

Abstract:

With this contribution, we want to show how the AiRT system could change the future way of working of a part of the creative industry and what new economic opportunities could arise for them. Remotely Piloted Aircraft Systems (RPAS), also more commonly known as drones, are now essential tools used by many different companies for their creative outdoor work. However, using this very flexible applicable tool indoor is almost impossible, since safe navigation cannot be guaranteed by the operator due to the lack of a reliable and affordable indoor positioning system which ensures a stable flight, among other issues. Here we present our first results of a European project, which consists of developing an indoor drone for professional footage especially designed for the creative industries. One of the main achievements of this project is the successful implication of the end-users in the overall design process from the very beginning. To ensure safe flight in confined spaces, our drone incorporates a positioning system based on ultra-wide band technology, an RGB-D (depth) camera for 3D environment reconstruction and the possibility to fully pre-program automatic flights. Since we also want to offer this tool for inexperienced pilots, we have always focused on user-friendly handling of the whole system throughout the entire process.

Keywords: virtual reality, 3D reconstruction, indoor positioning system, RPAS, remotely piloted aircraft systems, aerial film, intelligent navigation, advanced safety measures, creative industries

Procedia PDF Downloads 196

Authors: Aarnav Singh, Jatin Moolchandani

Abstract:

The proposed research work aims to build a query-based AI chatbot that can answer any question related to any topic. A chatbot is software that converses with users via text messages. In the proposed system, we aim to build a chatbot that generates a response based on the user’s query. For this, we use natural language processing to analyze the query and some set of texts to form a concise answer. The texts are obtained through web-scrapping and filtering all the credible sources from a web search. The objective of this project is to provide a chatbot that is able to provide simple and accurate answers without the user having to read through a large number of articles and websites. Creating an AI chatbot that can answer a variety of user questions on a variety of topics is the goal of the proposed research project. This chatbot uses natural language processing to comprehend user inquiries and provides succinct responses by examining a collection of writings that were scraped from the internet. The texts are carefully selected from reliable websites that are found via internet searches. This project aims to provide users with a chatbot that provides clear and precise responses, removing the need to go through several articles and web pages in great detail. In addition to exploring the reasons for their broad acceptance and their usefulness across many industries, this article offers an overview of the interest in chatbots throughout the world.

Keywords: Chatbot, Artificial Intelligence, natural language processing, web scrapping

Procedia PDF Downloads 66

Authors: Timur Zhukov, Yuko Yamashita

Abstract:

Music as a product of hedonic consumption has been researched at least since the early 20th century, but little light has been shed on how language affects its consumption process. At the intersection of music consumption, language impact, and consumer behavior, this research explores the influence of language on music consumption in Japan. Its aim is to clarify how listening to music in different languages affects the listener’s purchase intention and sharing intention by conducting a survey where respondents listen to three versions of the same song in different languages in random order. It uses an existing framework that views the flow of music consumption as a combination of responses (emotional response, sensory response, imaginal response, analytical responses) affecting the experiential response, which then affects the overall affective response, followed by the need to reexperience and lastly the purchase intention. In this research, the sharing intention has been added to the model to better fit the modern consumption model (e.g., AISAS). This research shows how positive and negative emotions and imaginal and analytical responses change depending on the language and what impact it has on consumer behavior. It concludes by proposing how modern music businesses can learn from the language differences and cater to the needs of the audiences who speak different languages.

Keywords: AISAS, consumer behavior, first language, music consumption, second language

Procedia PDF Downloads 133

Authors: Shih-Fang Lo, Tzu-Wei Guo, Chih-Hsuan Lee

Abstract:

Tropical fisheries are vulnerable to the physical and biogeochemical oceanic changes associated with climate change. Warmer temperatures and extreme weather have beendamaging the abundance and growth patterns of aquatic organisms. In recent year, the shrinking of fish stock and labor shortage have increased the threat to global aquacultural production. Thus, building a climate-resilient and sustainable mechanism becomes an urgent, important task for global citizens. To tackle the problem, Taiwanese fishermen applies the artificial intelligence (AI) technology. In brief, the AI system (1) measures real-time water quality and chemical parameters infish ponds; (2) monitors fish stock through segmentation, detection, and classification; and (3) implements fishermen’sprevious experiences, perceptions, and real-life practices. Applying this system can stabilize the aquacultural production and potentially increase the labor force. Furthermore, this AI technology can build up a more resilient and sustainable system for the fishermen so that they can mitigate the influence of extreme weather while maintaining or even increasing their aquacultural production. In the future, when the AI system collected and analyzed more and more data, it can be applied to different regions of the world or even adapt to the future technological or societal changes, continuously providing the most relevant and useful information for fishermen in the world.

Keywords: aquaculture, artificial intelligence (AI), real-time system, sustainable fishery

Procedia PDF Downloads 111