Search results for: automated vehicles

Authors: Kevin Spencer G. Anglim, Zhenyu Zhang, Qingbin Gao

Abstract:

Reusable launch vehicles (RLVs) present a more environmentally-friendly approach to accessing space when compared to traditional launch vehicles that are discarded after each flight. This paper studies the recyclable nature of RLVs by presenting a solution method for determining minimum-fuel optimal trajectories using principles from optimal control theory and particle swarm optimization (PSO). This problem is formulated as a minimum-landing error powered descent problem where it is desired to move the RLV from a fixed set of initial conditions to three different sets of terminal conditions. However, unlike other powered descent studies, this paper considers the highly nonlinear effects caused by atmospheric drag, which are often ignored for studies on the Moon or on Mars. Rather than optimizing the controls directly, the throttle control is assumed to be bang-off-bang with a predetermined thrust direction for each phase of flight. The PSO method is verified in a one-dimensional comparison study, and it is then applied to the two-dimensional cases, the results of which are illustrated.

Keywords: minimum-fuel optimal trajectory, particle swarm optimization, reusable rocket, SpaceX

Procedia PDF Downloads 277

Authors: Marek Dosbaba

Abstract:

Within the mining sector, SEM-based Automated Mineralogy (AM) has been the standard application for quickly and efficiently handling mineral processing tasks. Over the last decade, the trend has been to analyze larger numbers of samples, often with a higher level of detail. This has necessitated a shift from interactive sample analysis performed by an operator using a SEM, to an increased reliance on offline processing to analyze and report the data. In response to this trend, TESCAN TIMA Mineral Analyzer is designed to quickly create a virtual copy of the studied samples, thereby preserving all the necessary information. Depending on the selected data acquisition mode, TESCAN TIMA can perform hyperspectral mapping and save an X-ray spectrum for each pixel or segment, respectively. This approach allows the user to browse through elemental distribution maps of all elements detectable by means of energy dispersive spectroscopy. Re-evaluation of the existing data for the presence of previously unconsidered elements is possible without the need to repeat the analysis. Additional tiers of data such as a secondary electron or cathodoluminescence images can also be recorded. To take full advantage of these information-rich datasets, TIMA utilizes a new archiving tool introduced by TESCAN. The dataset size can be reduced for long-term storage and all information can be recovered on-demand in case of renewed interest. TESCAN TIMA is optimized for network storage of its datasets because of the larger data storage capacity of servers compared to local drives, which also allows multiple users to access the data remotely. This goes hand in hand with the support of remote control for the entire data acquisition process. TESCAN also brings a newly extended open-source data format that allows other applications to extract, process and report AM data. This offers the ability to link TIMA data to large databases feeding plant performance dashboards or geometallurgical models. The traditional tabular particle-by-particle or grain-by-grain export process is preserved and can be customized with scripts to include user-defined particle/grain properties.

Keywords: Tescan, electron microscopy, mineralogy, SEM, automated mineralogy, database, TESCAN TIMA, open format, archiving, big data

Procedia PDF Downloads 109

Authors: Melissa Hachem, Maxime Loizeau, Nadine Saleh, Isabelle Momas, Lynda Bensefa-Colas

Abstract:

Professional drivers are exposed inside their vehicles to high levels of air pollutants due to the considerable time they spend close to motor vehicle emissions. Little is known about ultrafine particles (UFP) or black carbon (BC) adverse respiratory health effects compared to the regulated pollutants. We aimed to study the short-term associations between UFP and BC concentrations inside vehicles and (1) the onset of mucosal irritation and (2) the acute changes in lung function of Parisian taxi drivers during a working day. An epidemiological study was carried out on 50 taxi drivers in Paris. UFP and BC were measured inside their vehicles with DiSCmini® and microAeth®, respectively. On the same day, the frequency and the severity of nose, eye, and throat irritations were self-reported by each participant and a spirometry test was performed before and after the work shift. Multivariate analysis was used to evaluate the associations between in-taxis UFP and BC concentrations and mucosal irritation and lung function, after adjustment for potential confounders. In-taxis UFP concentrations ranged from 17.9 to 37.9 × 103 particles/cm³ and BC concentrations from 2.2 to 3.9 μg/m³, during a mean of 9 ± 2 working hours. Significant dose-response relationships were observed between in-taxis UFP concentrations and both nasal irritation and lung function. The increase of in-taxis UFP (for an interquartile range of 20 × 103 particles/cm3) was associated to an increase in nasal irritation (adjusted OR = 6.27 [95% CI: 1.02 to 38.62]) and to a reduction in forced expiratory flow at 25–75% by −7.44% [95% CI: −12.63 to −2.24], forced expiratory volume in one second by −4.46% [95% CI: −6.99 to −1.93] and forced vital capacity by −3.31% [95% CI: −5.82 to −0.80]. Such associations were not found with BC. Incident throat and eye irritations were not related to in-vehicle particles exposure; however, they were associated with outdoor air quality (estimated by the Atmo index) and in-vehicle humidity, respectively. This study is the first to show a significant association, within a short-period of time, between in-vehicle UFP exposure and acute respiratory effects in professional drivers.

Keywords: black carbon, lung function, mucosal irritation, taxi drivers, ultrafine particles

Procedia PDF Downloads 178

Authors: Zachary Cooper-Baldock, Paulo E. Santos, Russell S. A. Brinkworth, Karl Sammut

Abstract:

In recent years, large unmanned underwater vehicles such as the Boeing Voyager and Anduril Ghost Shark have been developed. These vessels can be structured to contain onboard internal payload bays. These payload bays can serve a variety of purposes – including the launch and recovery (LAR) of smaller underwater vehicles. The LAR of smaller vessels is extremely important, as it enables transportation over greater distances, increased time on station, data transmission and operational safety. The larger vessel and its payload bay structure complicate the LAR of UUVs in contrast to static docks that are affixed to the seafloor, as they actively impact the local flow field. These flow field impacts require analysis to determine if UUV vessels can be safely launched and recovered inside the motherships. This research seeks to determine the hydrodynamic forces exerted on a vertically over-actuated, small, unmanned underwater vehicle (OUUV) during an internal LAR manoeuvre and compare this to an under-actuated vessel (UUUV). In this manoeuvre, the OUUV is navigated through the stern wake region of the larger vessel to a set point within the internal payload bay. The manoeuvre is simulated using ANSYS Fluent computational fluid dynamics models, covering the entire recovery of the OUUV and UUUV. The analysis of the OUUV is compared against the UUUV to determine the differences in the exerted forces. Of particular interest are the drag, pressure, turbulence and flow field effects exerted as the OUUV is driven inside the payload bay of the larger vessel. The hydrodynamic forces and flow field disturbances are used to determine the feasibility of making such an approach. From the simulations, it was determined that there was no significant detrimental physical forces, particularly with regard to turbulence. The flow field effects exerted by the OUUV are significant. The vertical thrusters exert significant wake structures, but their orientation ensures the wake effects are exerted below the UUV, minimising the impact. It was also seen that OUUV experiences higher drag forces compared to the UUUV, which will correlate to an increased energy expenditure. This investigation found no key indicators that recovery via a mothership payload bay was not feasible. The turbulence, drag and pressure phenomenon were of a similar magnitude to existing static and towed dock structures.

Keywords: underwater vehicles, submarine, autonomous underwater vehicles, AUV, computational fluid dynamics, flow fields, pressure, turbulence, drag

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Authors: Nur Syahidatul Idany Abdul Ghani, Rahizar Ramli, Jamilah Mohamad, Ahmad Saifizul, Mohamed Rehan Karim

Abstract:

Increasing fatalities in road accidents in Malaysia over the last 10 years are quite alarming. Based on Malaysian Institute of Road Safety Research (Miros) latest research ‘Predicting Malaysian Road Fatalities for year 2020; it is predicted that road fatalities in Malaysia for 2015 is 8,780 and 10,716 for the year 2020 which 30 percent of fatalities were caused by accidents involving commercial vehicles. Government, related agencies and NGOs have continuously and persistently work to reduce the statistics through enforcement, educating the public, training to drivers, road safety campaigns, advertisements etc. However, the trend of casualties does not show encouraging pattern but instead, steadily growing. Thus, this comparative study reviews the literature pertaining on method of measurement used to evaluate commercial drivers competency. In several studies driving competency has been assessed with different assessment based on the license procedures and requirements according to the country regulation. The assessment criteria that has been establish for commercial drivers generally focus on driving tasks and assessment e.g. theory test, medical test and road assessment rather than driving competency test or physical test. Realizing the importance of specific assessment test for drivers competency this comparative study reviews the most discussed literature related to competency assessment method to identify competency of the drivers include (1. judgement and reaction, 2. skill of drivers, 3. experiences and fatigue). The concluding analysis of this paper is a comparative table for assessment methodology to access driver’s competency. A comparative study is a further discussion reviewing past literature to provide an overview on existing assessment test and potential subject matters that can be identified for further studies to increase awareness of the drivers, passengers as well as the authorities about the importance of competent drivers in order to improve safety in commercial vehicles.

Keywords: commercial vehicles, driver’s competency, specific assessment

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Authors: V. Shapaval, N. K. Afseth, D. Tzimorotas, A. Kohler

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Globally there is a dramatic increase in the demand for food, energy, materials and clean water since natural resources are limited. As a result, industries are looking for ways to reduce rest materials and to improve resource efficiency. Microorganisms have a high potential to be used as bio factories for the production of primary and secondary metabolites that represent high-value bio-products (enzymes, polyunsaturated fatty acids, bio-plastics, glucans, etc.). In order to find good microbial producers, to design suitable substrates from food rest materials and to optimize fermentation conditions, rapid analytical techniques for quantifying target bio products in microbial cells are needed. In the EU project FUST (R4SME, Fp7), we have developed a fully automated high-throughput FUST system based on micro-cultivation and FTIR spectroscopy that facilitates the screening of microorganisms, substrates and fermentation conditions for the optimization of the production of different high-value metabolites (single cell oils, bio plastics). The automated system allows the preparation of 100 samples per hour. Currently, The FUST system is in use for screening of filamentous fungi in order to find oleaginous strains with the ability to produce polyunsaturated fatty acids, and the optimization of cheap substrates, derived from food rest materials, and the optimization of fermentation conditions for the high yield of single cell oil.

Keywords: FTIR spectroscopy, FUST system, screening, biotechnology

Procedia PDF Downloads 443

Authors: Zachary Cooper-Baldock, Paulo E. Santos, Russell S. A. Brinkworth, Karl Sammut

Abstract:

- In recent years, large unmanned underwater vehicles such as the Boeing Voyager and Anduril Ghost Shark have been developed. These vessels can be structured to contain onboard internal payload bays. These payload bays can serve a variety of purposes – including the launch and recovery (LAR) of smaller underwater vehicles. The LAR of smaller vessels is extremely important, as it enables transportation over greater distances, increased time on station, data transmission and operational safety. The larger vessel and its payload bay structure complicate the LAR of UUVs in contrast to static docks that are affixed to the seafloor, as they actively impact the local flow field. These flow field impacts require analysis to determine if UUV vessels can be safely launched and recovered inside the motherships. This research seeks to determine the hydrodynamic forces exerted on a vertically over-actuated, small, unmanned underwater vehicle (OUUV) during an internal LAR manoeuvre and compare this to an under-actuated vessel (UUUV). In this manoeuvre, the OUUV is navigated through the stern wake region of the larger vessel to a set point within the internal payload bay. The manoeuvre is simulated using ANSYS Fluent computational fluid dynamics models, covering the entire recovery of the OUUV and UUUV. The analysis of the OUUV is compared against the UUUV to determine the differences in the exerted forces. Of particular interest are the drag, pressure, turbulence and flow field effects exerted as the OUUV is driven inside the payload bay of the larger vessel. The hydrodynamic forces and flow field disturbances are used to determine the feasibility of making such an approach. From the simulations, it was determined that there was no significant detrimental physical forces, particularly with regard to turbulence. The flow field effects exerted by the OUUV are significant. The vertical thrusters exert significant wake structures, but their orientation ensures the wake effects are exerted below the UUV, minimising the impact. It was also seen that OUUV experiences higher drag forces compared to the UUUV, which will correlate to an increased energy expenditure. This investigation found no key indicators that recovery via a mothership payload bay was not feasible. The turbulence, drag and pressure phenomenon were of a similar magnitude to existing static and towed dock structures.

Keywords: underwater vehicles, submarine, autonomous underwater vehicles, auv, computational fluid dynamics, flow fields, pressure, turbulence, drag

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Authors: Kushal Kumar Chode

Abstract:

Increase in demand for fuel saving and demand for faster vehicles with decent fuel economy, researchers around the world started investigating in various passive flow control devices to improve the fuel efficiency of vehicles. In this paper, A roof fairing was investigated for reducing the aerodynamic drag of a bluff body. The bluff body considered for this work is Ahmed model with a rake angle of 25deg was and subjected to flow with a velocity of 40m/s having Reynolds number of 2.68million was analysed using a commercial Computational Fluid Dynamic (CFD) code Star CCM+. It was evident that pressure drag is the main source of drag on an Ahmed body from the initial study. Adding a roof fairing has delayed the flow separation and resulted in delaying wake formation, thus improving the pressure in near weak and reducing the wake region. Adding a roof fairing of height and length equal to 1/7H and 1/3L respectively has shown a drag reduction by 9%. However, an optimised fairing, which was obtained by changing height, length and width by 5% increase, recorded a drag reduction close 12%.

Keywords: Ahmed model, aerodynamic drag, passive flow control, roof fairing, wake formation

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Authors: Mortez Alijani, Anas Osman

Abstract:

Recently, the popularity of Unmanned aerial vehicles (UAVs) has skyrocketed amidst the unprecedented events and the global pandemic, as they play a key role in both the security and health sectors, through surveillance, taking test samples, transportation of crucial goods and spreading awareness among civilians. However, the process of designing and producing such aerial robots is suppressed by the internal and external constraints that pose serious challenges. Landing is one of the key operations during flight, especially, the autonomous landing of UAVs on a moving platform is a scientifically complex engineering problem. Typically having a successful automatic landing of UAV on a moving platform requires accurate localization of landing, fast trajectory planning, and robust control planning. To achieve these goals, the information about the autonomous landing process such as the intersection point, the position of platform/UAV and inclination angle are more necessary. In this study, the mathematical approach to this problem in the X-Y axis based on the inclination angle and position of UAV in the landing process have been presented. The experimental results depict the accurate position of the UAV, intersection between UAV and moving platform and inclination angle in the landing process, allowing prediction of the intersection point.

Keywords: autonomous landing, inclination angle, unmanned aerial vehicles, moving platform, X-Y axis, intersection point

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Authors: Tae-Uk Yun, Pullip Chung, Kee-Young Kwahk, Hyunchul Ahn

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Fake news is defined as the news articles that are intentionally and verifiably false, and could mislead readers. Spread of fake news may provoke anxiety, chaos, fear, or irrational decisions of the public. Thus, detecting fake news and preventing its spread has become very important issue in our society. However, due to the huge amount of fake news produced every day, it is almost impossible to identify it by a human. Under this context, researchers have tried to develop automated fake news detection using machine learning techniques over the past years. But, there have been no prior studies proposed an automated fake news detection method for Korean news to our best knowledge. In this study, we aim to detect Korean fake news using text mining and machine learning techniques. Our proposed method consists of two steps. In the first step, the news contents to be analyzed is convert to quantified values using various text mining techniques (topic modeling, TF-IDF, and so on). After that, in step 2, classifiers are trained using the values produced in step 1. As the classifiers, machine learning techniques such as logistic regression, backpropagation network, support vector machine, and deep neural network can be applied. To validate the effectiveness of the proposed method, we collected about 200 short Korean news from Seoul National University’s FactCheck. which provides with detailed analysis reports from 20 media outlets and links to source documents for each case. Using this dataset, we will identify which text features are important as well as which classifiers are effective in detecting Korean fake news.

Keywords: fake news detection, Korean news, machine learning, text mining

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Authors: Vertika Goswami, Gargi Sharma

Abstract:

The rapid evolution of fintech has led to the rise of robo-advisor platforms that utilize artificial intelligence (AI) and machine learning to offer personalized investment solutions efficiently and cost-effectively. This research paper conducts a comprehensive sentiment analysis of investor experiences with these platforms, employing natural language processing (NLP) and sentiment classification techniques. The study investigates investor perceptions, engagement, and satisfaction, identifying key drivers of positive sentiment such as clear communication, low fees, consistent returns, and robust security. Conversely, negative sentiment is linked to issues like inconsistent performance, hidden fees, poor customer support, and a lack of transparency. The analysis reveals that addressing these pain points—through improved transparency, enhanced customer service, and ongoing technological advancements—can significantly boost investor trust and satisfaction. This paper contributes valuable insights into the fields of behavioral finance and fintech innovation, offering actionable recommendations for stakeholders, practitioners, and policymakers. Future research should explore the long-term impact of these factors on investor loyalty, the role of emerging technologies, and the effects of ethical investment choices and regulatory compliance on investor sentiment.

Keywords: artificial intelligence in finance, automated investment, financial technology, investor satisfaction, investor sentiment, robo-advisors, sentimental analysis

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Authors: Ahmed Kiani

Abstract:

The United Arab Emirates is clearly facing a multitude of challenges in curbing its greenhouse gas emissions to meet its pre-allotted framework of Kyoto protocol and COP21 targets due to its hunger for modernization, industrialization, infrastructure growth, soaring population and oil and gas activity. In this work, we focus on the bonafide zero emission electric vehicles market penetration in the country’s transport industry for emission reduction. We study the global electric vehicle market trends, the complementary battery technologies and the trends by manufacturers, emission standards across borders and prioritized advancements which will ultimately dictate the terms of future conditions for the United Arab Emirate transport industry. Based on our findings and analysis at every stage of current viability and state-of-transport-affairs, we postulate policy recommendations to local governmental entities from a supply and demand perspective covering aspects of technology, infrastructure requirements, change in power dynamics, end user incentives program, market regulators behavior and communications amongst key stakeholders. 

Keywords: electric vehicles, greenhouse gas emission reductions, market analysis, policy recommendations

Procedia PDF Downloads 309

Authors: Ioan Corneliu Salisteanu, Valentin Dogaru Ulieru, Mihaita Nicolae Ardeleanu, Alin Pohoata, Bogdan Salisteanu, Stefan Broscareanu

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Creating a personalized statistic for an individual within the population using IT systems, based on the searches and intercepted spheres of interest they manifest, is just one 'atom' of the artificial intelligence analysis network. However, having the ability to generate statistics based on individual data intercepted from large demographic areas leads to reasoning like that issued by a human mind with global strategic ambitions. The DiaMOTO device is a technical sensory system that allows the interception of car events caused by a driver, positioning them in time and space. The device's connection to the vehicle allows the creation of a source of data whose analysis can create psychological, behavioural profiles of the drivers involved. The SiaMOTO system collects data from many vehicles equipped with DiaMOTO, driven by many different drivers with a unique fingerprint in their approach to driving. In this paper, we aimed to explain the software infrastructure of the SiaMOTO system, a system designed to monitor and improve driver driving behaviour, as well as the criteria and algorithms underlying the intelligent analysis process.

Keywords: artificial intelligence, data processing, driver behaviour, driver monitoring, SiaMOTO

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Authors: A. Ramalho, L. Romeiro, R. Ventura, A. Suleman

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A real-time path planning framework for Unmanned Air Vehicles, and in particular multi-rotors is proposed. The framework is designed to provide feasible trajectories from the current UAV position to a goal state, taking into account constraints such as obstacle avoidance, problem kinematics, and vehicle limitations such as maximum speed and maximum acceleration. The framework computes feasible paths online, allowing to avoid new, unknown, dynamic obstacles without fully re-computing the trajectory. These features are achieved using an iterative process in which the robot computes and optimizes the trajectory while performing the mission objectives. A first trajectory is computed using a modified Rapidly-Exploring Random Tree (RRT) algorithm, that provides trajectories that respect a maximum curvature constraint. The trajectory optimization is accomplished using the Interior Point Optimizer (IPOPT) as a solver. The framework has proven to be able to compute a trajectory and optimize to a locally optimal with computational efficiency making it feasible for real-time operations.

Keywords: interior point optimization, multi-rotors, online path planning, rapidly exploring random trees, trajectory optimization

Procedia PDF Downloads 135

Authors: Nikhat Iqbal Tamboli

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INTRODUCTION: road traffic accidents (RTAs) are one of the leading and common causes of ocular injuries especially in developing countries like India which are preventable with certain measures and so it is of public health importance. AIM: To study incidence and clinical presentation of ocular morbidity in road traffic accidents. METHOD: Prospective cross-sectional study was conducted on 360 patients reported in department of ophthalmology. Detailed ocular examination and relevant investigations done. RESULTS: Incidence of ocular injuries is 23%. male:female ratio is 4.5:1.Cases having Sub conjunctival haemorrhage [74].eccymosis[217]. lid lcerations [164]orbital fracture[12] corneal tear [7]corneal abrasion[2] sclera tear[6] hyphaema[4] traumatic mydriasis [7]traumatic cataract [2]vitreous haemorrhage [1]traumatic optic neuropathy[1].Maximum cases in age group 20-40 years, with two wheeler vehicles 94.7% .Under influence of alcohol 13.3%. CONCLUSION: Younger age group with male preponderance is involved in ocular trauma due to road traffic accidents .maximum cases reported are with anterior segment injuries. Alcohol and two wheeler vehicles are common risk factors. Injuries involving cornea had bad prognosis and involving retina had worst prognosis.

Keywords: ocular morbidity, eye trauma, RTA, eye injury

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Authors: Mohammed Elsayed Lotfy Elsayed Abouzeid, Tomonobu Senjyu

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A novel multi-objective park and ride control approach is presented in this research. Park and ride will encourage the owners of the vehicles to leave their cars in the nearest points (on the edges of the crowded cities) and use public transportation facilities (train, bus, metro, or mon-rail) to reach their work inside the crowded city. The proposed control scheme is used to design electric vehicle charging stations (EVCS) to charge 1000 electric vehicles (EV) during their owners' work time. Cairo, Egypt is used as a case study. Photovoltaic (PV) and battery energy storage system (BESS) are used to meet the EVCS demand. Two multi-objective optimization techniques (MOGA and epsilon-MOGA) are utilized to get the optimal sizes of PV and BESS so as to meet the load demand and minimize the total life cycle cost. Detailed analysis and comparison are held to investigate the performance of the proposed control scheme using MATLAB.

Keywords: Battery Energy Storage System, Electric Vehicle, Park and Ride, Photovoltaic, Multi-objective

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Authors: Seong Jun Yoon, Jasurbek Doliev, Sang Min Oh, Rodi Hartono, Kyoojae Shin

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Electric power steering (EPS), which is commonly used in electric vehicles recently, is an electric-driven steering device for vehicles. Compared to hydraulic systems, EPS offers advantages such as simple system components, easy maintenance, and improved steering performance. However, because the EPS system is a nonlinear model, difficult problems arise in controller design. To address these, various machine learning and artificial intelligence approaches, notably artificial neural networks (ANN), have been applied. ANN can effectively determine relationships between inputs and outputs in a data-driven manner. This research explores two main areas: designing an EPS identifier using an ANN-based backpropagation (BP) algorithm and enhancing the EPS system controller with an ANN-based Levenberg-Marquardt (LM) algorithm. The proposed ANN-based BP algorithm shows superior performance and accuracy compared to linear transfer function estimators, while the LM algorithm offers better input angle reference tracking and faster response times than traditional PID controllers. Overall, the proposed ANN methods demonstrate significant promise in improving EPS system performance.

Keywords: ANN backpropagation modelling, electric power steering, transfer function estimator, electrical vehicle driving system

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Authors: Ankit Sinha, Soham Banerjee, Pratik Chattopadhyay

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Automated product recognition in retail stores is an important real-world application in the domain of Computer Vision and Pattern Recognition. In this paper, we consider the problem of automatically identifying the classes of the products placed on racks in retail stores from an image of the rack and information about the query/product images. We improve upon the existing approaches in terms of effectiveness and memory requirement by developing a two-stage object detection and recognition pipeline comprising of a Faster-RCNN-based object localizer that detects the object regions in the rack image and a ResNet-18-based image encoder that classifies the detected regions into the appropriate classes. Each of the models is fine-tuned using appropriate data sets for better prediction and data augmentation is performed on each query image to prepare an extensive gallery set for fine-tuning the ResNet-18-based product recognition model. This encoder is trained using a triplet loss function following the strategy of online-hard-negative-mining for improved prediction. The proposed models are lightweight and can be connected in an end-to-end manner during deployment to automatically identify each product object placed in a rack image. Extensive experiments using Grozi-32k and GP-180 data sets verify the effectiveness of the proposed model.

Keywords: retail stores, faster-RCNN, object localization, ResNet-18, triplet loss, data augmentation, product recognition

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Authors: Ani Wilson Uchenna, Ogbonna Chikodi

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This study examined the impact of artificial intelligence driven applications on banks’ performance in West Africa using Nigeria and Ghana as case studies. Specifically, the study examined the extent to which deployment of smart automated teller machine impacts the banks’ net worth within the reference period in Nigeria and Ghana. It ascertained the impact of point of sale on banks’ net worth within the reference period in Nigeria and Ghana. Thirdly, it verified the extent to which webpay services can influence banks’ performance in Nigeria and Ghana and finally, determined the impact of mobile pay services on banks’ performance in Nigeria and Ghana. The study used automated teller machine (ATM), Point of sale services (POS), Mobile pay services (MOP) and Web pay services (WBP) as proxies for explanatory variables while Bank net worth was used as explained variable for the study. The data for this study were sourced from central bank of Nigeria (CBN) Statistical Bulletin as well as Bank of Ghana (BoGH) Statistical Bulletin, Ghana payment systems oversight annual report and world development indicator (WDI). Furthermore, the mixed order of integration observed from the panel unit test result justified the use of autoregressive distributed lag (ARDL) approach to data analysis which the study adopted. While the cointegration test showed the existence of cointegration among the studied variables, bound test result justified the presence of long-run relationship among the series. Again, ARDL error correction estimate established satisfactory (13.92%) speed of adjustment from long run disequilibrium back to short run dynamic relationship. The study found that while Automated teller machine (ATM) had statistically significant impact on bank net worth (BNW) of Nigeria and Ghana, point of sale services application (POS) statistically and significantly impact on bank net worth within the study period, mobile pay services application was statistically significant in impacting the changes in the bank net worth of the countries of study while web pay services (WBP) had no statistically significant impact on bank net worth of the countries of reference. The study concluded that artificial intelligence driven application have significant an positive impact on bank performance with exception of web pay which had negative impact on bank net worth. The study recommended that management of banks both in Nigerian and Ghanaian should encourage more investments in AI-powered smart ATMs aimed towards delivering more secured banking services in order to increase revenue, discourage excessive queuing in the banking hall, reduced fraud and minimize error in processing transaction. Banks within the scope of this study should leverage on modern technologies to checkmate the excesses of the private operators POS in order to build more confidence on potential customers. Government should convert mobile pay services to a counter terrorism tool by ensuring that restrictions on over-the-counter withdrawals to a minimum amount is maintained and place sanctions on withdrawals above that limit.

Keywords: artificial intelligence (ai), bank performance, automated teller machines (atm), point of sale (pos)

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Authors: Adrian Keith Caamino, Sina Shakibania, Lena Sunqvist-Öqvist, Jan Rosenkranz, Yousef Ghorbani

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Feldspar minerals are one of the main components of the earth’s crust. They are tectosilicate, meaning that they mainly contain aluminum and silicon. Besides aluminum and silicon, they contain either potassium, sodium, or calcium. Accordingly, feldspar minerals are categorized into three main groups: K-feldspar, Na-feldspar, and Ca-feldspar. In recent years, the trend to use K-feldspar has grown tremendously, considering its potential to produce potash and alumina. However, the feldspar minerals, in general, are difficult to decompose for the dissolution of their metallic components. Several methods, including intensive milling, leaching under elevated pressure and temperature, thermal pretreatment, and the use of corrosive leaching reagents, have been proposed to improve its low dissolving efficiency. In this study, as part of the POTASSIAL EU project, to overcome the low dissolution efficiency of the K-feldspar components, mechanical activation using intensive milling followed by leaching using hydrochloric acid (HCl) was practiced. Grinding operational parameters, namely time, rotational speed, and ball-to-sample weight ratio, were studied using the Taguchi optimization method. Then, the mineralogy of the grinded samples was analyzed using a scanning electron microscope (SEM) equipped with automated quantitative mineralogy. After grinding, the prepared samples were subjected to HCl leaching. In the end, the dissolution efficiency of the main elements and impurities of different samples were correlated to the mineralogical characterization results. K-feldspar component dissolution is correlated with ore mineralogy, which provides insight into how to best optimize leaching conditions for selective dissolution. Further, it will have an effect on purifying steps taken afterward and the final value recovery procedures

Keywords: K-feldspar, grinding, automated mineralogy, impurity, leaching

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Authors: Morteza Aien, Masoud Rashidinejad, Mahmud Fotuhi-Firuzabad

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Power systems are innately uncertain systems. To face with such uncertain systems, robust uncertainty assessment tools are appealed. This paper inspects the uncertainty assessment formulation of the load flow (LF) problem considering different kinds of uncertainties, developed in its companion paper through some case studies. The proposed methodology is based on the evidence theory and joint propagation of possibilistic and probabilistic uncertainties. The load and wind power generation are considered as probabilistic uncertain variables and the electric vehicles (EVs) and gas turbine distributed generation (DG) units are considered as possibilistic uncertain variables. The cumulative distribution function (CDF) of the system output parameters obtained by the pure probabilistic method lies within the belief and plausibility functions obtained by the joint propagation approach. Furthermore, the imprecision in the DG parameters is explicitly reflected by the gap between the belief and plausibility functions. This gap, due to the epistemic uncertainty on the DG resources parameters grows as the penetration level increases.

Keywords: electric vehicles, joint possibilistic- probabilistic uncertainty modeling, uncertain load flow, wind turbine generator

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Authors: Francesca Fermi, Paola Astegiano, Angelo Martino, Stephanie Heitel, Michael Krail

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In order to achieve the targeted emission reductions for the decarbonisation of the European economy by 2050, fundamental contributions are required from both energy and transport sectors. The objective of this paper is to analyse the impacts of a largescale diffusion of e-vehicles, either battery-based or fuel cells, together with the implementation of transport policies aiming at decreasing the use of motorised private modes in order to achieve greenhouse gas emission reduction goals, in the context of a future high share of renewable energy. The analysis of the impacts and challenges of future scenarios on transport sector is performed with the ASTRA (ASsessment of TRAnsport Strategies) model. ASTRA is a strategic system-dynamic model at European scale (EU28 countries, Switzerland and Norway), consisting of different sub-modules related to specific aspects: the transport system (e.g. passenger trips, tonnes moved), the vehicle fleet (composition and evolution of technologies), the demographic system, the economic system, the environmental system (energy consumption, emissions). A key feature of ASTRA is that the modules are linked together: changes in one system are transmitted to other systems and can feed-back to the original source of variation. Thanks to its multidimensional structure, ASTRA is capable to simulate a wide range of impacts stemming from the application of transport policy measures: the model addresses direct impacts as well as second-level and third-level impacts. The simulation of the different scenarios is performed within the REFLEX project, where the ASTRA model is employed in combination with several energy models in a comprehensive Modelling System. From the transport sector perspective, some of the impacts are driven by the trend of electricity price estimated from the energy modelling system. Nevertheless, the major drivers to a low carbon transport sector are policies related to increased fuel efficiency of conventional drivetrain technologies, improvement of demand management (e.g. increase of public transport and car sharing services/usage) and diffusion of environmentally friendly vehicles (e.g. electric vehicles). The final modelling results of the REFLEX project will be available from October 2018. The analysis of the impacts and challenges of future scenarios is performed in terms of transport, environmental and social indicators. The diffusion of e-vehicles produces a consistent reduction of future greenhouse gas emissions, although the decarbonisation target can be achieved only with the contribution of complementary transport policies on demand management and supporting the deployment of low-emission alternative energy for non-road transport modes. The paper explores the implications through time of transport policy measures on mobility and environment, underlying to what extent they can contribute to a decarbonisation of the transport sector. Acknowledgements: The results refer to the REFLEX project which has received grants from the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 691685.

Keywords: decarbonisation, greenhouse gas emissions, e-mobility, transport policies, energy

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Authors: Glenn Auld Knierim

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Automated robotic winding of high-temperature superconductors (HTS) addresses precision, efficiency, and reliability critical to the commercialization of products. Today’s HTS materials are mature and commercially promising but require manufacturing attention. In particular to the exaggerated rectangular cross-section (very thin by very wide), winding precision is critical to address the stress that can crack the fragile ceramic superconductor (SC) layer and destroy the SC properties. Damage potential is highest during peak operations, where winding stress magnifies operational stress. Another challenge is operational parameters such as magnetic field alignment affecting design performance. Winding process performance, including precision, capability for geometric complexity, and efficient repeatability, are required for commercial production of current HTS. Due to winding limitations, current HTS magnets focus on simple pancake configurations. HTS motors, generators, MRI/NMR, fusion, and other projects are awaiting robotic wound solenoid, planar, and spherical magnet configurations. As with conventional power cables, full transposition winding is required for long length alternating current (AC) and pulsed power cables. Robotic production is required for transposition, periodic swapping of cable conductors, and placing into precise positions, which allows power utility required minimized reactance. A full transposition SC cable, in theory, has no transmission length limits for AC and variable transient operation due to no resistance (a problem with conventional cables), negligible reactance (a problem for helical wound HTS cables), and no long length manufacturing issues (a problem with both stamped and twisted stacked HTS cables). The Infinity Physics team is solving manufacturing problems by developing automated manufacturing to produce the first-ever reliable and utility-grade commercial SC cables and magnets. Robotic winding machines combine mechanical and process design, specialized sense and observer, and state-of-the-art optimization and control sequencing to carefully manipulate individual fragile SCs, especially HTS, to shape previously unattainable, complex geometries with electrical geometry equivalent to commercially available conventional conductor devices.

Keywords: automated winding manufacturing, high temperature superconductor, magnet, power cable

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Authors: Dermot O'Brien, Vasileios Christaras, Georgios Fontaras, Igor Nai Fovino, Ioannis Kounelis

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With the rise of the Internet of Things (IoT), 5G, and blockchain (BC) technologies, vehicles are becoming ever increasingly connected and are already transmitting substantial amounts of data to the original equipment manufacturers (OEMs) servers. This data could be used to help detect mileage fraud and enable more accurate vehicle emissions monitoring. This would not only help regulators but could enable applications such as permitting efficient drivers to pay less tax, geofencing for air quality improvement, as well as pollution tolling and trading platforms for transport-related businesses and EU citizens. Other applications could include traffic management and shared mobility systems. BC enables the transmission of data with additional security and removes single points of failure while maintaining data provenance, identity ownership, and the possibility to retain varying levels of privacy depending on the requirements of the applied use case. This research performs simulations of vehicles interacting with European member state authorities and European Commission BC nodes that are running hyperleger fabric and explores whether the technology is currently feasible for transport applications such as the emission monitoring use-case.

Keywords: future transportation systems, technological innovations, policy approaches for transportation future, economic and regulatory trends, blockchain

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Authors: Sarah K. Hagi, Majdi Alnowaimi

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In the last decade, there were immense advancements in the medical imaging modalities. These advancements can scan a whole volume of the lung organ in high resolution images within a short time. According to this performance, the physicians can clearly identify the complicated anatomical and pathological structures of lung. Therefore, these advancements give large opportunities for more advance of all types of lung cancer treatment available and will increase the survival rate. However, lung cancer is still one of the major causes of death with around 19% of all the cancer patients. Several factors may affect survival rate. One of the serious effects is the breathing process, which can affect the accuracy of diagnosis and lung tumor treatment plan. We have therefore developed a semi automated algorithm to localize the 3D lung tumor positions across all respiratory data during respiratory motion. The algorithm can be divided into two stages. First, a lung tumor segmentation for the first phase of the 4D computed tomography (CT). Lung tumor segmentation is performed using an active contours method. Then, localize the tumor 3D position across all next phases using a 12 degrees of freedom of an affine transformation. Two data set where used in this study, a compute simulate for 4D CT using extended cardiac-torso (XCAT) phantom and 4D CT clinical data sets. The result and error calculation is presented as root mean square error (RMSE). The average error in data sets is 0.94 mm ± 0.36. Finally, evaluation and quantitative comparison of the results with a state-of-the-art registration algorithm was introduced. The results obtained from the proposed localization algorithm show a promising result to localize alung tumor in 4D CT data.

Keywords: automated algorithm , computed tomography, lung tumor, tumor localization

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Authors: G. Sandhya Devi, G. Suvarna Kumar, S. Chandini

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This research work aims to develop a system that will analyze and identify students who indulge in malpractices/suspicious activities during the course of an academic offline examination. Automated Video Surveillance provides an optimal solution which helps in monitoring the students and identifying the malpractice event immediately. This work is organized into three modules. The first module deals with performing an impersonation check using a PCA-based face recognition method which is done by cross checking his profile with the database. The presence or absence of the student is even determined in this module by implementing an image registration technique wherein a grid is formed by considering all the images registered using the frontal camera at the determined positions. Second, detecting such facial malpractices in which a student gets involved in conversation with another, trying to obtain unauthorized information etc., based on the threshold range evaluated by considering his/her mouth state whether open or closed. The third module deals with identification of unauthorized material or gadgets used in the examination hall by training the positive samples of the object through various stages. Here, a top view camera feed is analyzed to detect the suspicious activities. The system automatically alerts the administration when any suspicious activities are identified, thereby reducing the error rate caused due to manual monitoring. This work is an improvement over our previous work published in identifying suspicious activities done by examinees in an offline examination.

Keywords: impersonation, image registration, incrimination, object detection, threshold evaluation

Procedia PDF Downloads 230

Authors: Alyaa M. Younes, Nermine Harraz, Mohammad H. Elwany

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Lithium ion batteries are currently used for many applications including satellites, electric vehicles and mobile electronics. Their ability to store relatively large amount of energy in a limited space make them most appropriate for critical applications. Evaluation of the life of these batteries and their reliability becomes crucial to the systems they support. Reliability of Li-Ion batteries has been mainly considered based on its lifetime. However, another important factor that can be considered critical in many applications such as in electric vehicles is the cycle duration. The present work presents the results of an experimental investigation on the degradation behavior of a Laptop Li-ion battery (type TKV2V) and the effect of applied load on the battery cycle time. The reliability was evaluated using an accelerated life test. Least squares linear regression with median rank estimation was used to estimate the Weibull distribution parameters needed for the reliability functions estimation. The probability density function, failure rate and reliability function under each of the applied loads were evaluated and compared. An inverse power model is introduced that can predict cycle time at any stress level given.

Keywords: accelerated life test, inverse power law, lithium-ion battery, reliability evaluation, Weibull distribution

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Authors: Chun-Liang Wu, Kai-Ping Shaw, Cheng-Ping Yu, Wu-Chien Chien, Hsiao-Ting Chen, Shao-Huang Wu

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Objective: This study analyzed three criminal judicial cases. We applied the damage analysis of the two vehicles to verify other evidence, such as dashboard camera records of each accident, reconstruct the scenes, and pursue the truth. Methods: Evidence analysis, the method is to collect evidence and the reason for the results in judicial procedures, then analyze the involved damage evidence to verify other evidence. The collision damage analysis method is to inspect the damage to the vehicles and utilize the principles of tool mark analysis, Newtonian physics, and vehicle structure to understand the relevant factors when the vehicles collide. Results: Case 1: Sedan A turned right at the T junction and collided with Scooter B, which was going straight on the left road. The dashboard camera records showed that the left side of Sedan A’s front bumper collided with the body of Scooter B and rider B. After the analysis of the study, the truth was that the front of the left side of Sedan A impacted the right pedal of Scooter B and the right lower limb of rider B. Case 2: Sedan C collided with Scooter D on the left road at the crossroads. The dashboard camera record showed that the left side of the Sedan C’s front bumper collided with the body of Scooter D and rider D. After the analysis of the study, the truth was that the left side of the Sedan C impacted the left side of the car body and the front wheel of Scooter D and rider D. Case 3: Sedan E collided with Scooter F on the right road at the crossroads. The dashboard camera record showed that the right side of the Sedan E’s front bumper collided with the body of Scooter F and rider F. After the analysis of the study, the truth was that the right side of the front bumper and the right side of the Sedan F impacted the Scooter. Conclusion: The application of collision damage analysis in the reconstruction of a sedan-scooter collision could discover the truth and provide the basis for judicial justice. The cases and methods could be the reference for the road safety policy.

Keywords: evidence analysis, collision damage analysis, accident reconstruction, sedan-scooter collision, dashboard camera records

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Authors: Surajit Chakrabarty, Piyush Chauhan, Subhasis Panda, Sujoy Bhattacharya

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A large proportion of roads in India are not well maintained as per the laid down public safety guidelines leading to loss of direction control and fatal accidents. We propose a technique to detect speed breakers and potholes using mobile sensor data captured from multiple vehicles and provide a profile of the road. This would, in turn, help in monitoring roads and revolutionize digital maps. Incorporating randomness in the model formulation for detection of speed breakers and potholes is crucial due to substantial heterogeneity observed in data obtained using a mobile application from multiple vehicles driven by different drivers. This is accomplished with Hidden Markov Models, whose hidden state sequence is found for each time step given the observables sequence, and are then fed as input to LSTM network with peephole connections. A precision score of 0.96 and 0.63 is obtained for classifying bumps and potholes, respectively, a significant improvement from the machine learning based models. Further visualization of bumps/potholes is done by converting time series to images using Markov Transition Fields where a significant demarcation among bump/potholes is observed.

Keywords: deep learning, hidden Markov model, pothole, speed breaker

Procedia PDF Downloads 144

Authors: George Charkoftakis, Panagiotis Liosatos, Nicolas-Alexander Tatlas, Dimitrios Goustouridis, Stelios M. Potirakis

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E-maintenance is a relatively new concept, generally referring to maintenance management by monitoring assets over the Internet. One of the key links in the chain of an e-maintenance system is data acquisition and transmission. Specifically for the case of a fleet of heavy-duty vehicles, where the main challenge is the diversity of the vehicles and vehicle-embedded self-diagnostic/reporting technologies, the design of the data acquisition and transmission unit is a demanding task. This clear if one takes into account that a heavy-vehicles fleet assortment may range from vehicles with only a limited number of analog sensors monitored by dashboard light indicators and gauges to vehicles with plethora of sensors monitored by a vehicle computer producing digital reporting. The present work proposes an adaptable internet of things (IoT) sensor node that is capable of addressing this challenge. The proposed sensor node architecture is based on the increasingly popular single-board computer – expansion boards approach. In the proposed solution, the expansion boards undertake the tasks of position identification by means of a global navigation satellite system (GNSS), cellular connectivity by means of 3G/long-term evolution (LTE) modem, connectivity to on-board diagnostics (OBD), and connectivity to analog and digital sensors by means of a novel design of expansion board. Specifically, the later provides eight analog plus three digital sensor channels, as well as one on-board temperature / relative humidity sensor. The specific device offers a number of adaptability features based on appropriate zero-ohm resistor placement and appropriate value selection for limited number of passive components. For example, although in the standard configuration four voltage analog channels with constant voltage sources for the power supply of the corresponding sensors are available, up to two of these voltage channels can be converted to provide power to the connected sensors by means of corresponding constant current source circuits, whereas all parameters of analog sensor power supply and matching circuits are fully configurable offering the advantage of covering a wide variety of industrial sensors. Note that a key feature of the proposed sensor node, ensuring the reliable operation of the connected sensors, is the appropriate supply of external power to the connected sensors and their proper matching to the IoT sensor node. In standard mode, the IoT sensor node communicates to the data center through 3G/LTE, transmitting all digital/digitized sensor data, IoT device identity, and position. Moreover, the proposed IoT sensor node offers WiFi connectivity to mobile devices (smartphones, tablets) equipped with an appropriate application for the manual registration of vehicle- and driver-specific information, and these data are also forwarded to the data center. All control and communication tasks of the IoT sensor node are performed by dedicated firmware. It is programmed with a high-level language (Python) on top of a modern operating system (Linux). Acknowledgment: This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH—CREATE—INNOVATE (project code: T1EDK- 01359, IntelligentLogger).

Keywords: IoT sensor nodes, e-maintenance, single-board computers, sensor expansion boards, on-board diagnostics

Procedia PDF Downloads 154