Search results for: elliptic curve digital signature algorithm

Authors: Y. Zhang, J. Perez, S. Marnier

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To date, no study on goaltending butterfly motion has been completed in real conditions, during an ice hockey game or training practice, to the author's best knowledge. This motion, performed to save score, is unnatural, intense, and repeated. The target of this research activity is to identify representative biomechanical criteria for this goaltender-specific movement pattern. Determining specific physical parameters may allow to will identify the risk of hip and groin injuries sustained by goaltenders. Four professional or academic goalies were instrumented during ice hockey training practices with five inertial measurement units. These devices were inserted in dedicated pockets located on each thigh and shank, and the fifth on the lumbar spine. A camera was also installed close to the ice to observe and record the goaltenders' activities, especially the butterfly motions, in order to synchronize the captured data and the behavior of the goaltender. Each data recorded began with a calibration of the inertial units and a calibration of the fully equipped goaltender on the ice. Three butterfly motions were recorded out of the training practice to define referential individual butterfly motions. Then, a data processing algorithm based on the Madgwick filter computed hip and knee joints joint range of motion as well as angular specific angular velocities. The developed algorithm software automatically identified and analyzed all the butterfly motions executed by the four different goaltenders. To date, it is still too early to show that the analyzed criteria are representative of the trauma generated by the butterfly motion as the research is only at its beginning. However, this descriptive research activity is promising in its ecological assessment, and once the criteria are found, the tools and protocols defined will allow the prevention of as many injuries as possible. It will thus be possible to build a specific training program for each goalie.

Keywords: biomechanics, butterfly motion, human motion analysis, ice hockey, inertial measurement unit

Procedia PDF Downloads 125

Authors: I. Kade Wiratama

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As a result of the significant growth of wind turbines in size, blade load control has become the main challenge for large wind turbines. Many advanced techniques have been investigated aiming at developing control devices to ease blade loading. Amongst them, trailing edge flaps have been proven as effective devices for load alleviation. The present study aims at investigating the potential benefits of flaps in enhancing the energy capture capabilities rather than blade load alleviation. A software tool is especially developed for the aerodynamic simulation of wind turbines utilising blades equipped with flaps. As part of the aerodynamic simulation of these wind turbines, the control system must be also simulated. The simulation of the control system is carried out via solving an optimisation problem which gives the best value for the controlling parameter at each wind turbine run condition. Developing a genetic algorithm optimisation tool which is especially designed for wind turbine blades and integrating it with the aerodynamic performance evaluator, a design optimisation tool for blades equipped with flaps is constructed. The design optimisation tool is employed to carry out design case studies. The results of design case studies on wind turbine AWT 27 reveal that, as expected, the location of flap is a key parameter influencing the amount of improvement in the power extraction. The best location for placing a flap is at about 70% of the blade span from the root of the blade. The size of the flap has also significant effect on the amount of enhancement in the average power. This effect, however, reduces dramatically as the size increases. For constant speed rotors, adding flaps without re-designing the topology of the blade can improve the power extraction capability as high as of about 5%. However, with re-designing the blade pretwist the overall improvement can be reached as high as 12%.

Keywords: flaps, design blade, optimisation, simulation, genetic algorithm, WTAero

Procedia PDF Downloads 337

Authors: Sheel Chakraborty

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Modern society in developed countries has distanced itself from the earlier norm of joint family living, and with the increase of economic pressure, parents' availability for their children during their infant years has been consistently decreasing over the past three decades. During the same time, the pre-primary education system - built mainly on the developmental psychology theory framework of Jean Piaget and Lev Vygotsky, has been promoted in the US through the legislature and funding. Early care and education may have a positive impact on young minds, but a growing number of kids facing social challenges in making friendships in their teenage years raises serious concerns about its effectiveness. The survey-based primary research presented here shows a statistically significant number of millennials between the ages of 10 and 25 prefer to build friendships virtually than face-to-face interactions. Moreover, many teenagers depend more on their virtual friends whom they never met. Contrary to the belief that early social interactions in a non-home setup make the kids confident and more prepared for the real world, many shy-natured kids seem to develop a sense of shakiness in forming social relationships, resulting in loneliness by the time they are young adults. Reflecting on George Mead’s theory of self that is made up of “I” and “Me”, most functioning homes provide the required freedom and forgivable, congenial environment for building the "I" of a toddler; however, daycare or preschools can barely match that. It seems social images created from the expectations perceived by preschoolers “Me" in a non-home setting may interfere and greatly overpower the formation of a confident "I" thus creating a crisis around the inability to form friendships face to face when they grow older. Though the pervasive nature of social media can’t be ignored, the non-parental early care and education practices adopted largely by the urban population have created a favorable platform of teen psychology on which social media popularity thrived, especially providing refuge to shy Gen-Z teenagers. This can explain why young adults today perceive social media as their preferred outlet of expression and a place to form dependable friendships, despite the risk of being cyberbullied.

Keywords: digital socialization, shyness, developmental psychology, friendship, early education

Procedia PDF Downloads 128

Authors: Khanh D.Do, Loi V.Nguyen, Thanh N.Nguyen, Thang M.Nguyen, Vu T.Tran

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Along with the development of Secondary surveillance radar (SSR) in air traffic surveillance systems, the Multipath phenomena has always been a noticeable problem. This following article discusses the geometrical aspect and power aspect of the Multipath interference caused by reflection in SSR and proposes a method to deal with these unwanted multipath targets (ghosts) by false-target position predicting and adaptive target suppressing. A field-experiment example is mentioned at the end of the article to demonstrate the efficiency of this measure.

Keywords: multipath, secondary surveillance radar, digital signal processing, reflection

Procedia PDF Downloads 164

Authors: Luqman Afolabi

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This paper measures the impacts of trade and institutions on environmental quality in Sub-Saharan Africa (SSA). To examine the direction and the magnitude of the effects, the study employs the pooled mean group (PMG) estimation technique on the panel data obtained from the World Bank’s World Development and Governance Indicators, between 1996 and 2018. The empirical estimates validate the environmental Kuznets curve hypothesis (EKC) for the region, even though there have been inconclusive results on the environment – growth nexus. Similarly, a positive coefficient is obtained on the impact of trade on the environment, while the impact of the institutional indicators produce mixed results. A significant policy implication is that the governments of the SSA countries pursue policies that tend to increase economic growth, so that pollutants may be reduced. Such policies may include the provision of incentives for sustainable growth-driven industries in the region. In addition, the governance infrastructures should be improved in such a way that appropriate penalties are imposed on the pollutants, while advanced technologies that have the potentials to reduce environmental degradation should be encouraged. Finally, it is imperative from these findings that the governments of the region should promote their trade relations and the competitiveness of their local industries in order to keep pace with the global markets.

Keywords: environmental quality, institutional quality sustainable development goals, trade

Procedia PDF Downloads 143

Authors: Mohammedi Ferhate

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This work presents details of the study of the entire flow inside the facility where the exothermic chemical reaction process in the chemical laser cavity is analyzed. In our paper we will describe the principles of chemical lasers where flow reversal is produced by chemical reactions. We explain the device for converting chemical potential energy laser energy. We see that the phenomenon thus has an explosive trend. Finally, the feasibility and effectiveness of the proposed method is demonstrated by computer simulation

Keywords: genetic, lasers, nozzle, programming

Procedia PDF Downloads 94

Authors: Ali Seddiki, Mohamed Djebbouri, Driss Guerchi

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Medical imaging produces human body pictures in digital form. Since these imaging techniques produce prohibitive amounts of data, compression is necessary for storage and communication purposes. In some areas in medicine, it may be sufficient to maintain high image quality only in region of interest (ROI). This paper discusses a contribution to quality purpose compression in the region of interest of scintigraphic images based on dual tree complex wavelet transform (DT-CWT) using Run-Length coding (RLE) and Huffman coding (HC).

Keywords: DT-CWT, region of interest, run length coding, Scintigraphic images

Procedia PDF Downloads 282

Authors: Sreto Boljevic

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In the absence of considerable investment in electricity generation, transmission and distribution network (DN) capacity, the demand for electrical energy will quickly strain the capacity of the existing electrical power network. With anticipated growth and proliferation of Electric vehicles (EVs) and Heat pump (HPs) identified the likelihood that the additional load from EV changing and the HPs operation will require capital investment in the DN. While an area-wide implementation of EVs and HPs will contribute to the decarbonization of the energy system, they represent new challenges for the existing low-voltage (LV) network. Distributed energy resources (DER), operating both as part of the DN and in the off-network mode, have been offered as a means to meet growing electricity demand while maintaining and ever-improving DN reliability, resiliency and power quality. DN planning has traditionally been done by forecasting future growth in demand and estimating peak load that the network should meet. However, new problems are arising. These problems are associated with a high degree of proliferation of EVs and HPs as load imposes on DN. In addition to that, the promotion of electricity generation from renewable energy sources (RES). High distributed generation (DG) penetration and a large increase in load proliferation at low-voltage DNs may have numerous impacts on DNs that create issues that include energy losses, voltage control, fault levels, reliability, resiliency and power quality. To mitigate negative impacts and at a same time enhance positive impacts regarding the new operational state of DN, CHP system integration can be seen as best action to postpone/reduce capital investment needed to facilitate promotion and maximize benefits of EVs, HPs and RES integration in low-voltage DN. The aim of this paper is to generate an algorithm by using an analytical approach. Algorithm implementation will provide a way for optimal placement of the CHP system in the DN in order to maximize the integration of RES and increase in proliferation of EVs and HPs.

Keywords: combined heat & power (CHP), distribution networks, EVs, HPs, RES

Procedia PDF Downloads 202

Authors: Nam-Seok Joo, Da-Eun Jung, Beom-Hee Choi

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Background and Objectives: Recently, the triglyceride/high-density lipoprotine cholesterol (TG/HDL) ratio and pericardial adipose tissue (PAT) has gained attention as an indicator related to metabolic syndrome (MS). To date, there has been no research on the relationship between TG/HDL and PAT, we aimed to investigate the association between the TG/HDL and PAT. Methods: In this cross-sectional study, we investigated 627 patients who underwent coronary multidetector computed tomography and metabolic parameters. We divided subjects into two groups according to the cut-off PAT volume associated with MS, which is 142.2 cm³, and we compared metabolic parameters between those groups. We divided the TG/HDL ratio into tertiles according to Log(TG/HDL) and compared PAT-related parameters by analysis of variance. Finally, we applied logistic regression analysis to obtain the odds ratio of high PAT (PAT volume≥142.2 cm³) in each tertile, and we performed receiver operating characteristic (ROC) analysis to get the cut-off of TG/HDL ratio according to high PAT. Results: The mean TG/ HDL ratio of the high PAT volume group was 3.6, and TG/ HDL ratio had a strong positive correlation with various metabolic parameters. In addition, in the Log (TG/HDL) tertile groups, the higher tertile had more metabolic derangements, including PAT, and showed higher odds ratios of having high PAT (OR=4.10 in the second tertile group and OR=5.06 in their third tertile group, respectively) after age, sex, smoking adjustments. TG/HDL ratio according to the having increased PAT by ROC curve showed 1.918 (p < 0.001). Conclusion: TG/HDL ratio and high PAT volume have a significant positive correlation, and higher TG/HDL ratio showed high PAT. The cut-off value of the TG/HDL ratio was 1.918 to have a high PAT.

Keywords: triglyceride, high-density lipoprotein, pericardial adipose tissue, cut-off value

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Authors: Borhan Marzougui

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Road and Transport Authority (RTA) is moving ahead with the implementation of the leader’s vision in exploring all avenues that may bring better security and safety services to the community. Smart transport means using smart technologies such as IoT (Internet of Things). This technology continues to affirm its important role in the context of Information and Transportation Systems. In fact, IoT is a network of Internet-connected objects able to collect and exchange different data using embedded sensors. With the growth of IoT, Distributed Denial of Service (DDoS) attacks is also growing exponentially. DDoS attacks are the major and a real threat to various transportation services. Currently, the defense mechanisms are mainly passive in nature, and there is a need to develop a smart technique to handle them. In fact, new IoT devices are being used into a botnet for DDoS attackers to accumulate for attacker purposes. The aim of this paper is to provide a relevant understanding of dangerous types of DDoS attack related to IoT and to provide valuable guidance for the future IoT security method. Our methodology is based on development of the distributed algorithm. This algorithm manipulates dedicated intelligent and cooperative agents to prevent and to mitigate DDOS attacks. The proposed technique ensure a preventive action when a malicious packets start to be distributed through the connected node (Network of IoT devices). In addition, the devices such as camera and radio frequency identification (RFID) are connected within the secured network, and the data generated by it are analyzed in real time by intelligent and cooperative agents. The proposed security system is based on a multi-agent system. The obtained result has shown a significant reduction of a number of infected devices and enhanced the capabilities of different security dispositives.

Keywords: IoT, DDoS, attacks, botnet, security, agents

Procedia PDF Downloads 143

Authors: Aditya Verma

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E-voting using blockchain provides us with a distributed system where data is present on each node present in the network and is reliable and secure too due to its immutability property. This work compares various blockchain consensus algorithms used for e-voting applications in the past, based on performance and node scalability, and chooses the optimal one and improves on one such previous implementation by proposing solutions for the loopholes of the optimally working blockchain consensus algorithm, in our chosen application, e-voting.

Keywords: blockchain, parallel bft, consensus algorithms, performance

Procedia PDF Downloads 167

Authors: Jazim Sohail, Filipe Teixeira-Dias

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Mild traumatic brain injury (mTBI) within non-helmeted contact sports is a growing concern due to the serious risk of potential injury. Extensive research is being conducted looking into head kinematics in non-helmeted contact sports utilizing instrumented mouthguards that allow researchers to record accelerations and velocities of the head during and after an impact. This does not, however, allow the location of the impact on the head, and its magnitude and orientation, to be determined. This research proposes and validates two methods to quantify impact locations from instrumented mouthguard kinematic data, one using rigid body dynamics, the other utilizing machine learning. The rigid body dynamics technique focuses on establishing and matching moments from Euler’s and torque equations in order to find the impact location on the head. The methodology is validated with impact data collected from a lab test with the dummy head fitted with an instrumented mouthguard. Additionally, a Hybrid III Dummy head finite element model was utilized to create synthetic kinematic data sets for impacts from varying locations to validate the impact location algorithm. The algorithm calculates accurate impact locations; however, it will require preprocessing of live data, which is currently being done by cross-referencing data timestamps to video footage. The machine learning technique focuses on eliminating the preprocessing aspect by establishing trends within time-series signals from instrumented mouthguards to determine the impact location on the head. An unsupervised learning technique is used to cluster together impacts within similar regions from an entire time-series signal. The kinematic signals established from mouthguards are converted to the frequency domain before using a clustering algorithm to cluster together similar signals within a time series that may span the length of a game. Impacts are clustered within predetermined location bins. The same Hybrid III Dummy finite element model is used to create impacts that closely replicate on-field impacts in order to create synthetic time-series datasets consisting of impacts in varying locations. These time-series data sets are used to validate the machine learning technique. The rigid body dynamics technique provides a good method to establish accurate impact location of impact signals that have already been labeled as true impacts and filtered out of the entire time series. However, the machine learning technique provides a method that can be implemented with long time series signal data but will provide impact location within predetermined regions on the head. Additionally, the machine learning technique can be used to eliminate false impacts captured by sensors saving additional time for data scientists using instrumented mouthguard kinematic data as validating true impacts with video footage would not be required.

Keywords: head impacts, impact location, instrumented mouthguard, machine learning, mTBI

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Authors: Olesya Bolkhovskaya, Alexey Davydov, Alexander Maltsev

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In this paper approach to incoherent signal detection in multi-element antenna array are researched and modeled. Two types of useful signals with unknown wavefront were considered. First one is deterministic (Barker code), the second one is random (Gaussian distribution). The derivation of the sufficient statistics took into account the linearity of the antenna array. The performance characteristics and detecting curves are modeled and compared for different useful signals parameters and for different number of elements of the antenna array. Results of researches in case of some additional conditions can be applied to a digital communications systems.

Keywords: antenna array, detection curves, performance characteristics, quadrature processing, signal detection

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Authors: Johanna Lindstrom, Jacob Mickelsson

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Changes in the current media environment are transforming peoples’ everyday media consumption patterns all over the world. Digital media have become a natural, almost unavoidable, part of everyday lives of humans. While this has led to many positive consequences, there is also a growing concern for harmful effects. This paper contributes to knowledge about “the dark side” of media use by considering the topic of media addiction and subsequent coping strategies among young adults. The paper draws on a longitudinal media diary study conducted among young university students between the years 2013 and 2021. A total of 1029 diaries have been collected (approximately 100 each year), aiming at capturing the students’ everyday media behavior. In this paper, reflective narratives in the diaries have been analyzed, aiming at answering the following questions: Which of their own media behaviors do the students perceive as particularly destructive, addictive or problematic? How do they cope with such behaviors? Results from the study indicate a noticeable increase in reflections on addictive media behavior over the years. For example, compared to earlier years, the amount of such reflections significantly started to increase in the diaries in 2016 and 2017, and this trend has continued ever since. Furthermore, the nature of these reflections has changed, displaying a growing concern for one’s own excessive media use and general wellbeing. Media addiction seems particularly difficult to cope with as digital media is literally everywhere and media use in general is described as consistent and habitual, in terms of regularly repeated routines that are fragmented but performed continuously and often unintentionally throughout the day. Reflections on “the dark side” of everyday media consumption become particularly prominent in times of the Covid -19 pandemic. However, this trend was noticeable well before the pandemic started. The study also identifies a countertrend regarding reflections on how to deal and cope with problematic media behavioral patterns. This countertrend portrays a general development of increased awareness of factors that may trigger compulsive behavior and how to avoid or handle such trigger points. The countertrend is particularly evident in recent years, despite the ongoing pandemic and subsequent increases in time spent using media. Addictive media behavior may lead to severe consequences for students’ learning processes and general well-being. Increased awareness of this growing trend and coping strategies are needed on an individual as well as a broader educational level.

Keywords: coping strategies, media addiction, media behavior, well-being

Procedia PDF Downloads 202

Authors: Ali Kazemi

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Those within the volatile and interconnected international economic markets, appropriately predicting market trends, hold substantial fees for traders and financial establishments. Traditional device mastering strategies have made full-size strides in forecasting marketplace movements; however, monetary data's complicated and networked nature calls for extra sophisticated processes. This observation offers a groundbreaking method for monetary marketplace prediction that leverages the synergistic capability of Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks. Our suggested algorithm is meticulously designed to forecast the traits of inventory market indices and cryptocurrency costs, utilizing a comprehensive dataset spanning from January 1, 2015, to December 31, 2023. This era, marked by sizable volatility and transformation in financial markets, affords a solid basis for schooling and checking out our predictive version. Our algorithm integrates diverse facts to construct a dynamic economic graph that correctly reflects market intricacies. We meticulously collect opening, closing, and high and low costs daily for key inventory marketplace indices (e.g., S&P 500, NASDAQ) and widespread cryptocurrencies (e.g., Bitcoin, Ethereum), ensuring a holistic view of marketplace traits. Daily trading volumes are also incorporated to seize marketplace pastime and liquidity, providing critical insights into the market's shopping for and selling dynamics. Furthermore, recognizing the profound influence of the monetary surroundings on financial markets, we integrate critical macroeconomic signs with hobby fees, inflation rates, GDP increase, and unemployment costs into our model. Our GCN algorithm is adept at learning the relational patterns amongst specific financial devices represented as nodes in a comprehensive market graph. Edges in this graph encapsulate the relationships based totally on co-movement styles and sentiment correlations, enabling our version to grasp the complicated community of influences governing marketplace moves. Complementing this, our LSTM algorithm is trained on sequences of the spatial-temporal illustration discovered through the GCN, enriched with historic fee and extent records. This lets the LSTM seize and expect temporal marketplace developments accurately. Inside the complete assessment of our GCN-LSTM algorithm across the inventory marketplace and cryptocurrency datasets, the version confirmed advanced predictive accuracy and profitability compared to conventional and opportunity machine learning to know benchmarks. Specifically, the model performed a Mean Absolute Error (MAE) of 0.85%, indicating high precision in predicting day-by-day charge movements. The RMSE was recorded at 1.2%, underscoring the model's effectiveness in minimizing tremendous prediction mistakes, which is vital in volatile markets. Furthermore, when assessing the model's predictive performance on directional market movements, it achieved an accuracy rate of 78%, significantly outperforming the benchmark models, averaging an accuracy of 65%. This high degree of accuracy is instrumental for techniques that predict the course of price moves. This study showcases the efficacy of mixing graph-based totally and sequential deep learning knowledge in economic marketplace prediction and highlights the fee of a comprehensive, records-pushed evaluation framework. Our findings promise to revolutionize investment techniques and hazard management practices, offering investors and economic analysts a powerful device to navigate the complexities of cutting-edge economic markets.

Keywords: financial market prediction, graph convolutional networks (GCNs), long short-term memory (LSTM), cryptocurrency forecasting

Procedia PDF Downloads 66

Authors: Nigatu Tadesse, Ying Liu, Juan Li, Hong Ming Liu

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Gastric carcinogenesis is a lengthy process of histopathological transition from normal to atrophic gastritis (AG) to intestinal metaplasia (GIM), dysplasia toward gastric cancer (GC). The stage of GIM identified as precancerous lesions with resistance to H-pylori eradication and recurrence after endoscopic surgical resection therapies. GIM divided in to two morphologically distinct phenotypes such as complete GIM bearing intestinal type morphology whereas the incomplete type has colonic type morphology. The incomplete type GIM considered to be the greatest risk factor for the development of GC. Studies indicated the expression of the caudal type homeobox 2 (CDX2) gene is responsible for the development of complete GIM but its progressive downregulation from incomplete metaplasia toward advanced GC identified as the risk for IM progression and neoplastic transformation. The downregulation of CDX2 gene have promoted cell growth and proliferation in gastric and colon cancers and ascribed in chemo-treatment inefficacies. CDX2 downregulated through promoter region hypermethylation in which the methylation frequency positively correlated with the dietary history of the patients, suggesting the role of diet as methyl carbon donor sources such as methionine. However, the metabolism of exogenous methionine is yet unclear. Targeting exogenous methionine metabolism has become a promising approach to limits tumor cell growth, proliferation and progression and increase treatment outcome. This review article discusses molecular alterations that could shed light on the potential of exogenous methionine metabolisms, such as gut microbiota alteration as sources of methionine to host cells, metabolic pathway signaling via PI3K/AKt/mTORC1-c-MYC to rewire exogenous methionine and signature of increased gene methylation index, cell growth and proliferation in GIM, with insights to new treatment avenue via targeting methionine metabolism, and the need for future integrated studies on molecular alterations and metabolomics to uncover altered methionine metabolism and characterization of CDX2 methylation in gastric intestinal metaplasia for potential therapeutic exploitation.

Keywords: altered methionine metabolism, Intestinal metaplesia, CDX2 gene, gastric cancer

Procedia PDF Downloads 86

Authors: Loreta Kaklauskiene, Arturas Kaklauskas

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The Integrated Human Resources and Work Environment Management (HOWE) System optimises employee productivity, improves the work environment, and, at the same time, meets the employer’s strategic goals. The HOWE system has been designed to ensure an organisation can successfully compete in the global market, thanks to the high performance of its employees. The HOWE system focuses on raising workforce productivity and improving work conditions to boost employee performance and motivation. The methods used in our research are linear correlation, INVAR multiple criteria analysis, digital twin, and affective computing. The HOWE system is based on two patents issued in Lithuania (LT 6866, LT 6841) and one European Patent application (No: EP 4 020 134 A1). Our research analyses ways to make human resource management more efficient and boost labour productivity by improving and adapting a personalised work environment. The efficiency of human capital and labour productivity can be increased by applying personalised workplace improvement systems that can optimise lighting colours and intensity, scents, data, information, knowledge, activities, media, games, videos, music, air pollution, humidity, temperature, vibrations, and other workplace aspects. HOWE generates and maintains a personalised workspace for an employee, taking into account the person’s affective, physiological and emotional (APSE) states. The purpose of this project was to create a HOWE for the customisation of quality control in smart workspaces taking into account the user’s APSE states in an integrated manner as a single unit. This customised management of quality control covers the levels of lighting and colour intensities, scents, media, information, activities, learning materials, games, music, videos, temperature, energy efficiency, the carbon footprint of a workspace, humidity, air pollution, vibrations and other aspects of smart spaces. The system is based on Digital Twins technology, seen as a logical extension of BIM.

Keywords: human resource management, health economics, work environment, organizational behaviour and employee productivity, prosperity in work, smart system

Procedia PDF Downloads 75

Authors: Stojan Kravanja, Andrej Ivanič, Tomaž Žula

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This contribution focuses on structural optimization in civil engineering using mixed integer non-linear programming (MINLP). MINLP is characterized as a versatile method that can handle both continuous and discrete optimization variables simultaneously. Continuous variables are used to optimize parameters such as dimensions, stresses, masses, or costs, while discrete variables represent binary decisions to determine the presence or absence of structural elements within a structure while also calculating discrete materials and standard sections. The optimization process is divided into three main steps. First, a mechanical superstructure with a variety of different topology-, material- and dimensional alternatives. Next, a MINLP model is formulated to encapsulate the optimization problem. Finally, an optimal solution is searched in the direction of the defined objective function while respecting the structural constraints. The economic or mass objective function of the material and labor costs of a structure is subjected to the constraints known from structural analysis. These constraints include equations for the calculation of internal forces and deflections, as well as equations for the dimensioning of structural components (in accordance with the Eurocode standards). Given the complex, non-convex and highly non-linear nature of optimization problems in civil engineering, the Modified Outer-Approximation/Equality-Relaxation (OA/ER) algorithm is applied. This algorithm alternately solves subproblems of non-linear programming (NLP) and main problems of mixed-integer linear programming (MILP), in this way gradually refines the solution space up to the optimal solution. The NLP corresponds to the continuous optimization of parameters (with fixed topology, discrete materials and standard dimensions, all determined in the previous MILP), while the MILP involves a global approximation to the superstructure of alternatives, where a new topology, materials, standard dimensions are determined. The optimization of a convex problem is stopped when the MILP solution becomes better than the best NLP solution. Otherwise, it is terminated when the NLP solution can no longer be improved. While the OA/ER algorithm, like all other algorithms, does not guarantee global optimality due to the presence of non-convex functions, various modifications, including convexity tests, are implemented in OA/ER to mitigate these difficulties. The effectiveness of the proposed MINLP approach is demonstrated by its application to various structural optimization tasks, such as mass optimization of steel buildings, cost optimization of timber halls, composite floor systems, etc. Special optimization models have been developed for the optimization of these structures. The MINLP optimizations, facilitated by the user-friendly software package MIPSYN, provide insights into a mass or cost-optimal solutions, optimal structural topologies, optimal material and standard cross-section choices, confirming MINLP as a valuable method for the optimization of structures in civil engineering.

Keywords: MINLP, mixed-integer non-linear programming, optimization, structures

Procedia PDF Downloads 46

Authors: Waleed Ahmed, Huang Xiaohua, Wilayat Ali

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The wheelchair users are generally exposed to physical and psychological health problems, e.g., pressure sores and pain in the hip joint, associated with seating posture or being inactive in a wheelchair for a long time. Reclining Wheelchair with back, thigh, and leg adjustment helps in daily life activities and health preservation. The seat elevating function of an electric wheelchair allows the user (lower limb amputation) to reach different heights. An electric wheelchair is expected to ease the lives of the elderly and disable people by giving them mobility support and decreasing the percentage of accidents caused by users’ narrow sight or joystick operation errors. Thus, this paper proposed the design, analysis and obstacle avoidance control of an electric wheelchair with sit-sleep-seat elevation functions. A 3D model of a wheelchair is designed in SolidWorks that was later used for multi-body dynamic (MBD) analysis and to verify driving control system. The control system uses the fuzzy algorithm to avoid the obstacle by getting information in the form of distance from the ultrasonic sensor and user-specified direction from the joystick’s operation. The proposed fuzzy driving control system focuses on the direction and velocity of the wheelchair. The wheelchair model has been examined and proven in MSC Adams (Automated Dynamic Analysis of Mechanical Systems). The designed fuzzy control algorithm is implemented on Gazebo robotic 3D simulator using Robotic Operating System (ROS) middleware. The proposed wheelchair design enhanced mobility and quality of life by improving the user’s functional capabilities. Simulation results verify the non-accidental behavior of the electric wheelchair.

Keywords: fuzzy logic control, joystick, multi body dynamics, obstacle avoidance, scissor mechanism, sensor

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Authors: Morteza Raei Dehaghi, Seyed Mohammad Mirhashemi

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The current study explored the effect of economic development, financial development and institutional quality on environmental destruction in upper-middle income countries during the time period of 1999-2011. The dependent variable is logarithm of carbon dioxide emissions that can be considered as an index for destruction or quality of the environment given to its effects on the environment. Financial development and institutional development variables as well as some control variables were considered. In order to study cross-sectional correlation among the countries under study, Pesaran and Friz test was used. Since the results of both tests show cross-sectional correlation in the countries under study, seemingly unrelated regression method was utilized for model estimation. The results disclosed that Kuznets’ environmental curve hypothesis is confirmed in upper-middle income countries and also, financial development and institutional quality have a significant effect on environmental quality. The results of this study can be considered by policy makers in countries with different income groups to have access to a growth accompanied by improved environmental quality.

Keywords: economic development, environmental destruction, financial development, institutional development, seemingly unrelated regression

Procedia PDF Downloads 348

Authors: Mariano Gonzalez Cortina, Pablo Saiz Martinez, Francisco Fernandez Martinez, Antonio Rodriguez Sanchez

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Construction sector incessant activity of the last years preceding the crisis has originated a high waste generation and an increased use of raw materials. The main aim of this research is to compare three types of recycled aggregates and the feasibility to incorporate them into masonry mortar fabrication. The tests were developed using two types of binders: CEM II/B-L 32.5 N and CEM IV/B (V) 32.5 N. 50%, 75% and 100% of natural sand were replaced with three types of recycled aggregates. Cement-to-aggregate by dry weight proportions were 1:3 and 1:4. Physical and chemical characterization of recycled aggregates showed continues particle size distribution curve, lower density and higher absorption, which was the reason to use additive to obtain required mortar consistency. Main crystalline phases determined in the X-Ray diffraction test were calcite, quartz, and gypsum. Performed tests show that cement-based mortars fabricated with CEM IV/B (V) 32. 5 N can incorporate recycled aggregates coming from ceramic, concrete and mixed recycling processes, using 1:3 and 1:4 cement-to-aggregate proportions, complying with the limits established by the Spanish standards. It was concluded that recycled mortar coming from concrete recycling process is the one which presents better characteristics.

Keywords: construction and demolition waste, masonry mortar, mechanical properties, recycled aggregate, waste treatment

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Authors: A. Pokharel, A. Farooque, B. Acharya

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Pyrolysis can be used for energy production from waste biomass of agriculture and forestry. Biochar is the solid byproduct of pyrolysis and its cascading use can offset the cost of the process. A wide variety of research on biochar has highlighted its ability to absorb nutrients, metal and complex compounds; filter suspended solids; enhance microorganisms’ growth; retain water and nutrients as well as to increase carbon content of soil. In addition, sustainable biochar systems are an attractive approach for carbon sequestration and total waste management cycle. Commercially available biochar from Sigma Aldrich was studied for adsorption of nitrogen from effluent of municipal wastewater treatment plant. Adsorption isotherm and breakthrough curve were determined for the biochar. Similarly, biochar’s effects in aerobic as well as anaerobic bioreactors were also studied. In both cases, the biomass was increased in presence of biochar. The amount of gas produced for anaerobic digestion of fruit mix (apple and banana) was similar but the rate of production was significantly faster in biochar fed reactors. The cumulative goal of the study is to use biochar in various wastewater treatment units like aeration tank, secondary clarifier and tertiary nutrient recovery system as well as in anaerobic digestion of the sludge to optimize utilization and add value before being used as a soil amendment.

Keywords: biochar, nutrient recyling, wastewater treatment, soil amendment

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Authors: Noah Wayne, Andrea Tuka, Adrian Norbash, Bryan Garber, Paul Ritvo

Abstract:

Program/Intervention Description: The Canadian Armed Forces(CAF) Mental Health Clinicstreat a full spectrum of mental disorder, addictions, and psychosocial issues that include Major Depressive Disorder, Post-Traumatic Stress Disorder, Generalized Anxiety Disorder, and other diagnoses. We evaluated the feasibility of an online health coach interventiondelivering mindfulness based cognitive behavioral therapy (M-CBT) and behaviour changesupport for individuals receiving treatment at CAF Clinics. Participants were provided accounts on NexJ Connected Wellness, a digital health platform, and 16 weeks of phone-based health coaching,emphasizingmild to moderate aerobic exercise, a healthy diet, and M-CBT content. The primary objective was to assess the feasibility of the online deliverywith CAF members. Evaluation Methods: Feasibility was evaluated in terms of recruitment, engagement, and program satisfaction. Weadditionallyevaluatedhealth behavior change, program completion, and mental health symptoms (i.e. PHQ-9, GAD-7, PCL-5) at three time points. Results: Service members were referred from Vancouver, Esquimalt, and Edmonton CAF bases between August 2020 and January 2021. N=106 CAF personnel were referred, and n=77 consented.N=66 participated, and n=44 completed 4-month and follow-up measures. The platform received a mean rating of76.5 on the System Usability Scale, and health coaching was judged the most helpful program feature (95.2% endorsement), while reminders (53.7%), secure messaging (51.2%), and notifications (51.2%) were also identified. Improvements in mental health status during active interventions were observed on the PHQ-9 (-5.4, p<0.001), GAD-7 (-4.0, p<0.001), and PCL-5 (-4.1, p<0.05). Conclusion: Online health coaching was well-received amidst the COVID-19 pandemic and related lockdowns. Uptake and engagement were positively reported. Participants valuedcontacts and reported strong therapeutic alliances with coaches. Healthy diet, regular exercise, and mindfulness practice are important for physical and mental health. Engagements in these behaviors are associated with reduced symptoms. An online health coach program appears feasible for assisting Canadian Armed Forces personnel.

Keywords: coaching, CBT, military, depression, mental health, digital

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Authors: Sangamesh G. Sakri, Akash N. Patil, Sadashivappa M. Kotli

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Agro based industries in India are considered as the micro, small and medium enterprises (MSME). In India, MSMEs contribute approximately 8 percent of the country’s GDP, 42 percent of the manufacturing output and 40 percent of exports. The toor dal (scientific name Cajanus cajan, commonly known as yellow gram, pigeon pea) is the second largest pulse crop in India accounting for about 20% of total pulse production. The toor dal milling industry in India is one of the major agro-processing industries in the country. Most of the dal mills are concentrated in pulse producing areas, which are spread all over the country. In Karnataka state, Gulbarga is a district, where toor dal is the main crop and is grown extensively. There are more than 500 dal mills in and around the Gulbarga district to process dal. However, the majority of these dal milling units use traditional methods of processing which are energy and capital intensive. There exists a huge energy saving potential in these mills. An energy audit is conducted on a dal mill in Gulbarga to understand the energy consumption pattern to assess the energy saving potential, and an economic analysis is conducted to identify energy conservation opportunities.

Keywords: conservation, demand side management, load curve, toor dal

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Authors: Ata Khorami, Mohammad Sharifkhani

Abstract:

In the dynamic comparators, the pre-amplifier amplifies the input differential voltage and when the output Vcm of the pre-amplifier becomes larger than Vth of the latch input transistors, the latch is activated and finalizes the comparison. As a result, the pre-amplification delay is fixed to a value and cannot be set at the minimum required delay, thus, significant power and delay are imposed. In this paper, a novel structure is proposed through which the pre-amplification delay can be set at any low value saving power and time. Simulations show that using the proposed structure, by setting the pre-amplification delay at the minimum required value the power and comparison delay can be reduced by 55% and 100ps respectively.

Keywords: dynamic comparator, low power comparator, analog to digital converter, pre-amplification delay

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Authors: Vinod Ponnagandla, Brenton McLaury, Siamack Shirazi

Abstract:

Sand production can lead to deposition of particles or erosion. Low production rates resulting in deposition can partially clog systems and cause under deposit corrosion. Commercially available nonintrusive acoustic sand detectors are attractive as they claim to detect sand production. Acoustic sand detectors are used during oil and gas production; however, operators often do not know the threshold detection limits of these devices. It is imperative to know the detection limits to appropriately plan for cleaning of separation equipment or examine risk of erosion. These monitors are based on detecting the acoustic signature of sand as the particles impact the pipe walls. The objective of this work is to determine threshold detection limits for acoustic sand monitors that are commercially available. The minimum threshold sand concentration that can be detected in a pipe are determined as a function of flowing gas and liquid velocities. A large scale flow loop with a 4-inch test section is utilized. Commercially available sand monitors (ClampOn and Roxar) are evaluated for different flow regimes, sand sizes and pipe orientation (vertical and horizontal). The manufacturers’ recommend that the monitors be placed on a bend to maximize the number of particle impacts, so results are shown for monitors placed at 45 and 90 degree positions in a bend. Acoustic sand monitors that clamp to the outside of pipe are passive and listen for solid particle impact noise. The threshold sand rate is calculated by eliminating the background noise created by the flow of gas and liquid in the pipe for various flow regimes that are generated in horizontal and vertical test sections. The average sand sizes examined are 150 and 300 microns. For stratified and bubbly flows the threshold sand rates are much higher than other flow regimes such as slug and annular flow regimes that are investigated. However, the background noise generated by slug flow regime is very high and cause a high uncertainty in detection limits. The threshold sand rates for annular flow and dry gas conditions are the lowest because of high gas velocities. The effects of monitor placement around elbows that are in vertical and horizontal pipes are also examined for 150 micron. The results show that the threshold sand rates that are detected in vertical orientation are generally lower for all various flow regimes that are investigated.

Keywords: acoustic monitor, sand, multiphase flow, threshold

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Authors: Jae-Neung Lee, Keun-Chang Kwak

Abstract:

This paper describes an analysis of Yacht Simulator international trends and also explains about Yacht. Examples of yacht Simulator using Yacht Simulator include image processing for totaling the total number of vehicles, edge/target detection, detection and evasion algorithm, image processing using SIFT (scale invariant features transform) matching, and application of median filter and thresholding.

Keywords: yacht simulator, simulator, trends analysis, SIFT

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Authors: Rui Wu

Abstract:

In the volatile modern manufacturing environment, new orders randomly occur at any time, while the pre-emptive methods are infeasible. This leads to a real-time scheduling method that can produce a reasonably good schedule quickly. The dynamic Flexible Job Shop problem is an NP-hard scheduling problem that hybrid the dynamic Job Shop problem with the Parallel Machine problem. A Flexible Job Shop contains different work centres. Each work centre contains parallel machines that can process certain operations. Many algorithms, such as genetic algorithms or simulated annealing, have been proposed to solve the static Flexible Job Shop problems. However, the time efficiency of these methods is low, and these methods are not feasible in a dynamic scheduling problem. Therefore, a dynamic rule selection scheduling system based on the reinforcement learning method is proposed in this research, in which the dynamic Flexible Job Shop problem is divided into several parallel machine problems to decrease the complexity of the dynamic Flexible Job Shop problem. Firstly, the features of jobs, machines, work centres, and flexible job shops are selected to describe the status of the dynamic Flexible Job Shop problem at each decision point in each work centre. Secondly, a framework of reinforcement learning algorithm using a double-layer deep Q-learning network is applied to select proper composite dispatching rules based on the status of each work centre. Then, based on the selected composite dispatching rule, an available operation is selected from the waiting buffer and assigned to an available machine in each work centre. Finally, the proposed algorithm will be compared with well-known dispatching rules on objectives of mean tardiness, mean flow time, mean waiting time, or mean percentage of waiting time in the real-time Flexible Job Shop problem. The result of the simulations proved that the proposed framework has reasonable performance and time efficiency.

Keywords: dynamic scheduling problem, flexible job shop, dispatching rules, deep reinforcement learning

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Authors: Gulsah Olgun, Ozgur Bozdag, Yildirim Ertutar

Abstract:

This paper mainly focuses on performance assessment of buildings by associating the damage level with the damage cost. For this purpose a methodology is explained and applied to the representative mid-rise concrete building residing in Izmir. In order to consider uncertainties in occurrence of earthquakes, the structural analyses are conducted for all possible earthquakes in the region through the hazard curve. By means of the analyses, probability of the structural response being in different limit states are obtained and used to calculate expected damage cost. The expected damage cost comprises diverse cost components related to earthquake such as cost of casualties, replacement or repair cost of building etc. In this study, inter-story drift is used as an effective response variable to associate expected damage cost with different damage levels. The structural analysis methods performed to obtain inter story drifts are response spectrum method as a linear one, accurate push-over and time history methods to demonstrate the nonlinear effects on loss estimation. Comparison of the results indicates that each method provides similar values of expected damage cost. To sum up, this paper explains an approach which enables to minimize the expected damage cost of buildings and relate performance level to damage cost.

Keywords: expected damage cost, limit states, loss estimation, performance based design

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Authors: Richard O’Riordan, Saritha Unnikrishnan

Abstract:

Autonomous Vehicles (AVs) are incorporating an increasing number of ADAS features, including automated lane-keeping systems. In recent years, many research papers into lane detection algorithms have been published, varying from computer vision techniques to deep learning methods. The transition from lower levels of autonomy defined in the SAE framework and the progression to higher autonomy levels requires increasingly complex models and algorithms that must be highly reliable in their operation and functionality capacities. Furthermore, these algorithms have no room for error when operating at high levels of autonomy. Although the current research details existing computer vision and deep learning algorithms and their methodologies and individual results, the research also details challenges faced by the algorithms and the resources needed to operate, along with shortcomings experienced during their detection of lanes in certain weather and lighting conditions. This paper will explore these shortcomings and attempt to implement a lane detection algorithm that could be used to achieve improvements in AV lane detection systems. This paper uses a pre-trained LaneNet model to detect lane or non-lane pixels using binary segmentation as the base detection method using an existing dataset BDD100k followed by a custom dataset generated locally. The selected roads will be modern well-laid roads with up-to-date infrastructure and lane markings, while the second road network will be an older road with infrastructure and lane markings reflecting the road network's age. The performance of the proposed method will be evaluated on the custom dataset to compare its performance to the BDD100k dataset. In summary, this paper will use Transfer Learning to provide a fast and robust lane detection algorithm that can handle various road conditions and provide accurate lane detection.

Keywords: ADAS, autonomous vehicles, deep learning, LaneNet, lane detection

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