Search results for: train drivers

Authors: Jean-Paul Kapuya Bulaba Nyembwe, John Omomoluwa Ogundiran, Manuel Carlos Gameiro da Silva

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Indoor environmental quality (IEQ) remains a global concern because it impacts people's comfort, health, performance, and general well-being. People spend a significant amount of time in buildings or while commuting, hence ensuring the minimal risk in indoor spaces by ensuring suitable IEQ. IEQ studies are limited regarding developing sub-Saharan countries, whereas there is also a huge risk and concern for the current population and geometric growth as many cities in the region will become mega-cities by 2040 (World Bank report). The absence of suitable IEQ regulations and energy poverty are reasons to assess the IEQ gaps for increased awareness of sustainable interventions to minimize the associated risk. This study evaluates the gaps and potential hazards that exist in the IEQ of sub-Saharan countries using empirical studies of hospital occupants and BRT bus passengers and drivers. The Surveys were conducted in 3 cities of the Democratic Republic of Congo and Lagos metropolis of Nigeria. The results suggest that gaps exist in IEQ for these regions. The gaps indicate existential risk to people’s health, comfort, and well-being. The inferential conclusions are that there is a need for further scientific studies, improvement in IEQ conditions, and ensuring suitable regulations for developing sub-Saharan countries.

Keywords: health hazards, hospitals indoor environmental quality, indoor spaces, occupants, sub-Saharan countries, vehicles

Procedia PDF Downloads 71

Authors: Rene Davids

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Urban undergrounds spaces such as parking garages or metro stations are perceived as interludes before reaching desired destinations, as commodities devoid of aesthetic value. Within the encoded space of the city, commercial underground spaces are the closest expression to pure to structures of consumption and commodity. Even in the house, the cellar is associated with castoffs and waste or, as scholar Mircea Eliade has pointed out at best, with a place to store abandoned household and childhood objects, which lie forgotten and on rediscovery evoke a nostalgic and uncanny sense of the past. Despite a growing body of evidence presented by an increasing number of buildings situated entirely below or semi underground that feature exemplary spatial and sensuous qualities, critics and scholars see them largely as efforts to produce efforts in producing low consumption non-renewable energy. Buildings that also free space above ground. This critical approach neglects to mention and highlight other project drivers such as the notion that the ground and sky can be considered a building’s fundamental context, that underground spaces are conducive to the exploration of pure space, namely an architecture that doesn’t have to deal with facades and or external volumes and that digging into geology can inspire the textural and spatial richness. This paper will argue that while the assessment about the reduced energy consumption of underground construction is important, it does not do justice to the qualities underground buildings can contribute to a city’s expanded urban and or landscape experiences.

Keywords: low non-renewable energy consumption, pure space, underground buildings, urban and landscape experience

Procedia PDF Downloads 179

Authors: T. Pakulski, L. Kryza, A. Linossier

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The BEar Articulated TeleRobotic Inspection and Clasping Extremity is a lightweight, 5 DoF robotic manipulator for the Berlin Educational Assistant Rover (BEAR). BEAR is one of the educational planetary rovers developed under the Space Rover projects at the Chair of Space Technology of the Technische Universität Berlin. The projects serve to conduct research and train engineers by developing rovers for competitions like the European Rover Challenge and the DLR SpaceBot Cup. BEATRICE is the result of a cost-driven design process to deliver a simple but capable platform for a variety of competition tasks: object grasping and manipulation, inspection, instrument wielding and more. The manipulator’s simple mechatronic design, based on a combination of servomotors and stepper motors with planetary gearboxes, also makes it a practical tool for developing embedded control systems. The platform’s initial implementation relies on tele-operated control but is fully instrumented for future autonomous functionality. This paper describes BEATRICE’s development from its preliminary link model to its structural and mechatronic design, embedded control and AI and T. In parallel, it examines the influence of budget constraints and high personnel turnover commonly associated with student teams on the manipulator’s design. Finally, it comments on the utility of robot design projects for educating future engineers.

Keywords: education, low-cost, manipulator, robotics, rover

Procedia PDF Downloads 256

Authors: Muhammad Ali

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Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

Procedia PDF Downloads 125

Authors: Tahmina Sultana, Yasser Hassan

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Vehicle technologies rapidly evolving due to their multifaceted advantages. Adapted different vehicle technologies like connectivity and automation on the same roads with conventional vehicles controlled by human drivers may increase speed disparity in mixed vehicle technologies. Identifying relationships between speed distribution measures of different vehicles and road geometry can be an indicator of speed disparity in mixed technologies. Previous studies proved that speed disparity measures and traffic accidents are inextricably related. Horizontal curves from three geographic areas were selected based on relevant criteria, and speed data were collected at the midpoint of the preceding tangent and starting, ending, and middle point of the curve. Multiple linear mixed effect models (LME) were developed using the instantaneous speed measures representing the speed of vehicles at different points of horizontal curves to recognize relationships between speed variance (standard deviation) and road geometry. A simulation-based framework (Monte Carlo) was introduced to check the speed disparity on horizontal curves in mixed vehicle technologies when consideration is given to the interactions among connected vehicles (CVs), autonomous vehicles (AVs), and non-connected vehicles (NCVs) on horizontal curves. The Monte Carlo method was used in the simulation to randomly sample values for the various parameters from their respective distributions. Theresults show that NCVs had higher speed variation than CVs and AVs. In addition, AVs and CVs contributed to reduce speed disparity in the mixed vehicle technologies in any penetration rates.

Keywords: autonomous vehicles, connected vehicles, non-connected vehicles, speed variance

Procedia PDF Downloads 145

Authors: Rida Akzar, Risti Permani, Wahida , Wendy Umberger

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Adoption of farm innovations may increase farm productivity, and therefore improve market access and farm incomes. However, most studies that look at the level and drivers of innovation adoption only focus on a specific type of innovation. Farmers may consider multiple innovation options, and constraints such as budget, environment, scarcity of labour supply, and the cost of learning. There have been some studies proposing different methods to combine a broad variety of innovations into a single measurable index. However, little has been done to compare these methods and assess whether they provide similar information about farmer segmentation by their ‘innovativeness’. Using data from a recent survey of 220 dairy farm households in West Java, Indonesia, this study compares and considers different methods of deriving an innovation index, including expert-weighted innovation index; an index derived from the total number of adopted technologies; and an index of the extent of adoption of innovation taking into account both adoption and disadoption of multiple innovations. Second, it examines the distribution of different farming systems taking into account their innovativeness and farm characteristics. Results from this study will inform policy makers and stakeholders in the dairy industry on how to better design, target and deliver programs to improve and encourage farm innovation, and therefore improve farm productivity and the performance of the dairy industry in Indonesia.

Keywords: adoption, dairy, household survey, innovation index, Indonesia, multiple innovations dairy, West Java

Procedia PDF Downloads 336

Authors: Aya Effat, Ossama A. Hosny, Elkhayam M. Dorra

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Estimating costs is a crucial step in construction management and should be completed at the beginning of every project to establish the project's budget. The precision of the cost estimate plays a significant role in the success of construction projects as it allows project managers to effectively manage the project's costs. Site overhead costs constitute a significant portion of construction project budgets, necessitating accurate prediction and management. These costs are influenced by a multitude of factors, requiring a thorough examination and analysis to understand their relative importance and impact. Thus, the main aim of this research is to enhance the contractor’s ability to predict and manage site overheads by identifying and analyzing the main factors influencing the site overheads costs in the Egyptian construction industry. Through a comprehensive literature review, key factors were first identified and subsequently validated using a thorough comparative analysis of data from 55 real-life construction projects. Through this comparative analysis, the relationship between each factor and site overheads percentage as well as each site overheads subcategory and each project construction phase was identified and examined. Furthermore, correlation analysis was done to check for multicollinearity and identify factors with the highest impact. The findings of this research offer valuable insights into the key drivers of site overhead costs in the Egyptian construction industry. By understanding these factors, construction professionals can make informed decisions regarding the estimation and management of site overhead costs.

Keywords: comparative analysis, cost estimation, construction management, site overheads

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Authors: B. Moaveni, S. Morovati

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In recent years, increasing the usage of railway transportations especially in developing countries caused more attention to control systems railway vehicles. Consequently, designing and implementing the modern control systems to improve the operating performance of trains and locomotives become one of the main concerns of researches. Dynamic braking systems is an important safety system which controls the amount of braking torque generated by traction motors, to keep the adhesion coefficient between the wheel-sets and rail road in optimum bound. Adhesion force has an important role to control the braking distance and prevent the wheels from slipping during the braking process. Cascade control structure is one of the best control methods for the wide range of industrial plants in the presence of disturbances and errors. This paper presents cascade control structure based on two forward simple controllers with two feedback loops to control the slip ratio and braking torque. In this structure, the inner loop controls the angular velocity and the outer loop control the longitudinal velocity of the locomotive that its dynamic is slower than the dynamic of angular velocity. This control structure by controlling the torque of DC traction motors, tries to track the desired velocity profile to access the predefined braking distance and to control the slip ratio. Simulation results are employed to show the effectiveness of the introduced methodology in dynamic braking system.

Keywords: cascade control, dynamic braking system, DC traction motors, slip control

Procedia PDF Downloads 366

Authors: Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Fillia Makedon, Glenn Wylie

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Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that records neural activations in the brain by capturing the blood oxygen level in different regions based on the task performed by a subject. Given fMRI data, the problem of predicting the state of cognitive fatigue in a person has not been investigated to its full extent. This paper proposes tackling this issue as a multi-class classification problem by dividing the state of cognitive fatigue into six different levels, ranging from no-fatigue to extreme fatigue conditions. We built a spatio-temporal model that uses convolutional neural networks (CNN) for spatial feature extraction and a long short-term memory (LSTM) network for temporal modeling of 4D fMRI scans. We also applied a self-supervised method called MoCo (Momentum Contrast) to pre-train our model on a public dataset BOLD5000 and fine-tuned it on our labeled dataset to predict cognitive fatigue. Our novel dataset contains fMRI scans from Traumatic Brain Injury (TBI) patients and healthy controls (HCs) while performing a series of N-back cognitive tasks. This method establishes a state-of-the-art technique to analyze cognitive fatigue from fMRI data and beats previous approaches to solve this problem.

Keywords: fMRI, brain imaging, deep learning, self-supervised learning, contrastive learning, cognitive fatigue

Procedia PDF Downloads 190

Authors: Tamer E. Yared

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The use of driving simulators has made laboratory testing easier. It has been proven to be valid for testing driving ability by many researchers. One benefit of using driving simulators is keeping the human subjects away from traffic hazards, which drivers usually face in a real driving environment while performing a driving experiment. In this study, a driving simulator was developed with a navigation system using a game development software (Unity 3D) and C-sharp codes to measure and evaluate driving performance, safety, and workload for different driving tasks. The driving simulator hardware included a gaming steering wheel and pedals as well as a monitor to view the driving tasks. Moreover, driver distraction was evaluated by utilizing an eye-tracking system working in conjunction with the driving simulator. Twenty subjects were recruited to evaluate driver distraction, workload, driving safety, and performance, as well as provide their feedback about the driving simulator. The subjects’ feedback was obtained by filling a survey after conducting several driving tasks. The main question of that survey was asking the subjects to compare driving on the driving simulator with real driving. Furthermore, other aspects of the driving simulator were evaluated by the subjects in the survey. The survey revealed that the recruited subjects gave an average score of 7.5 out of 10 to the driving simulator when compared to real driving, where the scores ranged between 6 and 8.5. This study is a preliminary effort that opens the door for more improvements to the driving simulator in terms of hardware and software development, which will contribute significantly to driving ability testing.

Keywords: driver distraction, driving performance, driving safety, driving simulator, driving workload, navigation system

Procedia PDF Downloads 179

Authors: Reza Mohammadzadeh

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The major challenges in geotechnical engineering in underground spaces arise from uncertainties and different probabilities. The collection, collation, and collaboration of existing data to incorporate them in analysis and design for given prospect evaluation would be a reliable, practical problem solving method under uncertainty. Machine learning (ML) is a subfield of artificial intelligence in statistical science which applies different techniques (e.g., Regression, neural networks, support vector machines, decision trees, random forests, genetic programming, etc.) on data to automatically learn and improve from them without being explicitly programmed and make decisions and predictions. In this paper, a conceptual database schema of geotechnical risks in underground coal mining based on a cloud system architecture has been designed. A new approach of risk assessment using a three-dimensional risk matrix supported by the level of knowledge (LoK) has been proposed in this model. Subsequently, the model workflow methodology stages have been described. In order to train data and LoK models deployment, an ML platform has been implemented. IBM Watson Studio, as a leading data science tool and data-driven cloud integration ML platform, is employed in this study. As a Use case, a data set of geotechnical hazards and risk assessment in underground coal mining were prepared to demonstrate the performance of the model, and accordingly, the results have been outlined.

Keywords: data model, geotechnical risks, machine learning, underground coal mining

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Authors: Vertika Goswami, Gargi Sharma

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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: Rongbo Shen, Jianhua Yao, Kezhou Yan, Kuan Tian, Cheng Jiang, Ke Zhou

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This study proposes a generative adversarial networks (GAN) framework to perform synthetic sampling in feature space, i.e., feature augmentation, to address the class imbalance problem in medical image analysis. A feature extraction network is first trained to convert images into feature space. Then the GAN framework incorporates adversarial learning to train a feature generator for the minority class through playing a minimax game with a discriminator. The feature generator then generates features for minority class from arbitrary latent distributions to balance the data between the majority class and the minority class. Additionally, a data cleaning technique, i.e., Tomek link, is employed to clean up undesirable conflicting features introduced from the feature augmentation and thus establish well-defined class clusters for the training. The experiment section evaluates the proposed method on two medical image analysis tasks, i.e., mass classification on mammogram and cancer metastasis classification on histopathological images. Experimental results suggest that the proposed method obtains superior or comparable performance over the state-of-the-art counterparts. Compared to all counterparts, our proposed method improves more than 1.5 percentage of accuracy.

Keywords: class imbalance, synthetic sampling, feature augmentation, generative adversarial networks, data cleaning

Procedia PDF Downloads 127

Authors: Xia Fang

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Arid and semiarid areas of China (ASAC) have experienced significant land-use/cover changes (LUCC), along with intensified climate change. However, LUCC and climate changes and their individual and interactive effects on carbon stocks have not yet been fully understood in the ASAC. This study analyses the carbon stocks in the ASAC during 1980 - 2020 using the specific arid ecosystem model (AEM), and investigates the effects of LUCC and climate change on carbon stock trends. The results indicate that in the past 41 years, the ASAC carbon pool experienced an overall growth trend, with an increase of 182.03 g C/m2. Climatic factors (+291.99 g C/m2), especially the increase in precipitation, were the main drivers of the carbon pool increase. LUCC decreased the carbon pool (-112.27 g C/m2), mainly due to the decrease in grassland area (-2.77%). The climate-induced carbon sinks were distributed in northern Xinjiang, on the Ordos Plateau, and in Northeast China, while the LUCC-induced carbon sinks mainly occurred on the Ordos Plateau and the North China Plain, resulting in a net decrease in carbon sequestration in these regions according to carbon pool measurements. The study revealed that the combination of climate variability, LUCC, and increasing atmospheric CO2 concentration resulted in an increase of approximately 182.03 g C/m2, which was mainly distributed in eastern Inner Mongolia and the western Qinghai-Tibet Plateau. Our findings are essential for improving theoretical guidance to protect the ecological environment, rationally plan land use, and understand the sustainable development of arid and semiarid zones.

Keywords: AEM, climate change, LUCC, carbon stocks

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Authors: Pooja Rani, G. S. Mahapatra, S. K. Pandey

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In this paper, we propose a novel approach of Neural Network and Particle Swarm Optimization methods for software reliability prediction. We first explain how to apply compound function in neural network so that we can derive a Flexible Logistic (S-shaped) Growth Curve (FLGC) model. This model mathematically represents software failure as a random process and can be used to evaluate software development status during testing. To avoid trapping in local minima, we have applied Particle Swarm Optimization method to train proposed model using failure test data sets. We drive our proposed model using computational based intelligence modeling. Thus, proposed model becomes Neuro-Particle Swarm Optimization (NPSO) model. We do test result with different inertia weight to update particle and update velocity. We obtain result based on best inertia weight compare along with Personal based oriented PSO (pPSO) help to choose local best in network neighborhood. The applicability of proposed model is demonstrated through real time test data failure set. The results obtained from experiments show that the proposed model has a fairly accurate prediction capability in software reliability.

Keywords: software reliability, flexible logistic growth curve model, software cumulative failure prediction, neural network, particle swarm optimization

Procedia PDF Downloads 344

Authors: Sang-KeunBaik, Kyu-Soo Chong, Joon-Yeop Na

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This study attempts to reduce the wind load of road signs, improve roadside landscaping, and enhance the safety of road users by establishing criteria for the installation of small road signs. First, we derive the minimum font size that can be used on road signs according to the road’s design speed by considering the visibility and legibility of such road signs. We classify road junctions into eight types based on junction type (intersection, interchange, and expressway) and on the number of road lanes. Furthermore, we propose small sign alternatives, to which the minimum font size is applied, to be placed by each road junction. To verify the effects of the small signs, we implemented a 3D simulation road environment, to which the small road signs were applied, and performed experiments using the driving simulator targeting 50 drivers. The experiments compared and analyzed the effects, whether the driver proceeds to the desired exit and the average driving time, between the existing large road signs and the improved small road signs under the same road conditions and intersection type. We conducted a survey with the participants of the simulation experiment on the preference between graphical signs (large road signs) and exit-centric signs (small road signs). The results show that the participants prefer the exit-centric signs (60%) to the graphical signs (40%). We propose installation criteria for small road signs for intersections, interchanges, and expressways based on the results of the experiment and the survey.

Keywords: 3D simulation, driving simulator, legibility distance, minimum font size, small road signs

Procedia PDF Downloads 477

Authors: Yilin Liao, Hewen Li, Paula McConvey

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Speed skaters often face a risk of concussion when they fall on the ice floor and impact crash mats during practices and competitive races. Several variables, including those related to the skater, the crash mat, and the impact position (body side/head/feet impact), are believed to influence the severity of the skater's concussion. While computer simulation modeling can be employed to analyze these accidents, the simulation process is time-consuming and does not provide rapid information for coaches and teams to assess the skater's injury risk in competitive events. This research paper promotes the exploration of the feasibility of using AI techniques for evaluating skater’s potential concussion severity, and to develop a fast concussion prediction tool using artificial neural networks to reduce the risk of treatment delays for injured skaters. The primary data is collected through virtual tests and physical experiments designed to simulate skater-mat impact. It is then analyzed to identify patterns and correlations; finally, it is used to train and fine-tune the artificial neural networks for accurate prediction. The development of the prediction tool by employing machine learning strategies contributes to the application of AI methods in sports science and has theoretical involvements for using AI techniques in predicting and preventing sports-related injuries.

Keywords: artificial neural networks, concussion, machine learning, impact, speed skater

Procedia PDF Downloads 109

Authors: Rosa Hendijani, Mohammadhesam Hajighasemi

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As of late, the sharing economy has become an important type of business model. Online taxi services are one example that has grown rapidly around the world. This study examines the factors influencing the use of online taxis as one form of IT-enabled sharing services based on the theory of planned behavior (TPB). Based on the theory of planned behavior, these factors can be divided into three categories, including the ones related to attitude (e.g., image and perceived usefulness), normative believes (e.g., subjective norms), and behavioral control (e.g., technology facilitating conditions and self-efficacy). Three other factors were also considered based on the literature, including perceived economic benefits, openness towards using shared services, and perceived availability. The effect of all these variables was tested both directly and indirectly through intention as the mediating variable. A survey method was used to test the research hypotheses. In total, 361 individuals partook in the study. The results of a multiple regression analysis on behavior showed that perceived economic benefits, compatibility, and subjective norms were important factors influencing behavior among online taxi users. In addition, intention partially mediated the effect of perceived economic benefits and compatibility on behavior. It can be concluded that perceived economic benefits, compatibility, and subjective norms are the three main factors that influence behavior among online taxi users.

Keywords: collaborative consumption, IT-enabled sharing services model, online taxi, sharing economy, theory of planned behavior

Procedia PDF Downloads 138

Authors: Jane Osei, Kerry Brown, Mehran Nejati

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Numerous studies have established that solar energy is the second most prevalent form of alternative renewable energy globally, particularly in regions with abundant sunlight. The adoption and ongoing sustainability of solar technology are pivotal for the transition to renewable energy sources. However, the literature indicates that some countries, especially in the developing world, may impede this transition. Despite various policy initiatives aimed at supporting the adoption of solar technology, the long-term effectiveness of these policies remains uncertain. This study investigates the current policy drivers influencing the success or failure of solar energy technology adoption and sustainability. It employs a qualitative review approach to compare strategies for implementing the net-metering policy incentive in both developing and developed countries, identifying successful and unsuccessful strategies and drawing conclusions on the lessons learned. The study's findings reveal that the effective implementation of net metering depends on regional variations in solar radiation and differing levels of electricity demand across regions. Further, the study found that the implementation of net metering has faced challenges in some countries due to regulatory barriers and bottlenecks that hinder private sector involvement and business sustainability. Economic stability also significantly impacts net metering implementation. This study concludes that governments should strive to balance benefit-sharing to attract more private-sector investment in solar technology while ensuring the viability of government energy regulatory bodies.

Keywords: solar energy technology, adoption, sustainability, net-metering

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Authors: M. Bortolini, E. Ferrari, F. G. Galizia, C. Mora

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In the last decades, an increasing set of companies adopted lean philosophy to improve their productivity and efficiency promoting the so-called continuous improvement concept, reducing waste of time and cutting off no-value added activities. In parallel, increasing attention rises toward green practice and management through the spread of the green supply chain pattern, to minimise landfilled waste, drained wastewater and pollutant emissions. Starting from a review on contributions deepening lean and green principles applied to supply chain management, the most relevant drivers to measure the performance of industrial processes are pointed out. Specific attention is paid on the role of cost because it is of key importance and it crosses both lean and green principles. This analysis leads to figure out an original reference framework for integrating lean and green principles in designing and managing supply chains. The proposed framework supports the application, to the whole value chain or to parts of it, e.g. distribution network, assembly system, job-shop, storage system etc., of the lean-green integrated perspective. Evidences show that the combination of the lean and green practices lead to great results, higher than the sum of the performances from their separate application. Lean thinking has beneficial effects on green practices and, at the same time, methods allowing environmental savings generate positive effects on time reduction and process quality increase.

Keywords: environmental sustainability, green supply chain, integrated framework, lean thinking, supply chain management

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Authors: Renukswamy Chikkamath, Vishvapalsinhji Ramsinh Parmar, Christoph Hewel, Markus Endres

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Given a patent document, identifying distinct semantic annotations is an interesting research aspect. Text annotation helps the patent practitioners such as examiners and patent attorneys to quickly identify the key arguments of any invention, successively providing a timely marking of a patent text. In the process of manual patent analysis, to attain better readability, recognising the semantic information by marking paragraphs is in practice. This semantic annotation process is laborious and time-consuming. To alleviate such a problem, we proposed a dataset to train machine learning algorithms to automate the highlighting process. The contributions of this work are: i) we developed a multi-class dataset of size 150k samples by traversing USPTO patents over a decade, ii) articulated statistics and distributions of data using imperative exploratory data analysis, iii) baseline Machine Learning models are developed to utilize the dataset to address patent paragraph highlighting task, and iv) future path to extend this work using Deep Learning and domain-specific pre-trained language models to develop a tool to highlight is provided. This work assists patent practitioners in highlighting semantic information automatically and aids in creating a sustainable and efficient patent analysis using the aptitude of machine learning.

Keywords: machine learning, patents, patent sentiment analysis, patent information retrieval

Procedia PDF Downloads 91

Authors: Louiza C. Duncker, Duarte Gonçalves

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Wildlife crime is a complex problem with many interconnected facets, which are generally responded to in parts or fragments in efforts to “break down” the complexity into manageable components. However, fragmentation increases complexity as coherence and cooperation become diluted. A whole-of-society approach has been developed towards finding a common goal and integrated approach to preventing wildlife crime. As part of this development, research was conducted in rural communities adjacent to conservation areas in South Africa to define and comprehend the challenges faced by them, and to understand their perceptions of wildlife crime. The results of the research showed that the perceptions of community members varied - most were in favor of conservation and of protecting rhinos, only if they derive adequate benefit from it. Regardless of gender, income level, education level, or access to services, conservation was perceived to be good and bad by the same people. Even though people in the communities are poor, a willingness to stop rhino poaching does exist amongst them, but their perception of parks not caring about people triggered an attitude of not being willing to stop, prevent or report poaching. Understanding the nuances, the history, the interests and values of community members, and the drivers behind poaching mind-sets (intrinsic or driven by transnational organized crime) is imperative to create sustainable and resilient communities on multiple levels that make a substantial positive impact on people’s lives, but also conserve wildlife for posterity.

Keywords: community perceptions, conservation, rhino poaching, whole-of-society approach, wildlife crime

Procedia PDF Downloads 238

Authors: Jingting Luo, Yong Wang, Ying Wang

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Pedestrian detection in image or video data is a very important and challenging task in security surveillance. The difficulty of this task is to locate and detect pedestrians of different scales in complex scenes accurately. To solve these problems, a deep neural network (RT-YOLOv3) is proposed to realize real-time pedestrian detection at different scales in security monitoring. RT-YOLOv3 improves the traditional YOLOv3 algorithm. Firstly, the deep residual network is added to extract vehicle features. Then six convolutional neural networks with different scales are designed and fused with the corresponding scale feature maps in the residual network to form the final feature pyramid to perform pedestrian detection tasks. This method can better characterize pedestrians. In order to further improve the accuracy and generalization ability of the model, a hybrid pedestrian data set training method is used to extract pedestrian data from the VOC data set and train with the INRIA pedestrian data set. Experiments show that the proposed RT-YOLOv3 method achieves 93.57% accuracy of mAP (mean average precision) and 46.52f/s (number of frames per second). In terms of accuracy, RT-YOLOv3 performs better than Fast R-CNN, Faster R-CNN, YOLO, SSD, YOLOv2, and YOLOv3. This method reduces the missed detection rate and false detection rate, improves the positioning accuracy, and meets the requirements of real-time detection of pedestrian objects.

Keywords: pedestrian detection, feature detection, convolutional neural network, real-time detection, YOLOv3

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Authors: Varda Gil, Ella Shoval, Tussia Mira

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Teacher mentoring is a complex practical profession whose unique characteristic is the teacher-mentors' commitment to helping teachers link theory with teaching practice in the process of decision-making and in their reflections on teaching. The aim of this research is to examine the way practicing teacher-mentors participating in a teacher mentoring course made the connection between theory and practice. The researchers analyzed 20 final papers submitted by participants in a course to train teacher mentors. The participants were all veteran high-school teachers. The course comprised 112 in-class hours in addition to mentoring novices in the field. The course covered the following topics: The teacher-mentors' perception of their role; formative and summative evaluation of the novices; tutoring strategies and tools; types of learners; and ways of communicating and dealing with novice teachers' resistance to counseling. The course participants were required to write a 4-5 page reflective summary of their field mentoring practice. In addition, they were required to link theories explicitly learned in the course to their practice in the field. A qualitative analysis of the papers led to the creation of the taxonomy of the link between theory and practice relating to four topics: The kinds of links made between theory and practice, the quality of these links, the links made between private teaching theories and official teaching theory, and the qualities of these links. This taxonomy may prove to be a useful tool in the teacher-mentor training processes.

Keywords: taxonomy, teacher-mentors, theory, practice, teacher-mentor training

Procedia PDF Downloads 354

Authors: U. Borasi, Amit Kr. Jain, Rakesh, Piyush Jain

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Data collection, data preparation, model training, model evaluation, and deployment are all processes in a typical machine learning workflow. Training data needs to be gathered and organised. This often entails collecting a sizable dataset and cleaning it to remove or correct any inaccurate or missing information. Preparing the data for use in the machine learning model requires pre-processing it after it has been acquired. This often entails actions like scaling or normalising the data, handling outliers, selecting appropriate features, reducing dimensionality, etc. This pre-processed data is then used to train a model on some machine learning algorithm. After the model has been trained, it needs to be assessed by determining metrics like accuracy, precision, and recall, utilising a test dataset. Every time a new model is built, both data pre-processing and model training—two crucial processes in the Machine learning (ML) workflow—must be carried out. Thus, there are various Machine Learning algorithms that can be employed for every single approach to data pre-processing, generating a large set of combinations to choose from. Example: for every method to handle missing values (dropping records, replacing with mean, etc.), for every scaling technique, and for every combination of features selected, a different algorithm can be used. As a result, in order to get the optimum outcomes, these tasks are frequently repeated in different combinations. This paper suggests a simple architecture for organizing this largely produced “combination set of pre-processing steps and algorithms” into an automated workflow which simplifies the task of carrying out all possibilities.

Keywords: machine learning, automation, AUTOML, architecture, operator pool, configuration, scheduler

Procedia PDF Downloads 58

Authors: Mfaniseni Wiseman Mbatha

Abstract:

The extension officers are regarded as the key role players in the provision of agricultural facilities to farmers across the world. The government of South Africa has shown a commitment to provide extensive support to farmers by the means of disseminating information and other agricultural facilities. This qualitative study on the experiences of extension officers on the provision of agricultural facilities to rural farmers towards improving agricultural practice was conducted in Msinga Local Municipality. The data was collected through the use of semi-structured interviews with extension officers who were sampled using the purposive sampling method. The qualitative data was analysed through the use of content analysis. The critical part of the findings reveals that the availability of arable land for agricultural practice, availability of agricultural schemes and availability of proper functioning community gardens were indicators of the high level of agricultural practice in the Msinga area. Therefore, the extension officers from the municipality department have shown to provide the agricultural budget to support rural farmers. Whereas, the department of agriculture provides well knowledgeable staff to train farmers about the process of farming and how they can address issues of livestock and crop diseases and also adapting to issues of climate change. The rural farmers, however, find it very difficult to learn and put into practice things that were thought by extension officers during training. There is, therefore, a need for recruitment of more extension staff and the involvement of Non-Government Organizations to increase access to extension facilities to the farmers.

Keywords: agricultural facilities, agricultural practice, extension officers, rural farmers

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Authors: Shinji Sato, M. M. A. Alam, Manabu Takao

Abstract:

In recent, investigation of an unsteady flow inside the resonance tube have become a strongly motivated research field for their potential application as high-frequency actuators. By generating a shock wave inside the resonance tube, a high temperature and pressure can be achieved inside the tube, and this high temperature can also be used to ignite a jet engine. In the present research, a computational fluid dynamics (CFD) analysis was carried out to investigate the flow inside the resonance tube. The density-based solver of rhoCentralFoam in OpenFOAM was used to numerically simulate the flow. The supersonic jet that was driven by a cylindrical nozzle with a nominal exit diameter of φd = 20.3 mm impinged onto the resonance tube. The jet pressure ratio was varied between 2.6 and 7.8. The gap s between the nozzle exit and tube entrance was changed between 1.5d and 3.0d. The diameter and length of the tube were taken as D = 1.25d and L=3.0D, respectively. As a result, when a supersonic jet has impinged onto the resonance tube, a compression wave was found generating inside the tube and propagating towards the tube end wall. This wave train resulted in a rise in the end wall gas temperature and pressure. While, in an outflow phase, the gas near tube enwall was found cooling back isentropically to its initial temperature. Thus, the compression waves repeated a reciprocating motion in the tube like a piston, and a fluctuation in the end wall pressures and temperatures were observed. A significant change was found in the end wall pressures and temperatures with a change of jet flow conditions. In this study, the highest temperature was confirmed at a jet pressure ratio of 4.2 and a gap of s=2.0d

Keywords: compressible flow, OpenFOAM, oscillations, a resonance tube, shockwave

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Authors: Sinsze Koo, Benjin Luo, Matthew Henry

Abstract:

In order to increase the performance of an automatic transmission, the automatic transmission fluid is required to be warm up to an optimal operating temperature. In a conventional vehicle, cold starts result in friction loss occurring in the gear box and engine. The stop and go nature of city driving dramatically affect the warm-up of engine oil and automatic transmission fluid and delay the time frame needed to reach an optimal operating temperature. This temperature phenomenon impacts both engine and transmission performance but also increases fuel consumption and CO2 emission. The aim of this study is to develop know-how of the thermal behavior in order to identify thermal impacts and functional principles in automatic transmissions. Thermal behavior was studied using models and simulations, developed using GT-Suit, on a one-dimensional thermal and flow transport. A power train of a conventional vehicle was modeled in order to emphasis the thermal phenomena occurring in the various components and how they impact the automatic transmission performance. The simulation demonstrates the thermal model of a transmission fluid cooling system and its component parts in warm-up after a cold start. The result of these analyses will support the future designs of transmission systems and components in an attempt to obtain better fuel efficiency and transmission performance. Therefore, these thermal analyses could possibly identify ways that improve existing thermal management techniques with prioritization on fuel efficiency.

Keywords: thermal management, automatic transmission, hybrid, and systematic approach

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Authors: Seyed Esmail Seyedi Bariran, Khairul Salleh Mohamed Sahari

Abstract:

In multi-echelon supply chain networks, optimal supplier selection significantly depends on the accuracy of suppliers’ performance prediction. Different methods of multi criteria decision making such as ANN, GA, Fuzzy, AHP, etc have been previously used to predict the supplier performance but the “black-box” characteristic of these methods is yet a major concern to be resolved. Therefore, the primary objective in this paper is to implement an artificial intelligence-based gene expression programming (GEP) model to compare the prediction accuracy with that of ANN. A full factorial design with %95 confidence interval is initially applied to determine the appropriate set of criteria for supplier performance evaluation. A test-train approach is then utilized for the ANN and GEP exclusively. The training results are used to find the optimal network architecture and the testing data will determine the prediction accuracy of each method based on measures of root mean square error (RMSE) and correlation coefficient (R2). The results of a case study conducted in Supplying Automotive Parts Co. (SAPCO) with more than 100 local and foreign supply chain members revealed that, in comparison with ANN, gene expression programming has a significant preference in predicting supplier performance by referring to the respective RMSE and R-squared values. Moreover, using GEP, a mathematical function was also derived to solve the issue of ANN black-box structure in modeling the performance prediction.

Keywords: Supplier Performance Prediction, ANN, GEP, Automotive, SAPCO

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Authors: Victor Romanov, Daria Efimenko

Abstract:

The focus on innovation in modern economy is the main factor in surviving business in a competitive environment. The innovations are based on the search and use of knowledge in a global context. Nowadays consumers and market demand are the main innovation drivers. This leads to build a business as a system with feedback, promptly restructuring production and innovation implementation in response to market demands. In modern knowledge economy, because of speed of technical progress, the product's lifecycle became much shorter, what makes more stringent requirements for innovation implementation on the enterprises of and therefore the possibility for enterprise for receiving extra income is decreasing. This circumstance imposes additional requirements for the replacement of obsolete products and the prompt release of innovative products to the market. The development of information technologies has led to the fact that only in the conditions of partnership and knowledge sharing with partners it is possible to update products quickly for innovative products. Many companies pay attention to updating innovations through the search for new partners, but the task of finding new partners presents some difficulties. The search for a suitable one includes several stages such as: determining the moment of innovation-critical, introducing a search, identifying search criteria, justifying and deciding on the choice of a partner. No less important is the question of how to manage an innovative product in response to a changing market. The article considers the problems of information support for the search for the source of innovation and partnership to decrease the time for implementation of novelty products.

Keywords: partnership, novelty, market feedback, alliance

Procedia PDF Downloads 194