Search results for: train schedule

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

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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: Anees Raza, Liu Chunyan

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Renewable energy technologies are need of the time and are already making the big impact in the climatic outlook of the world. Biogas technology is one of those, and it has a lot of benefits for its users. It is cost effective because it is produced from the raw material which is available free of cost to the livestock farmers. Bio-slurry, a by-product of biogas, is being used as fertilizer for the crops production and increasing soil fertility. There are many other household benefits of technology. Research paper discusses the benefits of biogas as perceived by the biogas users as well as non-users of Tehsil Jhang. Data were collected from 60 respondents (30 users and 30 non-users) selected purposively through validated and pre-tested interview schedule from the respondents. Collected data were analyzed by using Statistical Package for Social Sciences (SPSS). Household benefits like ‘makes cooking easy,’ ‘Less breathing issues for working women in kitchens’ and ‘Use of bio-slurry as organic fertilizer’ had the highly significant relationship between them with t-values of 3.24, 4.39 and 2.80 respectively. Responses of the respondents about environmental benefits of biogas technology showed that ‘less air pollution’ had a significant relationship between them while ‘less temperature rise up than due to the burning of wood /dung’ had the non-significant relationship in the responses of interviewed respondents. It was clear from the research that biogas users were becoming influential in convincing non-users to adopt this technology due to its noticeable benefits. Research area where people were depending on wood to be used as fire fuel could be helped in reduction of cutting of trees which will help in controlling deforestation and saving the environment.People should be encouraged in using of biogas technology through providing them subsidies and low mark up loans.

Keywords: biogas technology, deforestation, environmental benefits, renewable energy

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Authors: Bokkasam Sasidhar, Ibrahim Aljasser

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The problem of finding optimal schedules for each equipment in a production process is considered, which consists of a single stage of manufacturing and which can handle different types of products, where changeover for handling one type of product to the other type incurs certain costs. The machine capacity is determined by the upper limit for the quantity that can be processed for each of the products in a set up. The changeover costs increase with the number of set ups and hence to minimize the costs associated with the product changeover, the planning should be such that similar types of products should be processed successively so that the total number of changeovers and in turn the associated set up costs are minimized. The problem of cost minimization is equivalent to the problem of minimizing the number of set ups or equivalently maximizing the capacity utilization in between every set up or maximizing the total capacity utilization. Further, the production is usually planned against customers’ orders, and generally different customers’ orders are assigned one of the two priorities – “normal” or “priority” order. The problem of production planning in such a situation can be formulated into a Multiple Arc Network (MAN) model and can be solved sequentially using the algorithm for maximizing flow along a MAN and the algorithm for maximizing flow along a MAN with priority arcs. The model aims to provide optimal production schedule with an objective of maximizing capacity utilization, so that the customer-wise delivery schedules are fulfilled, keeping in view the customer priorities. Algorithms have been presented for solving the MAN formulation of the production planning with customer priorities. The application of the model is demonstrated through numerical examples.

Keywords: scheduling, maximal flow problem, multiple arc network model, optimization

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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

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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

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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

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Authors: Sujatha Shanmugasundaram

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Background: It has been since one year that COVID-19 has emerged into the world. Since then, healthcare professionals facing a great challenge in to fight against this deadly virus. According to World Health Organization (WHO) 2021, it is estimated that more than 131 million confirmed cases and 2million deaths around the world due to this pandemic. Nurses are the frontline workers who play a major role in safeguarding the lives of the people in acute care settings. Evidence suggests that there are numbers of research have been carried out on nurses' and healthcare provider’s experiences during the pandemic. But, unfortunately, there are no or little evidence available on Muslim nurse’s perspective. Hence, this research will investigate the experiences of Muslim nurses working in an acute care setting during the pandemic. Purpose: The purpose of the study is to explore the experiences of Muslim nurses working in an acute setting during the COVID-19 pandemic. Research Methods: A qualitative research approach will be utilized for the study. Semi-structured interview schedule will be used to collect the data. Face to face interviews will be conducted. All interviews will be conducted in Arabic, and it will be audio recorded. Verbatim will be noted. Muslim nurses working in an acute setting will be included in the study. Convenient sampling technique will be used to recruit the participants. Ethical approval will be obtained from the study sites. Strauss and Corbin's thematic analysis will be used to analyze the data. Conclusion: Considering that nurses are the frontline workers, they have a significant role in dealing with this COVID-19. It is a great challenge for the nurses working in an acute care setting. Thus, this study will bring out significant findings that will impact the nursing practice.

Keywords: acute care, COVID-19, experiences, muslim nurses

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Authors: Inosha Bimali, Shambhu P. Adhikari, Sumana Baidya, Nishchal R. Shakya

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Background: An earthquake of 7.8 magnitudes with an epicenter in Gorkha on 25th April 2015 and second earthquake of 6.5 magnitudes with an epicenter at Sindhupalchwok on 12th May 2015 struck the beautiful country of Nepal, killing more than 8,500 people and over 18,500 individuals were left injured with various forms of disabilities. Objectives: To explore number, type and level of disability among post earthquake victims. A door to door physiotherapy rehabilitation program will be conducted at the community level as a continuation of this study. Methods: A survey was carried out in the catchment area of Bahunepati and Manekharka outreach centers of Sindhupalchowk district and Gaurishankar outreach center of Dolakha district of Dhulikhel Hospital. Physical disability was identified using a disability survey form given by Ministry of women, children and social welfare Nepal Government. World health organization disability assessment schedule-2 was used to identify the level of disability. Results: Twenty-nine person with disabilities at Bahunepati, four person with disabilities at Manekharkha and two person with disabilities at Gaurishankar and its catchment area were identified. Level of disability was an average of 56% with majority of survivors having upper extremities fractures followed by lower extremities fractures and miscellaneous injury. Few spinal cord injuries and head injuries were also identified. Conclusion: Though number of person with disabilities was found relatively less, disability level is high; hence an urgent need of physiotherapy rehabilitation is reflected to improve the quality of life of the affected people.

Keywords: community, disability, Nepal earthquake, physiotherapy

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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

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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: Chin-Chih Chang

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Many manufacturing systems suffer failures due to complex degradation processes and various environment conditions such as random shocks. Consider an operating system is subject to random shocks and works at random times for successive jobs. When successive jobs often result in production losses and performance deterioration, it would be better to do maintenance or replacement at a planned time. A preventive replacement (PR) policy is presented to replace the system before a failure occurs at a continuous time T. In such a policy, the failure characteristics of the system are designed as follows. Each job would cause a random amount of additive damage to the system, and the system fails when the cumulative damage has exceeded a failure threshold. Suppose that the deteriorating system suffers one of the two types of shocks with age-dependent probabilities: type-I (minor) shock is rectified by a minimal repair, or type-II (catastrophic) shock causes the system to fail. A corrective replacement (CR) is performed immediately when the system fails. In summary, a generalized maintenance model to scheduling replacement plan for an operating system is presented below. PR is carried out at time T, whereas CR is carried out when any type-II shock occurs and the total damage exceeded a failure level. The main objective is to determine the optimal continuous schedule time of preventive replacement through minimizing the mean cost rate function. The existence and uniqueness of optimal replacement policy are derived analytically. It can be seen that the present model is a generalization of the previous models, and the policy with preventive replacement outperforms the one without preventive replacement.

Keywords: preventive replacement, working time, cumulative damage model, minimal repair, imperfect maintenance, optimization

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Authors: G. Martino, F. Silva, E. Marchal

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The control over delivered iron ore blend characteristics is one of the most important aspects of the mining business. The iron ore price is a function of its composition, which is the outcome of the beneficiation process. So, end-to-end integrated planning of mine operations can reduce risks of penalties on the iron ore price. In a standard iron mining company, the production chain is composed of mining, ore beneficiation, and client supply. When mine planning and client supply decisions are made uncoordinated, the beneficiation plant struggles to deliver the best blend possible. Technological improvements in several fields allowed bridging the gap between departments and boosting integrated decision-making processes. Clusterization and classification algorithms over historical production data generate reasonable previsions for quality and volume of iron ore produced for each pile of run-of-mine (ROM) processed. Mathematical modeling can use those deterministic relations to propose iron ore blends that better-fit specifications within a delivery schedule. Additionally, a model capable of representing the whole production chain can clearly compare the overall impact of different decisions in the process. This study shows how flexibilization combined with a planning optimization model between the mine and the ore beneficiation processes can reduce risks of out of specification deliveries. The model capabilities are illustrated on a hypothetical iron ore mine with magnetic separation process. Finally, this study shows ways of cost reduction or profit increase by optimizing process indicators across the production chain and integrating the different plannings with the sales decisions.

Keywords: clusterization and classification algorithms, integrated planning, mathematical modeling, optimization, penalty minimization

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Authors: Uday S. Goel, G. Senthil Kumar, Biswajit Ghosh, V. V. Mahashabde, Dhirendra Kumar, H. Manjunath, Ritesh Kumar, Mahesh Bhagwat, Subodh Pandey

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Tata Steel Bhushan Steel Ltd.(TSBSL)’s Hot Mill at Angul , Orissa , India, was facing shape issues in Hot Rolled (HR) coils. This was resulting in a defect called as ‘Ridge’, which was appearing in subsequent cold rolling operations at various cold mills (CRM) and external customers. A collaborative project was undertaken to resolve this issue. One of the reasons identified was the strange drop in thermal crown after rolling of 20-25 coils in the finishing mill (FM ) schedule. On the shop floor, it was observed that work roll temperatures in the FM after rolling were very high and non uniform across the work roll barrel. Jammed work roll cooling nozzles, insufficient roll bite lubrication and inadequate roll cooling water quality were found to be the main reasons. Regular checking was initiated to check roll cooling nozzles health, and quick replacement done if found jammed was implemented. Improvements on roll lubrication, especially flow rates, was done. Usage of anti-peeling headers and inter stand descaling was enhanced. A subsequent project was also taken up for improving the quality of roll cooling water. Encouraging results were obtained from the project with a reduction in rejection due to ridge at CRM’s by almost 95% of the pre project start levels. Poor profile occurrence of HR coils at HSM was also reduced from a high of 32% in May’19 to <1% since Apr’20.

Keywords: hot rolling flat, shape, ridge, work roll, roll cooling nozzle, lubrication

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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

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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

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Authors: Mfaniseni Wiseman Mbatha

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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: Nnadi Ezekiel Ejiofor

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The construction industry plays a decisive role in the healthy development of any nation. Not only large but even small construction projects contribute to a country’s economic growth. There is a need for good management to ensure successful delivery and sustainability because of the plethora of risks that have resulted in low-profit margins for contractors, cost and schedule overruns, poor quality delivery, and abandoned projects. This research reviewed Capitalization on Sustainable Risk Management. Questionnaires and oral interviews conducted were utilized as means of data collection. One hundred and ninety-eight (198) large construction firms in Nigeria form the population of this study. 15 (fifteen) companies that emanated from merger and acquisition were used for the study. The instruments used for data collection were a researcher-developed structured questionnaire based on a five-point rating scale, interviews, focus group discussion, and secondary sources (bill of quantities and stock and exchange commission). The instrument was validated by two experts in the field. The reliability of the instrument was established by applying the split-half method. Kendall’s coefficient of concordance was used to test the data, and a degree of agreement was obtained. Data were subjected to descriptive statistics and analyzed using analysis of variance, t-test, and SPSS. The identified impacts of capitalization were an increase in turnover (24.5%), improvement in the image (24.5%), risk reduction (20%), business expansion (17.3%), and geographical spread (13.6%). The study strongly advocates the inclusion of risk management evaluation as part of the construction procurement process.

Keywords: capitalization, project delivery, risks, risk management, sustainability

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

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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

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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: Robin Molinier

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Industrial symbiosis (I.S) deals with both substitution synergies (exchange of waste materials, fatal energy and utilities as resources for production) and infrastructure/service sharing synergies. The latter is based on the intensification of use of an asset and thus requires to balance capital costs increments with snowball effects (network externalities) for its implementation. Initial investors must specify ex-ante arrangements (cost sharing and pricing schedule) to commit toward investments in capacities and transactions. Our model investigate the decision of 2 actors trying to choose cooperatively a level of infrastructure capacity oversizing to set a plug-and-play offer to a potential entrant whose capacity requirement is randomly distributed while satisficing their own requirements. Capacity cost exhibits sub-additive property so that there is room for profitable overcapacity setting in the first period. The entrant’s willingness-to-pay for the access to the infrastructure is dependent upon its standalone cost and the capacity gap that it must complete in case the available capacity is insufficient ex-post (the complement cost). Since initial capacity choices are driven by ex-ante (expected) yield extractible from the entrant we derive the expected complement cost function which helps us defining the investors’ objective function. We first show that this curve is decreasing and convex in the capacity increments and that it is shaped by the distribution function of the potential entrant’s requirements. We then derive the general form of solutions and solve the model for uniform and triangular distributions. Depending on requirements volumes and cost assumptions different equilibria occurs. We finally analyze the effect of a per-unit subsidy a public actor would apply to foster such sharing synergies.

Keywords: capacity, cooperation, industrial symbiosis, pricing

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Authors: Seyed Esmail Seyedi Bariran, Khairul Salleh Mohamed Sahari

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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: Kemka Ihemelandu, Chukwuemeka Ihemelandu

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Melanoma remains a public health crisis, with incidence rates increasing rapidly in the past decades. Improving diagnostic accuracy to decrease misdiagnosis using Artificial intelligence (AI) continues to be documented. Unfortunately, unintended racially biased outcomes, a product of lack of diversity in the dataset used, with a noted class imbalance favoring lighter vs. darker skin tone, have increasingly been recognized as a problem.Resulting in noted limitations of the accuracy of the Convolutional neural network (CNN)models. CNN models are prone to biased output due to biases in the dataset used to train them. Our aim in this study was the optimization of convolutional neural network algorithms to mitigate bias in the automated diagnosis of melanoma. We hypothesized that our proposed training algorithms based on a data augmentation method to optimize the diagnostic accuracy of a CNN classifier by generating new training samples from the original ones will reduce bias in the automated diagnosis of melanoma. We applied geometric transformation, including; rotations, translations, scale change, flipping, and shearing. Resulting in a CNN model that provided a modifiedinput data making for a model that could learn subtle racial features. Optimal selection of the momentum and batch hyperparameter increased our model accuracy. We show that our augmented model reduces bias while maintaining accuracy in the automated diagnosis of melanoma.

Keywords: bias, augmentation, melanoma, convolutional neural network

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Authors: Alina P. Colling, Robert G. Hekkenberg

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Recent years have seen extensive technological advancement in truck platooning, as reflected in the literature. Its main benefits are the improvement of traffic stability and the reduction of air drag, resulting in less fuel consumption, in comparison to using individual trucks. Platooning is now being adapted to the waterborne transport sector in the NOVIMAR project through the development of a Vessel Train (VT) concept. The main focus of VT’s, as opposed to the truck platoons, is the decrease in manning on board, ultimately working towards autonomous vessel operations. This crew reduction can prove to be an important selling point in achieving economic competitiveness of the waterborne approach when compared to alternative modes of transport. This paper discusses the expected benefits and drawbacks of the VT concept, in terms of the technical logistic performance and generalized costs. More specifically, VT’s can provide flexibility in destination choices for shippers but also add complexity when performing special manoeuvres in VT formation. In order to quantify the cost and performances, a model is developed and simulations are carried out for various case studies. These compare the application of VT’s in the short sea and inland water transport, with specific sailing regimes and technologies installed on board to allow different levels of autonomy. The results enable the identification of the most important boundary conditions for the successful operation of the waterborne platooning concept. These findings serve as a framework for future business applications of the VT.

Keywords: autonomous vessels, NOVIMAR, vessel trains, waterborne platooning

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Authors: Claudio Díaz, Mabel Ortiz

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An EFL teacher education programme in Chile takes five years to train a future teacher of English. Preservice teachers are prepared to learn an advanced level of English and teach the language from 5th to 12th grade in the Chilean educational system. In the context of their first EFL Methodology course in year four, preservice teachers have to create a five-minute digital story that starts from a critical incident they have experienced as teachers-to-be during their observations or interventions in the schools. A critical incident can be defined as a happening, a specific incident or event either observed by them or involving them. The happening sparks their thinking and may make them subsequently think differently about the particular event. When they create their digital stories, preservice teachers put technology, teaching practice and theory together to narrate a story that is complemented by still images, moving images, text, sound effects and music. The story should be told as a personal narrative, which explains the critical incident. This presentation will focus on the creation process of 50 Chilean preservice teachers’ digital stories highlighting the critical incidents they started their stories. It will also unpack preservice teachers’ beliefs and reflections when approaching their teaching practices in schools. These beliefs will be coded and categorized through content analysis to evidence preservice teachers’ most rooted conceptions about English teaching and learning in Chilean schools. The findings seem to indicate that preservice teachers’ beliefs are strongly mediated by contextual and affective factors.

Keywords: beliefs, digital stories, preservice teachers, practicum

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Authors: Yasin Tadayonrad

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Most of the supply chain networks have many nodes starting from the suppliers’ side up to the customers’ side that each node sends/receives the raw materials/products from/to the other nodes. One of the major concerns in this kind of supply chain network is finding the best schedule for loading /unloading the shipments through the whole network by which all the constraints in the source and destination nodes are met and all the shipments are delivered on time. One of the main constraints in this problem is loading/unloading capacity in each source/ destination node at each time slot (e.g., per week/day/hour). Because of the different characteristics of different products/groups of products, the capacity of each node might differ based on each group of products. In most supply chain networks (especially in the Fast-moving consumer goods industry), there are different planners/planning teams working separately in different nodes to determine the loading/unloading timeslots in source/destination nodes to send/receive the shipments. In this paper, a mathematical problem has been proposed to find the best timeslots for loading/unloading the shipments minimizing the overall delays subject to respecting the capacity of loading/unloading of each node, the required delivery date of each shipment (considering the lead-times), and working-days of each node. This model was implemented on python and solved using Python-MIP on a sample data set. Finally, the idea of a heuristic algorithm has been proposed as a way of improving the solution method that helps to implement the model on larger data sets in real business cases, including more nodes and shipments.

Keywords: supply chain management, transportation, multiple-multiple network, timeslots management, mathematical modeling, mixed integer programming

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Authors: Satish Kumar Peddapelli

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This paper presents advances in pulse width modulation techniques which refers to a method of carrying information on train of pulses and the information be encoded in the width of pulses. Pulse Width Modulation is used to control the inverter output voltage. This is done by exercising the control within the inverter itself by adjusting the ON and OFF periods of inverter. By fixing the DC input voltage we get AC output voltage. In variable speed AC motors the AC output voltage from a constant DC voltage is obtained by using inverter. Recent developments in power electronics and semiconductor technology have lead improvements in power electronic systems. Hence, different circuit configurations namely multilevel inverters have become popular and considerable interest by researcher are given on them. A fast Space-Vector Pulse Width Modulation (SVPWM) method for five-level inverter is also discussed. In this method, the space vector diagram of the five-level inverter is decomposed into six space vector diagrams of three-level inverters. In turn, each of these six space vector diagrams of three-level inverter is decomposed into six space vector diagrams of two-level inverters. After decomposition, all the remaining necessary procedures for the three-level SVPWM are done like conventional two-level inverter. The proposed method reduces the algorithm complexity and the execution time. It can be applied to the multilevel inverters above the five-level also. The experimental setup for three-level diode-clamped inverter is developed using TMS320LF2407 DSP controller and the experimental results are analysed.

Keywords: five-level inverter, space vector pulse wide modulation, diode clamped inverter, electrical engineering

Procedia PDF Downloads 377

Authors: Qin Long, Li Xiaoge

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The Knowledge Graphs have now become a new form of knowledge representation. However, there is no consensus in regard to a plausible and definition of entities and relationships in the domain-specific knowledge graph. Further, in conjunction with several limitations and deficiencies, various domain-specific entities and relationships recognition approaches are far from perfect. Specifically, named entity recognition in Chinese domain is a critical task for the natural language process applications. However, a bottleneck problem with Chinese named entity recognition in new domains is the lack of annotated data. To address this challenge, a domain distant supervised named entity recognition framework is proposed. The framework is divided into two stages: first, the distant supervised corpus is generated based on the entity linking model of graph attention neural network; secondly, the generated corpus is trained as the input of the distant supervised named entity recognition model to train to obtain named entities. The link model is verified in the ccks2019 entity link corpus, and the F1 value is 2% higher than that of the benchmark method. The re-pre-trained BERT language model is added to the benchmark method, and the results show that it is more suitable for distant supervised named entity recognition tasks. Finally, it is applied in the computer field, and the results show that this framework can obtain domain named entities.

Keywords: distant named entity recognition, entity linking, knowledge graph, graph attention neural network

Procedia PDF Downloads 81

Authors: Saritdikhun Somasa

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3D printing fabrication has empowered many artists in many fields. Artists who work in stop motion, 3D modeling, toy design, product design, sculpture, and fine arts become one-stop shop operations–where they can design, prototype, and distribute their designs for commercial or fine art purposes. The author has developed a digital sculpting course that fosters digital software, peripheral hardware, and 3D printing with traditional sculpting concept techniques to address the complexities of this multifaceted process, allowing the students to produce complex 3d-printed work. The author will detail the preparation and planning for pre- to post-process 3D printing elements, including software, materials, space, equipment, tools, and schedule consideration for small to medium figurine design statues in a semester-long class. In addition, the author provides insight into teaching challenges in the non-studio space that requires students to work intensively on post-printed models to assemble parts, finish, and refine the 3D printed surface. Even though this paper focuses on the 3D printing processes and techniques for small to medium design statue projects for the Digital Media program, the author hopes the paper will benefit other fields of study such as craft practices, product design, and fine-arts programs. Other schools that might implement 3D printing and fabrication in their programs will find helpful information in this paper, such as a teaching plan, choices of equipment and materials, adaptation for non-studio spaces, and putting together a complete and well-resolved project for students.

Keywords: 3D digital modeling, 3D digital sculpting, 3D modeling, 3D printing, 3D digital fabrication

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Authors: Amah Nnachi Lofty, Oyefeso Olufemi, Ibiam Udu Ama

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New and more powerful communications technologies continue to emerge at a rapid pace and their uses in education are widespread and the impact remarkable in the developing societies. This study investigates Mobile Collaboration Learning Technique (MCLT) on learners’ outcome among students in tertiary institutions of developing nations (a case of Nigeria students). It examines the significance of retention achievement scores of students taught using mobile collaboration and conventional method. The sample consisted of 120 students using Stratified random sampling method. Three research questions and hypotheses were formulated, and tested at a 0.05 level of significance. A student achievement test (SAT) was made of 40 items of multiple-choice objective type, developed and validated for data collection by professionals. The SAT was administered to students as pre-test and post-test. The data were analyzed using t-test statistic to test the hypotheses. The result indicated that students taught using MCLT performed significantly better than their counterparts using the conventional method of instruction. Also, there was no significant difference in the post-test performance scores of male and female students taught using MCLT. Based on the findings, the following recommendations was made that: Mobile collaboration system be encouraged in the institutions to boost knowledge sharing among learners, workshop and trainings should be organized to train teachers on the use of this technique and that schools and government should formulate policies and procedures towards responsible use of MCLT.

Keywords: education, communication, learning, mobile collaboration, technology

Procedia PDF Downloads 210